Development of an Automated, Real Time Surveillance Tool for Predicting Readmissions at a Community Hospital

References
1 Medicare Program; Hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and FY 2012 rates. 42 C. F. R. Pt. 412.150 - 412.154 2011
2 McCarthy D, Johnson MB, Audet A-M. Recasting readmissions by placing the hospital role in community context. JAMA 2013; 309 (04) 351-352.
3 Brock J, Mitchell J, Irby K, Stevens B, Archibald T, Goroski A, Lynn J. Association between quality improvement for care transitions in communities and rehospitalizations among medicare beneficiaries. JAMA 2013; 309 (04) 381-389.
4 Hansen L, Young R, Hinami K, Leung A, Williams M. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Int Med 2011; 155: 520-528.
5 Kansagara D, Englander H, Salanitro A. et al. Risk prediction models for hospital readmission: A systematic review. JAMA 2011; 306 (15) 1688-1698.
6 Coleman EA, Min SJ, Chomiak A. et al. Posthospital care transitions: patterns, complications, and risk identification. Health Serv Res 2004; 39 (05) 1449-1465.
7 van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, Austin PC, Forster AJ. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ 2010; 182 (06) 551-557.
8 Charlson ME, Pompei P, Ales KL. et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40 (05) 373-383.
9 Schneeweiss S, Wang PS, Avorn J. et al. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res 2003; 38: 1103-1120.
10 Grunier A, Dhalla IA, van Walraven C. et al. Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. Open Medicine 2011; 5 (02) E104.
11 Amarasingham R, Moore BJ, Tabak YP. et al. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care 2010; 48 (11) 981-988.
12 Billings J, Dixon J, Mijanovich T, Wennberg D. Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. BMJ 2006; 333 7563 327.
13 Billings J, Blunt I, Steventon A, Georghiou T, Lewis G, Bardsley M. Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30). BMJ Open 2012; 00:e001667.doi:10.1136/bmjopen-2012-001667.
14 Hasan O, Meltzer DO, Shaykevich SA, Bell CM, Kaboli PJ, Auerbach AD, Wetterneck TB, Arora VM, Zhang J, Schnipper JL. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med 2009; 25 (03) 211-219.
15 Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi J. et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care. 2005 43:(11)
16 Hall W, Ramachandran R, Narayan S, Jani A, Vijayajumar S. An electronic application for rapidly calculating Charlson comorbidity score. BMC Cancer 2004; 4: 94 .l
17 Garrison G, Mansukhani M, Bohn B. Predictors of thirty-day readmission among hospitalized family medicine patients. J Am Board Fam Med 2013; 26: 71-77.
18 Arbaje A, Wolff J, Yu Q, Powe N, Anderson G, Boult C. Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. Gerontologist 2008; 48: 495-504.
19 Prins H, Hasman A. Appropriateness of ICD-9 coded diagnostic inpatient hospital discharge data for medical practice assessment: a systematic review. Methods Inf Med 2013; S2: 3-17.