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The American Journal of Managed Care December 2018
Feasibility of Expanded Emergency Department Screening for Behavioral Health Problems
Mamata Kene, MD, MPH; Christopher Miller Rosales, MS; Sabrina Wood, MS; Adina S. Rauchwerger, MPH; David R. Vinson, MD; and Stacy A. Sterling, DrPH, MSW
From the Editorial Board: Jonas de Souza, MD, MBA
Jonas de Souza, MD, MBA
Risk Adjusting Medicare Advantage Star Ratings for Socioeconomic Status
Margaret E. O’Kane, MHA, President, National Committee for Quality Assurance
Reducing Disparities Requires Multiple Strategies
Melony E. Sorbero, PhD, MS, MPH; Susan M. Paddock, PhD; and Cheryl L. Damberg, PhD
Cost Variation and Savings Opportunities in the Oncology Care Model
James Baumgardner, PhD; Ahva Shahabi, PhD; Christopher Zacker, RPh, PhD; and Darius Lakdawalla, PhD
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Patient Attribution: Why the Method Matters
Rozalina G. McCoy, MD, MS; Kari S. Bunkers, MD; Priya Ramar, MPH; Sarah K. Meier, PhD; Lorelle L. Benetti, BA; Robert E. Nesse, MD; and James M. Naessens, ScD, MPH
Relationships Between Provider-Led Health Plans and Quality, Utilization, and Satisfaction
Natasha Parekh, MD, MS; Inmaculada Hernandez, PharmD, PhD; Thomas R. Radomski, MD, MS; and William H. Shrank, MD, MSHS
Primary Care Burnout and Populist Discontent
James O. Breen, MD
Adalimumab Persistence for Inflammatory Bowel Disease in Veteran and Insured Cohorts
Shail M. Govani, MD, MSc; Rachel Lipson, MSc; Mohamed Noureldin, MBBS, MSc; Wyndy Wiitala, PhD; Peter D.R. Higgins, MD, PhD, MSc; Sameer D. Saini, MD, MSc; Jacqueline A. Pugh, MD; Dawn I. Velligan, PhD; Ryan W. Stidham, MD, MSc; and Akbar K. Waljee, MD, MSc
The Value of Novel Immuno-Oncology Treatments
John A. Romley, PhD; Andrew Delgado, PharmD; Jinjoo Shim, MS; and Katharine Batt, MD
Medicare Advantage Control of Postacute Costs: Perspectives From Stakeholders
Emily A. Gadbois, PhD; Denise A. Tyler, PhD; Renee R. Shield, PhD; John P. McHugh, PhD; Ulrika Winblad, PhD; Amal Trivedi, MD; and Vincent Mor, PhD
Provider-Owned Insurers in the Individual Market
David H. Howard, PhD; Brad Herring, PhD; John Graves, PhD; and Erin Trish, PhD
Mixed Messages to Consumers From Medicare: Hospital Compare Grades Versus Value-Based Payment Penalty
Jennifer Meddings, MD, MSc; Shawna N. Smith, PhD; Timothy P. Hofer, MD, MSc; Mary A.M. Rogers, PhD, MS; Laura Petersen, MHSA; and Laurence F. McMahon Jr, MD, MPH

Patient Attribution: Why the Method Matters

Rozalina G. McCoy, MD, MS; Kari S. Bunkers, MD; Priya Ramar, MPH; Sarah K. Meier, PhD; Lorelle L. Benetti, BA; Robert E. Nesse, MD; and James M. Naessens, ScD, MPH
Reliable identification of the physician–patient relationship is necessary for accurate evaluation. Standardization of evidence-based attribution methods is essential to improve the value of healthcare.
DISCUSSION

Measuring and reporting indices of health outcomes, utilization, and costs of care are important for improving the value of delivered care, particularly in the context of primary care medical homes, ACOs, and bundled payment programs. Effective population health management, accurate assessment of performance-based contracts, and informed patient engagement are predicated on accurately and reliably attributing patients to providers. Although prior studies have examined the impact of patient attribution methods on select clinical outcomes2,5,19,20,27,28 or more broadly evaluated the impact of different dimensions of attribution algorithms,6 our study extended these findings by evaluating the impact of existing primary care attribution methods on measured care quality, utilization, and costs of care within an integrated healthcare delivery system. Integrated healthcare delivery practices provide an increasing fraction of medical care in the United States,29 yet how attribution methods originally designed for primary care settings translate to multispecialty groups has not been assessed.

