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Development of a framework to identify patient pathways through a segment of the health care cycle
h [electronic resource] /
by Abhik Bhattacharya.
[Tampa, Fla] :
b University of South Florida,
Title from PDF of title page.
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Thesis (M.S.I.E.)--University of South Florida, 2009.
Includes bibliographical references.
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ABSTRACT: The US spends more money on health care than other industrialized nations. Nevertheless, the US lags behind them in life expectancies, access to care, and other health indicators. This can be attributed to the numerous issues that afflict the US health care sector the lack of a universal health coverage, increasing medical errors, over and under-treatment of patients, lack of standardization, and so on. It is believed that the structure of health care delivery as it exists in the US is broken, which consequently reduces the quality of provided care and increases costs. There is a growing consensus among the different players in the sector that a complete overhaul of the health care system is required. This study presents an approach to identify patient treatment over a cycle of care. Every medical condition has a care cycle over which treatment is provided.The complete cycle of care of most medical conditions comprise of both inpatient and ambulatory care and start from the onset of the disease to its resolution. There are established guidelines that state what care should be provided during various points of this cycle. It is important to identify and analyze the flow of patients through this cycle of care. Once the flow is identified, various analyses can then be conducted to identify bottlenecks, delays, redundancies and other issues that reduce efficiency and increase costs. Unfortunately, due to the fact that medical data is collected for either medical or billing purposes and not for an operational analysis, it is very difficult to analyze the flow of patients over this cycle of care. This study developed a framework to extract relevant patient medical information from existing administrative databases of health care organizations.This was used to create patient flow paths across a segment of the care cycle to enable the analysis of the care treatment. A case study was conducted at a federal health care provider to identify and map the flow over the care cycle of patients with lung cancer.
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Co-advisor: Jose L. Zayas-Castro, Ph.D.
Co-advisor: Peter J. Fabri, M.D., Ph.D.
x Industrial and Management Systems
t USF Electronic Theses and Dissertations.
Development of a Framework to Identify Patient Pathways through a Segment of the Health Care Cycle by Abhik Bhattacharya A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Industrial Engineering Department of Industrial and Ma nagement Systems Engineering College of Engineering University of South Florida Co-Major Professor: Jos L. Zayas-Castro, Ph.D. Co-Major Professor: Peter J. Fabri, M.D., Ph.D. Ali Yalcin, Ph.D. Date of Approval: March 10, 2009 Keywords: health care, care cycle, patient flow, clinical pathways, medical databases Copyright 2009, Abhik Bhattacharya
Dedication To my parents and brother.
Acknowledgements I would like to thank Dr. Jos L. Zaya s-Castro for his continued support and guidance through the years. I would like to th ank Dr. Peter J. Fabri for his invaluable insight into the health care domain. I would also like to thank Dr. Ali Yalcin for his help and support over the years. I would also like to thank my parents for their support through all th ese years. Nothing in my life would have been possibl e without their constant encouragement and support. Finally, thanks to my friends and collea gues for their help in completing this study.
i Table of Contents List of Tables ..................................................................................................................... iii List of Figures .................................................................................................................... vi Abstract ............................................................................................................................. vii Chapter 1 Introduction ........................................................................................................ 1 1.1 The Health Care Sector ..................................................................................... 3 1.2 Issues in Health Care ........................................................................................ 4 1.2.1 The US Hospital Care System ........................................................... 5 1.2.2 The Overarching Grand Challenge s in the Health Care Sector ......... 7 1.3 Overview of Research Objectives ..................................................................... 8 1.4 Thesis Organization .......................................................................................... 8 Chapter 2 Literature Review ............................................................................................. 10 2.1 Patient Flow .................................................................................................... 10 2.1.1 Outpatient Flow ............................................................................... 11 2.1.2 Inpatient Flow .................................................................................. 13 2.2 Clinical Pathways ............................................................................................ 15 2.3 Electronic Medical Records ............................................................................ 17 2.4 Summary ......................................................................................................... 18 Chapter 3 Problem Statement ........................................................................................... 21 3.1 Introduction and Motivation ........................................................................... 21 3.2 Research Objective ......................................................................................... 23 3.3 Benefits of the Research ................................................................................. 24 3.4 Research Methodology ................................................................................... 25 3.4.1 Development of the Timeline .......................................................... 27 Chapter 4 Identifying the Flow of Lung Resection Patients ............................................. 30 4.1 Creating the Timeline ..................................................................................... 30
ii 4.2 Determining the Windows .............................................................................. 32 4.3 The Pre-Admission Flow ................................................................................ 34 4.4 The Inpatient Episode: Admission, Resection and Discharge ........................ 36 4.5 The Post-Discharge Flow ................................................................................ 38 Chapter 5 The Framework Rules ...................................................................................... 41 5.1 Rules for Patients Undergoing Lung Resection .............................................. 41 5.1.1 The Pre-Admission Period ............................................................... 42 5.1.2 The Surgical Inpatient Period .......................................................... 43 5.1.3 The Post-Discharge Period .............................................................. 44 5.2 The Universal Framework Rules .................................................................... 48 5.2.1 Identifying and Arranging the Sample ............................................. 48 5.2.2 The Pre-Intervention Period ............................................................. 49 5.2.3 The Intervention Period ................................................................... 50 5.2.4 The Post-Intervention Period ........................................................... 52 Chapter 6 Conclusions and Future Work .......................................................................... 54 List of References ............................................................................................................. 58 Appendices ........................................................................................................................ 67 Appendix 1: Encounter Grouping ......................................................................... 68 Appendix 2: Pre-Admission Patient Flow ............................................................ 69 Appendix 3: Inpatient Ward Descriptions ............................................................ 79 Appendix 4: Inpatient Flow during Surgical Episode ........................................... 81 Appendix 5: Detailed Post-D ischarge Flow of Patients ....................................... 87 Appendix 6: Pre-Admission Encounter Frequencies ............................................ 96 Appendix 7: Post-Discharge Encounter Frequencies ........................................... 98 Appendix 8: Information from the Observed Patient Flow ................................ 100
iii List of Tables Table 1. The Three Periods in the Segment of the Care Cycle ......................................... 28Table 2. The Care Cycle Windows ................................................................................... 28Table 3. Relative Days in the Segment of Care Cycle ...................................................... 31Table 4. Windows in the Pre-Admission Period ............................................................... 34Table 5. Frequency of Each Enc ounter in the Pre-Admission Period .............................. 35Table 6. Encounters in Each of the Pre-Admission Windows .......................................... 36Table 7. Common Inpatient Flows and their Frequencies ................................................ 38Table 8. Windows in the Post-Discharge Period .............................................................. 38Table 9. Frequency of Each Encount er in the Post-Discharge Period .............................. 39Table 10. Encounters in Each Post-Discharge Window ................................................... 40Table 11. Related Clinic Visits Irr espective of When They Occur .................................. 42Table 12. Related Clinic Visits Only if they Occur Within 30 days of Admission .......... 42Table 13. Related Procedures Irre spective of When They Occur ..................................... 43Table 14. Tests that are Related Whenever they Occur in the Pre-Admission Period ..... 43Table 15. Units that were Visited by Patie nts During the Surgical Admission Period .... 44Table 16. Related Clinic Visits Irr espective of When They Occur .................................. 44Table 17. Related Clinic Visits Only if They Occur Within 30 Days of Discharge ......... 45Table 18. Related Procedures Irre spective of When They Occur ..................................... 45Table 19. Tests that are Related Whenever They Occur in the Po st-Discharge Period .... 45Table 20. Flow of a Patient in the Care Cycle .................................................................. 46
iv Table 21. Care Cycle Flow of Another Patient ................................................................. 47Table 22. Encounter Grouping .......................................................................................... 68Table 23. The Flow of Patients (1 -3) in the Pre-Admission Period. ................................. 69Table 24. The Flow of Patients (4 -6) in the Pre-Admission Period. ................................. 70Table 25. The Flow of Patients (7 -9) in the Pre-Admission Period. ................................. 71Table 26. The Flow of Patients (1 0-12) in the Pre-Admission Period. ............................. 71Table 27. The Flow of Patients (1 3-15) in the Pre-Admission Period. ............................. 72Table 28. The Flow of Patients (1 6-18) in the Pre-Admission Period. ............................. 72Table 29. The Flow of Patients (1 9-21) in the Pre-Admission Period. ............................. 73Table 30. The Flow of Patients (2 2-24) in the Pre-Admission Period. ............................. 73Table 31. The Flow of Patients (2 5-27) in the Pre-Admission Period. ............................. 74Table 32. The Flow of Patients (2 8-30) in the Pre-Admission Period. ............................. 75Table 33. The Flow of Patients (3 1-33) in the Pre-Admission Period. ............................. 75Table 34. The Flow of Patients (3 4-36) in the Pre-Admission Period. ............................. 76Table 35. The Flow of Patients (3 7-39) in the Pre-Admission Period. ............................. 76Table 36. The Flow of Patients (4 0-42) in the Pre-Admission Period. ............................. 77Table 37. The Flow of Patients (4 3-45) in the Pre-Admission Period. ............................. 77Table 38. The Flow of Patients (4 6-48) in the Pre-Admission Period. ............................. 78Table 39. Detailed Flow of Patients (1-5 ) in the Inpatient Surgical Episode ................... 81Table 40. Detailed Flow of Patients (610) in the Inpatient Surgical Episode ................. 81Table 41. Detailed Flow of Patients (11-15) in the Inpatient Surgical Episode ............... 82Table 42. Detailed Flow of Patients (16-20) in the Inpatient Surgical Episode ............... 83Table 43. Detailed Flow of Patients (21-25) in the Inpatient Surgical Episode ............... 84Table 44. Detailed Flow of Patients (26-30) in the Inpatient Surgical Episode ............... 84Table 45. Detailed Flow of Patients (31-35) in the Inpatient Surgical Episode ............... 85
v Table 46. Detailed Flow of Patients (36-40) in the Inpatient Surgical Episode ............... 85Table 47. Detailed Flow of Patients (41-45) in the Inpatient Surgical Episode ............... 85Table 48. Detailed Flow of Patients (46-48) in the Inpatient Surgical Episode ............... 86Table 49. Detailed Flow of Patients (1-3) in the Post-Discharge Period .......................... 87Table 50. Detailed Flow of Patients (4-7) in the Post-Discharge Period .......................... 88Table 51. Detailed Flow of Patients (8 -11) in the Post-Discharge Period ........................ 88Table 52. Detailed Flow of Patients ( 12-15) in the Post-Discharge Period ...................... 89Table 53. Detailed Flow of Patients ( 16-19) in the Post-Discharge Period ...................... 89Table 54. Detailed Flow of Patients ( 20-21) in the Post-Discharge Period ...................... 90Table 55. Detailed Flow of Patients ( 22-25) in the Post-Discharge Period ...................... 91Table 56. Detailed Flow of Patients ( 26-30) in the Post-Discharge Period ...................... 91Table 57. Detailed Flow of Patients ( 31-33) in the Post-Discharge Period ...................... 92Table 58. Detailed Flow of Patients ( 34-36) in the Post-Discharge Period ...................... 93Table 59. Detailed Flow of Patients ( 37-40) in the Post-Discharge Period ...................... 93Table 60. Detailed Flow of Patient (41) in the Post-Discharge Period ............................. 94Table 61. Detailed Flow of Patients ( 42-45) in the Post-Discharge Period ...................... 94Table 62. Detailed Flow of Patients ( 46-48) in the Post-Discharge Period ...................... 95Table 63. Encounter Frequencie s in the Pre-Admission Period ....................................... 96Table 64. Encounter Frequencies in the Post-Discharge Period ....................................... 98Table 65. Basic Statistical Information of the Pre-Admission Period ............................ 100Table 66. Basic Statistical Information of the Post-Discharge Period ............................ 100Table 67. Expected Encounters in the Pre-Admission Period ........................................ 101Table 68. Expected Encounters in the Post-Discharge Period ........................................ 101
vi List of Figures Figure 1. The Care Cycle (Source: (Fabri, 2008)) ............................................................ 21Figure 2. The Data Packages Used in the Extraction Process .......................................... 26Figure 3. Encounter Frequenc y in Pre-Admission Windows ........................................... 35Figure 4. Number of Visits Made to Each Inpatient Unit ................................................. 37Figure 5. Expected Flow of Inpatients Undergoing Lung Surgery ................................... 37Figure 6. Encounter Frequency in Post-Discharge Windows ........................................... 40Figure 7. Frequency of Encounters in E ach Block of the Pre-Admission Period ............. 97Figure 8. Frequency of Encounters in Each Block of the Post-Discharge Period ............ 99
vii Development of a Framework to Identify Patient Pathways through a Segment of the Health Care Cycle Abhik Bhattacharya ABSTRACT The US spends more money on health care than other indus trialized nations. Nevertheless, the US lags behind them in life expectancies, access to care, and other health indicators. This can be attributed to the numerous issues that afflict the US health care sector the lack of a universal health coverage, increasing me dical errors, over and under-treatment of patients, lack of standardization, and so on. It is believed that the structure of health care delivery as it exists in the US is broken, which consequently reduces the quality of provided care and increases costs. There is a growing consensus among the different players in the sector that a complete overhaul of the health care system is required. This study presents an approach to identify patient treatment over a cycle of care. Every medical condition has a care cycle over which treatment is provided. The complete cycle of care of most medical conditions comprise of both inpatient and ambulatory care and start from the onset of the disease to its resolution. There are established guidelines that st ate what care should be provided during various points of
viii this cycle. It is important to identify and anal yze the flow of patients through this cycle of care. Once the flow is identif ied, various analyses can then be conducted to identify bottlenecks, delays, redundancie s and other issues that reduc e efficiency and increase costs. Unfortunately, due to the fact that medical data is collected for either medical or billing purposes and not for an operational analys is, it is very difficult to analyze the flow of patients over this cycle of care. This study developed a framework to extract relevant patient medical information from existing administrative database s of health care organizations. This was used to create pati ent flow paths across a segment of the care cycle to enable the analysis of the care tr eatment. A case study was conducted at a federal health care provider to identify and map the fl ow over the care cycle of patients with lung cancer.
1 Chapter 1 Introduction The United States spends significantly mo re on health care than other developed countries in the world (OECD, 2007). In 2006, the national health care expenditures amounted to $2.1 trillion and formed 16 per cent of the Gross Domestic Product (GDP) (National Center for Health Statistics, 2007). Despite the staggering amount spent on health care, the US lags behind other developed nations and the UN Human Development Report mentions that countries spending less than the US have h ealthier populations (United Nations Development Program, 2005). One of the major issues with health care in the US is the lack of universal health in surance as the US is the only industrialized country in the world without a universal health insurance system (Hoffman, Hoffman & Hoffman, 2005). In a CBS/New York Ti mes poll conducted in 2007, 90 percent of Americans believe the American health care system is broken and needs fundamental changes or needs to be completely rebuilt a nd 64 percent said that the government should guarantee health insurance for all (CBS/NYT, 2007). A lot of interest and energy in recent year s has been focused on reducing the number of uninsured in the US, but that may not be the only solution to the problem. Porter & Teisberg (2006) mention that while the vast majority of attention has been focused on insurance, we believe that the structure of health care delivery is the most fundamental issue. The structure of health care delivery drives the cost and quality of the entire system, and ultimately the cost of insurance a nd the amount of coverage that is feasible.
2 The fundamental problem in the U.S. health car e system is that the structure of health care delivery is broken. This is what all the data about ri sing costs and alarming quality are telling us. The World Health Organization (WHO) define s health care delivery as one of the functions of the health system, which deals with the medical and therapeutic measures intended to preserve or improve the health condition of a patient. WHO mentions that the objectives of reforming health care deli very are to provide health care that is oriented towards outcome; based on evid ence and focused on effectiveness and efficiency; to increase the availability of se rvices, patient satisfac tion and the quality of care.(World Health Organization Europe, 2006) Providing timely care is important to prev ent adverse outcomes. Unfortunately, the broken structure of health care delivery coupled with the high demand for health care has led to overcrowding and delays, and timely care is not always provided. Identifying delays in the system at the provider leve l and addressing the root causes behind them helps in delivering care on time. A significant way to identify delays is to study the flow of patients. Patient flow analysis has been defined as that representing the study of how patients move through the health care system (Hall, 2007). This study presents an approach to study patient flow over the care cycle, with the aim of reducing unnecessary delays and improving efficiency at the provider level. Once fl ow has been identified and understood, different approaches can then be used to balance ut ilization across the process, make it more patient-centric, redu ce costs, and improve quality and so on. The next few sections describe the US health ca re sector and the vari ous challenges it faces.
