Does Process Quality of Inpatient Care Serve as a Guide to Reduce Potentially Preventable Readmission (PPR)?

의료서비스의 과정적 질과 잠재적으로 예방 가능한 재입원율과의 관계

  • Choi, Jae-Young (Healthcare Management Track, Department of Business Administration, Hallym University)
  • 최재영 (한림대학교 경영학과 의료경영트랙)
  • Received : 2018.02.06
  • Accepted : 2018.03.15
  • Published : 2018.03.31

Abstract

Objective: The objective of this study is to examine the association between process quality of inpatient care and risk-adjusted, thirty-day potentially preventable hospital readmission (PPR) rates. Data Sources/Study Setting: This was an observational cross-sectional study of nonfederal acute-care hospitals located in two states California and Florida, discharging Medicare patients with a principal discharge diagnosis of heart failure, acute myocardial infarction, or pneumonia January through December 31, 2007. Data were obtained from the Healthcare Cost and Utilization Project State Inpatient Database of the Agency for Healthcare Research and Quality, Centers for Medicare and Medicaid Services Hospital Compare database, and the American Hospital Association Annual Survey of Hospitals. Study Design: The dependent variable of this study is condition-specific, risk-adjusted, thirty-day potentially preventable hospital readmission (PPR). 3M's PPR software was utilized to determine whether a readmission was potentially preventable. The independent variable of this study is hospital performance for process quality of inpatient care, measured by hospital adherence to recommended processes of care. We used multivariate hierarchical logistic models, clustered by hospitals, to examine the relationship between condition-specific, risk-adjusted, thirty-day PPR rates and process quality of inpatient care, after taking clinical and socio-demographic characteristics of patients and structural and operational characteristics of hospitals into account. Findings: Better performance on the process quality metrics was associated with better patient outcome (i.e., low thirty-day PPR rates) in pneumonia, but not generally in two cardiovascular conditions (i.e., heart failure and acute myocardial infarction). Practical Implication: Adherence to the process quality metrics currently in use by CMS is associated with risk-adjusted, thirty-day PPR rates for patients with pneumonia, but not with cardiovascular conditions. More evidence-based process quality metrics closely linked to 30-day PPR rates, particularly for cardiovascular conditions, need to be developed to serve as a guideline to reduce potentially preventable readmissions.

Purpose: 본 연구는 미국 캘리포니아와 플로리다에 위치한 의료기관을 대상으로 급성심근경색증, 심부전, 폐렴을 주진단으로 받은 메디케어 입원환자들에게 제공된 의료서비스의 과정적 질과 잠재적으로 예방이 가능한 30일 이내 위험 보정 재입원율과의 관계를 살펴보았다. Methods: 본 연구의 종속변수는 잠재적으로 예방이 가능한 30일 이내 위험 보정 질환별 재입원율이며 3M PPR 소프트웨어를 이용하여 재입원의 예방 가능 여부를 결정하였다. 미연방 의료 비용 및 이용 프로젝트 데이터베이스, 미국병원협회의 병원조사 자료, 미연방 보건복지부소속 메디케어 및 메디케이드 서비스 센터의 병원비교 자료를 이용하였다. 자료의 위계적 구조를 고려하여 다수준 로지스틱 회귀분석을 이용하여 분석하였다. Findings: 의료서비스의 과정적 품질과 퇴원 후 30일 이내 잠재적 예방 가능 위험도 보정 재입원율과의 관계는 질환별로 차이를 보였다. 폐렴의 경우 의료서비스의 과정적 질은 30일 이내 잠재적 예방 가능 보정 재입원율과 유의한 부(-)의 관계를 보였으나, 급성심근경색증과 심부전의 경우 대체로 유의한 관계를 관찰할 수 없었다. Practical Implications: 잠재적으로 예방 가능한 급성심근경색증, 심부전 재입원율을 줄이기 위해서는 의료기관에서 가이드라인으로 따를 수 있는 더욱 다양한 근거 중심의 과정적 질 지표의 개발에 대한 정부와 보건의료계의 노력이 필요하다.