There was substantial variability among methods in the number of attributed patients and their characteristics, with several attribution dimensions emerging as particularly important. First, 3 methods attributed patients based on the majority rather than plurality of visits (ie, HP, private payer, and MNCM). These attributed fewer patients than methods attributing based on the plurality of visits and matched a lower proportion of patients to their paneled PCPs; attributed patients were older with more comorbidities and higher utilization and costs. Another key dimension is the time frame of measurement. Methods permitting 2 years (ie, ACO, private payer, and MNCM) had more attributed-matched patients without face-to-face encounters during the assignment year that could be attributed using encounters during the extended look-back period. These patients were younger and healthier, with fewer medical services in the assignment year. Restricting attribution of patients to those with a majority of services from a single PCP and just 1 attribution-eligible year selects for costly high utilizers and misses younger, healthier patients who are less likely to have office visits and more likely to have lower costs. This has important implications for PCPs (who would not receive feedback on care provided to all their patients), patients (who may not be identified as needing services through population management approaches), and health systems (which may misallocate resources away from undercounted patients). Methods that attribute patients without healthcare expenditures during the measurement year may support and reward proactive population health management. Specifically, patient portals, e-consults, telemedicine, and other approaches to manage patients without reliance on face-to-face clinical encounters may deliver high-quality, patient-centered care yet not be captured by attribution methods, quality-of-care metrics, and utilization indices that focus exclusively on having face-to-face visits. Such methods may facilitate chronic disease management among those who already have chronic disease and utilize health services, but they are not optimal for disease prevention and risk management among patients without current health needs or utilization of health services.

Other aspects of attribution methodologies may not alter the accuracy of attribution in primary care practices but have important implications for multispecialty practices and integrated care delivery systems. Methods that include specialists or ED providers as attribution-eligible providers (ie, ACO and public payer) had minimal impact on the proportion of attributed-matched patients but did attribute more paneled patients to specialists. These methods also attributed unpaneled patients; these patients are not part of our primary care population and likely receive their primary care elsewhere, potentially biasing profiling. Another feature that has greater bearing for multispecialty and academic practices is the classification of APPs and trainees as PCPs irrespective of their practice setting. As a result, referral patients were misattributed as primary care patients. We attempted to minimize this in our analyses by considering service location; public payer is the only attribution method to formally do so. Although APPs and medical trainees are not currently available in the National Provider Index, we propose that for the purposes of attribution, they should be accounted by the type and location of care that they provide.

We did not find much variation in quality measures across methods, despite marked differences in the number of individuals assessed. This is reassuring for patients, providers, and payers. However, these measures reflect chronic disease management, and all methods effectively attribute patients with existing diseases who already utilize health services. The goal of population health is to prevent disease, deliver primary and preventive care, and thus reduce the personal and economic impacts of illness. The process of empanelment facilitates this for all patients, whereas most attribution methods—even the ACO method, which captures the most patients overall and the most patients without utilization or chronic disease—fall short.

Limitations

Our findings must be considered in the context of their limitations. All 5 attribution methods were applied to institutional administrative data, which do not capture services and encounters received outside of our institution. However, paneled primary care patients have been shown to have higher provider continuity,30 particularly as a large portion of the included primary care patients have employer-sponsored health benefit plans with higher out-of-pocket costs for services outside our system. These conditions limit the extent of missing services. Some attribution criteria were not incorporated in comparisons across methods to allow for consistent and more meaningful analyses. For example, the public payer method attributes certified medical home patients as the first step; we did not have access to these data and were unable to incorporate this step. In addition, the private payer method excludes high-cost patients (>$200,000); however, because we were interested in understanding how high-cost patients were attributed across methods, we included them in our analyses. Although these specific criteria might improve attribution in certain situations, they are unique to each method and thus limit comparisons. How these methods apply to specialty care providers is outside the scope of this study. Finally, this study is based on a single integrated health system, which potentially limits its generalizability. However, this does not change the main implications of the study—specifically, the marked variation in measured utilization and cost profiles depending on the attribution methodology of measurement-eligible patients.

CONCLUSIONS

Accurate and reliable outcome, utilization, and cost data are needed by providers, administrators, and payers to appropriately allocate resources, focus interventions on areas of highest need, and improve the value of care. Population health management is predicated on data-driven stewardship of patient outcomes and healthcare utilization, replacing the traditional fee-for-service models of care. To achieve these aims, patients must be accurately and equitably attributed to the providers and systems managing their care. The marked variability in attribution methodologies hampers progress in population health management, and broader agreement on the key dimensions underlying these methods is necessary.