3 1.1 The Health Care Sector In 2006, the national health ca re expenditures amounted to $2.1 trillion and formed 16 percent of the Gross Domestic Product (GDP) (National Center for Health Statistics, 2007). Health care expenditures rose 6.7 percen t from the previous year (2005) and are forecasted to grow at the same rate until 2017. In comparison, the overall US economy was expected to grow at 4.9 percent through th e same period. At this rate, health care expenditures would total more than $4.3 trillion in 2017; or 19.5 percent of the GDP. In contrast, in1960 the health care expenditures totaled $27.5 billion, just 5.2 percent of the GDP. During that period, the US population has grown from 186 million to 300 million resulting in the per capita increase on health care from $148 (in 1960) to over $7000 (in 2006) and is expected to go over $13,000 in 2017 (National Center for Health Statistics, 2007). Health care consists of two major segments: outpatient (or ambul atory) and inpatient services. In 2006, hospital care expenses fell to less than 31 % of total expe nditures while physician expenses rose to 21%. The average length of stay for inpatients drastically decreased from 11.4 days in 1975 to 6.5 days in 2005, while outpatient visits increased from 254 million to 673 million in that pe riod. Significantly, outpatient surgeries increased from 16.3% in 1980 to 66.3% in 2005 (N ational Center for Health Statistics, 2007), confirming a trend towards more am bulatory services. Unfortunately, higher expenditures have not translated into pr oviding greater access to health care nor significantly improved its quality.
4 1.2 Issues in Health Care In 2005, more than 40 million adults could not afford health care (National Center for Health Statistics, 2007). Life expectanci es havent improved the US has among the lowest life expectancy rates in all indust rialized nations (World Health Organization, 2008). Regrettably, the issues do not end there. Medica l errors are on the rise and the Institute of Medicine (IOM) estimates that between 44,000 to 98,000 people die annually due to medical errors (Committee on Quality of Health Care in America, Institute of Medicine, 2000). Additionally, over and under-treatment of patients have become commonplace. Americans receive only about 55 percent of care suggested by established medical standards (Porter & Teisberg, 2006). The low qua lity of provided care leads to avoidable adverse medical conditions resulting in additi onal care, and thus, even higher expenses. There are wide variations in medical practice a nd costs, with differences in the patterns of practice and variation in the frequency of specialist care a nd hospitalization that drive regional variations in spending. Additionally, malpractice premiums and laws uits, which increase the cost of care, promote defensive medicine by inducing unnecessary tests and over-diagnosis, which result in excessive care and expenses. Therefore, it is not surprising to read that US consumers report higher dissatisfaction with th eir health care system than do consumers in other developed nations (CBS/NYT, 2007). Th e list of concerns afflicting US health care keeps increasing, and the above are onl y a few that need to be addressed.
5 1.2.1 The US Hospital Care System The US health care system is made up of both outpatient faciliti es (clinics, doctors offices) and inpatient facilities (hospitals). There has been a downward trend in the number of operating hospita ls in the US. The number of community hospitals has decreased from 5813 in 1981 to 4927 in 2006, decreasing the number of available beds during the same period by about 200,000 (Ame rican Hospital Association, 2008). This has led to only 2.68 beds per 1000 persons in 2006 compared to 4.37 in 1981. National expenditures on hospital care have incr eased from 3.4% in 1996 to 7% in 2006, (American Hospital Association, 2008). Even though the US population has increas ed by about 70 million from 1981 to 2006, inpatient admissions have decreased (from more than 36 million in 1981 to about 35 million in 2006). The average inpatient stay has reduced significantly from 7.6 days to 5.6 during the same period, showing a growing trend towards more outpatient care. Outpatient visits increased from 202 million in 1981 to almost 600 million in 2006. More surgical procedures are bei ng done in ambulatory care, as inpatient surgeries decreased from over 15 million in 1981 to 10 million in 2006, while ambulatory surgeries increased from 3.5 million to over 17 million duri ng the same period (American Hospital Association, 2008). Hospital systems are facing myriad problems that affect the quality of care received by patients. About 25% of hospitals reported negative revenue margins in 2006 (American Hospital Association, 2006). Delays in emer gency departments (EDs) are routine and have become expected. There were about 600 less EDs in 2006 than in 1991 reflecting
6 the downward trend (American Hospital Association, 2008), though the number of ED visits have risen from 88.5 million in 1981 to 118.4 million in 2006, a 33 percent increase. This has resulted in considerable delays in accessing timely care in EDs. The Centers for Disease Control and Prevention (C DC), in their 2005 survey found that the average waiting time for a patient in the ED was almost an hour and has been increasing steadily from 38 minutes in 1997. Twelve percent of those ED visits resulted in inpatient admission, while almost half of hospital admissions were through the ED in 2006, up from 36 percent in 1996 (Pitts et al., 2008). One of the major reasons for ED overcrowding ha s been attributed to the lack of inpatient bed capacity, which blocks new patients fr om being treated (American College of Emergency Physicians, 2006). Forty-seven perc ent of hospitals in 2007 reported their ED at or over capacity and 36 percent of all hosp itals had to go on ambulance diversion. Fifty-nine percent of hospitals reported that the ambulance diversion was due to either an overcrowded ED or lack of staffed critical care beds (American Hospital Association, 2008). Research shows that the real gridlock in emergency department crowding is a "throughput" problem, caused by the lack of inpatient bed capacity in hospitals. The General Accountability Office (GAO) reported in 2003 that boarding" of critically ill patients causes overcrowding, tying up staff a nd resources, making them unable to treat any more patients from the waiting room or from an ambulance (United States General Accounting Office, 2003).
7 1.2.2 The Overarching Grand Challenges in the Health Care Sector To conclude, the US health care sector is facing many challenges and it is very important that effective solutions be found to combat those challenges and ensure that people receive proper and timely care. In turn, these difficulties reduce the quality of care received. About 44 million Americans (17 per cent of the total population) do not have health coverage (National Center for Health Statistics, 2007). In su mmary, health care costs have been escalating by about 7 per cent annually, many indivi duals are unable to access basic medical care due to these rising costs, and errors and mistreatments in medical practice have been creeping up. Another challenge is the lack of standardization in health care. There are wide regional variations in cost, access, phys ician visits, and procedures. Medical standards do exist for treatment of specific medical conditions, but, there is significant over and undertreatment of patients, resulting in unnecessary and avoidable interventions that increase costs. The quality of care also suffers due to preventable adverse events. The fragmented nature of the health care system result s in minimal information sharing among the different providers, increasing th e possibility of over-treatment or of losing critical health information. A multipronged effort is required to tackle th e challenges mentioned. As has been said by several people, a complete revamp of the US health care sector is needed. One way of reducing costs and increasing efficiency is to reduce variations in treatment. Variations can be determined by assessing the treatments patients receive, which form the flow of a patient through their care cycle. Information obtained from patient provider interactions
8 can be used to improve the provided care. This study addresses the need to evaluate patient flow over the treatment care cycle. By identifying and analyzing flow, decisions can be made to improve the efficiency, e ffectiveness and the timeliness of care. 1.3 Overview of Research Objectives Information sharing among different providers is the only way to create patient pathways, where all treatments can be chronologically identified and arra nged. Unfortunately, the fragmented nature of health care has resulted in information silos where information is not easily shared among different providers. Hence, it is not an easy task to trace a patients path through multiple episodes of care. The flow identification is an insurmountable task when patients have to go to different unaffiliated providers during the course of their treatment. The objective of this study is to develop a fr amework to identify patient flow over a cycle of care from fragmented databases. This fram ework can be used to track patient flow for any diagnosis or treatment. The subsequent analysis of the flow can be used to make the care delivery more patie nt-centric. Further details on th e study objective can be found in chapter 3. 1.4 Thesis Organization This thesis is organized into six chapters. Ch apter 2 is a review of pertinent literature on patient flow in both outpatient and inpatient settings. Sections on c linical pathways and electronic medical records (EMR ) are also presented. Chapter 3 introduces the research objectives, methodology and the co rresponding benefits of this study. Chapter 4 describes the patient data obtained on implementing th e rules for patients who underwent a lung
9 resection. Chapter 5 outlines the framework rules. The first section of chapter 5 describes the protocols and rules for identifying rele vant information of patients undergoing lung resection. The second section of chapter 5 expa nds these rules and describes the protocol to follow to identify relevant patient info rmation for any intervention. Conclusions and future work are discussed in chapter 6.
10 Chapter 2 Literature Review This chapter outlines the major efforts that have been made towards improving health care delivery, especially in th e area of improving patient flow The first section reviews studies on patient flow, while sections 2.2 and 2.3 are devoted to cl inical pathways and electronic medical records (EMR). 2.1 Patient Flow Increased focus on improving efficiency in he alth care delivery came about in the 1980s when the federal government adopted a prospe ctive payment system (PPS) that resulted in fixed payments for medical services. Consequently, providers had to focus on efficiency to reduce costs due to delays (Cote, 2000). This resulted in a surge in studies on improving flow in the delivery of health car e services, since analyses had shown that just adding resources would not improve the s ituation, as the delays are due to problems in flow and not necessarily with resources (Haraden & Resar, 2004). Multiple approaches and techniques have been used to enhan ce flow, including disc rete-event simulation (DES), scheduling, optimization, Markov chai ns, linear programming, queuing theory, and data mining. Smoothing demand for elect ive surgeries (ES), reducing wait times, allocating resources, and achieving timely and e fficient transfer of patients have been the
11 common areas of focus (Haraden & Resa r, 2004). Jun, Jacobson & Swisher (1999) provide a comprehensive survey of studies that have been conducted by using DES, which has become a widely used tool for modeling patient flow. Hall (2007) expands upon that survey and provides updates. The next two sub-sections outline studies in outpatient and inpatient segments. 2.1.1 Outpatient Flow Many of the papers on improving health care de livery are in the outpatient or ambulatory sector. The ambulatory sector comprises doct ors offices, single and multiple clinic systems, and ambulatory surgery systems. Most studies in this ar ea are in improving the scheduling of appointments, reducing waiting times, the callback of patients in the waiting room, improving the layout in ambulator y clinics, and reducing demand peaks for ambulatory ES. Cayirli & Vera (2003) provide a comprehe nsive review of studies on appointment scheduling in outpatient services. AharonsonDaniel, Paul & Hedl ey (1996) built a simulation model in MedModel of an outpatien t clinic in Hong Kong to demonstrate the use of DES to reduce patients waiting time to see a physician. Clague et al. (1997) used a computer simulation model to study the eff ects of changes in clin ic size, consultation time, patient mix, appointment scheduling and non-arrivals on the patient and physician waiting times. They discovered that a 1 in 4 ratio of new to follow-up patients reduced the patient waiting time considerably and ne w patient appointments should be optimally spread throughout the clinic to have a signi ficant impact on the waiting time of patients. Dexter (1999) provides guideli nes on designing appointment systems for pre-anesthesia
12 evaluation clinics. According to this author, the major factor s that lead to long patient waits are patient punctuality, provider tardiness and walk-on patients. With the help of a simulation, he demonstrated that wait times can be reduced by decreasing the standard deviation of consultation times (some of which, in his study, were as high as the mean consultation time itself) and deliberately reduce provider productivity by scheduling breaks or longer appointment times to ta ke into account walk-on patients. Edward et al. (2008) used two simulation models to reduce the maximum waiting time to 10 minutes for 95% of all patients in a preoperative assessment clinic. Edwards et al. (1994) used a simulation model of two outpati ent clinics and was ab le to reduce waiting time by 30%. Harper & Gamlin (2003) eval uated multiple scenarios or policies by building simulation models to reduce patient waiting time in an Ear, Nose and Throat (ENT) clinic in the UK. Klassen & Rohled er (1996) built a simulation model of a dynamic medical outpatient environment to e fficiently schedule different types of patients and found that the leas t waiting time occurred if pati ents with large service time standard deviations were scheduled towards the end of the appointment session. One of the major issues in outpatient care de livery has been patient walk-ins and noshows, which disrupt appointme nt schedules. As a result, many clinics now provide open access scheduling where patients make appoint ments only a few days before seeing the physician. Kopach et al. (2007) studied the effect s of four variables fraction of patients served on open access, the scheduling horiz on, provider care groups, and overbooking on the waiting time, and concluded that correctly configured open access scheduling leads to improvements in patient throughput. Rohrer et al. (2007) studied the difference in access
13 between clinics with traditional schedu ling systems and those with open access scheduling. Studies have also been conducte d on how staffing policies and resource allocations affect waiting time. Huarng & Lee (1996) built a simulation model to study how changes in the appointment system, staf fing policies and servic e units affected the patient waiting queues. Swisher et al. ( 2001) used simulation to conduct what-if analyses on staffing levels, facility desi gn, scheduling policies and operating hours in a family practice health care clinic. 2.1.2 Inpatient Flow As with outpatient facilities, a number of studies have been done in the inpatient segment or hospitals. Most studies in these facilities are on sche duling admissions, bed planning, optimizing the emergency department (ED), reducing admission and discharge delays, and smoothing demand for ES. The common areas of focus have been the ED, the operation room (OR), and the acute care units (ACU). Magerlein & Martin (1976) published a comprehensive review of ES scheduling studies. Lowery (1996) used a simulation model to design an admissions sche duling system that re duces variability in hospital occupancy. The ED has been the most common focus of inpatient flow studies. Cowan & Trzeciak (2005) review the causes and effects of ED overcrowding in the US and explores its impact on critically ill ED patie nts. Trzeciak & Rivers (2003) also conducted a review of studies on ED overcrowding and came to the conclusion that overcrowded EDs compromise patient safety and jeopardize th e reliability of the entire US emergency management system (EMS). Schneider et al. (2003) and Spaite et al (2002) also studied
14 the level of crowding in EDs. Ambulance diversions have increased as more overcrowded EDs stop seeing new patients. Fatovich, Nagree & Sprivulis (2005), Olshaker & Rathlev (2006a) and Schull et al. (2003) studied the rela tionship between ED overcrowding and ambulance diversions and offe red solutions to reduce them. Eckstein et al. (2005) studied the impact of crowding on EMS personnel who are unable to transfer their patients to an ED. Multiple studies have evaluated or identified factors to reduce ED overcrowding. Terris et al. (2004) studied the impact on the patient wait time when an emergency medicine consultant and an ED nurse assisted in triage. The US General Accounting Office (United States General Accounting Office, 2003) identified the inability to transfer emergency patients to inpatient beds once a decision has been made to admit them as hospital patients rather than to treat and release them as the factor most commonly associated with ED overcro wding. Olshaker & Rathlev (2006b) and Schafermeyer & Asplin (2003) offer solutions to reduce ED overcrow ding. Miller, Ferrin & Szymanski (2003) and Howell, Bessman & Rubin (2004) studied two approaches to reduce ED overcrowding. The former utilized simulation and a six sigma approach to evaluate multiple scenarios to improve ED performance, while Howell et al. implemented a new direct admission system based on telephone consultation between ED physicians and in-house hospitalists and were able to reduce average admissi on time to 18 minutes compared to the previous average of 2.5 hours. Effective allocation of beds has become an important point to consider, since ineffective bed utilization has consequences on various other areas within a hospital. Proudlove, Gordon & Boaden (2003) concluded that an e ffective bed management system solved the ED overcrowding issue. Marshall, Vasilaki s & El-Darzi (2005) reviewed studies on
15 length of stay-based patient flow mode ls. Christodoulou & Taylor (2001) used a continuous time hidden Markov process to m odel bed occupancy of geriatric patients. Simulation models have also been used to improve bed allocation in hospitals (Dumas, 1984; el-Darzi et al., 1998; Mackay & Millard, 1999; McCl ean & Millard, 1995; Ridge et al., 1998; Mackay & Lee, 2005). 2.2 Clinical Pathways Clinical pathways are quickly gaining favor among practitioners as an effective tool to reduce delays and improve treatment efficienc y. (Coffey et al., 2005) define a clinical pathway as an optimal sequencing and timi ng of interventions by physicians, nurses, and other staff for a particular diagnosis or procedure, desi gned to minimize delays and resource utilization and to maximize the qua lity of care. Cheah (2000) mentions that clinical pathways have been shown to re duce unnecessary varia tion in patient care, reduce delays in discharge through more e fficient discharge planning, and improve the cost-effectiveness of clinical services. Furt her arguments state that clinical pathways delineate a plan to execute the best practices in patient care. Granted the pathways must have flexibility since they represent the majority, but not all patients. Numerous studies have been done on the im pact of implementing a clinical pathway. Walter et al. (2007) successfully implemented a clinical pathway for patients having total joint arthroplasty, and were able to not only reduce the length of stay and costs, but also increase patient satisfaction. Chen et al. (2000) found sim ilar results on implementing a pathway approach for patients undergoing head and neck oncologic surgery. Konishi & Agawa (2000), Chang et al. (1999), Kim et al. (2003), Husbands et al. (1999), Rauh et al.
