In recent years state and federal policies have encouraged the use of telemedicine by formalizing payments for it. Telemedicine has the potential to expand access to timely care and reduce costs, relative to in-person care. Using information from the Minnesota All Payer Claims Database, we conducted a population-level analysis of telemedicine service provision in the period 2010–15, documenting variation in provision by coverage type, provider type, and rurality of patient residence. During this period the number of telemedicine visits increased from 11,113 to 86,238, and rates of use varied extensively by coverage type and rurality. In metropolitan areas telemedicine visits were primarily direct-to-consumer services provided by nurse practitioners or physician assistants and covered by commercial insurance. In nonmetropolitan areas telemedicine use was chiefly real-time provider-initiated services delivered by physicians to publicly insured populations. Recent federal and state legislation that expanded ...

Purpose: We sought to examine the demographic, market, and policy-related factors influencing first year enrollment rates for the population targeted by the Health Insurance Marketplaces (HIMs) established as part of the Affordable Care Act. In particular, we analyzed differences in enrollment rates across urban and rural counties in 32 states served by the Federally Facilitated Marketplace. Methods: We used enrollment data from the Assistant Secretary for Planning and Evaluation of the US Department of Health and Human Services and demographic data from the American Community Survey, supplemented with other market and policy-related information. Using multivariate regression, we investigated how county-level enrollment rates are associated with demographic, market and policy-related characteristics, including rurality. Findings: Relative to an adjusted mean enrollment rate of 17.1% for large metropolitan counties, small metropolitan counties have a 2.8% lower enrollment rate and rural counties have a 2.7% lower enrollment rate. States decisions to expand Medicaid and to have the federal government fully manage the HIM are both negatively associated with enrollment rates. Partnership HIMs exhibit a positive association with enrollment rates as do navigator grants, but the latter relationship is only present in counties located in Medicaid expansion states. Conclusions: Enrollment rates vary by rurality, but differences are statistically significant only between large metropolitan counties and all other types of countiessmall metropolitan, micropolitan, and noncore. State-level policies, particularly Medicaid expansion, have the largest association with enrollment rates among the explanatory variables examined in the model.

OBJECTIVE: Examine the factors that are associated with awareness of physician quality information (PQI) among older people with one or more chronic illnesses and the implications for Medicare. DATA SOURCES/STUDY SETTING: Random digit-dial survey of adults with one or more chronic illnesses. RESEARCH DESIGN: Structural equation modeling to examine factors related to awareness of PQI. RESULTS: Awareness of PQI is low (13 percent), but comparable to findings in general population surveys. Age, race, education, and self-reported health status are associated with PQI awareness. Trust in the Internet as a source of health care information and not trusting one's physician as a source of information both are associated with a greater likelihood of being aware of PQI. Patients with high levels of activation have greater trust in physicians as information sources, but this is not associated with awareness, nor is degree of satisfaction with their care experience. CONCLUSIONS: Awareness of PQI among older persons with chronic illnesses is relatively low across all socio-economic and demographic subgroups. Changes in population characteristics over time are unlikely to improve awareness in this population, nor are changes in patient activation or satisfaction with care. Medicare would need a broad-based effort if it wishes to raise PQI awareness among Medicare beneficiaries in the near term. Before undertaking resource-intensive efforts to increase awareness, Medicare may want to consider what level of awareness actually is needed to accomplish the overall objective for PQI transparency, which is raising the quality of care received by beneficiaries. It may be that relatively low levels of awareness are sufficient.

Increasing the quality of care and reducing cost growth are core objectives of numerous private- and public-sector performance improvement initiatives. Using a unique panel data set for a commercially insured population and multivariate regression analysis, this study examines the relationship between medical care spending and diabetes-related quality measures, including provider-initiated processes of care and patient-dependent quality activities. Empirical evidence generated from this analysis of the relationship between a comprehensive set of diabetes quality measures and diabetes-related spending does not lend support for the assumption that high-quality preventive and primary care combined with effective patient self-management can lead to lower costs in the near term. Finally, we find no relationship between adjusted spending and intermediate clinical outcomes (e.g., HbA1c level) measured at the clinic level.

