A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD

Health Care Management Science

Volumn 18, Issue 1, 2015, Pages 19-34

  Shams, Issac ^a   Ajorlou, Saeede ^a   Yang, Kai ^a  

^a WAYNE STATE UNIVERSITY   (United States)

Author keywords

Markov chain; Patient flow; Phase type distribution; Predictive analytics; Readmission

Indexed keywords

AGED; ALGORITHM; COMORBIDITY; FEMALE; GOVERNMENT; HEALTH CARE QUALITY; HEART FAILURE; HOSPITAL READMISSION; HUMAN; LENGTH OF STAY; MALE; MIDDLE AGED; MYOCARDIAL INFARCTION; PNEUMONIA; PROBABILITY; PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; RISK FACTOR; SOCIOECONOMICS; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA; TIME; UNITED STATES;

AGED; ALGORITHMS; CENTERS FOR MEDICARE AND MEDICAID SERVICES (U.S.); COMORBIDITY; DATA INTERPRETATION, STATISTICAL; FEMALE; HEART FAILURE; HUMANS; LENGTH OF STAY; MALE; MICHIGAN; MIDDLE AGED; MYOCARDIAL INFARCTION; PATIENT READMISSION; PNEUMONIA; PROBABILITY; PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; QUALITY OF HEALTH CARE; RISK FACTORS; SOCIOECONOMIC FACTORS; TIME FACTORS; UNITED STATES; UNITED STATES DEPARTMENT OF VETERANS AFFAIRS;

EID: 84936996800     PISSN: 13869620     EISSN: 15729389     Source Type: Journal    
DOI: 10.1007/s10729-014-9278-y     Document Type: Article

References (38)