16 (1999) and Calligaro et al. (1995) studied the impact of c linical pathways on various surgical procedures and concl uded that pathways not only reduce length of stay and costs, but also increases patient and provider sati sfaction. Zehr et al. (1998) found that the implementation of a standardized clinical pathway for major thoracic surgeries reduced hospital length of stay and co sts. Collier (1997) found simila r results for major vascular surgeries. But not all pathway implementations have been successful. Bailey et al. (1998) found no significant difference in length of stay between pathway and non-pathway patient groups after the implementation of a clinical pathway in asthma, though they did observe cost savings due to increased use of alternate treatment opti ons. Weingarten et al. (1998) also observed a mix in success rates in reducing length of stay for patients having hip or knee replacements. Cardoen & Demeulemeester (2008) devel oped a DES model of a consultation and surgical suite in a Belgian hospital to evaluate the efficiency of clinical pathways and their complex interdependencies of resource usage and patient throughput. Napolitano (2005) outlines the creation of clinical path ways from national guidelines into a local setting for the management of patients under going common surgical procedures. Forkner (1996) mentions the liabilities associated with clinical paths, while Sheehan (2002) provides a liability checklist for ensuring safe and responsible use of pathways. Eccles & Mason (2001) explores the methods to incorpor ate cost issues within clinical guidelines. Asadi & Baltz (1996) provides a methodology to create pathways that combine both clinical and financial data to measure how efficiently human, material and capital resources are allocated to provide services. Smith & Hillner (2001) conducted a review of studies on improvements in oncology treatm ent processes or outcomes and found a mix
17 of successful and unsuccessful attempts. They mention that programs that have not succeeded have relied on voluntary change in practice behavior without incentives to change or have had no accountability component. Every et al. (2000) and Butterworth (1997) present guidelines on creating clinical pathways and successfully implementing them. Kingston, Krumberger & Peruzzi (2000) e xplored the benefits and barriers to using clinical guidelines and examines processes th at are critical to constructing valid tools. Barnette & Clendenen (1996) provides an exam ple of a community behavioral centers transition to clinical pathways. 2.3 Electronic Medical Records EMRs are garnering widespread discussion, w ith researchers, provi ders, and the public increasingly identifying it as a way to im prove the quality of care, reduce costs and ensure a continuum of care for the patient. Numerous studies have been conducted that identify the benefits of EMRs. Strategi es for implementation and surveys of EMR adoption rates are also widely discussed in the literature. Kazley & Ozcan (2008) compared quality out come between hospita ls using EMR with those that do not use EMR in 3 clinical conditions acute myocardial infarction, congestive heart failure, and pneumonia. Th ey found a positive significant relationship between EMR use and increased quality in the first two c onditions. Asch et al. (2004) studied 12 VHA health care systems and concluded that VHA patients received higher quality care due to the introduc tion of an integrated EMR. Spencer et al. (1999) found that EMRs along with continuous quality im provement lead to improvements in the screening and documentation for smoking status at a clinic. Kinn et al. (2001) studied the
18 impact of EMR on patient outcomes at an outpatient cardiology clinic and found that patients with EMRs received significantly more appropriate care than those without. Garrido et al. (2005) evaluated the impact of EMR implementation on the use and quality of ambulatory care at Kaiser Permanente and concluded that readily available, comprehensive, integrated clinical inform ation reduced use of ambulatory care while maintaining quality Several other studies have also come to similar conclusions. Besides improving the quality of care, EMRs also have the potential to reduce costs. Wang et al. (2003) estimated the net cost bene fit from using an EMR in primary care for a 5 year period was $86,400 per provider. Even though there are poten tial benefits in implementing EMRs, only about 24 percent of physicians in ambulatory care and 5 percent of hospitals used EMRs through 2005 (Jha et al., 2006). Burt & Sisk (2005) analyzes the relationship between EMR a doption rates and physician and practice characteristics. Providers cite various factors that affect their decision to implement EMRs. These factors include a lack of national health care polic y, multiple EMR informatics standards, EMR implementation costs, privacy, and data en try issues. Vishwanath & Scamurra (2007) developed a comprehensive empirically based con ceptual model of the barriers that affect EMR adoption among physicians. Unless these issues are sorted, EMR adoption rates would not significantly increase. 2.4 Summary The studies previously reviewed have been conducted to improve the delivery of care. Various methodologies and approaches have been used towards that aim. Most of these
19 studies have been limited to individual clin ics or units within a hospital. Only a few address multiple units within an organizati on or multi-clinic facilities. The literature barely focuses on the entire care cycle of a patients medical condition from an operational viewpoint, though there are numerous papers from a medical perspective. As Porter & Teisberg (2006) mention, value can only be measured over the care cycle, not for an individual procedure, servi ce, office visit, or test. They define value as the health outcome per dollar of cost expended. Therefor e, there is an opport unity for studies on the entire care cycle of the patients medical condition, as th at is the only way to identify if the provided care is effective. Technological advances have made it possible to conduct procedures or interventions that could be highly beneficial to a patients treatment. These advant ages, though, come at a higher cost, and decisions have to be ma de regarding the effectiveness of these procedures to alleviate the patients medi cal condition. These decisions can be better made when the entire care cycle of the pati ent is analyzed, and the different treatment options weighed. For example, it could be mo re cost-effective to have an expensive intervention now and improve the patients cond ition that reduces th e level (and cost) of future care. However, the health care system is not struct ured to collect inform ation over a patients entire care cycle. Most providers store thei r own facilitys inform ation, which is episodic in nature. Information beyond th eir network is stored in pa per form; scanned and stored electronically, but as an object that is neither easily accessible nor searchable; or worse,
20 are shredded and relegated to the trash bin. It is believed that a universal electronic medical record (EMR) will improve information sharing among providers. Thus, to enable the analysis of the care cycle and improve care delivery, information not only needs to be shared and easily accessibl e among the different providers, but also a way has to be developed to glean pertinen t information from these multiple variant databases. This study attempts to address the latter by developing a framework to extract information from databases. It is believ ed that this will serve as a foundation for increased studies on developing methods to ex tract relevant information from medical databases that will enable the evaluation of care over the care cycle. This will enable further analysis, including effectiveness, costs, and other factors.
Chapter 3 Problem Statement 3.1 Introduction and Motivation Every medical condition has a treatment care cy cle with guidelines that state the type of care or treatment to be provided. Treatment is usually provided through multiple interventions within the cycle. The care cycl e starts from the onset of the disease and ends with its resolution or the patients death. For a majority of medical conditions, especially those considered serious, th e care cycle encompasses both ambulatory and inpatient care. Figure 1 il lustrates the care cycle. Figure 1. The Care Cycle (Source: (Fabri, 2008)) 21
22 In figure 1, insert 1 depicts the complete care cycle, while inserts 2-5 are smaller portions that together comprise the entire care cy cle the multiple episodes of care (insert 3), interventions (insert 4) or physician visits (insert 5). In insert 2 of figure 1, we introduce a segme nt of the care cycle. The segment of care can be defined as being comprised of multiple episodes of care, and is equal to or smaller than the complete cycle of care. Often patien ts visit multiple providers for receiving care pertaining to the same medical condition, and due to the non-sharing of data among providers, it is not always possi ble to obtain all care informati on of a patient. Hence, this makes it difficult to identify the specific ev ent(s) or encounter(s) that started, ended or form part of the care cycle. In situations wh ere that information is available, and one can say with a certain degree of confidence that the care cycle started and concluded with an identifiable specific encounter and all care pertaining to the cycle is available, the segment of care is equal to the complete care cycle. In other cases, and this is often the case, it is smaller th an the care cycle. To analyze the operational aspects of the treat ment, it is necessary to identify and study the flow of patients th rough the care cycle for a particular medical condition. An analysis of this kind will help to identify bottlenecks, redundancies, and unnecessary delays in the system, all of which constitute some of the major causes for exploding costs in the health care sector. Further, the actual treatments during the care cycle can be compared with the established guidelines to identi fy variations in the expected patient pathways. Over and under-treatment of patients can also be id entified through such an analysis. Yet,
23 conducting a study of this nature could be a da unting task due to th e inaccessibility of pertinent patient trea tment information. One of the major problems is obtaining a patients care information from the multitude of providers treating him/her for the same me dical condition. This makes it impossible to obtain information on every encounter that a patient undergoes during his/her treatment. The reason behind this is that the fragmented nature of health care has resulted in information silos where information is not easily shared among different providers. Even among in-network providers, the informa tion is stored in disparate databases that often do not lend themselves to easy portability. This problem becomes even more acute when patients go to out-of-network providers during the course of their treatment. Fortunately, a few providers have developed an integrated ne twork where all care can be provided within the network itself, and the patients EMR is shared among them. By accessing the patients EMR in these integrat ed networks, one can identify all clinic visits, inpatient admissions, and clinical or surgical procedures th at the patients have undergone over several years in that specific network. This study attempts to remove this barrier by developing a set of rules that govern a framework to enable the extraction of pe rtinent information from the fragmented databases of providers and use it to create a pathway of patients undergoing treatment in a specific care cycle. 3.2 Research Objective As figure 3.1 shows, the complete care cycle is comprised of sma ller episodes of care. Information on these episodes can be obtained through patient records, but due to the
24 fragmented nature of the collected data, it is not an easy task. The main objective of this study is to develop a set of ru les that form a framework to extract pertinent information from databases to identify and map patient pathways over a segment of the care cycle for a specific medical condition. The framework outli nes how data from administrative (even fragmented) databases of health care organi zations could be stru ctured to identify relevant patient provider interactions that are part of the care cy cle. It will enable providers, patients, medical researchers, and other stakeholders to identify and analyze the patients care over the care cycle. The set of rules are implemen table if all patient encounters are stored electroni cally. Paper records need to be entered into the electronic system for it to be used in the process. It is expected that this coul d be a foundation that will pave the way to better address some of the issues, including delays, rising costs, and medical errors that are plaguing the health care sector, and provide ti mely, effective, efficient, a nd patient-centric care four of the six aims identified by the Institute of Medicine for improvi ng the quality of care (Committee on Quality of Health Care in America, Institute of Medicine, 2001). The benefits that can be achieved by being able to identify the fl ow of patients across the care cycle are outlined in the next section. 3.3 Benefits of the Research The benefits of this analysis are as follows: identify the expected flow of pa tients on a particular care cycle help providers make resource allocation decisions based on the expected flow
25 help providers in comparing current treat ment guidelines to the care provided, and identify variations in a particular patient group. The framework can be modified to map patient flow for various procedures and medical conditions across diverse inpatient and outpa tient settings. Additional analyses, that would help reduce overcrowding and delays, can be conducted on the identified flow of patients. In addition, adheren ce to appropriate standards shou ld reduce the wide variation in treatment that is common today, reducing costs and delays, and increasing patient and staff satisfaction. Overall the treatment will be more patient-centri c, thus improving the quality of care. 3.4 Research Methodology This research was conducted using inform ation obtained from a Veterans Health Administration (VHA) medica l facility. VHA is one of the largest health care organizations in the US and has a nati onwide network of more than 1400 medical facilities (Department of Vete ran Affairs, 2008). Though the information used in this research came from a single facility, it can be duplicated at other VHA facilities since the electronic medical record system is iden tical across the network. James A. Haley Veterans Affairs Hospital (JAHVAH), where this study was based, stores patient treatment data into separate computer pack ages. These are categorized according to the type of data stored. There are more than a dozen packages with self-explanatory titles. For example, patient data related to operations are in a package titled surgery, inpatient movements are in another called patient move ment file, outpatient appointments in the package are titled appointments, and so on. Since the packages were designed for
specific transactions, multiple encounters of a patient often need to be obtained from different packages, yet redundancies also exist. A list of the packages was obtained from JAHVAH, from which a shortlist was created of those relevant for the study. After receiving the necessary IRB approval, JAHVA H provided the data, which was retrieved from the packages shown in figure 2. Figure 2. The Data Packages Used in the Extraction Process The VHA uses a unique patient identification number called Internal Entry Number (IEN), given to each and every patient obtaini ng treatment at a VHA facility. The IEN is provided at the first point of contact of the patient with a VHA facility, and is thereafter used for every transaction with any VHA facility (which assures that users cannot identify the patient).Through the use of IENs, it was possible to track patients care treatments over multiple episodes of care. Fo r the purposes of this study, it was assumed that the patients included in th e study got all their medical care requirements from a VHA facility, though, in reality, this might not always be the case. 26
27 The research team decided to use lung cancer as the medical condition, restricted only to patients who underwent a lung resection. The reasoning was that lung resection is a specific and identifiable event. Also, an overwhelming majority of patients undergoing lung resection have lung cancer and the treatme nt period is short and easily identifiable. From the surgery package, we developed a li st of de-identified pa tients who had a lung resection during the cal endar year 2007 by using Current Procedural Terminology (CPT) codes for lung resection proce dures. A total of 49 lung rese ctions were identified along with the 48 patients who underwent them. Then the other packages were queried for those particular patients. Using the IENs, the different packages were lin ked together with the help of a database application. The resulting da tasets were arranged chronol ogically and each encounter (visit, test, or procedure) was analyzed to de termine if it was related to the lung resection. 3.4.1 Development of the Timeline The first step in developing the timeline was to classify the care cycle into three distinct periods: The pre-admission period the period pr ior to the admission for the lung resection. The surgical period the inpatient ep isode that included the lung resection. The post-discharge period the period after discharge from the lung resection. The three periods are shown in table 1.
Table 1. The Three Periods in the Segment of the Care Cycle Pre-Admission Period Surgical Period Post-Discharge Period The pre-admission and post-discharge periods were further classified into three distinct phases or chronologic windows based on the following: the window closest to the resection was defined as having all encounters during that phase related to the res ection this is Window 1 the next window was defined as having en counters that were likely related to the resection except a few that were clearly id entified as unrelated this is Window 2. Most encounters in this window were included. the window furthest from the resection was defined as having encounters that were not likely related to the resection ex cept a few that were clearly identified to be related this is Window 3. Most en counters in this window were excluded. The structure of this classifi cation is shown in table 2. Table 2. The Care Cycle Windows Window 3 Window 2 Window 1 Surgical Episode Window 1 Window 2 Window 3 Pre-Admission Period Post-Discharge Period Clinic Visits Clinic Visits Clinic Visits Surgery Clinic Visits Clinic Visits Clinic Visits Procedures Procedures Procedures Procedures Procedures Procedures Tests Tests Tests Tests Tests Tests 28
29 The probability of an event being related to th e lung resection decreases as we move from window 1 to window 3 in both pre-admission and post-discharge periods. To develop a timeline for the care cycle, relative dates were assigned to each encounter based on their relation to the resection and is discussed in the next chapter. Based on the final version of the related events a clinical pathway (over a segment of the cycle) was developed for the patients undergoing lung resec tion at JAHVAH; the results are mentioned in the next chapter.