Keywords

References

  1. Smith VK, Gifford K, Ellis E, Rudowitz R, Snyder L, Hinton E: Medicaid reforms to expand coverage, control costs and improve care: Results from a 50-state Medicaid budget survey for state fiscal years 2015 and 2016. Menlo Park, CA: The Kaiser Family Foundation, and National Association of Medicaid Directors 2015.
  2. State initiatives [http://www.3m.com/3M/en_US/health-information-systems-us/resources/our-partners/state-initiatives/]
  3. Borzecki AM, Chen Q, Restuccia J, Mull HJ, Shwartz M, Gupta K, Hanchate A, Strymish J, Rosen A: Do pneumonia readmissions flagged as potentially preventable by the 3M PPR software have more process of care problems? A crosssectional observational study. BMJ quality & safety 2015, 24(12):753-763. https://doi.org/10.1136/bmjqs-2014-003911
  4. Borzecki AM, Chen Q, Mull HJ, Shwartz M, Bhatt DL, Hanchate A, Rosen AK: Do Acute Myocardial Infarction and Heart Failure Readmissions Flagged as Potentially Preventable by the 3M Potentially Preventable Readmissions Software Have More Process-of-Care Problems? Circulation Cardiovascular quality and outcomes 2016, 9(5):532-541. https://doi.org/10.1161/CIRCOUTCOMES.115.002509
  5. Hernandez AF, Hammill BG, Peterson ED, Yancy CW, Schulman KA, Curtis LH, Fonarow GC: Relationships between emerging measures of heart failure processes of care and clinical outcomes. American heart journal 2010, 159(3):406-413. https://doi.org/10.1016/j.ahj.2009.12.024
  6. Patterson ME, Hernandez AF, Hammill BG, Fonarow GC, Peterson ED, Schulman KA, Curtis LH: Process of care performance measures and long-term outcomes in patients hospitalized with heart failure. Medical care 2010, 48(3):210-216. https://doi.org/10.1097/MLR.0b013e3181ca3eb4
  7. Goldfield NI, McCullough EC, Hughes JS, Tang AM, Eastman B, Rawlins LK, Averill RF: Identifying potentially preventable readmissions. Health care financing review 2008, 30(1):75-91.
  8. 3M: Potentially Preventable Readmissions Classification System: Methodology Overview. In.: 3M Health Information Systems; 2010.
  9. Nasir K, Lin Z, Bueno H, Normand SL, Drye EE, Keenan PS, Krumholz HM: Is same-hospital readmission rate a good surrogate for all-hospital readmission rate? Medical care 2010, 48(5):477-481. https://doi.org/10.1097/MLR.0b013e3181d5fb24
  10. Rubin HR, Pronovost P, Diette GB: The advantages and disadvantages of process-based measures of health care quality. International journal for quality in health care : journal of the International Society for Quality in Health Care / ISQua 2001, 13(6):469-474. https://doi.org/10.1093/intqhc/13.6.469
  11. Cromwell J, Trisolini MG, Pope GC, Mitchell JB, Greenwald LM: Pay for Performance in Health Care: Methods and Approaches, vol. BK-0002-1103.: RTI Press 2011.
  12. Flather MD, Yusuf S, Kober L, Pfeffer M, Hall A, Murray G, Torp-Pedersen C, Ball S, Pogue J, Moye L et al: Long-term ACE-inhibitor therapy in patients with heart failure or left-ventricular dysfunction: a systematic overview of data from individual patients. ACE-Inhibitor Myocardial Infarction Collaborative Group. Lancet 2000, 355(9215):1575-1581. https://doi.org/10.1016/S0140-6736(00)02212-1
  13. Soumerai SB, McLaughlin TJ, Spiegelman D, Hertzmark E, Thibault G, Goldman L: Adverse outcomes of underuse of beta-blockers in elderly survivors of acute myocardial infarction. JAMA : the journal of the American Medical Association 1997, 277(2):115-121. https://doi.org/10.1001/jama.1997.03540260029031
  14. Indications for ACE inhibitors in the early treatment of acute myocardial infarction: systematic overview of individual data from 100,000 patients in randomized trials. ACE Inhibitor Myocardial Infarction Collaborative Group. Circulation 1998, 97(22):2202-2212. https://doi.org/10.1161/01.CIR.97.22.2202
  15. Shepperd S, McClaran J, Phillips CO, Lannin NA, Clemson LM, McCluskey A, Cameron ID, Barras SL: Discharge planning from hospital to home. Cochrane database of systematic reviews 2010(1):CD000313.
  16. Quality Indicator User Guide: Pediatric Quality Indicators (PDI) Composite Measures [http://www.qualityindicators.ahrq.gov/Downloads/Modules/PDI/V43/Composite_User_Technical_Specification_PDI_4.3.pdf]
  17. Measures Development, Methodology, and Oversight Advisory Committee: Recommendations to PCPI Work Groups on Composite Measures [http://www.ama-assn.org/resources/doc/cqi/composite-measures-framework.pdf]
