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The American Journal of Managed Care June 2017
Comparative Effectiveness and Costs of Insulin Pump Therapy for Diabetes
Ronald T. Ackermann, MD, MPH; Amisha Wallia, MD, MS; Raymond Kang, MA; Andrew Cooper, MPH; Theodore A. Prospect, FSA, MAAA; Lewis G. Sandy, MD, MBA; and Deneen Vojta, MD
Radical Prostatectomy Innovation and Outcomes at Military and Civilian Institutions
Jeffrey J. Leow, MBBS, MPH; Joel S. Weissman, PhD; Linda Kimsey, PhD; Andrew Hoburg, PhD; Lorens A. Helmchen, PhD; Wei Jiang, MS; Nathanael Hevelone, MPH; Stuart R. Lipsitz, ScD; Louis L. Nguyen, MD, MPH, MBA; and Steven L. Chang, MD, MS
Patients' Views of a Behavioral Intervention Including Financial Incentives
Judy A. Shea, PhD; Aderinola Adejare, BA; Kevin G. Volpp, MD, PhD; Andrea B. Troxel, ScD; Darra Finnerty, MPH; Karen Hoffer, BS; Thomas Isaac, MD, MPH, MBA; Meredith Rosenthal, PhD; Thomas D. Sequist, MD, MPH; and David A. Asch, MD, MBA
How Do Medicare Advantage Beneficiary Payments Vary With Tenure?
Paul D. Jacobs, PhD, and Eamon Molloy, PhD
Patient Ratings of Veterans Affairs and Affiliated Hospitals
Paul A. Heidenreich, MD, MS; Aimee Zapata, MS; Lisa Shieh, MD, PhD; Nancy Oliva, PhD, RN; and Anju Sahay, PhD
Using "Roll-up" Measures in Healthcare Quality Reports: Perspectives of Report Sponsors and National Alliances
Jennifer L. Cerully, PhD; Steven C. Martino, PhD; Lise Rybowski, MBA; Melissa L. Finucane, PhD; Rachel Grob, PhD; Andrew M. Parker, PhD; Mark Schlesinger, PhD; Dale Shaller, MPA; and Grant Martsolf, PhD, MPH, RN
Does the Offer of Free Prescriptions Increase Generic Prescribing?
Bruce Stuart, PhD; Franklin Hendrick, PhD; J. Samantha Dougherty, PhD; and Jing Xu, PhD
Patients' Views on Price Shopping and Price Transparency
Hannah L. Semigran, BA; Rebecca Gourevitch, MS; Anna D. Sinaiko, PhD; David Cowling, PhD; and Ateev Mehrotra, MD, MPH
Heterogeneity of Nonadherent Buprenorphine Patients: Subgroup Characteristics and Outcomes
Charles Ruetsch, PhD; Joseph Tkacz, MS; Vijay R. Nadipelli, MS, BPharm; Brenna L. Brady, PhD; Naoko Ronquest, PhD; Hyong Un, MD; and Joseph Volpicelli, MD, PhD
Currently Reading
A Comparison of Retrospective Attribution Rules
Lucas Higuera, MA, and Caroline Carlin, PhD

A Comparison of Retrospective Attribution Rules

Lucas Higuera, MA, and Caroline Carlin, PhD
The authors compare methods of retrospectively attributing patients to provider systems by the fraction of patients attributed and the stability of attribution over time.
Objectives: To compare the performance of methods to retrospectively attribute patients to provider systems by comparing the fraction attributed and the stability of assignment over time. 

Study Design: Retrospective cross-sectional study.   

Methods: Descriptive statistics are used to measure the fraction of patients attributed and stability of attribution from year to year. This study uses a panel of administrative claims data (2010-2011). Attribution rules were defined by unit of measure (count of physician visits, dollars paid), type of providers (primary care physicians [PCPs], all physicians), type of encounters (all visits, evaluation and management visits only), and level of concentration of care (majority, plurality). We created 32 retrospective attribution rules, spanning PCP-only rules, all-physician rules, hierarchical rules based on PCPs then all physicians, and lookback rules based on current-year PCP visits then prior-year experience.

Results: All methods exhibit a tradeoff between stability of attribution and fraction of the population attributed. This tradeoff is minimized when PCP-based rules are supplemented by a 1-year lookback when the current-year experience does not result in attribution. 