30 Chapter 4 Identifying the Flow of Lung Resection Patients The patient flow data were analyzed and th e results evaluated to identify flow patterns among the 48 patients undergoing lung resecti on at the hospital. The flow includes every encounter, i.e., a clinic visit, a test, or a procedure, related to the surgery. This flow represents a segment of care of the patient, and can be broken down into inpatient and outpatient episodes. Every patient had their lung resection as an inpatient, and that episode was further analyzed to identify flow patte rns during that part icular episode of care. As mentioned in the previous chapter, the segment of care was categorized into three periods the pre-admission period which leads to th e admission for surgery, the surgical inpatient episode that includes the lung surgery, and the post-discharge period following discharge from the hospital after th e resection. To develop a timeline for the care cycle, relative dates were assigned to each encounter based on their relation to the surgery. 4.1 Creating the Timeline The following rule was used to create the timeline. The inpatient episode of car e, which includes the lung re section, is considered a time interval.
o This interval is from admission to di scharge of the patient and includes the lung resection. o This interval differs for each patient, and could vary from a few days to several weeks. o This characterization ensures cons istency, since all encounters on the timeline are episodic. Also, it create s a clear demarcation point between the pre-admission period, the surgical episode and the post-discharge period. The day of every encounter that occurs before admi ssion, viz., the pre-admission period, has been assigned negative number s according to their chronological order from the admission date. For example, -1 is one day prior to the admission, -2 is two days prior and so on. Similarly, encounters after discharge fr om the resection the post-discharge period, were assigned positive numbers, e.g., +1 is one day after discharge, +2 is two days after discharge, and so on. The timeline is exemplified in table 3. Table 3. Relative Days in the Segment of Care Cycle Pre-Admission Period Surgical Episode Post-Discharge Period -365 -3 -2 -1 1 2 3 365 31
32 Based on the nature of cancer progression a nd the treatment options for lung cancer, the pre-admission period was considered to be a maximum of one year (365 days). The postdischarge period was truncated after one year (365 days) si nce treatment is usually an ongoing process after surgery. Th e sections that follow describe the results for the inpatient surgical episode, the pr e-admission and post-discharge periods. 4.2 Determining the Windows The length of each window in both the preadmission and post-discharge periods was determined separately by the following process: All encounters that a patient had were arranged chronologically. o Pre-admission encounters were arranged from the patients first encounter up to a year before surgery and le ading to the surgical admission. o Post-discharge encounters were arra nged from the surgical discharge going forward one year after discharge. Every encounter was classified as being ei ther related or not related to the lung resection. It is important to note that encounters had to be related to the lung resection but not the lung cancer. The related encounters were extracted into a matrix, with encounters as rows and the relative dates as columns. o The first occurrence of encounters that due to their nature had periodic occurrences was included in the matr ix. An example is visits to an
33 The encounters were clustered into 30 day blocks based on their relative dates (e.g., relative dates 1-30 were grouped in to block 1, relative dates 31-60 became block 2, and so on). 30 day blocks were chosen since they are generally used to define timelines. The frequencies of the related encounters in each month were determined and were sorted in descending order. The fr equencies were used to determine the time lengths that formed each of the windows defined earlier. The windows for each period were determined as follows: Pre-admission period (days before surgical admission) o Window 1: Days (-1) to (-30) o Window 2: Days (-31) to (-180) o Window 3: Days (-181) to (-365) Post-discharge period (days after surgical discharge) o Window 1: Days 1-30 o Window 2: Days 31-180 o Window 3: Days 181-365
34 Even though the data were analyzed sepa rately for pre-admission and post-discharge periods, both periods were found to have similar window lengths. The next three sections explain the encounters that we re identified after applying the rules in the three periods defined above. 4.3 The Pre-Admission Flow The pre-admission period starts 365 days before admission to surgery and ends the day before the admission of the patient to lung re section. As mentioned before, this period was divided in three windows based on the anal ysis of the data, and shown in table 4. Table 4. Windows in the Pre-Admission Period Window 3 Window 2 Window 1 Admission for Lung Surgery Days 365-181 Days 180-31 Days 30-1 Clinic Visits Clinic Visits Clinic Visits Surgery Procedures Procedures Procedures Tests Tests Tests This period is comprised of outpatient or ambul atory encounters, viz., clinic visits, tests, and outpatient procedures. After grouping sim ilar encounters, there were a total of 535 encounters, out of which 25 were unique. The encounters are listed in table 5. Appendix 1 depicts the classification of these encounters. Appendix 2 shows the flow of each patient in the pre-admission period.
Table 5. Frequency of Each Encount er in the Pre-Admission Period Encounter Name Number of Total Encounters Encounter Name Number of Total Encounters All Encounters 535 Myocardial Perfusion 10 Xray 63 Cardio ABG 7 Oncology 62 Gated Spect 6 CT 58 CT Guidance 4 Lung Nodule Clinic 48 Stress Test 4 Pre-Op Clinic 48 Lung Biopsy 2 PACM 48 Barium 1 Pre-Anesthesia Clinic 48 Bone Scan 1 PFT 41 Bone Surv Comp 1 Radiology 26 EKG 1 PET 20 MRI 1 Pulmonary Procedure 19 Quantitative Perfusion 1 Thoracic Surgery 14 Urology Oncology 1 Table 6 shows the encounters in each of the wi ndows. Figure 3 shows the frequency of the encounters in each window. Appendix 7 provides a detailed view of the frequencies of each encounter in the pre-admission timeline. Figure 3. Encounter Frequenc y in Pre-Admission Windows 35
Table 6. Encounters in Each of the Pre-Admission Windows Days Clinic VisitsProcedures Tests OncologyPulmonary ProcedurePFT Radiology Lung Nodule Thoracic Surgery OncologyPulmonary ProcedureCT Lung Nodule Myocardial PerfusionXray RadiologyCardio ABG PFT Thoracic SurgeryGated Spect PET Lung Biopsy Stress Test BA CT Guidance Gastronomy Tube PACS Bone Surv Comp Quantitative Perfusion Pre-Op Pulmonary ProcedureXray PACM Gated Spect PFT Pre-AnesthesiaMyocardial PerfusionCT Lung Nodule Cardio ABG PET Oncology CT Guidance Thoracic Surgery Bone Scan Urology Oncology EKG MRI Window 2 (180-31) Window 1 (30-1) Window 3 (360-181) Stress Test 4.4 The Inpatient Episode: Admi ssion, Resection and Discharge The inpatient episode corresponds to the pe riod when the patient was admitted to the hospital and underwent lung surgery. This period starts with the ad mission of the patient and ends with the discharge of the patient. During that period, the pa tient was transferred from one unit or ward to anot her to undergo the resection. There were a total of 12 units having at le ast one patient visit. Figure 3 depicts the number of visits that were made to each of the above 12 areas of the hospital, categorized into preand post-surgery. Appendix 3 pr ovides a description of these units. 36
Figure 4. Number of Visits Made to Each Inpatient Unit The usual flow for patients w ithout any complications is de picted in figure 4. Table 7 shows the frequency of patients following sp ecific paths. Appendix 4 shows the detailed flow of each inpatient episode. Figure 5. Expected Flow of Inpa tients Undergoing Lung Surgery 37
38 Table 7. Common Inpatient Flows and their Frequencies Series Number Inpatient Flow Number of Patients Percentage of all Patients 1 6ST-Hold Area-OR-PACU-SICU-6ST-Discharge 13 26.5 2 6ST-Hold Area-OR-SICU-6ST-Discharge 5 10.2 3 6ST-Hold Area-OR-PACU-SICU-6ST-RAD-6STDischarge 4 10.2 4 6ST-Hold Area-OR-SICU-6ST-RAD-6STDischarge 4 8.2 5 6ST-Hold Area-OR-PACU-SICU-6ST-RAD-6STRAD-6ST-Discharge 3 6.1 6 6ST-Hold Area-OR-PACU-Discharge 2 4.1 7 6ST-Hold Area-OR-PACU-6ST-Discharge 2 4.1 Unique Single Patient Paths 17 34.7 4.5 The Post-Discharge Flow The post-discharge period starts the day afte r the discharge of the patient from surgery and extends 365 days after discharge. Sim ilar to the pre-admission period, the postdischarge period has also been divided in to three windows as shown in table 8. Table 8. Windows in the Post-Discharge Period Discharge from Lung Surgery Window 1 Window 2 Window 3 Day 0 Days 1-30 Days 31-180 Days 181-365 Surgery Clinic Visits Clinic Visits Clinic Visits Procedures Procedures Procedures Tests Tests Tests The encounters in this period are outpatient episodes including clin ic visits, tests and procedures. There were a total of 439 enc ounters, out of whic h 25 unique encounters
39 were identified after grouping similar encounters. Table 9 sh ows the different encounters and their frequencies. The cl assification of these encounter s can be seen in Appendix 1, while a detailed flow of patients in the pos t-discharge period can be found in Appendix 5. Table 9. Frequency of Each Encounter in the Post-Discharge Period Encounter Name Number of Total Encounters Encounter Name Number of Total Encounters All Encounters 439 PACM 3 Oncology 115 Pre-Anesthesia 3 Xray 72 PET 2 CT 68 Bone Surv Comp 1 Thoracic Surgery 68 CT Guidance 1 Oncology Procedure 50 ER 1 Radiology 15 Fluoro 1 Resp Care/Home Oxygen (1BS) 9 Lung Biopsy 1 Pulmonary Procedure 8 Lung Nodule 1 Bone Scan 5 PCC Women 1 MRI 4 Scan 1 PFT 4 Urgent Care 1 Pre-Op 4 Urology Oncology 1 Figure 6 displays the frequency of the en counters in each window. Table 10 shows the encounters in each of the windows. Appendix 8 provides a detailed view of the frequencies of each encounter in the post-discharge timeline.
Figure 6. Encounter Frequency in Post-Discharge Windows Table 10. Encounters in Each Post-Discharge Window Days Clinic Visits ProceduresTests Thoracic Surgery Pulmonary ProcedureXray Oncology Oncology ProcedureCT Radiology PFT Resp Care/Home Oxygen (1BS) MRI PCC Women PACM Pre-Op Pre-Anesthesia Urology Oncology Urgent Care ER Oncology Oncology ProcedureCT Thoracic Surgery Pulmonary ProcedureXray Radiology Lung BiopsyBone Scan Resp Care/Home Oxygen (1BS)Bone Surv CompMRI PACM PET Pre-Op PFT Pre-Anesthesia Bone Scan CT Guidance Oncology Oncology ProcedureCT Thoracic Surgery Pulmonary ProcedureXray Radiology Fluoro Resp Care/Home Oxygen (1BS) Pre-Op Pre-Anesthesia PACM Lung Nodule Window 1 (1-30) Window 2 (31-180) Window 3 (181-360) 40
41 Chapter 5 The Framework Rules This chapter describes the rules that we re developed to govern the framework to identify and extract relevant patient informa tion from medical databases. The first section outlines the protocol and rules to identif y relevant patient encounters for patients undergoing a lung resection, while section 2 de lineates a generic vers ion of those rules that can be used for patients undergoing any intervention. 5.1 Rules for Patients Undergoing Lung Resection This section describes the protocol for iden tifying relevant patient information from medical databases for patients undergoing a lung resection. These ru les can be easily implemented for VHA databases, while a s light modification, viz., changing the VHA clinic names to the providers corresponding names, might be needed for other extracting the information from other providers databases. The segment of care (a two year time frame) was broken down into pre-admission period, surgical inpatient period, and post-discharge period. The next sub-sections describe the rules in each of these periods.
42 5.1.1 The Pre-Admission Period This period starts 1 year (365 days) before the surgical admission and culminates with the admission for lung resection. Th e three windows in this period were determined as described in section 4.2. The related encounters (clinic visits, tests and procedures) were identified as: Clinic Visits Table 11 identifies the clinic visits that are related whenever they occur in the pre-admission period, while table 12 identifies clinic visits that are related only if they occur within 30 da ys prior to the surgical admission. Table 11. Related Clinic Visits Irr espective of When They Occur Clinic Visits Oncology Lung Nodule Thoracic Surgery Radiology Table 12. Related Clinic Visits Only if they Occur Within 30 days of Admission Clinic Visits Pre-Op PACM Pre-Anesthesia Urology
43 Procedures Table 13 identifies procedures that are related whenever they occur in the pre-admission period. Table 13. Related Procedures Irre spective of When They Occur Procedures Pulmonary Procedure Lung Biopsy Tests Table 14 identifies tests that are related whenever they occur in the preadmission period. Table 14. Tests that are Related Whenever they Occur in the Pre-Admission Period Tests Tests Tests Xray CT Guidance Stress Test CT EKG BA PFT MRI Bone Scan PET Cardio ABG Bone Surv Comp Myocardial Perfusion Gated Spect Quantitative Perfusion 5.1.2 The Surgical Inpatient Period The surgical inpatient period wa s divided into 3 categories: Pre-surgical stay: Admission to surgery Surgery Post-surgical stay: Surgery to discharge Table 15 shows the units that were visite d by patients during the surgical admission period.
44 Table 15. Units that were Visited by Patie nts During the Surgical Admission Period Pre-Surgical Stay Surgery Post-Surgical Stay 6 South Thoracic ward OR Post Anesthesia Care Unit 6 South Cardio ward Surgical Intensive Care Unit 4 South Thoracic ward Medical Intensive Care Unit Medical Intensive Care Unit Radiology Spinal Cord Unit ward 6 South Thoracic ward Genito-Urinary Clinic 6 South Cardio ward Pre-Op Hold Area 5.1.3 The Post-Discharge Period This period starts from the discharge from lung resection and culminates 1 year (365 days) after the surgical discharge. The three windows in this period were determined as described in section 4.2. The related encounters (clinic visits, tests and procedures) were identified as: Clinic Visits Table 16 identifies the clinic visits that are related whenever they occur in the post-discharge period, while tabl e 17 identifies clinic visits that are related only if they occur within 30 days after the surgical discharge. Table 16. Related Clinic Visits Irr espective of When They Occur Clinic Visits Oncology Thoracic Surgery Lung Nodule Radiology Resp Care/Home Oxygen
45 Table 17. Related Clinic Visits Only if They Occur Within 30 Days of Discharge Clinic Visits ER Urgent Care Urology Primary Care Clinic Procedures Table 18 identifies procedures that are related whenever they occur in the post-discharge period. Table 18. Related Procedures Irre spective of When They Occur Procedures Oncology Procedure Pulmonary Procedure Lung Biopsy Tests Table 19 identifies tests that are related whenever they occur in the postdischarge period, but only if they are requested by oncology, thoracic surgery or lung nodule clinics PFT is related only if it occu rs within 2 months of the resection. Table 19. Tests that are Related Whenever Th ey Occur in the Post -Discharge Period Tests Tests Xray PET CT Bone Surv Comp Bone Scan CT Guidance MRI Fluoro
These rules were used to determine the flow of each of the 48 patients during the selected care cycle. An example of the flow is show n in table 20. It depicts flow in the three periods defined above. The identified care cycl e starts 133 days before surgical admission and ends 319 days after surgical discharge. In each of the periods, the column on the right is the encounter type and the column on the left is the relative date of occurrence of that encounter. In the inpatient episode, the i de notes an inpatient episode where the surgery was given a relative date of zero in that period. Table 20. Flow of a Patient in the Care Cycle 133Xray -0i6ST 11Thoracic Surgery 98CT -0iHold Area20PCC Women 79Thoracic Surgery 0iOR 20Oncology 73Oncology 0iPACU 46Thoracic Surgery 52PET 0iSICU 139Oncology 51PFT 3i6ST 139Xray 51Pulmonary Procedure 6iRAD 247Oncology 38Oncology 6i6ST 262Oncology 11CT 7iRAD 319Oncology 9Lung Nodule 7i6ST 3PACM 8iDischarge 3Pre Op 3Pre Anesthesia 3Xray Pre Admission Period Inpatient Episode Post-Discharge Period Table 21 shows the path followed by another patient. This patients identified care cycle started 175 days before surgical admission a nd culminated 335 days after the discharge from the surgical episode. 46
Table 21. Care Cycle Flow of Another Patient \ 175Radiology -0i6ST 6Urgent Care 151Oncology -0iHold Area 6ER 151Xray 0iOR 6Xray 95RAD CT 0iSICU 12Thoracic Surgery 94CT 3i6ST 12Xray 88Oncology 4iCCU 13Resp Care/Home Oxygen (1BS) 62CT Guidance 7i6ST 25Resp Care/Home Oxygen (1BS) 62Lung Biopsy 10iDischarge41Oncology 61Xray 74Xray 60Oncology 74CT 59Xray 95Oncology 47Lung Nodule 117PET 18Oncology 139Oncology 14PACM 139Resp Care/Home Oxygen (1BS) 14Pre Op 140CT Guidance 14PFT 140Lung Biopsy 13Xray 153Pulmonary Procedure 188Oncology 227CT 242Xray 265Oncology 328Resp Care/Home Oxygen (1BS) 333Oncology 335Oncology Pre Admission Period Inpatient Episode Post-Discharge Period A comparison of the two paths (table 20 & 21) shows the differences in the path of the patients. These paths can be further studied by medical experts to iden tify variations and standardize care. The complete paths of all 48 patients are provided in Appendices 2, 4 and 5. This section described the rules for dete rmining the flow of lung resection patients in a segment of the care cycle. The next sec tion describes the generalized rules that can be implemented in any organization and for any intervention 47
48 5.2 The Universal Framework Rules This section lists the rules that govern the framework for extracting pertinent information from databases. The following rules have b een generalized for im plementation in any organization and for any intervention. The firs t sub-section describes the protocol to identify and arrange the sample of patients. The second sub-section describes the rules for identifying the related encounters (to the intervention) of that patient and the time frames during which they are related. It is important to keep in mind that before embarking on such a study, it might be essential to obtain the necessary IRB approvals. 5.2.1 Identifying and Arranging the Sample Following is the procedure to identify the sample of patients that are going to be studied: Identify an intervention that a se t of patients underwent during their care cycle, e.g., an inpatient or outpatient procedure. Define this as the event Determine the period of study dependi ng on the nature of the medical condition and its associated care cycle, e.g., 1 year before and after the event. Identify all occurrences of that even t using CPT codes from the list of all patients undergoing interventions. From that list, identify the IENs of all those patients (for patients outside the VA system, IENs are the same as patient IDs or numbers) Identify all encounters by querying for thos e IENs in all patient data files, e.g., clinic appointments, operations, admi ssions, tests, clin ical procedures. Create a chronological order of all patie nt encounters during the period under study
49 Identify and remove encounters that ar e clearly unrelated to the event. The remaining list now consists of mostly related encounters. Call this List 1. Group encounters that have the same purpose, e.g., lung nodule clinic and pulmonary nodule clinic serve the same purpose and can be grouped together. Divide the care cycle segment into 3 categories o Pre-interven tion period o Intervention period o Post-intervention period 5.2.2 The Pre-Intervention Period This sub-section provides rules to determine th e related encounters and to further classify the time frames of each of the periods the pre-intervention period, the intervention period and the post-intervention period. The following are the rules for the pre-intervention period. Create a matrix of all related pre-intervention encounters, wi th the encounters as rows and relative dates as columns. Identify encounters that have periodic occurrences and only retain the first occurrence. Call this list 2. From list 2, determine the frequencies of the encounters and arrange them in descending order. It might be easier to group the dates into larger time periods, e.g., months rather than days. Divide the pre-intervention peri od into 3 groups or clusters.