Although we compared 5 commonly used attribution methods with each other and with institutional PCP empanelment, proposing a gold-standard definition for patient attribution was beyond the scope of this analysis. Patient empanelment, just like each of the attribution approaches, has limitations. In particular, empanelment is not predicated on having encounters with that PCP and does not change on the basis of future encounters. Patients are paneled prior to their first primary care appointment on the basis of provider panel size. The discrepancy between seen and paneled PCPs was demonstrated in our study. Thus, as the healthcare community works toward a standardized and objective attribution method, our findings demonstrate how nuances of attribution approaches, such as specialty care, encounter types, and provider types, must be considered, because these factors significantly affect measured care utilization, quality, and cost.

Author Affiliations: Division of Health Care Policy & Research, Department of Health Sciences Research (RGM, SKM, JMN), Division of Primary Care Internal Medicine, Department of Internal Medicine (RGM), Department of Family Medicine (KSB, REN), and Robert D. and Patricia E. Kern Center for Science of Health Care Delivery (PR, LLB, JMN), Mayo Clinic, Rochester, MN.

Source of Funding: The authors would like to acknowledge the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery for funding support. Dr McCoy is supported by the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery and by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Number K23DK114497. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author Disclosures: The authors’ institution, Mayo Clinic, is subject to public reporting based on attribution methods.

Authorship Information: Concept and design (RGM, KSB, PR, SKM, REN, JMN); acquisition of data (PR, LLB, JMN); analysis and interpretation of data (RGM, PR, LLB, REN, JMN); drafting of the manuscript (RGM, KSB, PR, SKM, REN, JMN); critical revision of the manuscript for important intellectual content (RGM, KSB, PR, SKM, JMN); statistical analysis (PR, LLB, JMN); obtaining funding (JMN); and administrative, technical, or logistic support (PR, JMN).

Address Correspondence to: Rozalina G. McCoy, MD, Division of Health Care Policy & Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905. Email: mccoy.rozalina@mayo.edu.
REFERENCES

1. Atlas SJ, Chang Y, Lasko TA, Chueh HC, Grant RW, Barry MJ. Is this “my” patient? development and validation of a predictive model to link patients to primary care providers. J Gen Intern Med. 2006;21(9):973-978. doi: 10.1111/j.1525-1497.2006.00509.x.

2. Atlas SJ, Grant RW, Ferris TG, Chang Y, Barry MJ. Patient-physician connectedness and quality of primary care. Ann Intern Med. 2009;150(5):325-335. doi: 10.7326/0003-4819-150-5-200903030-00008.

3. Sandmeyer B, Fraser I. New evidence on what works in effective public reporting. Health Serv Res. 2016;51(suppl 2):1159-1166. doi: 10.1111/1475-6773.12502.

4. Modest health benefit cost growth continues as consumerism kicks into high gear [news release]. New York, NY: Mercer; November 19, 2014. mercer.com/newsroom/modest-health-benefit-cost-growth-continues-as-consumerism-kicks-into-high-gear.html. Accessed February 15, 2018.

5. McCoy RG, Tulledge-Scheitel SM, Naessens JM, et al. The method for performance measurement matters: diabetes care quality as measured by administrative claims and institutional registry. Health Serv Res. 2016;51(6):2206-2220. doi: 10.1111/1475-6773.12453.

6. Higuera L, Carlin C. A comparison of retrospective attribution rules. Am J Manag Care. 2017;23(6):e180-e185.

7. Mehrotra A, Burstin H, Raphael C. Raising the bar in attribution. Ann Intern Med. 2017;167(6):434-435. doi: 10.7326/M17-0655.

8. Accelerating and aligning population-based payment models: patient attribution. Health Care Payment Learning & Action Network website. hcp-lan.org/pa-whitepaper. Published June 30, 2016. Accessed February 15, 2018.

9. Attribution: principles and approaches. National Quality Forum website. www.qualityforum.org/Publications/2016/12/Attribution_-_Principles_and_Approaches.aspx. Published December 2016. Accessed February 15, 2018.

10. American Academy of Family Physicians; American Academy of Pediatrics; American College of Physicians; American Osteopathic Association. Joint principles of the patient-centered medical home. American Academy of Family Physicians website. aafp.org/dam/AAFP/documents/practice_management/pcmh/initiatives/PCMHJoint.pdf. Published March 2007. Accessed August 9, 2015.

