A successful hospital-based disease management program to reduce admissions among patients with multiple chronic illnesses

Article Sidebar

Requires Subscription or Fee PDF (USD 25)
Published: Dec 20, 2011
DOI: https://doi.org/10.5750/ijpcm.v1i4.142

Main Article Content

Ariel Linden
University of Michigan, Ann Arbor, MI, USA
Marco Bonollo
Monash University, Prahran, Victoria, Australia
Kaylene Fiddes
Disease Management Unit, The Alfred Hospital, Melbourne, Australia

Abstract

Objectives: To examine the effect of a hospital-based disease management program in reducing monthly hospital admission rates among patients with multiple chronic illnesses.Design: Interrupted time series analysis.Setting: A public hospital system comprised of three campuses in suburban Melbourne, Australia.Participants: 2,341 patients with three or more chronic illnesses enrolled in a hospital-based disease management program upon discharge.Intervention: Prior to hospital discharge, an inpatient coordinator refers eligible patients to the disease management unit (DMU). A DMU care coordinator invites patients to enroll and immediately schedules a comprehensive hospital-based outpatient clinic visit. The clinic utilizes a patient-centered team approach including a physician trained in multi-specialty care, a pharmacist, and a DMU nurse. Additional clinic visits are scheduled as needed. Between clinic visits, patients receive continued intensive contact with the DMU team, home visits by a pharmacist if necessary and optional patient education classes. The DMU liaises with the patient’s general practitioner throughout the program until the patient is stable.Measurement: Admissions per 1,000 patients per month (PTPM), evaluated 50 months before and 50 months after enrollment in the DMU program.Results: During the 50 month period pre-intervention period, admissions trended significantly upward at a rate of 2.43 admissions PTPM (95% confidence interval = 1.47, 3.38). Admissions PTPM during the 50-month period after enrollment trended significantly downward at a rate of 3.54 admissions PTPM (95% confidence interval = -4.71, -2.37). Conclusion: A comprehensive hospital-based disease management program successfully reduced monthly admissions for complex chronically ill patients during the 50 months following enrollment in the program compared to the prior 50 months. Contrary to many recent disease management evaluations, these findings suggest that it is possible to design a program to effectively reduce admissions, the largest cost driver in a chronically ill population, but that a person-centered closed-loop system involving both inpatient and outpatient services is likely required.

Article Details

Issue
Vol. 1 No. 4 (2011): December
Section
Person-centered care and chronic disease

References

Mattke, S., Seid, M. & Ma, S. (2007). Evidence for the effect of disease management: Is $1 billion a year a good investment? American Journal of Managed Care 13 (12), 670-676.

Brown, R., Peikes, D., Chen, A. & Schore, J. (2008). 15-site randomized trial of coordinated care in medicare FFS. Health Care Financing Review 30 (1), 5–25.

Esposito, D., Brown, R., Chen, A., Schore, J. & Shapiro, R. (2008). Impacts of a disease management program for dually eligible beneficiaries. Health Care Financing Review 30 (1), 27–45.

McCall, N., Cromwell, J. & Bernard, S. Evaluation of Phase I of Medicare Health Support (formerly voluntary chronic care improvement) Pilot Program under Traditional Fee-For-Service Medicare. RTI International, June 2007. Available at: http://www.cms.hhs.gov/Reports/Downloads/McCall.pdf Accessed Sep 4, 2011.

Linden, A. & Adler-Milstein, J. (2008). Medicare disease management in a policy context. Health Care Finance Review 29 (3),1-11.

American Academy of Family Physicians (AAFP), American Academy of Pediatrics (AAP), American College of Physicians (ACP) & American Osteopathic Association (AOA). Joint Principles of the Patient-Centered Medical Home. February 2007. http://www.acponline.org/running_practice/pcmh/demonstrations/jointprinc_05_17.pdf. Accessed Sep 7, 2011.

American College of Physicians. Patient Centered Medical Home. http://www.acponline.org/running_practice/pcmh Accessed Sep 7, 2011.

Centers for Medicare and Medicaid Services. Details for Multi-payer Advanced Primary Care Initiative. http://www.cms.gov/demoprojectsevalrpts/md/itemdetail.asp?itemid=cms1230016 Accessed Sep 7, 2011.

