Patient Medical Group Continuity and Healthcare Utilization
Published Online: August 23, 2012
Louise H. Anderson, PhD; Thomas J. Flottemesch, PhD; Patricia Fontaine, MD, MS; Leif I. Solberg, MD; and Stephen E. Asche, MA
Fisher et al proposed the accountable care organization (ACO) as a way to increase coordination of care and financial responsibility while lowering costs and improving quality.1 The ACO, an organization of physicians and institutional providers such as hospitals, home health agencies, and nursing homes, would be accountable for all of the care for a defined population.2 The Patient Protection and Affordable Care Act authorized Medicare to contract with ACOs to provide care for fee-for-service beneficiaries beginning in January 2012.3 The Medicare contracts include a shared savings program to give financial incentive to ACOs to improve quality and contain costs.4 As the methods to define patient populations, quality measures, and cost containment goals are being developed for the Medicare Shared Savings Program, private payers have also been testing ACO models for their insured populations.5-8
The ACO model is not limited to a single organizational structure. Five types of arrangements have been suggested: integrated delivery systems, multispecialty group practices, physician-hospital organizations, independent practice associations, and virtual physician organizations.9 Central to the ACO concept is the organization’s accountability for a defined population. Defining that population requires assigning each patient to a clinician and, by extension, to the ACO. The assignment, or attribution, method can take various forms. It can be retrospective or prospective, can involve single or multiple clinicians, or can be based on patient choice, patient visits, or dollars.7,10 Regardless of the method, attribution is assignment of a patient to an ACO for a single year. As patients seek care from different clinicians from year to year, they may be attributed to different ACOs. Because the ACO assumes financial risk for its attributed patients, the ACO will be concerned not only about the annual attribution but also about the stability of the population and associated financial impacts.
Large medical groups are organizations of healthcare providers that contract with health plans to provide medical care to health plan members. They are potential ACOs because they have the ability to implement clinical, managerial, and financial systems to coordinate the entire spectrum of patient care.11 In this study we explored the importance of patient mobility among medical groups as a proxy for understanding patient mobility among ACOs.
Our objectives were to measure continuity of insured patients among medical groups over a 5-year period and to test whether group continuity of care was associated with healthcare utilization and costs. Thus, it was a retrospective observational study of natural patient behavior tracked through insurance claims data in the absence of any medical group or health plan incentives for continuity. We addressed 2 questions relevant to ACO success. (1) What patterns of patient continuity could be identified? (2) What were the associations among those continuity patterns and total medical costs, inpatient utilization and costs, and emergency department (ED) utilization? Our results will be helpful to policy makers as they evaluate the potential for ACOs to deliver cost savings and as ACOs evaluate the risk they are assuming for their patient populations.
We compared annual medical utilization and costs in patient groups, defined by their pattern of medical group attribution and continuity over a 5-year period. Each year, patients were attributed to the medical group where they received the greatest number of primary care visits, without regard to numbers of providers seen. In case of ties, patients were attributed to the medical group where the most recent visit occurred. Primary care visits were defined by location and specialty of the billing physician and included the following specialties: family medicine, internal medicine, pediatrics, geriatrics, and obstetrics and gynecology. Nurse practitioner and physician assistant visits were also included. Patients without primary care visits in a year were not attributed in that year.
Patients were categorized into 3 groups on the basis of their attribution: not attributed (NotAtt), or patients who were not attributable to a medical group in any of the 5 study years; infrequently attributed (InfAtt), or patients who were attributed only 1 or 2 years during the study period; and frequently attributed (FreqAtt), or patients who were attributable for 3 or more years.
The FreqAtt group was further segmented into 3 continuity categories according to the number of moves the patients made among medical groups: high continuity (HiCont), or patients who were always attributable to the same medical group; medium continuity (MedCont), or patients who made 1 move between medical groups; and low continuity (Low- Cont), or patients who made 2 or more moves among medical groups. A move was defined as a change in attributed medical group. For example, a member who was attributed to medical group A in 2005, who was not attributable in 2006, and who was attributed to medical group A in 2007, did not move. The same member who was not attributable in 2008 and who was attributed to medical group B in 2009 moved once. Patient moves among clinics in the same medical group were not considered moves for this study.
Study Population and Data Sources
This was a retrospective analysis of administrative data from 2005 to 2009 from HealthPartners, a large nonprofit Minnesota health plan with more than 600,000 members. All HealthPartners members during the study period were eligible if they met the following criteria: (1) at least 10 months of health plan enrollment in each study year; (2) aged 19 years or older on January 1, 2005; (3) pharmacy coverage for each study year; and (4) commercial, Medicaid, or Medicare insurance coverage.
The medical groups included in the study all provided primary care and were equally divided between those having 7 to 40 physicians, 41 to 110 physicians, and more than 110 physicians. Thirty-seven percent were single-specialty primary care groups, and 54% were located outside the metropolitan area of Minneapolis-St. Paul.
Utilization, costs, and patient demographic data came from HealthPartners administrative databases and included information describing type of insurance coverage, diagnostic codes, procedure codes, billed amounts, plan reimbursed amounts, and member liability (ie, patient paid). These data were used to identify major medical comorbidities for all members (asthma, cardiovascular disease, congestive heart failure, chronic obstructive pulmonary disease, depression, and diabetes).
Yearly total cost of care was defined as the total amount paid for medical services to medical providers during the year. To avoid variation from differences in contracted reimbursement rates across medical groups, all costs were based on a standardized measure, the HealthPartners relative resource value unit. These units are based on Centers for Medicare & Medicaid Services relative value units, inpatient diagnosisrelated groups, and ambulatory payment classification weights. Use of standardized costs for each Current Procedural Terminology code, diagnosis-related group, and pharmacy claim made patient costs independent of the provider contract or type of insurance coverage. All costs are expressed as 2005 dollars. Costs and utilization counts were annualized for members with fewer than 12 months of enrollment in a given year.
Multilevel multiple regression models were used to estimate the association of annualized medical cost and utilization with attribution and continuity categories (NotAtt, InfAtt, HiCont, MedCont, LowCont). To account for within-subject correlation across observation years, a generalized estimating equations approach was used. For continuous outcomes of total and inpatient costs, a 2-part Heckman estimator was used.12 First, the association between patient categories and positive expenditure was determined by logistic regression. Second, the association between categories and expenditures was estimated for those with expenditures. Because both continuous outcomes were heavily skewed, a log transformation with Duan’s smearing estimator was used.13,14 For the count outcome of ED utilization, a zero-inflated Poisson model was used, first with logistic regression to estimate the association between patient categories and positive ED utilization, and second, with Poisson regression to estimate the association between categories and number of ED visits among patients with ED use.
The multivariate models adjusted for patient demographics, complexity, comorbidities, and study year. Number of medications was our measure of patient complexity because it was reliably available from pharmacy claims data, and it is a validated, transparent, and easily reproducible measure.15 We included interaction and polynomial terms that improved model performance.
To more easily interpret model results, we estimated predicted outcomes (probabilities, costs, and utilization) for each patient category, with covariate values set at the mean value within each category. We also estimated the marginal effect of changing category assignment. The predicted outcomes for the average MedCont and LowCont patients were estimated as if they were HiCont patients. These estimates quantified the impact of patient continuity.
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