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Patient Medical Group Continuity and Healthcare Utilization

Louise H. Anderson, PhD; Thomas J. Flottemesch, PhD; Patricia Fontaine, MD, MS; Leif I. Solberg, MD; and Stephen E. Asche, MA
Patients who often change medical groups have the highest healthcare costs. The improved coordination of an accountable care organization may reduce those costs.
There were several significant covariates in the multivariate analysis. For total and inpatient costs, the outcomes increased with age, were higher for females, and were higher for those insured by Medicare or Medicaid. Costs and utilization trended upward over time and increased with care complexity (number of prescription medications) and comorbidities. Patients with asthma, coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, and depression had marginally higher costs. Surprisingly, patients with diabetes had slightly lower costs than patients without comorbidities, after adjusting for covariates. However, because the claims-based algorithms used to identify diabetes required 2 outpatient diagnostic codes or 1 inpatient diagnostic code and an active prescription, they indicated active diabetes management. Costs for diabetes associated with medication management and comorbidites were reflected in covariates; therefore, the result can be interpreted as the marginal impact of behavioral diabetes management, after adjusting for covariates.

The probability of positive ED utilization decreased with age and was less likely among females than males. The number of ED visits also decreased with age, but was not significantly different for females than for males.

Estimated model coefficients for patient attribution and continuity category variables are displayed in Table 2. Estimates from the positive versus zero utilization models indicate likelihood of utilization compared with the HiCont group. The NotAtt and InfAtt groups had significantly lower likelihood of positive total costs, inpatient costs, and ED visits, while the LowCont and MedCont groups had significantly higher likelihood of positive inpatient costs and ED visits compared with the HiCont group. Results for probability of positive total costs were mixed for the LowCont (more likely) and MedCont (less likely) groups. Estimates from the models on positive values are the log percentage differences of utilization compared with the HiCont group; positive estimates indicate higher utilization. Among the patients with positive utilization, the LowCont (18.8%) and MedCont (8.7%) groups had significantly higher total costs than the HiCont group. The NotAtt (12.8%), InfAtt (10.9%), and MedCont (4.6%) groups had significantly higher inpatient costs, and the MedCont (3.8%) had significantly more ED visits than the HiCont group.

Predicted outcomes estimated from model results on the average patient within each category are presented in Table 3. The predicted probability of positive total costs, inpatient costs, and ED use was lowest for the NotAtt and InfAtt categories. Across the continuity categories, the HiCont group had the lowest probability of inpatient (6.4%) and ED (9.6%) use. The NotAtt and InfAtt groups had the lowest predicted amounts for total cost and ED visits. HiCont patients had the highest predicted costs; at $9184, they were significantly higher than the costs for all other categories. However, those cost estimates reflected the average age, sex, complexity, comorbidity, and insurance type makeup of each category. HiCont patients had higher costs because they were older, more likely to be female, and more likely to have comorbidities.

To illustrate the effect of continuity, the probabilities and amounts of utilization for LowCont and MedCont patients were estimated as if they had high continuity (Table 4). Continuity had a significant effect on the probabilities of positive inpatient and ED utilization, and predicted total costs. The predicted probabilities of positive inpatient expenditure (5.5%, 5.8%) and positive ED visits (9.1%, 9.5%) for the average LowCont patient and MedCont patient, respectively, would drop significantly if they were in the HiCont category. Predicted annual costs would be 17% lower for the average LowCont patient and 8% lower for the average MedCont patient if they were in the HiCont category.

DISCUSSION

The interesting comparisons in our study were among the majority of members who made at least 1 annual visit to a primary care provider in 3 or more of the study years. In that group, we found that although HiCont patients were older and had more comorbidities than MedCont or LowCont patients, they had a consistently lower probability of positive inpatient expenditure or positive ED utilization and lower total medical costs. This finding extends our previous work showing that HiCont in a single medical group or clinic is associated with lower ambulatory costs for primary and specialty care.16 The association of continuity and utilization is important for medical group management as these groups take on financial risks for total cost of care as ACOs and invest their scarce resources in systems to improve their abilities to manage comprehensive medical utilization. In addition to building technical, managerial, and financial systems, medical groups will also want to understand patient mobility.