Linden, A. (2006). What will it take for disease management to demonstrate a return on investment? New perspectives on an old theme. American Journal of Managed Care 12 (4),61-67.

Jencks, S.F., Williams, M.V. & Coleman, E.A. (2009). Rehospitalizations among Patients in the Medicare Fee-for-Service Program. New England Journal of Medicine 360, 1418-1428.

Campbell, D.T. & Stanley, J.C. (1966). Experimental and quasi-experimental designs for research. Chicago: Rand McNally.

Cook, T.D. & Campbell, D.T. (1979). Quasi-experimentation: design and analysis issues for field settings. Chicago: Rand McNally College Publishing.

Shadish, S.R., Cook, T.D. & Campbell, D.T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.

Simonton, D.K. (1977). Cross sectional time-series experiments: some suggested statistical analyses. Psychological Bulletin 84 (3), 489-502.

Matowe, L.K., Leister, C.A., Crivera, C. & Korth-Bradley, J.M. (2003). Interrupted times series analysis in clinical research. Annals of Pharmacotherapy 37, 1110-1116.

Linden, A. & Adams, J.L. (2010). Applying a propensity-score based weighting model to interrupted time series data: improving causal inference in program evaluation. Journal of Evaluation in Clinical Practice. Online version published October 25, 2010.

Newey, W.K. & West, K.D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55, 703–708.

Liang, K-Y. & Zeger, S.L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22.

U.S. Renal Data System. USRDS 2010 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda: MD, 2010.

Ljung, G.M. & Box, G.E.P. (1978). On a measure of lack of fit in time series models. Biometrika 65, 297–303.

Naylor, M.D., Brooten, D., Campbell, R., Jacobsen, B.S., Mezey, M.D., Pauley, M.V. & Schwartz, J.S. (1999). Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. Journal American Medical Association 281, 613-620.

Naylor, M.D., Brooten, D.A., Campell, R.L., Maislin, G., McCauley, K.M. & Schwartz, J.S. (2004). Transitional care of older adults hospitalized with heart failure: a randomized, controlled trial. Journal American Geriatric Society 52, 675-684.

Coleman, E.A. & Chalmers, S. (2006). The care transitions intervention: results of a randomized controlled trial. Archives of Internal Medicine 166, 1822-1828.

Agency for Healthcare Research and Quality. Comparative Effectiveness of Case Management for Adults with Medical Illness and Complex Care Needs. Available at: http://www.effectivehealthcare.ahrq.gov/ehc/products/240/732/Case-Management_Draft-Report_CER_20110726.pdf Accessed Sep 19, 2011.

Koechler, J.A., Abramowitz, P.W., Swim, S.E. & Daniels, C.E. (1989). Indicators for the selection of ambulatory patients who warrant pharmacist monitoring. American Journal of Hospital Pharmacy 46, 729-732.

Hepler, C.D. & Strand, L.M. (1990). Opportunities and responsibilities in pharmaceutical care. American Journal of Hospital Pharmacy 47, 533-543.

Strand, L.M., Morley, P.C. & Cipolle, R.J. (1990). Drug-related problems: their structure and function. Annals of Pharmacotherapy 24, 1093-1097.

Linden, A., Adams, J. & Roberts, N. (2003). An assessment of the total population approach for evaluating disease management program effectiveness. Disease Management 6 (2), 93-102.

Linden, A. (2007). Estimating the effect of regression to the mean in health management programs. Disease Management and Health Outcomes 15 (1), 7-12.

Ezekiel, E.J. (1996). Cost savings at the end of life: What do the data show? Journal American Medical Association 275, 1907-1914.

Hogan, C., Lunney, J., Gabel, J. & Lynn, J. (2001). Medicare beneficiaries’ costs of care in the last year of life. Health Affairs 204, 188–195.

Barnato, A., McClellan, M.B., Kagay, C.R. & Garber, A.M. (2004). Trends in inpatient treatment intensity among Medicare beneficiaries at the end of life. Health Services Research 39 (2), 363–376.

Campbell, D., Lynn, J., Louis, T. & Shugarman, L. (2004). Medicare program expenditures associated with hospice use. Annals of Internal Medicine 140, 269–277.