OBJECTIVES: An important feature of the Affordable Care Act is the creation of insurance exchanges, which are organized marketplaces through which individuals could begin to shop for and purchase coverage beginning in 2014. This study analyzes the decisions of new insurers and incumbent insurers already operating in a major market within a state to participate in state and federally facilitated exchanges. STUDY DESIGN: Utilizing secondary data from the National Association of Insurance Commissioners* and government websites, we describe each state's insurance market in 2012, summarizing the number of incumbent insurers by size and operations in the individual market segment. Next, we investigate the organizational, market, and policy-related factors associated with incumbent insurers' participation decisions. Finally, we discuss the entry patterns of new insurers and briefly assess their potential impact on the market. METHODS: We use multivariate regression analysis to identify the organizational, market, and policy-related factors related to insurer participation in exchanges. RESULTS: Only 10% of incumbent insurers are participating in exchanges in 2014, although considerable variation exists across states. Participation is more prevalent among larger insurers, local and regional insurers, and those with prior experience in other market segments in the same state. The entry of newly formed organizations, such as cooperatives (co-ops), and of existing insurers into new states is modest. CONCLUSIONS: Robust participation of insurers is an important prerequisite to ensure competition in health insurance markets. Exchange administrators will need to better understand the strategic or operational reasons why insurers chose not to participate in the individual market exchanges in 2014. *The NAIC does not endorse any analysis or conclusions based upon the use of its data.

BACKGROUND: Starting in 2011, the Affordable Care Act stipulates that insurers meet the minimum medical loss ratio (MLR) standards or issue rebates. An MLR is the proportion of premium revenues spent on clinical benefits, and must be at least 80% in the individual and small-group markets. Although some insurers have issued rebates, it is unclear whether they also adjusted MLRs and their components in ways to move toward compliance. OBJECTIVE: To investigate early responses of individual and small-group insurers' MLR-related outcomes to the Affordable Care Act provisions. RESEARCH DESIGN: Descriptive and multivariate analyses using 2010-2011 data from the National Association of Insurance Commissioners and other sources. MEASURES: Outcomes include MLRs, MLR components (claims incurred, premiums earned, quality improvement expenses, and fraud detection/recovery expenses), and administrative expenses. RESULTS: In 2010, only 44.3% of individual market insurers reported MLRs of at least the stipulated level; by 2011, this percentage was 63.2%. Among small-group insurers, 74.9% had 2010 MLRs at or above the stipulated level, with little change in 2011. Individual insurers with 2010 MLRs >10 percentage points below the minimum exhibited the largest increases in MLRs, with changes occurring through increases in claims and indirectly through decreases in administrative expenses. CONCLUSIONS: Early responses to MLR regulation seem more pronounced in the individual versus small-group market, with insurers using both direct and indirect strategies for compliance. Because insurers learned of final MLR regulations only in late 2010, early responses may be limited and skewed more toward greater use of rebates than other adjustments.

The Affordable Care Act of 2010 recommends that consumer incentives be employed to increase the use of preventive care by Medicaid beneficiaries, but few evaluative studies exist. This study evaluates a Target gift card incentive employed by a Minnesota health plan serving Medicaid beneficiaries over the period 2002-2003. Lacking a contemporaneous control group, the proximity between the child's residence and the nearest Target store was used as the intervention variable. Using alternative specifications for the intervention variable, results of the difference-in-differences equations suggest that the incentive program significantly increased the likelihood that a Medicaid beneficiary would have a well-child visit.

OBJECTIVE: Conventional wisdom suggests that health promotion programs yield a positive return on investment (ROI) in year 3. In the case of the University of Minnesota's program, a positive ROI was achieved in the third year, but it was due entirely to the effectiveness of the disease management (DM) program. The objective of this study is to investigate why. METHODS: Differences-in-differences regression equations were estimated to determine the effect of DM participation on spending (overall and service specific), hospitalizations, and avoidable hospitalizations. RESULTS: Disease management participation reduced expenditures overall, and especially in the third year for employees, and reduced hospitalizations and avoidable hospitalizations. CONCLUSIONS: The positive ROI at Minnesota was due to increased effectiveness of DM in the third year (mostly due to fewer hospitalizations) but also to the simple durability of the average DM effect.

BACKGROUND: Health promotion programs for the workplace are often sold to employers with the promise that they will pay for themselves with lowered health care expenditures and reduced absenteeism. In a recent review of the literature, it was noted that analysts often caution not to expect a positive return on investment until the third year of operation. OBJECTIVE: This study investigates whether a positive return on investment was generated in the third year for the health promotion program used by the University of Minnesota. It further investigates what it is about the third year that would explain such a phenomenon. MEASURES: The study uses health care expenditure data and absenteeism data from 2004 to 2008 to investigate the effect of the University's lifestyle and disease management programs. It also investigates the effectiveness of participation in Minnesota's 10,000 Steps walking program and Miavita self-help programs. RESEARCH DESIGN: A differences-in-differences equations approach is used to address potential selection bias. Possible regression to the mean is dealt with by using only those who were eligible to participate as control observations. Propensity score weighting was used to balance the sample on observable characteristics and reduce bias due to omitted variables. RESULTS: The study finds that a 1.76 return on investment occurs in the third year of operation that is generated solely by the effect of disease management program participation in reducing health care expenditures. However, neither of the explanations for a third-year effect we tested seemed to be able to explain this phenomenon.