1. Chassin MR, Loeb JM, Schmaltz SP, Wachter RM (2010) Accountability measures—using measurement to promote quality improvement. N Engl J Med 363(7):683–688
2. Jencks SF, Williams MV, Coleman EA (2009) Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 360(14):1418–1428
3. Medicare Payment Advisory Commission (2007) Report to the Congress: Promoting greater efficiency in Medicare. Medicare Payment Advisory Commission (MedPAC)
4. Tsai TC, Joynt KE, Orav EJ, Gawande AA, Jha AK (2013) Variation in surgical-readmission rates and quality of hospital care. N Engl J Med 369(12):1134–1142
5. Westert GP, Lagoe RJ, Keskimäki I, Leyland A, Murphy M (2002) An international study of hospital readmissions and related utilization in Europe and the USA. Health Policy 61(3):269–278
6. Hansen LO, Young RS, Hinami K, Leung A, Williams MV (2011) Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med 155(8):520–528
7. Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S (2011) Risk prediction models for hospital readmission: a systematic review. J Am Med Assoc 306(15):1688–1698. doi:10.1001/jama.2011.1515
8. van Walraven C, Bennett C, Jennings A, Austin PC, Forster AJ (2011) Proportion of hospital readmissions deemed avoidable: a systematic review. Can Med Assoc J 183(7):E391–E402
9. Au AG, McAlister FA, Bakal JA, Ezekowitz J, Kaul P, van Walraven C (2012) Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization. Am Heart J 164(3):365–372. doi:10.1016/j.ahj.2012.06.010
10. Joynt KE, Orav EJ, Jha AK (2011) Thirty-day readmission rates for Medicare beneficiaries by race and site of care. J Am Med Assoc 305(7):675–681. doi:10.1001/jama.2011.123
11. Shulan M, Gao K, Moore CD (2013) Predicting 30-day all-cause hospital readmissions. Health Care Manag Sci 16(2):167–175. doi:10.1007/s10729-013-9220-8
12. Fetter RB, Shin Y, Freeman JL, Averill RF, Thompson JD (1980) Case mix definition by diagnosis-related groups. Med Care 18(2):1–53
13. Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Ingber MJ, Levy JM, Robst J (2004) Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev 25(4):119–141
14. Sales AE, Liu C-F, Sloan KL, Malkin J, Fishman PA, Rosen AK, Loveland S, Nichol WP, Suzuki NT, Perrin E (2003) Predicting costs of care using a pharmacy-based measure risk adjustment in a veteran population. Med Care 41(6):753–760
15. Berry JG, Toomey SL, Zaslavsky AM, Jha AK, Nakamura MM, Klein DJ, Feng JY, Shulman S, Chiang VW, Kaplan W, Hall M, Schuster MA (2013) Pediatric readmission prevalence and variability across hospitals. J Am Med Assoc 309(4):372–380. doi:10.1001/jama.2012.188351
16. Goldfield NI, McCullough EC, Hughes JS, Tang AM, Eastman B, Rawlins LK, Averill RF (2008) Identifying potentially preventable readmissions. Health Care Financ Rev 30(1):75–91
17. Centers for Medicare and Medicaid Services (2011) Hospital 30-day readmissions measures. Available at http://www.qualitynet.org/dcs/ContentServer?c=Page&pagename=QnetPublic%2FPage%2FQnetTier3&cid=1219069855841. Accessed 10 March 2014
18. 3M Health Information Systems (2008) Potentially preventable readmissions classification system. Available at http://multimedia.3m.com/mws/mediawebserver?66666UuZjcFSLXTtNXMtmxMEEVuQEcuZgVs6EVs6E666666. Accessed 10 March 2014
19. Mull HJ, Chen Q, O’Brien WJ, Shwartz M, Borzecki AM, Hanchate A, Rosen AK (2013) Comparing 2 methods of assessing 30-day readmissions: what is the impact on hospital profiling in the Veterans Health Administration? Med Care 51(7):589–596
20. Horwitz L, Partovian C, Lin Z, Herrin J, Grady J, Conover M, Montague J, Dillaway C, Bartczak K, Ross J (2011) Hospital-wide (all-condition) 30-day risk-standardized readmission measure. Available at http://146.123.140.205/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/downloads/MMSHospital-WideAll-ConditionReadmissionRate.pdf. Accessed 27 Oct 2013
21. National Quality Measures Clearinghouse (2012) Ambulatory care sensitive conditions. Agency for Healthcare Research and Quality (AHRQ). Available at http://www.qualitymeasures.ahrq.gov/content.aspx?id=35186. Accessed 03 Sep 2013
22. Ajorlou S, Shams I, Yang K (2014) Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs. arXiv preprint arXiv:14031210
23. Fackrell M (2009) Modelling healthcare systems with phase-type distributions. Health Care Manag Sci 12(1):11–26
24. Asmussen S (1996) EMpht software. Available at http://home.imf.au.dk/asmus/pspapers.html. Accessed 14 Nov 2013
25. Breiman L (2001) Random forests. Mach Learn 45(1):5–32
26. Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008) Random survival forests. Ann Appl Stat 2:841–860
27. Wu T-J, Sepulveda A (1998) The weighted average information criterion for order selection in time series and regression models. Stat Probab Lett 39(1):1–10
28. Andridge RR, Little RJ (2010) A review of hot deck imputation for survey non‐response. Int Stat Rev 78(1):40–64
29. Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: ACM Sigmod Record, vol 2. ACM, pp 93–104
30. Healthcare Cost Utilization Project (2012) Comorbidity Software, Version 3.7. Available at http://www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp. Accessed 10 Nov 2013
31. Kaboli PJ, Go JT, Hockenberry J, Glasgow JM, Johnson SR, Rosenthal GE, Jones MP, Vaughan-Sarrazin M (2012) Associations between reduced hospital length of stay and 30-day readmission rate and mortality: 14-year experience in 129 Veterans Affairs hospitals. Ann Intern Med 157(12):837–845
32. Wikipedia: The Free Encyclopedia. Wikimedia Foundation Inc. (2014) Receiver operating characteristic. Accessed 09 March 2014
33. RDC Team (2005) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria, 2013. http://www.R-project.org
34. Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18–22
35. Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A (2008) Misc functions of the Department of Statistics (e1071), TU Wien. R Packag 1:5–24
36. Marschner IC (2011) glm2: fitting generalized linear models with convergence problems. R Journal 3(2):12–15
37. Demuth H, Beale M (1993) Neural network toolbox for use with MATLAB
38. Kalbfleisch JD, Wolfe RA (2013) On monitoring outcomes of medical providers. Stat Biosci 5(2)286–302