50 o The first group (closest to the inte rvention) should have all encounters within it related to th e event. This is group 1. o The second group (next closest to the intervention) should have almost all encounters related to the event except a few clearly identified as unrelated. This is group 2. o The third group (furthest from the intervention) should have only a few clearly identified encounters related to the event from all encounters within it. This is group 3 and will also form the end of the period under study. From list 1, extract encounters in the preintervention period and classify them into clinic visits, procedures and tests o Clinic Visits, Procedures and Tests Identify clinic visits, procedures and tests that are related to the event, e.g., an encounter might be related to the event throughout the entire period, while another might be related only if it occurs within a certain time frame from the event. 5.2.3 The Intervention Period Depending on where the intervention occurre d, in an ambulatory or inpatient setting, there are two ways to identify patient movements during this period. Intervention in an inpatient setting: Divide the intervention interval into 3 categories: o Pre-intervention stay: Admission to intervention
51 o Intervention (the event ) o Post-intervention stay: In tervention to discharge Identify all patient movements during the period of the intervention and classify them according to the three cat egories defined in the above step. Establish the dates in the intervention pe riod relative to the date of the event. Hence, all inpatient movements from the admission to the intervention will have negative numbers and inpatient movements from the intervention to discharge will have positive numbers. To differentiate this numbering polic y from the previous one, denote all inpatient episodes with a subscript i. For example, -1i would be an inpatient encounter occurring one day befo re the intervention. Similarly, 1i is the inpatient encounter occurring one day after the intervention. Intervention in an ambulatory setting: Interventions in the ambulatory sett ing are same day interventions and patients do not stay overnight. If desired, the patient movements can also be identified in the ambulatory setting starting from prepping the patient for the intervention, the intervention itself and the subsequent recovery from the intervention. These encounters will occur with hours (or mi nutes) as the time unit since all encounters in this period will occur on the same day. To differentiate these ambulatory en counters, the relative dates can be denoted by the subscript a in the time line, e.g., -1a will be one hour (or
52 another time unit) before the intervention and 1a; 2a will be one hour and two hours after the intervention (or event), respectively. 5.2.4 The Post-Intervention Period The rules in this period are similar to the pre-intervention pe riod, except that the encounters occurred after the intervention. Create a matrix of all rela ted post-intervention encounter s, with the encounters as rows and relative dates as columns Identify encounters that have periodic encounters and remove them from the list of encounters for the frequenc y analysis. Call this list 2. From list 3, determine the frequencies of the encounters and arrange them in descending order. It might be easier to group the dates into larger time periods, e.g., months rather than days. Divide the postintervention peri od into 3 groups or clusters. o The first group (closest to the inte rvention) should have all encounters within it related to th e event. This is group 1. o The second group (next closest to the intervention) should have almost all encounters related to the event except a few clearly identified as unrelated. This is group 2. o The third group (furthest from the intervention) should have only a few clearly identified encounters related to the event from all encounters within it. This is group 3 and will also form the end of the period under study.
53 From list 1, extract encounters in the post-intervention period and classify them into clinic visits, procedures and tests o Clinic Visits, Procedures and Tests Identify clinic visits, procedures and tests that are related to the event, e.g., an encounter might be related to the event throughout the entire period, while another might be related only if it occurs within a certain time frame from the event. The above section defined the protocol to follow to extract relevant patient medical information for identifying patient flow duri ng a care cycle. The best way to do it is by going through each patients medical records. Unfortunately, that is not an efficient approach when dealing with large datasets (at a regional or national level). The approach described above provides a set of rules that can be implemented to extract information from large datasets. It is hoped that research ers will utilize these ru les to develop patient pathways in the care cycle to enable furt her understanding of the care delivery system. Conclusions and future work are discussed in the next chapter.
54 Chapter 6 Conclusions and Future Work Aspects related to rising health care costs, lack of standardizati on, increasing medical errors, growing delays, and reduced access have diminished the quality of the US health care delivery system. As stated before, numer ous studies have been conducted to seek ways to improve care delivery. Unfortunately, most of them have been limited to single clinics or units, with barely any focus on the health care cycle. We reiterate that focusing on the health care cycle, and not the individual episodes within it, is what provides value to the patient. The highest valu e that a patient obtains is wh en (s)he receives the highest quality at the lowest cost. However, one ma jor gap that patients, providers, researchers and other stakeholders confront is the lack of access to congruent health and operational information over the care cycle. The current co llection and storage of health information makes it very difficult or even impossible to derive relevant information over the care cycle. The fragmented nature of data gathering processes and of the supporting information systems has led to minimal sharing of information among providers. This results in the loss of critical health informa tion often proving detrimen tal to the quality of the provided care. Unless systems and mechan isms are developed to improve information gathering and extraction that he lps illustrate and document the entire care cycle, the quality of care delivery will continue to be crippled. What has been accomplished in this research is a step to fill the need for c oherent data in a patients care cycle.
55 The authors of this study developed a set of rules that govern a fr amework that enables providers and researchers to extract meani ngful information from the disconnected database systems to create patient pathways through the care cycle of a patient. A specific event (lung resection) was chosen and encounters related to that event were identified and chronologically arranged to create the path way of the patients undergoing that event. Rules were determined such that they can be utilized with other databases to extract similar information. These rules can be modified and adapted to other medical conditions, as well as to other environments and/or providers. The research team successfully overcame the challenges of accessing data from disparate sources in a care cycle and demonstrated how the extracted data can be utilized to construct care paths. An example on how thes e care paths can be developed is presented for lung resection patients. The research cont ributes to the analysis of care cycles by developing an approach to obtai n relevant clinical information so as to subsequently identify patient flow over the entire care cycle, not just indivi dual interventions. This research leads to various opportunities for further work. One possible next step is to implement these rules in bigger datasets that will permit larger sample sizes, using a bootstrapping technique to refine the rules. A relevant approach is to access a large dataset (e.g., a national dataset of lung rese ction patients) and random ly select a sample of patients and apply these rules. Subsequent re-sampling from the database can be used to derive estimates of commonalities or differences, errors, perhaps confidence intervals and other information to refine the rules presen ted in this work. The modified set of rules can be implemented in other (including non-VHA) database systems to obtain more
56 comprehensive and accurate data over the care cycle, as well as to compare the federal and the private health care sector. Another consideration is to utilize the extrac ted clinical information to create the pathway of patients during a specific care cycle. Tw o kinds of analysis, from two distinct perspectives, can be conducted on the i ndividual pathways. From an operational viewpoint, the pathways can be evaluated to identify instances of delays, and further analysis may be conducted to determine th e causes of those delays. Reducing, if not altogether removing the delays will greatly lessen the time it takes for patients to receive proper care, thus, significantly reducing the potential for a de terioration of the patients health condition. Also, historical information on the flow of pa tients will enable providers to adjust their resources to the demand patte rns, improving the timelin ess of the provided care. In addition to improving the quality of care and increasing pa tient satisfaction, it could assist in identifying and linking sources of costs in the longer term. From a treatment policy perspective, these pathways can be analyzed by medical subject matter experts (SMEs) to evaluate vari ous aspects of care and improve existing treatment practices. Through the use of outcome data, one can evalua te the effectiveness of the differing pathways in a care cycle. Li nking cost data, as di scussed below, to the pathways (and the care cycle) will allow cost-benefit analyses. For example, one could use the quality-adjusted life year (QALY) inde xes but extended over the care cycle rather than just a single medical intervention. An im portant consideration of this approach is that patients with different co-morbidities may significantly deviate from the expected care path. Analysts have to ensure that th e cost-benefit analysis does not overlook these
57 deviations and any policies that are devel oped should have sufficient flexibility to incorporate the needed variations. A third possibility is to focus on effective wa ys to estimate costs linked to each encounter and allow for the analysis and evaluation of the true cost of the entire care cycle. It is apparent that much of the operational cost da ta, either derived or estimated, is in the form of billing (or claims), and does not necessarily represent the actual cost over the care cycle. As said before, many of the estimates of the rising costs of healthcare are at an aggregate (macro) level, but ve ry little is really known or understood at the operational or care cycle level. By undertaking this study, th e research team addressed the current challenge of unavailability of care cycle da ta to close the gap in studi es on care delivery over the health care cycle. It is believed that this will form the foundation for further studies on reconciling medical information from current medical database system s. It is expected that these rules will be used by providers and researchers to identify treatments over the complete care cycle and provide greater insi ght into the efficiency, effectiveness and timeliness of the care delivery system. Consequently, delays and costs will reduce and more standardized care will be delivered, resu lting in an improvement in the quality of provided care and making it more patient-centered.
58 List of References Aharonson-Daniel, L., Paul, R.J. & Hedley, A.J. 1996, "Management of Queues in OutPatient Departments: The Use of Computer Simulation", Journal of Management in Medicine, vol. 10, no. 6, pp. 50-8, 3. American College of Emergency Phys icians 2006, March 31, 2006-last update Gridlock in Nation's Emergency Departments Caus ed by Lack of Inpatient Bed Capacity, Not Patients with Nonurgent Medical Conditions [Homepage of ACEP], [Online]. Available: http://www.acep.org/pressroom.aspx?id=25662 [2008, American Hospital Association 2008, TrendWatch Chartbook American Hospital Association. American Hospital Association 2006, "Annual Survey Data", Asadi, M.J. & Baltz, W.A. 1996, "Activity-B ased Costing for Clinical Paths: An Example to Improve Clinical Cost & Efficiency", Journal of the Society for Health Systems, vol. 5, no. 2, pp. 1-7. Asch, S.M., McGlynn, E.A., Hogan, M.M., Hayw ard, R.A., Shekelle, P., Rubenstein, L., Keesey, J., Adams, J. & Kerr, E.A. 2004, "Comparison of Quality of Care for Patients in the Veterans Health Administ ration and Patients in a National Sample", Annals of Internal Medicine, vol. 141, no. 12, pp. 938-945. Bailey, R., Weingarten, S., Lewis, M. & M ohsenifar, Z. 1998, "Impact of Clinical Pathways and Practice Guidelines on the Management of Acute Exacerbations of Bronchial Asthma", Chest, vol. 113, no. 1, pp. 28-33. Barnette, J.E. & Clendenen, F. 1996, "Making the Transition to Critical Pathways: A Community Behavioral Hea lth Center's Approach", Best Practices and Benchmarking in Healthcare, vol. 1, no. 3, pp. 147-156. Burt, C.W. & Sisk, J.E. 2005, "Which Physicians And Practices Are Using Electronic Medical Records?", Health Affairs, vol. 24, no. 5, pp. 1334-1343.
59 Butterworth, J. 1997, "Clinical Pathwa ys for the High-Risk Patient", Journal of Cardiothoracic and Vascular Anesthesia, vol. 11, no. 2 Suppl 1, pp. 16-8; discussion 24-5. Calligaro, K.D., Dougherty, M.J., Raviola, C.A., Musser, D.J. & DeLaurentis, D.A. 1995, "Impact of Clinical Pathways on Hospita l Costs and Early Outcome after Major Vascular Surgery, ,", Journal of Vascular Surgery, vol. 22, no. 6, pp. 649-660. Cardoen, B. & Demeulemeester, E. 2008, "Capacity of Clinical Pathways A Strategic Multi-level Evaluation Tool", Journal of Medical Systems, vol. 32, no. 6, pp. 443. Cayirli, T. & Vera, E. 2003, "Outpatient Scheduling in Health Care: A Review of Literature", Production and Operations Management, vol. 12, no. 4, pp. 519. CBS/NYT 2007, US Health Care Politics. Chang, P.L., Wang, T.M., Huang, S.T., Hsieh, M.L., Tsui, K.H. & Lai, R.H. 1999, "Effects of Implementation of 18 Clinical Pathways on Costs and Quality of Care Among Patients Undergoing Urological Surgery", The Journal of Urology, vol. 161, no. 6, pp. 1858-1862. Cheah, J. 2000, "Clinical Pathways: An Evaluation of its Impact on the Quality of Care in an Acute Care General Hospital in Singapore", Singapore Medical Journal, vol. 41, no. 7, pp. 335-346. Chen, A.Y., Callender, D., Mansyur, C., Reyna K.M., Limitone, E. & Goepfert, H. 2000, "The Impact of Clinical Pathways on th e Practice of Head and Neck Oncologic Surgery: The University of Texas M. D. Anderson Cancer Center Experience", Archives of Otolaryngology Head & Neck Surgery, vol. 126, no. 3, pp. 322-326. Christodoulou, G. & Taylor, G.J. 2001, "Using a Continuous Time Hidden Markov Process, with Covariates, to Model Bed Occupancy of People Aged over 65 Years", Health Care Management Science, vol. 4, no. 1, pp. 21-24. Clague, J.E., Reed, P.G., Barlow, J., Ra da, R., Clarke, M. & Edwards, R.H. 1997, "Improving Outpatient Clinic Effici ency using Computer Simulation", International Journal of Health Care Quality Assurance, vol. 10, no. 4-5, pp. 197201. Coffey, R.J., Richards, J.S., Remmert, C.S., LeRoy, S.S., Schoville, R.R. & Baldwin, P.J. 2005, "An Introduction to Critical Paths", Quality Management in Health Care, vol. 14, no. 1, pp. 46-55.