  18. Werner RM, Bradlow ET: Relationship between Medicare's hospital compare performance measures and mortality rates. JAMA : the journal of the American Medical Association 2006, 296(22):2694-2702. https://doi.org/10.1001/jama.296.22.2694
  19. Jha AK, Orav EJ, Li Z, Epstein AM: The inverse relationship between mortality rates and performance in the Hospital Quality Alliance measures. Health affairs 2007, 26(4):1104-1110. https://doi.org/10.1377/hlthaff.26.4.1104
  20. Krumholz HM, Normand SL, Spertus JA, Shahian DM, Bradley EH: Measuring performance for treating heart attacks and heart failure: the case for outcomes measurement. Health affairs 2007, 26(1):75-85. https://doi.org/10.1377/hlthaff.26.1.75
  21. Gilstrap LG, Joynt KE: Both processes and readmissions matter for heart failure: How can we align them? American heart journal 2015, 170(5):968-970. https://doi.org/10.1016/j.ahj.2015.07.012
  22. Freemantle N, Cleland J, Young P, Mason J, Harrison J: beta Blockade after myocardial infarction: systematic review and meta regression analysis. Bmj 1999, 318(7200):1730-1737. https://doi.org/10.1136/bmj.318.7200.1730
  23. Bisognano M, Boutwell A: Improving transitions to reduce readmissions. Frontiers of health services management 2009, 25(3):3-10.
  24. Bonow RO, Bennett S, Casey DE, Jr., Ganiats TG, Hlatky MA, Konstam MA, Lambrew CT, Normand SL, Pina IL, Radford MJ et al: ACC/AHA clinical performance measures for adults with chronic heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Performance Measures (Writing Committee to Develop Heart Failure Clinical Performance Measures) endorsed by the Heart Failure Society of America. Journal of the American College of Cardiology 2005, 46(6):1144-1178. https://doi.org/10.1016/j.jacc.2005.07.012
  25. Spertus JA, Eagle KA, Krumholz HM, Mitchell KR, Normand SL, American College of Cardiology/American Heart Association Task Force on Performance M: American College of Cardiology and American Heart Association methodology for the selection and creation of performance measures for quantifying the quality of cardiovascular care. Journal of the American College of Cardiology 2005, 45(7):1147-1156. https://doi.org/10.1016/j.jacc.2005.03.011
  26. Jollis JG, Ancukiewicz M, DeLong ER, Pryor DB, Muhlbaier LH, Mark DB: Discordance of databases designed for claims payment versus clinical information systems. Implications for outcomes research. Annals of internal medicine 1993, 119(8):844-850. https://doi.org/10.7326/0003-4819-119-8-199310150-00011
  27. Dans PE: Looking for answers in all the wrong places. Annals of internal medicine 1993, 119(8):855-857. https://doi.org/10.7326/0003-4819-119-8-199310150-00014
  28. Fisher ES, Whaley FS, Krushat WM, Malenka DJ, Fleming C, Baron JA, Hsia DC: The accuracy of Medicare's hospital claims data: progress has been made, but problems remain. American journal of public health 1992, 82(2):243-248. https://doi.org/10.2105/AJPH.82.2.243
  29. Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T: Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? JAMA : the journal of the American Medical Association 1992, 267(16):2197-2203. https://doi.org/10.1001/jama.1992.03480160055034
  30. Iezzoni LI: The risks of risk adjustment. JAMA : the journal of the American Medical Association 1997, 278(19):1600-1607. https://doi.org/10.1001/jama.1997.03550190064046
  31. Elixhauser A, Steiner C, Harris DR, Coffey RM: Comorbidity measures for use with administrative data. Medical care 1998, 36(1):8-27. https://doi.org/10.1097/00005650-199801000-00004
  32. Southern DA, Quan H, Ghali WA: Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Medical care 2004, 42(4):355-360. https://doi.org/10.1097/01.mlr.0000118861.56848.ee
  33. Federal Register. 2011, 76(160):51476-51846.
  34. Boozary AS, Manchin J, 3rd, Wicker RF: The Medicare Hospital Readmissions Reduction Program: Time for Reform. Jama 2015, 314(4):347-348. https://doi.org/10.1001/jama.2015.6507
  35. Rosen AK, Chen Q, Shwartz M, Pilver C, Mull HJ, Itani KF, Borzecki A: Does Use of a Hospital-wide Readmission Measure Versus Condition-specific Readmission Measures Make a Difference for Hospital Profiling and Payment Penalties? Medical care 2016, 54(2):155-161. https://doi.org/10.1097/MLR.0000000000000455