Conclusions: We recommend using this lookback method when multiple years of data are available. In absence of multiple years of data, PCP-based rules maximize stability; hierarchical rules result in a greater fraction attributed with less loss of stability than simple all-provider rules.
Takeaway Points
The process of assigning patients to provider systems is a key component of many payment reform efforts. The majority of these payment systems rely on retrospective attribution of patients to provider systems, but there is no generally accepted method. We evaluated retrospective attribution rules, measuring the fraction attributed and the stability of attribution. 
  • The performance of attribution rules varies in terms of patients attributed and the stability of attribution. 
  • We recommend attribution rules based on primary care physician visits, with a look back to the prior year’s experience if the most recent year does not result in attribution.
Much of provider payment reform (bundled payments,1 pay-for-performance,2 shared savings programs3-6) requires patients to be linked to an individual provider or provider systems. Because prospective selection of a medical home is rare, many compensation methods rely on retrospective attribution of patients to providers. Retrospective attribution is the process of assigning responsibility for patients to an individual provider or provider systems after care is received. However, there is no generally accepted method of retrospective attribution, and methods are sometimes proprietary, making comparison of methods difficult.3,6,7 Attribution rules are typically defined by: 1) level of attribution (provider system, clinic location, individual physician), 2) provider types included (primary care physicians [PCPs], specialty physicians, nurse practitioners, physician assistants), 3) encounter type (any office visit, evaluation and management [E&M] visits), 4) unit of measure (visit counts, allowed charges), and 5) level of concentration of care required (majority, plurality).

For instance, the Medicare Shared Savings Program (MSSP) uses visits to PCPs (ie, general practice, family practice, internal medicine, and geriatric medicine physicians) to attribute patients to provider systems. In the MSSP, a provider system is responsible for a patient if the overall cost of all types of visits to PCPs was in that system. If a patient did not have PCP visits, then they use visits to all physicians as the attribution criterion.8 Both attribution and financial evaluation are retrospective. Specifically, after 2015 claims data were completed in 2016, Medicare used this 2015 experience to retrospectively attribute patients to provider systems and evaluate whether the year’s financial goals were achieved.

An ideal attribution rule balances 2 conflicting goals: maximizing the fraction of patients attributed and maximizing the stability of attribution to a particular provider system. An attribution rule assigning a large fraction of patients to a provider system makes payment reform relevant to a larger portion of the insured population. However, such rules may compromise stability. Conversely, attribution rules that consistently assign patients to the same provider system generate stable incentives for managing population health. An unstable attribution rule may assign patients to different provider systems over time due to variations in patterns of care rather than a true change in patient–provider affiliation; however, maximizing stability may require criteria so strict that the rule attributes few patients.

Our goal was to evaluate a variety of attribution rules by comparing the fraction attributed with the stability of assignment for each rule. Because we focused on shared savings contracts and other provider reimbursement methods administered at the level where reimbursement is negotiated, we attributed patients at the provider system level. We expected that more restrictive measures (PCPs only, E&M visits only) would exhibit greater stability of attribution over time and broader measures (all types of physicians, all types of visits) would attribute a greater fraction of the population but may be less stable.

There is a small body of literature evaluating attribution methods, much of it focused on attribution at the individual provider level. Mehrotra et al7 used commercial health plan data to compare 12 rules attributing patients to individual physicians and the subsequent categorization of physicians in cost categories. They found that the attribution rule could substantially affect the cost category assignment. Pham et al9 tested the fraction attributed and stability of attribution of Medicare patients to individual physicians; attribution rules were based on E&M visits and differed by concentration of care (majority, plurality), provider type (PCPs only, any provider), and a rule that allowed a patient to be attributed to several providers. The fraction attributed ranged from 79% to 94%, and stability of attribution ranged from 48% to 74%. In one of the few studies comparing attribution rules at the provider system level, Lewis et al3 compared prospective and retrospective application of the Medicare Shared Savings rule to Medicare beneficiaries. They found retrospective attribution more accurately identified patients who received care from an accountable care organization (ACO). A robustness check using claims from all types of physicians yielded the same results.

Our study builds on and improves the literature in several ways. First, we attributed health plan members at the provider system level to support public and private shared savings contracts. Second, since these compensation methods are being adopted by private health plans, we expand the literature by assessing how attribution performs among commercially insured and managed Medicaid populations.


Setting and Data

The study used data from a regional health plan headquartered in Minnesota, which is at the forefront of consolidation of providers into vertically integrated delivery systems (IDSs) capable of functioning as ACOs and of shared savings contracting with public and private payers.6,10 Yet, there remains significant exposure to other care settings, with 64% of the office visits in our data coming from providers practicing outside of an IDS setting. We believe that Minnesota is a mature shared savings environment that offers insights into the future of delivery of care in America.

