Currently Viewing:
The American Journal of Managed Care September 2013
Referring Patients for Telephone Counseling to Promote Colorectal Cancer Screening
Roger Luckmann, MD, MPH; Mary E. Costanza, MD; Milagros Rosal, PhD; Mary Jo White, MS, MPH; and Caroline Cranos, MPH
Improving BP Control Through Electronic Communications: An Economic Evaluation
Paul A. Fishman, PhD; Andrea J. Cook, PhD; Melissa L. Anderson, MS; James D. Ralston, PhD, MPH; Sheryl L. Catz, PhD; David Carrell, PhD; James Carlson, PharmD; and Beverly B. Green, MD, MPH
Currently Reading
Risk-Stratification Methods for Identifying Patients for Care Coordination
Lindsey R. Haas, MPH; Paul Y. Takahashi, MD; Nilay D. Shah, PhD; Robert J. Stroebel, MD; Matthew E. Bernard, MD; Dawn M. Finnie, MPA; and James M. Naessens, ScD
FDA Warning and Removal of Rosiglitazone From VA National Formulary
Sherrie L. Aspinall, PharmD, MSc; Xinhua Zhao, PhD; Chester B. Good, MD, MPH; Roslyn A. Stone, PhD; Kenneth J. Smith, MD, MS; and Francesca E. Cunningham, PharmD
Emerging and Encouraging Trends in E-Prescribing Adoption Among Providers and Pharmacies
Meghan H. Gabriel, PhD; Michael F. Furukawa, PhD; and Varun Vaidya, PhD
Improving Pneumococcal and Herpes Zoster Vaccination Uptake: Expanding Pharmacist Privileges
Michael S. Taitel, PhD; Leonard E. Fensterheim, MPH; Adam E. Cannon, MPH; and Edward S. Cohen, PharmD
Testimonials Do Not Convert Patients From Brand to Generic Medication
John Beshears, PhD; James J. Choi, PhD; David Laibson, PhD; Brigitte C. Madrian, PhD; and Gwendolyn Reynolds, MTS
Outpatient Parenteral Antimicrobial Therapy at Large Veterans Administration Medical Center
Andrew Lai, MD; Thuong Tran, PharmD; Hien M. Nguyen, MD; Jacob Fleischmann, MD; David O. Beenhouwer, MD; and Christopher J. Graber, MD, MPH
Adherence, Persistence, and Switching Patterns of Dabigatran Etexilate
Kimberly Tsai, PharmD; Sara C. Erickson, PharmD; Jianing Yang, MS; Ann S. M. Harada, PhD, MPH; Brian K. Solow, MD; and Heidi C. Lew, PharmD

Risk-Stratification Methods for Identifying Patients for Care Coordination

Lindsey R. Haas, MPH; Paul Y. Takahashi, MD; Nilay D. Shah, PhD; Robert J. Stroebel, MD; Matthew E. Bernard, MD; Dawn M. Finnie, MPA; and James M. Naessens, ScD
Identifying which patients are likely to benefit from care coordination is important. We evaluated the performance of 6 risk-screening instruments in predicting healthcare utilization.
Each of these risk-stratification methods has potential advantages and disadvantages. There is considerable overlap across the risk-stratification methods because each system relies on the number of comorbid illnesses that a patient has. In fact, at least 40% of our clinically identified patients (based on combination of MN Tiering and ERA) were among the highest risk patients using any of the risk-stratification methods. However, none of the models explained more than half of the variability in outcomes—a clear limitation suggesting that other factors could enable better identification of patients who need care coordination in order to reduce the need for hospitalization and other high-cost healthcare services. Future research is needed to determine whether incorporating additional factors (eg, living situation, high-risk medications, lifestyle, patient preferences) would improve prediction of high-risk individuals or might suggest that certain risks suggest certain interventions.

CONCLUSION

We found good concordance among all 6 different risk screening instruments for predicting hospitalization. Use of any of the tools may provide some support for providers and health plans who undertake case management. Focusing care coordination efforts within the medical home on patients likely to benefit most requires appropriate identification of the highest risk, highest utilizing patients. Use of risk screening tools is a promising method.

Author Affiliations: From Department of Health Sciences (LRH, NDS, DMF, JMN), Division of Health Care Policy and Research, Department of Internal Medicine (RJS, PYT), Division of Primary Care Internal Medicine, Department of Family Medicine (MEB), Mayo Clinic, Rochester, MN
.
Funding Source: None.

Author Disclosures: The authors (LRS, PYT, NDS, RJS, MEB, DMF, JMN) report no relationship or financial interest with any  entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (LRS, PYT, NDS, RJS, MEB, DMF, JMN); acquisition of data (LRS, JMN); analysis and interpretation of data (LRS, NDS, RJS, MEB, DMF, JMN); drafting of the manuscript (LRS, PYT, MEB, JMN); critical revision of the manuscript for important intellectual content (LRS, PYT, NDS, RJS, MEB, JMN); statistical analysis (LRS, JMN); obtaining funding (JMN); and administrative, technical, or logistic support (DMF).

Address correspondence to: Lindsey R. Haas, MPH, Health Care Policy and Research, Mayo Clinic, 200 First St Southwest, Rochester, MN 55905. E-mail: haas.lindsey@mayo.edu.
1. National Committee for Quality Assurance. NCQA Patient-Centered Medical Home 2011. http://www.ncqa.org/LinkClick.aspx?fileticket=ycS4coFOGnw%3d&tabid=631. Accessed May 1, 2012.