60 Collier, P.E. 1997, "Do Clinical Pathways for Major Vascular Surgery Improve Outcomes and Reduce Cost?", Journal of Vascular Surgery, vol. 26, no. 2, pp. 179-185. Committee on Quality of Health Care in America, Institute of Medicine 2001, Crossing the Quality Chasm: A New Health System for the 21st Century National Academies Press. Committee on Quality of Health Care in America, Institute of Medicine 2000, To Err Is Human: Building a Safer Health System National Academy Press. Cote, M.J. 2000, "Understanding Patient Flow", Decision Line, vol. 31, no. 2, pp. 8. Cowan, R.M. & Trzeciak, S. 2005, "Clini cal Review: Emergency Department Overcrowding and the Potential Impact on the Critically Ill", Critical Care, vol. 9, no. 3, pp. 291-295. Department of Veteran Affairs 2008, VA Organizational Briefing Book. Available at http://www.va.gov/ofcadmin/docs/vaorgbb.pdf Dexter, F. 1999, "Design of Appointment Systems for Preanesthesia Evaluation Clinics to Minimize Patient Waiting Times: A Review of Computer Simulation and Patient Survey Studies", Anesthesia and Analgesia, vol. 89, no. 4, pp. 925-931. Dumas, M.B. 1984, "Simulation Mode ling for Hospital Bed Planning", Simulation, vol. 43, no. 2, pp. 69-78. Eccles, M. & Mason, J. 2001, "How to Develop Cost-Conscious Guidelines", Health Technology Assessment, vol. 5, no. 16, pp. 1-69. Eckstein, M., Isaacs, S.M., Slovis, C.M., Ka ufman, B.J., Loflin, J.R., O'Connor, R.E. & Pepe, P.E. 2005, "Facilitating EMS Turnaround Intervals at Hospitals in the Face of Receiving Facility Overcrowding", Prehospital Emergency Care, vol. 9, no. 3, pp. 267-275. Edward, G.M., Das, S.F., Elkhuizen, S.G., Bakker, P.J., Hontelez, J.A., Hollmann, M.W., Preckel, B. & Lemaire, L.C. 2008, "Simula tion to Analyse Planning Difficulties at the Preoperative Assessment Clinic", British Journal of Anaesthesia, vol. 100, no. 2, pp. 195-202. Edwards, R.H., Clague, J.E., Barlow, J ., Clarke, M., Reed, P.G. & Rada, R. 1994, "Operations Research Survey and Comput er Simulation of Waiting Times in Two Medical Outpatient Clinic Structures", Health Care Analysis, vol. 2, no. 2, pp. 164169.
61 el-Darzi, E., Vasilakis, C., Chaussalet, T. & Millard, P.H. 1998, "A Simulation Modelling Approach to Evaluating Length of Sta y, Occupancy, Emptiness and Bed Blocking in a Hospital Geriatric Department", Health Care Management Science, vol. 1, no. 2, pp. 143-149. Every, N.R., Hochman, J., Becker, R., Kopecky, S. & Cannon, C.P. 2000, "Critical Pathways: A Review", Circulation, vol. 101, no. 4, pp. 461-465. Fabri, P.J. 2008, "Cost-Effectiveness Analysis", First Annual Healthcare Engineering Symposium, Research Triangle Park, NC, April 6-8, 2008. Fatovich, D.M., Nagree, Y. & Sprivulis P. 2005, "Access Block Causes Emergency Department Overcrowding and Ambulance Di version in Perth, Western Australia", Emergency Medicine Journal, vol. 22, no. 5, pp. 351-354. Forkner, J. 1996, "Clinical Pathwa ys: Benefits and Liabilities", Nursing Management, vol. 27, no. 11, pp. 35-38. Garrido, T., Jamieson, L., Zhou, Y., Wiesenthal, A. & Liang, L. 2005, "Effect of Electronic Health Records in Ambulator y Care: Retrospective, Serial, Crosssectional Study", British Medical Journal, vol. 330, no. 7491, pp. 581. Hall, R.W. (ed) 2007, Patient Flow: Reducing Delay in Healthcare Delivery 1st edn, Springer. Haraden, C. & Resar, R. 2004, "Patient Flow in Hospitals: Understanding and Controlling it Better", Frontiers of Health Services Management, vol. 20, no. 4, pp. 3-15. Harper, P.R. & Gamlin, H.M. 2003, "Reduced Outpatient Waiting Times with Improved Appointment Scheduling: A Simulation Modeling Approach", OR Spectrum, vol. 25, no. 2, pp. 207. Hoffman, R., Hoffman, D. & Hoffman, C. 2005, The Impact of Health Insurance Coverage on Health Disparities in the United States. Howell, E.E., Bessman, E.S. & Rubin, H. R. 2004, "Hospitalist s and an Innovative Emergency Department Admission Process", Journal of General Internal Medicine, vol. 19, no. 3, pp. 266-268. Huarng, F. & Lee, M.H. 1996, "Using Simulation in Out-Patient Queues: A Case Study", International Journal of Health Care Quality Assurance, vol. 9, no. 6, pp. 21-25.
62 Husbands, J.M., Weber, R.S., Karpati, R.L ., Weinstein, G.S., Chalian, A.A., Goldberg, A.N., Thaler, E.R. & Wolf, P.F. 1999, "Clinical Care Pathways: Decreasing Resource Utilization in Head a nd Neck Surgical Patients", Otolaryngology Head and Neck Surgery, vol. 121, no. 6, pp. 755-759. Jha, A.K., Ferris, T.G., Donelan, K., DesR oches, C., Shields, A., Rosenbaum, S. & Blumenthal, D. 2006, "How Common Are Electronic Health Records In The United States? A Summary Of The Evidence", Health Affairs, vol. 25, no. 6, pp. w496-507. Jun, J.B., Jacobson, S.H. & Swisher, J. R. 1999, "Application of Discrete-Event Simulation in Health Care Clinics: A Survey", The Journal of the Operational Research Society, vol. 50, no. 2, pp. 109-123. Kazley, A.S. & Ozcan, Y.A. 2008, "Do Hosp itals With Electronic Medical Records (EMRs) Provide Higher Quality Care? An Examination of Three Clinical Conditions", Medical Care Research and Review, vol. 65, no. 4, pp. 496-513. Kim, S., Losina, E., Solomon, D.H., Wright, J. & Katz, J.N. 2003, "Effectiveness of Clinical Pathways for Total Knee and Tota l Hip Arthroplasty: Literature Review", The Journal of Arthroplasty, vol. 18, no. 1, pp. 69-74. Kingston, M.E., Krumberger, J.M. & Peruzzi, W.T. 2000, "Enhancing Outcomes: Guidelines, Standards, and Protocols", AACN Clinical Issues, vol. 11, no. 3, pp. 363-374. Kinn, J.W., OToole, M.F., Rowley, S.M., Marek, J.C., Bufalino, V.J. & Brown, A.S. 2001, "Effectiveness of the Electronic Medica l Record in Cholesterol Management in Patients with Coronary Artery Disease (Virtual Lipid Clinic)", The American Journal of Cardiology, vol. 88, no. 2, pp. 163-165. Klassen, K.J. & Rohleder, T.R. 1996, "Scheduling Outpatient Appointments in a Dynamic Environment", Journal of Operations Management, vol. 14, no. 2, pp. 83-101. Konishi, T. & Agawa, S. 2000, "Clinical Pathways in Oncology", Gan to Kagaku Ryoho:Cancer & Chemotherapy, vol. 27, no. 5, pp. 655-670. Kopach, R., DeLaurentis, P.C., Lawley, M ., Muthuraman, K., Ozsen, L., Rardin, R., Wan, H., Intrevado, P., Qu, X. & Willis, D. 2007, "Effects of Clinical Characteristics on Successful Open Access Scheduling", Health Care Management Science, vol. 10, no. 2, pp. 111-124.
63 Lowery, J.C. 1996, "Design of Hospital Admissions Scheduling System using Simulation", Simulation Conference, 1996. Proceedings. Winter pp. 1199. Mackay, M. & Lee, M. 2005, "Choice of Models for the Analysis and Forecasting of Hospital Beds", Health Care Management Science, vol. 8, no. 3, pp. 221-230. Mackay, M. & Millard, P.H. 1999, "Applica tion and Comparison of Two Modelling Techniques for Hospital Bed Management", Australian Health Review, vol. 22, no. 3, pp. 118-143. Magerlein, J.M. & Martin, J.B. 1976, "Surgical Demand Scheduling: A Review", Health Services Research, vol. 11, pp. 53. Marshall, A., Vasilakis, C. & El-Darzi, E. 2005, "Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions", Health Care Management Science, vol. 8, no. 3, pp. 213-220. McClean, S. & Millard, P.H. 1995, "A Deci sion Support System for Bed-Occupancy Management and Planning Hospitals", IMA Journal of Mathematics Applied in Medicine and Biology, vol. 12, no. 3-4, pp. 249-257. Miller, M.J., Ferrin, D.M. & Szymanski, J. M. 2003, "Simulating Six Sigma Improvement Ideas for a Hospital Emergency Department", Simulation Conference, 2003. Proceedings of the 2003 Winter pp. 1926. Napolitano, L.M. 2005, "Standardization of Perioperative Management: Clinical Pathways", The Surgical Clinics of North America, vol. 85, no. 6, pp. 1321-7, xiii. National Center for Health Statistics 2007, Health, United States With Chartbook on Trends on the Health of Americans OECD 2007, "Organisation for Economic Coope ration and Development (OECD) Health Data", Olshaker, J.S. & Rathlev, N.K. 2006a, "E mergency Department Overcrowding and Ambulance Diversion: The Impact and Po tential Solutions of Extended Boarding of Admitted Patients in the Emergency Department", The Journal of Emergency Medicine, vol. 30, no. 3, pp. 351-356. Olshaker, J.S. & Rathlev, N.K. 2006b, "E mergency Department Overcrowding and Ambulance Diversion: The Impact and Po tential Solutions of Extended Boarding of Admitted Patients in the Emergency Department", The Journal of Emergency Medicine, vol. 30, no. 3, pp. 351-356.
64 Pitts, S.R., Niska, R.W., Jianmin, X. & Burt, C.W. 2008, National Hospital Ambulatory Medical Care Survey: 2006 Emergency Department Summary National Center for Health Statistics, Hyattsville, MD. Porter, M.E. & Teisberg, E.O. 2006, Redefining Health Care: Creating Value-Based Competition on Results, Harvard Business School Publishing. Proudlove, N.C., Gordon, K. & Boaden, R. 2003, "Can Good Bed Management Solve the Overcrowding in Accident and Emergency Departments?", Emergency Medicine Journal, vol. 20, no. 2, pp. 149-155. Rauh, R.A., Schwabauer, N.J., Enger, E.L. & Moran, J.F. 1999, "A Community HospitalBased Congestive Heart Failure Program: Impact on Length of Stay, Admission and Readmission Rates, and Cost", The American Journal of Managed Care, vol. 5, no. 1, pp. 37-43. Ridge, J.C., Jones, S.K., Nielsen, M.S. & Shahani, A.K. 1998, "Capacity Planning for Intensive Care Units", European Journal of Operational Research, vol. 105, no. 2, pp. 346-355. Rohrer, J.E., Bernard, M., Naessens, J., Fu rst, J., Kircher, K. & Adamson, S. 2007, "Impact of Open-Access Scheduling on Realized Access", Health Services Management Research, vol. 20, no. 2, pp. 134-139. Schafermeyer, R.W. & Asplin, B.R. 2003, "Hospital and Emergency Department Crowding in the United States", Emergency Medicine, vol. 15, no. 1, pp. 22-27. Schneider, S.M., Gallery, M.E., Schafermeyer, R. & Zwemer, F.L. 2003, "Emergency Department Crowding: A Point in Time", Annals of Emergency Medicine, vol. 42, no. 2, pp. 167-172. Schull, M.J., Lazier, K., Vermeulen, M., Mawhinney, S. & Morrison, L.J. 2003, "Emergency Department Contributors to Ambulance Diversion: A Quantitative Analysis", Annals of Emergency Medicine, vol. 41, no. 4, pp. 467-476. Sheehan, J.P. 2002, "A Liability Checklist for Clinical Pathways", Nursing Management, vol. 33, no. 2, pp. 23-25. Smith, T.J. & Hillner, B.E. 2001, "Ensuring Qua lity Cancer Care by th e use of Clinical Practice Guidelines and Critical Pathways", Journal of Clinical Oncology, vol. 19, no. 11, pp. 2886-2897.
65 Spaite, D.W., Bartholomeaux, F., Guisto, J., Lindberg, E., Hull, B., Eyherabide, A., Lanyon, S., Criss, E.A., Valenzuela, T.D. & Conroy, C. 2002, "Rapid Process Redesign in a University-Based Emerge ncy Department: Decreasing Waiting Time Intervals and Improving Patient Satisfaction", Annals of Emergency Medicine, vol. 39, no. 2, pp. 168-177. Spencer, E., Swanson, T., Hueston, W.J. & Edberg, D.L. 1999, "Tools to Improve Documentation of Smoking Status: Continuous Quality Improvement and Electronic Medical Records", Archives of Family Medicine, vol. 8, no. 1, pp. 1822. Swisher, J.R., Jacobson, S.H., Jun, J.B. & Balci, O. 2001, "Modeling and Analyzing a Physician Clinic Environment using Di screte-Event (vis ual) Simulation", Computers & Operations Research, vol. 28, no. 2, pp. 105-125. Terris, J., Leman, P., O'Connor, N. & Wood, R. 2004, "Making an IMPACT on Emergency Department Flow: Improv ing Patient Processing Assisted by Consultant at Triage", Emergency Medicine Journal, vol. 21, no. 5, pp. 537-541. Trzeciak, S. & Rivers, E.P. 2003, "Emergency Department Overcrowding in the United States: An Emerging Threat to Patie nt Safety and Public Health", Emergency Medicine Journal, vol. 20, no. 5, pp. 402-405. United Nations Development Program 2005, Human Development Report 2005: International Cooperation at the Crossroads Aid, Trade and Security in an Unequal World, UNDP. United States General Accounting Office 2003, Hospital Emergency Departments: Crowded Conditions Vary among Hospitals and Communities GAO. Vishwanath, A. & Scamurra, S.D. 2007, "Barriers to the Adoption of Electronic Health Records: Using Concept Mapping to Deve lop a Comprehensive Empirical Model", Health Informatics Journal, vol. 13, no. 2, pp. 119-134. Walter, F.L., Bass, N., Bock, G. & Markel, D.C. 2007, "Success of Clinical Pathways for Total Joint Arthroplasty in a Community Hospital", Clinical Orthopaedics and Related Research, vol. 457, pp. 133-137. Wang, S.J., Middleton, B., Prosse r, L.A., Bardon, C.G., Spurr, C.D., Carchidi, P.J., Kittler, A.F., Goldszer, R.C., Fairchild, D.G., Sussman, A.J., Kuperman, G.J. & Bates, D.W. 2003, "A Cost-Benefit Analysis of Electronic Medical Records in Primary Care", The American Journal of Medicine, vol. 114, no. 5, pp. 397-403.
66 Weingarten, S., Riedinger, M.S., Sandhu, M., Bowers, C., Ellrodt, A.G., Nunn, C., Hobson, P. & Greengold, N. 1998, "Can Pr actice Guidelines Safely Reduce Hospital Length of Stay? Results from a Multicenter Interventional Study", The American Journal of Medicine, vol. 105, no. 1, pp. 33-40. World Health Organization 2008, World Health Statistics World Health Organization. World Health Organization Eu rope 2006, July 18, 2006-last update Health Care Delivery Available: http://www.euro.who.int/healthcaredelivery [2008, December 3] Zehr, K.J., Dawson, P.B., Yang, S.C. & Heitmiller, R.F. 1998, "Standardized Clinical Care Pathways for Major Thoracic Cases Reduce Hospital Costs", Annals of Thoracic Surgery, vol. 66, no. 3, pp. 914-919.