We used claims and enrollment data from 2010 and 2011 for plan members (“patients”) aged 18 to 64 years. Patients were enrolled in commercial group, individual and family, and managed Medicaid products. The study was limited to adults younger than 65 years because Medicare paid on a primary basis for the plan’s Medicare Cost products, so we may not have observed all physician visits for these products.

Attribution measures were based on visits that occurred in a home or clinic setting or, for Medicaid only, the emergency department (as Medicaid patients may seek primary or specialty care at the emergency department in nonemergent settings11). Encounters were categorized as E&M visits (identified by the current procedural terminology [CPT] codes 99050, 99053-99057, 99058, 99201-99205, 99211-99215, 99241-99245, 99324-99328, 99334-99337, 99339-99345, 99347-99350, 99354, 99355, 99374, 99375, 99381-99387, 99391-99397) or non-E&M visits. We excluded most nonphysician encounters, although visits to nurse practitioners and physician assistants were retained with visits to physicians, categorized as primary or specialty care according to their area of practice. Providers specializing in general practice, internal medicine, pediatrics, family practice, or obstetrics-gynecology were categorized as primary care. Cost of care was defined as allowed charges, including both plan and member payments.

Table 1 shows sociodemographic characteristics of the population. Demographics (age, sex, plan type) were drawn from enrollment data. A categorical measure of health status (resource utilization band) and indicators of frailty and chronic conditions (diabetes, depression, high cholesterol, and high blood pressure) were computed by the Johns Hopkins Adjusted Clinical Groups (ACG) System12 based on diagnosis and pharmacy history. Neighborhood effects were based on 2010 US Census and 2011 5-year average American Community Survey data, within block (percent non-Hispanic whites), block group (levels of median income, education distribution), and census tract (urban, percent foreign-born).

Attribution Rules

Our attribution rules retrospectively assigned patients to a provider system based on the most recent calendar year of claims experience. These provider systems range from small practices to large IDSs with multiple clinic locations. We created provider systems by grouping providers within Tax Identification Numbers (TINs), additionally grouping TINs into the level at which the system contracts with the health plan. Each attribution rule is defined by: 1) provider type (any physician, PCPs only), 2) encounter type (any visits, E&M visits only), 3) unit of measure (number of visits, allowed charges for visits), and 4) required concentration of care (majority, plurality with a minimum fraction of 35%). Criteria 1 and 2 define the scope of data needed, criterion 3 defines the metric used, and criterion 4 defines the decision rule. Together, these criteria define 16 attribution rules. For example, a patient could be attributed to the provider system from which they receive the majority of E&M visits with a PCP. In the case of a tie, attribution was made to the provider system providing the greater fraction of the alternate unit. For example, if an attribution rule was tied at 50% of visits in 2 provider systems, the tie was broken by allowed charges.

We tested 2 additional sets of 8 attribution rules that increase the fraction attributed. These rules first used a PCP-only method and, for those not initially attributed, a supplementary method. In the hierarchical method, if a member was not initially attributed, the analogous all-physician method was used. In the lookback method, if a member was not initially attributed (2011), we looked back to the member’s experience in the prior year (2010) to see if they had coverage and would be attributed in the prior year. If so, this method carried that attribution forward to 2011. Altogether, we evaluated 32 attribution rules.


Fraction Attributed and Stability

Figure 1 displays the results for the 32 rules, showing fraction attributed and stability of attribution, with details available in the eAppendix [eAppendices available at]. Fraction attributed is simply the percentage of patients attributed. Stability is the percentage of patients attributed in the current and prior years that stayed in the same system in both years. Figure 1 plots the fraction of patients attributed against the stability of attribution for the all-physician (squares), PCP (diamonds), hierarchical (triangles), and lookback (circles) attribution rules, including enrollees with both partial and full years of coverage. eAppendix Figure 1 shows that while enrollees with partial years have a lower fraction attributed, the relative performance of the attribution rules is consistent between partial-year and full-year enrollees. The type of visit used for attribution is indicated by the size of the symbol, with larger symbols indicating the use of all visits and smaller symbols indicating the data were restricted to E&M visits. If the attribution units were cost (allowed charges), the outline of the symbol matched the color of the fill; black outlines indicate that the count of visits was used. Where the decision rule was based on majority of cost or counts, the symbol is filled with a darker shade; lighter fill indicates a plurality rule. Figure 1 shows that the lookback rules clearly dominate in both fraction attributed and stability. For these rules, the choice of cost versus count or majority versus plurality makes little difference in their performance. Restricting our attribution process to E&M visits only results in a minor reduction in the fraction attributed for lookback methods.

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