We found that among health plan members who were insured during the entire 5-year study period, the minority who did not visit a primary care provider in any year, or who visited in only 1 or 2 of the 5 years, were young and healthy, and had low overall healthcare utilization and associated costs. This is not surprising.

Our results show that health plan members who visited a primary care provider but had medium or low continuity among medical groups had greater inpatient and ED use than those with high continuity. Our results suggest that the medium- and low-continuity patients had total costs of care 9% to 18% higher than those of the high-continuity patients (Table 2). These results support the published studies that found that patient stability with a primary care provider was associated with lower utilization and costs.17-22

Our study is limited by its use of data from a single Midwestern heath plan, which may not be representative of other markets and geographic areas because of differences in patients, care providers, medical group orientation, or payment and coverage arrangements. We excluded patients who were not enrolled with HealthPartners in any year or who had fewer than 10 months of enrollment in each year of our 5-year study. While the enrollment criteria greatly limited our sample, we could not include members with coverage gaps because the gaps led to missing data on our variables of interest, medical group attribution, and utilization.

We included covariates obtained from administrative claims data to adjust our utilization and cost models; however, they did not include perceived health status and socioeconomic status, and thus may not have fully adjusted for patient risk. Our definition of primary care included obstetrics and gynecology office visits because many young women view obstetrician/gynecologists as their primary care providers and visit them for routine preventive health visits. However, some of those visits may have been referrals for obstetrics care that resulted in high costs of delivery, potentially biasing results.

We also examined patient continuity under a single attribution method. Our focus was on the impact of a consistent source of care over time. However, these findings may be affected by how patients are attributed. Determining that impact was beyond the scope of the work, but it is an important topic for future work because consensus has not been reached on methodology.10 While our analysis is limited to a single attribution methodology, it suggests that patient stability is an important consideration for ACOs.

Our study was of natural patient medical care–seeking behavior. Because the study is based on administrative data, we do not know the reasons for mobility. Although members had coverage from the same insurance carrier during all years of the study, they may have had a financial incentive to change medical groups. For example, contractual arrangements between the employer and the health plan may have resulted in a change in cost sharing, or their provider may have been placed in a more costly grouping. Patients also change providers for nonfinancial reasons such as dissatisfaction, relocation, or a change in medical needs. Although our study cannot address reasons that our members changed medical groups over time, the salient point for ACOs is the potential impact of patient mobility on financial results.

One might speculate on potential unintended consequences that could develop from health plan and payer actions to steer patients to selected providers. While the goal of the plans and payers may be to give patients incentive to seek care from high-quality, low-cost providers to reduce overall healthcare expenditures, the outcomes may be unexpected. Health plan and payer actions that encourage patient mobility in the name of cost savings may, in fact, result in higher medical costs. Given the popularity of provider tiering and related patient cost sharing, more study in this area is warranted.

CONCLUSIONS

Patient continuity with an ACO and its relationship to medical expenditures and healthcare utilization are of interest for financial risk management of ACO payment arrangements. We explored patient medical group continuity to better understand the potential impact of ACOs on the attainment of desired medical cost savings. A category of patients we called FreqAtt who are using healthcare services including primary care exhibit care-seeking behavior that is correlated with utilization and costs. Although a small proportion, patients who move among medical groups the most often have the highest total costs and the greatest use of inpatient and ED care. Improved coordination and integration have the potential to lower utilization and costs in this group.

Acknowledgments

We are very grateful to Krista Van Vorst, MS, for her work in preparing the analytic files for this study.

Author Affiliations: From HealthPartners Research Foundation (LA, TJF, PF, LIS, SEA), Minneapolis, MN.

Funding Source: Supported by the Agency for Healthcare Research and Quality Contract #HHSA290 2007 10010 TO 4.

Author Disclosures: The authors (LA, TF, PF, LIS, SEA) 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 (LA, PF); analysis and interpretation of data (LA, TF, PF, LIS); drafting of the manuscript (LA, TF, SEA); critical revision of the manuscript for important intellectual content (LA, PF, LIS, SEA); statistical analysis (LA, TF, SEA); and obtaining funding (PF).

Address correspondence to: Louise Anderson, PhD, Research Associate, HealthPartners Research Foundation, 8170 33rd Ave S, Mail Stop 21111R, Minneapolis, MN 55425. E-mail: landerson@technomicsresearch.com.
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