2. Brown RS, Peikes D, Peterson G, Schore J, Razafindrakoto CM. Six features of medicare coordinated care demonstration programs that cut hospital admissions of high-risk patients. Health Aff (Millwood). 2012;31(6):1156-1166.

3. Minnesota Department of Human Services. Health Care Homes: Minnesota Health Care Programs (MHCP) Fee-for-Service Care Coordination Rate Methodology. http://www.dhs.state.mn.us/main/ idcplg?IdcService=GET_FILE&RevisionSelectionMethod=LatestReleased&noSaveAs=1&Rendition=Primary&allowInterrupt=1&dDocName=dhs16_148943. Accessed March 20, 2012.

4. National Quality Forum. Preferred Practices and Performance Measures for Measuring and Reporting Care Coordination. Washington, DC: National Quality Forum; October 2010.

5. Bodenheimer T. Coordinating care—a perilous journey through the health care system. N Engl J Med. 2008;358(10):1064-1071.

6. Hoffman C, Rice D, Sung HY. Persons with chronic conditions: their prevalence and costs. JAMA. 1996;276(18):1473-1479.

7. Reid RJ, Fishman PA, Yu O, et al. Patient-centered medical home demonstration: a prospective, quasi-experimental, before and after evaluation. Am J Manag Care. 2009;15(9):e71-e87.

8. Gilfillan RJ, Tomcavage J, Rosenthal MB, et al. Value and the medical home: effects of transformed primary care. Am J Manag Care. 2010;16(8):607-614.

9. Lemke KW, Weiner JP, Clark JM. Development and validation of a model for predicting inpatient hospitalization. Med Care. 2012;50(2): 131-139.

10. Bernstein RH. New arrows in the quiver for targeting care management: high-risk versus high-opportunity case identification. J Ambul Care Manage. 2007;30(1):39-51.

11. Meenan RT, Goodman MJ, Fishman PA, Hornbrook MC, O’Keeffe- Rosetti MC, Bachman DJ. Using risk-adjustment models to identify high-cost risks. Med Care. 2003;41(11):1301-1312.

12. Huntley AL, Johnson R, Purdy S, Valderas JM, Salisbury C. Measures of multimorbidity and morbidity burden for use in primary care and community settings: a systematic review and guide. Ann Fam Med. 2012;10(2):134-141.

13. Melton L 3rd. History of the Rochester Epidemiology Project. Mayo Clinic proceedings. Mayo Clinic. 1996;71(3):266-274.

14. Weiner JP, Starfield BH, Steinwachs DM, Mumford LM. Development and application of a population-oriented measure of ambulatory care case-mix. Med Care. 1991;29(5):452-472.

15. The Johns Hopkins University. About the ACG system. http://www.acg.jhsph.org/index.php?option=com_content&view=article&id=46&It emid=366. Accessed March 23, 2012.

16. Mosley DG, Peterson E, Martin DC. Do hierarchical condition category model scores predict hospitalization risk in newly  enrolled Medicare advantage participants as well as probability of repeated admission scores? J Am Geriatr Soc. 2009;57(12):2306-2310.

17. Pope GC, Kautter J, Ellis RP, et al. Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev. 2004;25(4):119-141.

18. Li P, Kim MM, Doshi JA. Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality. BMC Health Serv Res. 2010;10:245.

19. Lin PJ, Maciejewski ML, Paul JE, Biddle AK. Risk adjustment for Medicare beneficiaries with Alzheimer’s disease and  related dementias.Am J Manag Care. 2010;16(3):191-198.

20. Crane SJ, Tung EE, Hanson GJ, Cha S, Chaudhry R, Takahashi PY. Use of an electronic administrative database to identify older community dwelling adults at high-risk for hospitalization or emergency department visits: the elders risk assessment index. BMC Health Serv Res. 2010;10:338.

21. Naessens JM, Stroebel RJ, Finnie DM, et al. Effect of multiple chronic conditions among working-age adults. Am J Manag Care. 2011;17(2):118-122.

22. Hwang W, Weller W, Ireys H, Anderson G. Out-of-pocket medical spending for care of chronic conditions. Health Aff (Millwood). 2001; 20(6):267-278.

23. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383.

24. Li B, Evans D, Faris P, Dean S, Quan H. Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases. BMC Health Serv Res. 2008;8:12.

25. Shelton P, Sager MA, Schraeder C. The community assessment risk screen (CARS): identifying elderly persons at risk for hospitalization or emergency department visit. Am J Manag Care. 2000;6(8):925-933.

26. Leibson CL, Katusic SK, Barbaresi WJ, Ransom J, O’Brien PC. Use and costs of medical care for children and adolescents with and without attention-deficit/hyperactivity disorder. JAMA. 2001;285(1):60-66.

27. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39(4):561-577.

28. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928-935.

29. Lemke KW, Weiner JP, Clark JM. Development and validation of a model for predicting inpatient hospitalization. Med Care. 2012;50(2): 131-139.

30. Minnesota Department of Health. Care Coordination Tier Assignment Tool, Version 1.0. Health Care Home Initiative.  http://www.health.state.mn.us/healthreform/homes/payment/HCHComplexityTierTool_March2010.pdf. Published March 2010. Accessed March 20, 2012.

31. St Sauver JL, Grossardt BR, Yawn BP, Melton LJ 3rd, Rocca WA. Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester epidemiology project. Am J Epidemiol. 2011;173(9):1059-1068.

32. US Census. State & County Quick Facts. Olmsted County, Minnesota. 2010. http://quickfacts.census.gov/qfd/states /27/27109.html. Accessed June 5, 2012.
PDF
 
Copyright AJMC 2006-2018 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up