11. Wagner EH, Coleman K, Reid RJ, Phillips K, Sugarman JR. Guiding transformation: how medical practices can become patient-centered medical homes. The Commonwealth Fund website.
commonwealthfund.org/publications/fund-reports/2012/feb/guiding-transformation-how-medical-practices-can-become-patient. Published February 2012. Accessed August 9, 2015.

12. Safety Net Medical Home Initiative; Coleman CF, Phillips KE, eds. Empanelment Implementation Guide: Establishing Patient-Provider Relationships. 1st ed. Seattle, WA: The MacColl Institute for Healthcare Innovation at the Group Health Research Institute and Qualis Health; 2010. improvingchroniccare.org/downloads/empanelment.pdf. Accessed August 9, 2015.

13. Hong CS, Atlas SJ, Chang Y, et al. Relationship between patient panel characteristics and primary care physician clinical performance rankings. JAMA. 2010;304(10):1107-1113. doi: 10.1001/jama.2010.1287.

14. Mehrotra A, Adams JL, Thomas JW, McGlynn EA. Cost profiles: should the focus be on individual physicians or physician groups? Health Aff (Millwood). 2010;29(8):1532-1538. doi: 10.1377/hlthaff.2009.1091.

15. Lewis VA, McClurg AB, Smith J, Fisher ES, Bynum JP. Attributing patients to accountable care organizations: performance year approach aligns stakeholders’ interests. Health Aff (Millwood). 2013;32(3):587-595. doi: 10.1377/hlthaff.2012.0489.

16. Dowd B, Li CH, Swenson T, Coulam R, Levy J. Medicare’s Physician Quality Reporting System (PQRS): quality measurement and beneficiary attribution. Medicare Medicaid Res Rev. 2014;4(2):e1-e14. doi: 10.5600/mmrr.004.02.a04.

17. Episode-based cost measure development for the Quality Payment Program. CMS website. cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs/Episode-Based-Cost-Measure-Development-for-the-Quality-Payment-Program.pdf. Published December 23, 2016. Accessed February 15, 2018.

18. Total cost of care attribution. Minnesota Community Measurement website. mncm.org/reports-and-websites/reports-and-data. Published 2013. Accessed December 11, 2013.

19. Nyman MA, Cabanela RL, Liesinger JT, Santrach PJ, Naessens JM. Inclusion of short-term care patients affects the perceived performance of specialists: a retrospective cohort study. BMC Health Serv Res. 2015;15:99. doi: 10.1186/s12913-015-0757-8.

20. Bynum JPW, Bernal-Delgado E, Gottlieb D, Fisher E. Assigning ambulatory patients and their physicians to hospitals: a method for obtaining population-based provider performance measurements. Health Serv Res. 2007;42(1, pt 1):45-62. doi: 10.1111/j.1475-6773.2006.00633.x.

21. Attribution use in total cost of care: an observational study of commercial administrative methodsHealthPartners website. healthpartners.com/tcoc. Published 2013. Accessed January 12, 2014.

22. 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.

23. 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. doi: 10.1377/hlthaff.20.6.267.

24. Clinical Classifications Software (CCS) for ICD-9-CM. Agency for Healthcare Research and Quality website. hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed August 9, 2015.

25. ACG System version 10.0 installation and usage guide. Johns Hopkins ACG System website. hopkinsacg.org/document/acg-system-version-10-0-installation-and-usage-guide. Published June 6, 2013. Accessed August 9, 2015.

26. Visscher SL, Naessens JM, Yawn BP, Reinalda MS, Anderson SS, Borah BJ. Developing a standardized healthcare cost data warehouse. BMC Health Serv Res. 2017;17(1):396. doi: 10.1186/s12913-017-2327-8.

27. DiMatteo MR. The physician-patient relationship: effects on the quality of health care. Clin Obstet Gynecol. 1994;37(1):149-161.

28. Rodriguez HP, von Glahn T, Chang H, Rogers WH, Safran DG. Patient samples for measuring primary care physician performance: who should be included? Med Care. 2007;45(10):989-996. doi: 10.1097/MLR.0b013e318074ce63.

29. Kane CK, Emmons DW. New data on physician practice arrangements: private practice remains strong despite shifts toward hospital employment. American Medical Association website. ama-assn.org/sites/default/files/media-browser/premium/health-policy/prp-physician-practice-arrangements_0.pdf. Published 2013. Accessed February 15, 2018.

30. Christiansen E, Hampton MD, Sullivan M. Patient empanelment: a strategy to improve continuity and quality of patient care. J Am Assoc Nurse Pract. 2016;28(8):423-428. doi: 10.1002/2327-6924.12341.
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