68 Appendix 1: Encounter Grouping Below is the grouping of encounters into thr ee types: visits, pro cedures and tests. Table 22. Encounter Grouping Clinic Visits Procedures Tests Oncology Pulmonary Procedure Xray Lung Nodule Lung Biopsy CT Pre-Op Oncology Procedure PFT PACM PET Thoracic Surgery Myocardial Perfusion Radiology CT Guidance Resp Care/Home Cardio ABG ER Gated Spect Urgent Care Stress Test Primary Care Clinic Barium/Fluoro Bone Scan Bone Surv Comp EKG MRI Quantitative Perfusion
Appendix 2: Pre-Admission Patient Flow Below are the detailed flows of each patient in the pre-admission period. For each patient, the column on the left is the number of days before the surgical admission. The column on the right is the encounter type. Table 23. The Flow of Patients (1 -3) in the Pre-Admission Period. 133Xray 168Oncology 175Radiology 98CT 162Pulmonary Procedure 151Oncology 79Thoracic Surgery 147Oncology 151Xray 73Oncology 125Lung Nodule 95RAD CT 52PET 77Xray 94CT 51PFT 62Lung Nodule 88Oncology 51Pulmonary Procedure 48Xray 62CT Guidance 38Oncology 12Oncology 62Lung Biopsy 11CT 5PACM 61Xray 9Lung Nodule 5Pre Op 60Oncology 3PACM 5Pre Anesthesia 59Xray 3Pre Op 5Xray 47Lung Nodule 3Pre Anesthesia 18Oncology 3Xray 14PACM 14Pre Op 14PFT 13Xray Patient 1P a t i e n t 2P a t i e n t 3 69
Appendix 2: (Continued) Table 24. The Flow of Patients (4 -6) in the Pre-Admission Period. 7Lung Nodule 126Oncology 151CT 7PFT 123CT 151CT 1PACM 123CT 143Oncology 1Pre Op 122Pulmonary Procedure 136Radiology 1Pre Anesthesia 115Oncology 136Gastronomy Tube PACS 1Xray 115Xray 134PFT 97Cardio ABG 130Radiology 89Lung Nodule 130Barium 76PET 128Lung Nodule 21Oncology 102PET 5PACM 80Oncology 5Pre Op 59Radiology 5Pre Anesthesia 58Radiology 49CT 45CT 16Thoracic Surgery 7PACM 7Pre Op 7Xray 7Pre Anesthesia 7PFT 4Pulmonary Procedure Patient 4P a t i e n t 5P a t i e n t 6 70
Appendix 2: (Continued) Table 25. The Flow of Patients (7 -9) in the Pre-Admission Period. 125CT 33Lung Nodule 162Radiology 104Lung Nodule 20PACM 137Cardio ABG 88RAD CT 20Pre Op 56Radiology 70PET 53Myocardial Perfusion 61PFT 53Myocardial Perfusion 18Xray 48Oncology 15PACM 27Pulmonary Procedure 15Pre Op 27Thoracic Surgery 27Xray 1PACM 1Pre Op 1Pre Anesthesia 1Xray Patient 7Patient 8P a t i e n t 9 Table 26. The Flow of Patients (1012) in the Pre-Admission Period. 172Pulmonary Procedure 53PET 75Lung Nodule 171CT 49Cardio ABG 60Oncology 129Oncology 48Oncology 58Stress Test 114Lung Nodule 25Cardio ABG 58Myocardial Perfusion 107Xray 15Oncology 58Gated Spect 59Oncology 13Lung Nodule 27PFT 42Stress Test 8PACM 26Lung Nodule 42Myocardial Perfusion 8Pre Op 7PACM 42Gated Spect 8Xray 7Pre Op 15CT 7Pre Anesthesia 4PACM 7Xray 4Pre Op 4Pre Anesthesia 4PFT 3Xray Patient 10 Patient 11 Patient 12 71
Appendix 2: (Continued) Table 27. The Flow of Patients (1315) in the Pre-Admission Period. 72 6Xray 3Pre Op 3Pre Anesthesia 3PFT 3Xray 43Lung Nodule 138PFT 136Xray 34Lung Nodule 71CT 66CT 20PET 19CT Guidance 66CT 7PACM 19CT Guidance 59Xray 7Pre Op 19CT 48CT 7PFT 6PACM 24PET 7Pre Anesthesia 6Pre Op 5Thoracic Surgery 7Xray 6Pre Anesthesia 3PACM Patient 13 Patient 14 Patient 15 Table 28. The Flow of Patients (1618) in the Pre-Admission Period. 168Xray 35Pulmonary Procedure 159Oncology 137CT 28Pulmonary Procedure 40CT 120Oncology 15Radiology 40CT 63PET 2CT 40CT 51PFT 2CT 40Oncology 37Thoracic Surgery 27Lung Nodule 18Stress Test 18Bone Scan 18Gated Spect 15PET 18Gated Spect 6PACM 18Myocardial Perfusion 6Pre Op 18EKG 6Pre Anesthesia 17Myocardial Perfusion 6PFT 16Thoracic Surgery 2PACM 2Pre Op 2PFT 2Pre Anesthesia 2Xray Patient 16 Patient 17 Patient 18
Appendix 2: (Continued) Table 29. The Flow of Patients (1921) in the Pre-Admission Period. 73 3MRI 7PFT 7Pre Anesthesia 1PACM 1Xray 148CT 134Oncology 120CT 146PFT 78RAD 86Oncology 28CT 30Lung Nodule 41Oncology 27Thoracic Surgery 30PFT 22Radiology 11PACM 15PACM 21Radiology 11Pre Op 15Pre Op 15Thoracic Surgery 11Xray 7Pre Op Patient 19 Patient 20 Patient 21 Table 30. The Flow of Patients (2224) in the Pre-Admission Period. 57Lung Nodule 102CT 98Radiology 56Oncology 86Lung Nodule 85Radiology 56Pulmonary Procedure 73PET 84Xray 55Xray 72Thoracic Surgery 84CT 49Oncology 65PFT 64CT 16PET 18PACM 58PFT 15CT 18Pre Op 50Oncology 15CT 17Xray 41Pulmonary Procedure 8PFT 27Thoracic Surgery 1PACM 1PACM 1Pre Op 1Pre Op 1Pre Anesthesia 1Pre Anesthesia 1Xray 1Xray 1PFT Patient 22 Patient 23 Patient 24
Appendix 2: (Continued) Table 31. The Flow of Patients (2527) in the Pre-Admission Period. 74 3Xray 2Radiology 2Myocardial Perfusion 2Gated Spect 144CT 148Lung Nodule 144CT 120Lung Nodule 144CT 114PET 47CT 77RAD 26Lung Nodule 37CT Guidance 4PACM 37Lung Biopsy 4Pre Op 37Xray 4Pre Anesthesia 37Xray 4PFT 29Lung Nodule 4Xray 14CT 3PACM 3Pre Op 3Pre Anesthesia 3PFT Patient 25 Patient 26 Patient 27
Appendix 2: (Continued) Table 32. The Flow of Patients (2830) in the Pre-Admission Period. 48Lung Nodule 138Radiology 153Oncology 20PFT 137CT 118CT 8Pre Op 130Oncology 97Oncology 8PACM 117Pulmonary Procedure 76Pulmonary Procedure 7Xray 115Xray 76Xray 109Oncology 62Oncology 109Cardio ABG 19Lung Nodule 84PET 7PACM 83Lung Nodule 7Pre Op 81Oncology 7Pre Anesthesia 78Radiology 77Myocardial Perfusion 77Myocardial Perfusion 75Oncology 19Pre Op 19Xray 12Oncology 1PACM 1Pre Op 1Pre Anesthesia Patient 28 Patient 29 Patient 30 Table 33. The Flow of Patients (3133) in the Pre-Admission Period. 145Oncology 60Xray 124Lung Nodule 62CT 39Bone Surv Comp 39CT 62CT 34PFT 33Lung Nodule 62CT 34PFT 12Lung Nodule 43Oncology 20Lung Nodule 12PET 35Oncology 20Oncology 5PACM 29Pulmonary Procedure 7PACM 5Pre Op 21PET 7Pre Op 5Pre Anesthesia 19Thoracic Surgery 7Pre Anesthesia 5Xray 6PACM 7Xray 6Pre Op 5Oncology 6Pre Anesthesia 1RAD 6PFT 6Xray Patient 31 Patient 32 Patient 33 75
Appendix 2: (Continued) Table 34. The Flow of Patients (3436) in the Pre-Admission Period. 76 6PACM 6Pre Op 6Pre Anesthesia 5Xray 177Oncology 169Radiology 53Xray 105Oncology 116Radiology 46CT 92Lung Nodule 100Lung Nodule 26Lung Nodule 57Lung Nodule 93Cardio ABG 18Oncology 48Pulmonary Procedure 30Lung Nodule 5PACM 44Stress Test 11PACM 5Pre Op 44Myocardial Perfusion 11Pre Op 5Pre Anesthesia 44Gated Spect 5Xray 38Quantitative Perfusion 37CT 35Oncology 22Lung Nodule Patient 34 Patient 35Patient 36 Table 35. The Flow of Patients (3739) in the Pre-Admission Period. 53Xray 322Oncology 84Lung Nodule 26Lung Nodule 154Oncology 84PFT 11Oncology 115Oncology 69Oncology 11PFT 104Pulmonary Procedure 69Pulmonary Procedure 5PACM 99Lung Nodule 69Xray 5Pre Op 99PFT 32PET 5Pre Anesthesia 71Oncology 30PACM 5Xray 50Lung Nodule 30Pre Op 5PFT 30CT 29Xray 30CT 6Urology Oncology 30CT 29Lung Nodule 22Thoracic Surgery 17PET 10PACM 10Pre Op 10Xray Patient 37 Patient 38 Patient 39
Appendix 2: (Continued) Table 36. The Flow of Patients (4042) in the Pre-Admission Period. 77 21Pulmonary Procedure 3Pre Anesthesia 8Xray 16Pre Op 1Xray 8PFT 16PACM 14PFT 122Oncology 100Lung Nodule 41CT 88Radiology 86PFT 41CT 45Xray 25Radiology 41CT 36Xray 9Lung Nodule 34Lung Nodule 35CT 3PACM 8PACM 30Pulmonary Procedure 3Pre Op 8Pre Op Patient 40 Patient 41Patient 42 Table 37. The Flow of Patients (4345) in the Pre-Admission Period. 141Oncology 36Lung Nodule 174Radiology 133Oncology 22Thoracic Surgery 168Xray 90Oncology 20PET 98CT 89CT 20PFT 98CT 82Pulmonary Procedure 6PACM 98CT 64Oncology 6Pre Op 76Oncology 53PET 6Pre Anesthesia 49PET 32Cardio ABG 6PFT 47Thoracic Surgery 32Oncology 6Xray 34PFT 22Oncology 34Oncology 13Lung Nodule 33Xray 5PACM 8PACM 5Pre Op 8Pre Op 5Xray 5Pre Anesthesia Patient 43 Patient 44 Patient 45
Appendix 2: (Continued) Table 38. The Flow of Patients (4648) in the Pre-Admission Period. 78 1Pre Op 1Pre Anesthesia 1PFT 1Xray 118PFT 16Lung Nodule 170CT 16PFT 170CT 130Radiology 115Radiology 63CT 52PFT 51Oncology 51Radiology 51Xray 37Lung Nodule 1PACM Patient 46Patient 47Patient 48
79 Appendix 3: Inpatient Ward Descriptions The descriptions of each of the units or ward s within the hospital that were visited by at least one of the patients during their inpatient episode of care: Ward 6 South Thoracic (6ST) a surgical wa rd that is the most common point of admission for patients undergoing el ective surgery (ES) of the lung Ward 6 South Cardio (6SC) ward that shares space with 6ST and is for cardiac patients Ward 4 South (4S) ward fo r most patients undergoing ES Spinal Cord Injury Service (SCI) ward fo r patients with spinal cord injuries Pre-Operation Hold Area the unit that is responsible for prepping the patient before surgery, including administering anesthesia Operation Room the designated area for surgeries Post Anesthesia Care Unit (PACU) unit patients are wheeled into post surgery to recover from anesthesia and for observation Surgical Intensive Care Unit (SICU) an acute care unit (ACU) for patients to recover from the surgery Critical Care Unit (CCU) an ACU for cardiac patients
80 Appendix 3: (Continued) Medical Intensive Care Un it (MICU) an ACU for patients that do not fall under surgical or cardiac care Genito-Urinary Clinic (GUC) unit/clinic for patients with genitor-urinary problems Radiology (RAD) the radiol ogy unit within the hospital
Appendix 4: Inpatient Flow during Surgical Episode Below are the detailed flows of each patients inpatient episode for lung resection. For each patient, the column on the left is the order of each encounte r and the corresponding unit where the encounter occurred. The OR (whe re the resection took place) is step 0. Table 39. Detailed Flow of Patients (1-5 ) in the Inpatient Surgical Episode -26ST-26ST-26ST-26ST-26ST -1Hold Area-1Hold Area-1Hold Area-1Hold Area-1Hold Area 0OR0OR0OR0OR0OR 1PACU1SICU1SICU1SICU1SICU 2SICU26ST26ST26ST26ST 36ST3CCU3Discharge3RAD3RAD 4RAD46ST 4RAD46ST 56ST5Discharge 56ST5Discharge 6RAD 6Discharge 76ST 8Discharge Patient 1 Patient 3 Patient 2 Patient 5 Patient 4 Table 40. Detailed Flow of Patients (610) in the Inpatient Surgical Episode -26ST-26ST-26ST-26ST-26ST -1Hold Area-1Hold Area-1Hold Area-1Hold Area-1Hold Area 0OR0OR0OR0OR0OR 1PACU1SICU1PACU1SICU1PACU 2SICU26ST2SICU26ST2SICU 36ST3Discharge36ST3RAD36ST 4Discharge 4RAD46ST4Discharge 56ST5Discharge 6RAD 76ST 8Discharge Patient 10 Patient 9 Patient 8 Patient 7 Patient 6 81
Appendix 4: (Continued) Table 41. Detailed Flow of Patients (11-15) in the Inpatient Surgical Episode 82 76ST 8RAD 96ST 10Discharge -26ST-26ST-26ST-26ST-26ST -1Hold Area-1Hold Area-1Hold Area-1Hold Area-1Hold Area 0OR0OR0OR0OR0OR 1PACU1SICU1PACU1PACU1PACU 2SICU26ST2SICU2SICU2SICU 36ST3Discharge36ST36ST36ST 4RAD 4RAD4RAD4RAD 56ST 56ST56ST56ST 6RAD 6Discharge6Discharge6Discharge Patient 15 Patient 14 Patient 13 Patient 12 Patient 11
Appendix 4: (Continued) Table 42. Detailed Flow of Patients (16-20) in the Inpatient Surgical Episode -26ST-1SCI-E-26ST-26ST-26ST -1Hold Area0OR-1Hold Area-1Hold Area-1Hold Area 0OR1PACU0OR0OR0OR 1SICU2SCI-E1PACU1PACU1PACU 2OR3Discharge26ST2Discharge2SICU 3SICU 3Discharge 36ST 4RAD -26ST 4Discharge 5SICU -1Hold Area 66ST 0OR 7CP 1PACU 86ST 2SICU 9RAD 36ST 106ST 4Discharge 11SICU 12RAD 13SICU 146ST 15MICU 16SICU 176ST 18SICU 19RAD 20SICU 216ST 22Discharge Patient 20 Patient 19 Patient 18 Patient 17 Patient 16 83
Appendix 4: (Continued) Table 43. Detailed Flow of Patients (21-25) in the Inpatient Surgical Episode -26ST-26SC-26ST-26ST-46ST -1Hold Area-1Hold Area-1Hold Area-1Hold Area-3RAD 0OR0OR0OR0OR-26ST 1SICU1SICU1PACU1PACU-1Hold Area 2RAD26ST2SICU2SICU0OR 36ST3Discharge36ST36ST1PACU 4Discharge 4Discharge4Discharge2SICU 36ST 4RAD 56ST 6RAD 76ST 8Discharge Patient 25 Patient 24 Patient 23 Patient 22 Patient 21 Table 44. Detailed Flow of Patients (26-30) in the Inpatient Surgical Episode -26ST-26ST-26ST-26ST-26ST -1Hold Area-1Hold Area-1Hold Area-1Hold Area-1Hold Area 0OR0OR0OR0OR0OR 1PACU1PACU1PACU1SICU1PACU 2SICU2SICU2SICU2Discharge2Discharge 36ST36ST36ST 4CP4Discharge4Discharge 56ST 6RAD 76ST 8SICU 96ST 10Discharge Patient 30 Patient 29 Patient 28 Patient 27 Patient 26 84
