Abstract
This research investigates whether and how expanding association health plans (AHPs) would generate more cost savings and enhance availability and affordability in the individual health insurance markets. In our analyses, we extend the AHP’s commonality of interest to include geographic proximity and form hypothetical statewide AHPs. We use modified context-dependent traditional and slack-based data envelopment analysis models from various perspectives of stakeholders. We find that, by structuring and operating the expanded individual AHPs following the efficient practices of large-group plans, significant premium and expense reductions would be achieved while preserving the health benefits compliant with the Affordable Care Act. We recommend each insurer create a statewide pseudo-association to pool all its individual enrollees and offer them a large-group health plan; and suggest a hybrid experience and retrospective approach to improve the AHPs’ operations through efficiency-aligned optimizations of premiums and health expenditures, with cross-subsidies from an individual guaranty fund. We find that the efficiency-based cross-subsidies from group plans would significantly reduce government subsidies to the individual health insurance markets.
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Notes
Per the National Association of Insurance Commissioners (NAIC), health insurance is classified into the following business lines: comprehensive coverage, Medicare, Medicaid, Medicare supplement, vision, dental, and other. The comprehensive line consists of individual and group (small-group and large-group) plans.
In this research, the health plans are comprehensive health insurance policies, not ancillary or supplemental policies such as dental, vision or critical illness insurance. The aggregation of health plans helps alleviating the potential concern of the plans’ individual heterogeneity.
Our sample large-group plans are a subset of the group plans (for which medical service utilization data are available). In this paper, they are not compared to group plans, but are employed as the efficiency benchmarks for individual plans.
Small employers may also band together to get ERISA-qualified large-group plans, instead of small-group plans. A similar sample of small-group plans (the subset of group plans with no less than 90% of the group enrollment in small-group plans) is very small: only 15, 17, 11, 16, 21, 14, and 18 insurers in 2014–2020 respectively, so small-group plans are not analyzed separately in this research. In the future, similar analyses can be conducted for small-group plans when their separate utilization data are available.
The average enrollment, premiums, hospital/medical expenses, and other expenses of our sample large-group plans are 89%, 85%, 85%, and 80% of the counterparts of all large-group plans. Ambulatory encounters and hospital patient days are not available for large-group plans, but they are highly correlated with enrollment, premiums, hospital/medical expenses, and other expenses for all the group plans (the correlation coefficients range from 0.86 to 0.93). Therefore, on average, the DEA inputs and outputs of our sample large-group plans are smaller than those of all large-group plans, but still comparable. For future research, we encourage researchers to conduct similar analyses using all large-group plans when their separate medical service utilization data are available.
Underwriting profits are defined as the difference between premiums and expenses, so non-positive values are valid.
Health care quality improvement expenses were included in claim adjustment expenses or general administrative expenses before the ACA’s minimum medical loss ratio (MLR) rule, and they only account for a very small share of total expenses.
The major objective of health care reforms is the provision of health services at reasonable costs, not the profitability of insurers. Therefore, the profitability model with underwriting profits as the only output is not employed in this current research (Golden and Yang 2019). It should be noted that health insurers may lack motives to implement the efficient moves from the perspectives of other stakeholders.
The input-oriented BCC model is not translation invariant to inputs. The output-oriented BCC model is translation invariant to inputs, but not outputs. The CCR model is not translation invariant (Lovell and Pastor 1995).
Compared to other major health insurance business lines, the enrollees’ risk profiles of individual plans are very similar to those of large-group plans in the sample period, but not Medicare or Medicaid. Specifically, the average ambulatory encounters and hospital patient days are 10.2 and 0.31 (individual plans), 11.8 and 0.28 (large-group plans), 25.7 and 1.9 (Medicare), and 14.0 and 1.4 (Medicaid).
The group plans’ intra-group cost reductions are computed using the median efficiency of the group plans as the efficiency goal. The group plans are not compared to large-group plans or individual plans.
Suffering significant underwriting losses with low premiums, the insurer’ individual business line would not be sustainable or the market would not be viable.
Bichay (2020) indicates that switching to single-payer insurance would reduce the total US health expenditures by 4.4%.
The SBM DEA is not applied to the societal/insurer model due to the translated underwriting profits.
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Alzubi, J., Fung, D., Yang, C. et al. Improving health insurance markets: cost efficiency, implementation, and financing of expanding association health plans. Rev Quant Finan Acc 59, 671–694 (2022). https://doi.org/10.1007/s11156-022-01054-y
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DOI: https://doi.org/10.1007/s11156-022-01054-y
Keywords
- Data envelopment analysis
- Cost efficiency
- Association health plans
- Individual health insurance
- Health expenditures