$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

의료서비스의 과정적 질과 잠재적으로 예방 가능한 재입원율과의 관계
Does Process Quality of Inpatient Care Serve as a Guide to Reduce Potentially Preventable Readmission (PPR)? 원문보기

병원경영학회지 = Korea journal of hospital management, v.23 no.1, 2018년, pp.87 - 106  

최재영 (한림대학교 경영학과 의료경영트랙)

초록
AI-Helper 아이콘AI-Helper

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.

Abstract AI-Helper 아이콘AI-Helper

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 ...

주제어

참고문헌 (35)

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  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. 

  27. Dans PE: Looking for answers in all the wrong places. Annals of internal medicine 1993, 119(8):855-857. 

  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. 

  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. 

  30. Iezzoni LI: The risks of risk adjustment. JAMA : the journal of the American Medical Association 1997, 278(19):1600-1607. 

  31. Elixhauser A, Steiner C, Harris DR, Coffey RM: Comorbidity measures for use with administrative data. Medical care 1998, 36(1):8-27. 

  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. 

  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. 

  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. 

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로