Appendix 4: (Continued) Table 45. Detailed Flow of Patients (31-35) in the Inpatient Surgical Episode 85 56ST5Discharge56ST 5RAD 6Discharge 6Discharge 66ST 7Discharge -26ST-26ST-26ST-26ST-26ST -1Hold Area-1Hold Area-1Hold Area-1Hold Area-1Hold Area 0OR0OR0OR0OR0OR 1PACU1SICU1PACU1PACU1SICU 2SICU26ST2SICU2SICU26ST 36ST3RAD36ST36ST3RAD 4RAD46ST4RAD4Discharge46ST Patient 35 Patient 34 Patient 33 Patient 32 Patient 31 Table 46. Detailed Flow of Patients (36-40) in the Inpatient Surgical Episode -26ST-26ST-26ST-64S-26ST -1Hold Area-1Hold Area-1Hold Area-5Hold Area-1Hold Area 0OR0OR0OR-4GUC B0OR 1PACU1PACU1SICU-3PACU1PACU 2SICU26ST2PACU-24S2SICU 36ST3Discharge3SICU-1Hold Area36ST 4Discharge 4Discharge0OR4Discharge 1SICU 26ST 3Discharge Patient 40 Patient 39 Patient 38 Patient 37 Patient 36 Table 47. Detailed Flow of Patients (41-45) in the Inpatient Surgical Episode -26ST-26ST-26ST-26ST-26ST -1Hold Area-1Hold Area-1Hold Area-1Hold Area-1Hold Area 0OR0OR0OR0OR0OR 1PACU1SICU1SICU1SICU1PACU 2SICU26ST26ST26ST2SICU 36ST3Discharge3RAD3Discharge36ST 4Discharge 46ST 4Discharge 5Discharge Patient 45 Patient 44 Patient 43 Patient 42 Patient 41
Appendix 4: (Continued) Table 48. Detailed Flow of Patients (46-48) in the Inpatient Surgical Episode 86 66SC 7EMG 86SC 9Discharge -8MICU-26SC-26ST -7RAD-1Hold Area-1Hold Area -6MICU0OR0OR -5RAD1PACU1PACU -4MICU2SICU2SICU -3RAD36ST36ST -2RAD4Discharge4RAD -1MICU 56ST 0OR 6RAD 1PACU 76ST 26SC 8Discharge 3RAD 46SC 5RAD Patient 48 Patient 47 Patient 46
Appendix 5: Detailed Post-Dis charge Flow of Patients For each patient, the column on the left is th e number of days after discharge from the surgical inpatient episode. The column on the right is th e encounter type. Table 49. Detailed Flow of Patients (1 -3) in the Post-Discharge Period 11Thoracic Surgery6Urgent Care 31Oncology 20PCC Women6ER 45CT 20Oncology 6Xray 65Resp Care/Home Oxygen (1BS) 46Thoracic Surgery12Thoracic Surgery 94Oncology 139Oncology12Xray 151CT 139Xray 13Resp Care/Home Oxygen (1BS)151CT 247Oncology25Resp Care/Home Oxygen (1BS)151CT 262Oncology41Oncology 171Oncology 319Oncology74Xray 269Oncology 74CT 95Oncology 117PET 139Oncology 139Resp Care/Home Oxygen (1BS) 140CT Guidance 140Lung Biopsy 153Pulmonary Procedure 188Oncology 227CT 242Xray 265Oncology 328Resp Care/Home Oxygen (1BS) 333Oncology 335Oncology Patient 1 Patient 2 Patient 3 87
Appendix 5: (Continued) Table 50. Detailed Flow of Patients (4 -7) in the Post-Discharge Period 12Thoracic Surgery 27Thoracic Surgery7Oncology5Xray 14CT 32Xray 15Radiology8Radiology 14Xray 49Oncology196Oncology9Pulmonary Procedure 19Pulmonary Procedure 62Thoracic Surgery209Radiology19PFT 40Thoracic Surgery 94Xray 24Thoracic Surgery 42Xray 124Oncology 25Xray 46Oncology 215CT 59Thoracic Surgery 61Thoracic Surgery 223Oncology 123CT 67Radiology 295Oncology 123CT 69MRI 299Pulmonary Procedure 82Thoracic Surgery 310Oncology 100CT 100CT 100CT 112Oncology 209Resp Care/Home Oxygen (1BS) 209CT 209CT 235Oncology 326Oncology Patient 4 Patient 5Patient 6 Patient 7 Table 51. Detailed Flow of Patients (8 -11) in the Post-Discharge Period 15Thoracic Surgery11Thoracic Surgery 35Radiology 11Xray 35Oncology 32Oncology 35Xray 54MRI 63Oncology 63Xray 108CT 130Xray 155Oncology Patient 8 Patient 9 Patient 10Patient 11 88
Appendix 5: (Continued) Table 52. Detailed Flow of Patients ( 12-15) in the Post-D ischarge Period 89 50Oncology 74Oncology 80Oncology 99Oncology Procedure 10Thoracic Surgery0Xray 27Oncology 0Xray 10Xray 12Xray 31Xray 9Thoracic Surgery 24Oncology 29Thoracic Surgery41Xray 9Xray 38Thoracic Surgery64Thoracic Surgery47CT 36Oncology 39Xray 106Thoracic Surgery47CT 43Xray 46Bone Scan 47CT 44Thoracic Surgery 50CT 47Thoracic Surgery98CT 50CT 54Bone Scan102Oncology 50CT Patient 12 Patient 13 Patient 14 Patient 15 Table 53. Detailed Flow of Patients ( 16-19) in the Post-D ischarge Period 160Oncology91CT7Oncology12Thoracic Surgery 213CT18PACM13Xray 316CT18Pre-Op 18Pre-Anesthesia 18Xray 41Thoracic Surgery 42Xray 49CT 49CT 70Oncology 154Oncology 154CT 154CT 154CT Patient 16Patient 17Patient 18 Patient 19
Appendix 5: (Continued) Table 54. Detailed Flow of Patients ( 20-21) in the Post-D ischarge Period 90 264CT 273Oncology 299CT 299CT 14Oncology 15Thoracic Surgery 19Oncology Procedure15Xray 40Oncology Procedure45Oncology 63Oncology 45Xray 70Oncology Procedure79CT 91Oncology 85Oncology 91Oncology Procedure142Oncology 112Oncology Procedure177Oncology 133Oncology 133Oncology Procedure 154Oncology Procedure 175Oncology Procedure 175Oncology 175Xray Patient 20 Patient 21
Appendix 5: (Continued) Table 55. Detailed Flow of Patients ( 22-25) in the Post-D ischarge Period 12Xray 13Thoracic Surgery20PFT 6Thoracic Surgery 12Thoracic Surgery14Xray 20Radiology 7Xray 14MRI 48Thoracic Surgery21Xray 33Oncology 53Xray 28Oncology 60CT 76Xray 28Thoracic Surgery 60CT 83Thoracic Surgery47Radiology 66Oncology 47PFT 68Oncology Procedure 47Xray 69Oncology Procedure 47Radiology 70Oncology Procedure 48CT 89Oncology Procedure 48CT 89Oncology 48CT 90Oncology Procedure 49Thoracic Surgery 91Oncology Procedure 50Oncology 136Oncology Procedure 61Oncology Procedure 136Oncology 82Oncology Procedure 137Oncology Procedure 82Oncology 138Oncology Procedure 103Oncology Procedure 157Oncology Procedure 103Oncology 157Oncology 124Oncology Procedure 158Oncology Procedure 124Oncology 159Oncology Procedure 167Oncology 297Oncology 258Oncology 322Oncology 350Oncology 355Oncology Procedure 356Pre-Op 356Oncology Procedure 357Oncology Procedure Patient 22 Patient 23 Patient 24 Patient 25 Table 56. Detailed Flow of Patients ( 26-30) in the Post-D ischarge Period 91 24CT8Thoracic Surgery28Xray 2Xray 24CT43Thoracic Surgery28Thoracic Surgery 15Thoracic Surgery 63Oncology 16Xray 65Xray Patient 26Patient 27 Patient 28Patient 29Patient 30
Appendix 5: (Continued) Table 57. Detailed Flow of Patients ( 31-33) in the Post-D ischarge Period 92 333Oncology 333Oncology Procedure 354Oncology Procedure 354Oncology 359Oncology Procedure 10Thoracic Surgery3Oncology 20Thoracic Surgery 15Radiology3Oncology Procedure22Xray 8Xray 82Resp Care/Home Oxygen (1BS) 37Oncology 156Xray 51Xray 208Resp Care/Home Oxygen (1BS) 52Xray 52Xray 58Bone Surv Comp 65Oncology 107Oncology 120Xray 127Pulmonary Procedure 135Oncology 162PET 166Pulmonary Procedure 170Oncology 191Oncology 199Oncology 221Oncology 227Thoracic Surgery 256Lung Nodule 269Pulmonary Procedure 275Oncology 277Oncology 288Oncology 288Oncology Procedure 291Oncology Procedure 305Oncology Procedure 312Oncology 312Oncology Procedure Patient 31 Patient 32 Patient 33
Appendix 5: (Continued) Table 58. Detailed Flow of Patients ( 34-36) in the Post-D ischarge Period 1Xray 12Thoracic Surgery8Thoracic Surgery 5Xray 13Xray 9Xray 5Xray 17Radiology36Oncology 7Thoracic Surgery 47Thoracic Surgery43Thoracic Surgery 17Xray 122Xray 133CT 22Xray 235CT 316CT 24Xray 346Radiology 42Oncology 346CT 42Thoracic Surgery 63Oncology 63Bone Scan 92Resp Care/Home Oxygen (1BS) 156CT 161Oncology 245CT 252Oncology 329Thoracic Surgery Patient 34 Patient 35 Patient 36 Table 59. Detailed Flow of Patients ( 37-40) in the Post-D ischarge Period 13Thoracic Surgery8Xray 15Thoracic Surgery13Thoracic Surgery 19CT 8Thoracic Surgery16Xray 13Xray 19CT 29Thoracic Surgery16Urology Oncology 20CT 30Xray 50Thoracic Surgery 36Oncology99Thoracic Surgery121Oncology 101Xray 154CT 288Oncology154CT 288Thoracic Surgery154CT 157MRI 226Oncology 328Oncology 358PACM 358Pre-Op 358Pre-Anesthesia Patient 37 Patient 38 Patient 39 Patient 40 93
Appendix 5: (Continued) Table 60. Detailed Flow of Patient ( 41) in the Post-Discharge Period 8Thoracic Surgery Patient 41 8Xray 72Xray 72CT 113Scan 120Thoracic Surgery Table 61. Detailed Flow of Patients ( 42-45) in the Post-D ischarge Period 17Thoracic Surgery9Thoracic Surgery9Xray 18Thoracic Surgery 17Xray 11Xray 9Thoracic Surgery18Xray 31Oncology25Oncology 38PACM 38Radiology119Oncology 38Pre-Op 38Xray 165CT 38PFT 45Thoracic Surgery212Oncology 38Pre-Anesthesia 58Radiology 38Xray 58Bone Scan 67Thoracic Surgery 65CT 83Oncology 65CT 100Oncology 65CT 101Oncology Procedure 66Oncology 116Oncology Procedure 94Oncology 116Oncology 129Oncology 118Oncology Procedure 134CT 125Oncology Procedure 134CT 130Oncology Procedure 134CT 132Oncology Procedure 136Thoracic Surgery 151Oncology Procedure 150Oncology 151Oncology 178Oncology 153Oncology Procedure 206Fluoro 165Oncology Procedure 206CT 165Oncology 206CT 172Oncology Procedure 206CT 206CT 213Oncology 291Radiology 291Radiology 297Oncology 360Oncology Patient 42 Patient 43 Patient 44 Patient 45 94
Appendix 5: (Continued) Table 62. Detailed Flow of Patients ( 46-48) in the Post-D ischarge Period 2Xray55Thoracic Surgery20Thoracic Surgery 70Oncology 22Oncology 103CT 22Xray 207CT 35Oncology Procedure 223Thoracic Surgery54Oncology 237Pulmonary Procedure55Oncology Procedure 244Thoracic Surgery76Thoracic Surgery 328Thoracic Surgery77Oncology Procedure 329Oncology 99Oncology Procedure 99Oncology Patient 46 Patient 47 Patient 48 95
Appendix 6: Pre-Admission Encounter Frequencies Table 63. Encounter Frequencies in the Pre-Admission Period Block 1Block 2Block 3Block 4Block 5Block 6Block 7Block 8Block 9Block 10Block 11Block 12Total Xray 37135323 63 Oncology 1017126106 162 CT 121410895 58 Lung Nodule 1812674 1 48 Pre Op 48 48 PACM 48 48 Pre Anesthesia 48 48 PFT 247423 1 41 Radiology 221236431226 PET 8552 20 Pulmonary Procedure553212 1 19 Thoracic Surgery1012 1 14 Myocardial Perfusion352 10 Cardio ABG 12 31 7 Gated Spect 33 6 CT Guidance 211 4 Stress Test 13 4 Lung Biopsy 11 2 Barium 1 1 Bone Surv Comp 1 1 EKG 1 1 Bone Scan 1 1 MRI 1 1 Quantitative Perfusion 1 1 Urology Oncology 1 1 Total 2849352353422041523535 96
Appendix 6: (Continued) Figure 7. Frequency of Encounters in E ach Block of the Pre-Admission Period97
Appendix 7: Post-Discharge Encounter Frequencies Table 64. Encounter Frequencies in the Post-Discharge Period Block 1Block 2Block 3Block 4Block 5Block 6Block 7Block 8Block 9Block 10Block 11Block 12Total Oncology 101715121113466876115 Xray 42165332 1 72 CT 6146761174222168 Thoracic Surgery 3516731 211268 Oncology Procedure 239989 22650 Radiology 5311221 15 Resp Care/Home Oxygen (1BS)2 211 2 1 9 Pulmonary Procedure 2 12111 8 Bone Scan 31 4 MRI 111 1 4 PFT 22 4 Pre Op 11 24 PACM 11 13 Pre Anesthesia 11 13 PET 1 1 2 Bone Surv Comp 1 1 CT Guidance 1 1 ER 1 1 Fluoro 11 Scan 1 1 Lung Biopsy 1 1 Lung Nodule 11 PCC Women 1 1 Urgent Care 1 1 Urology Oncology 1 1 Total 1147946383441141513141417439 98
Appendix 7: (Continued) Figure 8. Frequency of Encounters in Each Block of the Post-Discharge Period 99
Appendix 8: Information from the Observed Patient Flow Below are the information obtained regardi ng the flow paths of the lung resection patients. Table 65 identifies basic statistica l information on the length and the number of encounters each patient had in the pre-admi ssion period. Table 66 is for patients in the post-discharge period. Table 65. Basic Statistical Information of the Pre-Admission Period Number of DaysNumber of Encounters Average178.1 9.5 Max Value360 35 Min Value2 1 Std. Dev.129.1 8.5 Table 66. Basic Statistical Informati on of the Post-Discharge Period Number of DaysNumber of Encounters Average116 10.9 Max Value322 22 Min Value7 1 Std. Dev.58.9 4.6 Tables 67 and 68 identify the expected encounters that a patient undergoing lung resection will have in each of the window s of the pre-admission and post-discharge periods respectively. This is based on our observation of the patients sample and is ordered according to most frequent. 100
Appendix 8: (Continued) Table 67. Expected Encounters in the Pre-Admission Period Clinic VisitsProcedures Tests Window 3 Radiology OncologyPulmonary ProcedureCT Lung Nodule Xray Pre Op Xray PACM PFT Pre Anesthesia Window 2 Window 1 Table 68. Expected Encounters in the Post-Discharge Period Clinic VisitsProcedures Tests Window 1 Thoracic Surgery Xray OncologyOncology ProcedureCT Thoracic Surgery Xray Window 3 OncologyOncology ProcedureCT Window 2 101