Sixteen states have used Section 1332 waivers to implement reinsurance programs that aim to reduce premiums and increase enrollment in the Affordable Care Act's health insurance Marketplaces. Although reinsurance programs have successfully reduced premiums for unsubsidized enrollees, little is known about how reinsurance affects Marketplace premiums, minimum cost of coverage, and enrollment for the large majority of Marketplace enrollees who receive premium subsidies. Using a difference-in-differences analysis of matched counties straddling Georgia's borders to examine Georgia's 2022 implementation of its reinsurance program, we found that reinsurance increased the minimum cost of enrolling in subsidized Marketplace coverage by approximately 30 percent and decreased enrollment by roughly a third for Marketplace enrollees with incomes of 251-400 percent of the federal poverty level. Marketplace reinsurance programs may have the unintended consequences of increasing the minimum cost of subsidized coverage and reducing enrollment. These outcomes are especially relevant in the present policy context of enhanced subsidies, which have substantially reduced the number of unsubsidized enrollees who would benefit most from reinsurance.

<h3>Importance</h3><p>US primary care physicians (PCPs) have lower mean incomes than specialists, likely contributing to workforce shortages. In 2021, the Centers for Medicare &amp; Medicaid Services increased payment for evaluation and management (E/M) services and relaxed documentation requirements. These changes may have reduced the gap between primary care and specialist payment.</p><h3>Objectives</h3><p>To simulate the effect of the E/M payment policy change on total Medicare physician payments while holding volume constant and to compare these simulated changes with observed changes in total Medicare payments and E/M coding intensity, before (July-December 2020) and after (July-December 2021) the E/M payment policy change.</p><h3>Design, Setting, and Participants</h3><p>Retrospective observational study of US office-based physicians who were in specialties with 5000 or more physicians billing Medicare and who had 50 or more fee-for-service Medicare visits before and after the E/M payment policy change.</p><h3>Exposures</h3><p>E/M payment policy changes.</p><h3>Main Outcomes and Measures</h3><p>Outcomes included physician-level simulated volume-constant payment change, total observed Medicare payment change, and share of high-intensity (ie, level 4 or 5) E/M visits before and after the E/M payment policy change. For each specialty, the median change in each outcome was reported. The payment gap between primary care and specialty physicians was calculated as the difference between total Medicare payments to the median primary care and median specialty physician.</p><h3>Results</h3><p>The study population included 180 624 physicians. Repricing 2020 services yielded a simulated volume-constant payment change ranging from a 3.3% (−$4557.0) decrease for the median radiologist to an 11.0% ($3683.1) increase for the median family practice physician. After the E/M payment change, the median high-intensity share of E/M visits increased for physicians of nearly all specialties, ranging from a −4.4 percentage point increase (dermatology) to a 17.8 percentage point increase (psychiatry). The median change in total Medicare payments by specialty ranged from −4.2% (−$1782.9) for general surgery to 12.1% ($3746.9) for family practice. From July-December 2020 to July-December 2021, the payment gap between the median primary care physician and the median specialist shrank by $825.1, from $40 259.8 to $39 434.7 (primary care, $41 193.3 in July-December 2020 and $45 962.4 in July-December 2021; specialist, $81 453.1 in July-December 2020 and $85 397.1 in July-December 2021)—a relative decrease of 2.0%.</p><h3>Conclusions and Relevance</h3><p>Among US office-based physicians receiving Medicare payments in 2020 and 2021, E/M payment policy changes were associated with changes in Medicare payment by specialty, although the payment gap between primary care physicians and specialists decreased only modestly. The findings may have been influenced by the COVID-19 pandemic, and further research in subsequent years is needed.</p>

<h3>Importance</h3><p>Time is a valuable resource in primary care, and physicians and patients consistently raise concerns about inadequate time during visits. However, there is little evidence on whether shorter visits translate into lower-quality care.</p><h3>Objective</h3><p>To investigate variations in primary care visit length and quantify the association between visit length and potentially inappropriate prescribing decisions by primary care physicians.</p><h3>Design, Setting, and Participants</h3><p>This cross-sectional study used data from electronic health record systems in primary care offices across the US to analyze adult primary care visits occurring in calendar year 2017. Analysis was conducted from March 2022 through January 2023.</p><h3>Main Outcomes and Measures</h3><p>Regression analyses quantified the association between patient visit characteristics and visit length (measured using time stamp data) and the association between visit length and potentially inappropriate prescribing decisions, including inappropriate antibiotic prescriptions for upper respiratory tract infections, coprescribing of opioids and benzodiazepines for painful conditions, and prescriptions that were potentially inappropriate for older adults (based on the Beers criteria). All rates were estimated using physician fixed effects and were adjusted for patient and visit characteristics.</p><h3>Results</h3><p>This study included 8 119 161 primary care visits by 4 360 445 patients (56.6% women) with 8091 primary care physicians; 7.7% of patients were Hispanic, 10.4% were non-Hispanic Black, 68.2% were non-Hispanic White, 5.5% were other race and ethnicity, and 8.3% had missing race and ethnicity. Longer visits were more complex (ie, more diagnoses recorded and/or more chronic conditions coded). After controlling for scheduled visit duration and measures of visit complexity, younger, publicly insured, Hispanic, and non-Hispanic Black patients had shorter visits. For each additional minute of visit length, the likelihood that a visit resulted in an inappropriate antibiotic prescription changed by −0.11 percentage points (95% CI, −0.14 to −0.09 percentage points) and the likelihood of opioid and benzodiazepine coprescribing changed by −0.01 percentage points (95% CI, −0.01 to −0.009 percentage points). Visit length had a positive association with potentially inappropriate prescribing among older adults (0.004 percentage points; 95% CI, 0.003-0.006 percentage points).</p><h3>Conclusions and Relevance</h3><p>In this cross-sectional study, shorter visit length was associated with a higher likelihood of inappropriate antibiotic prescribing for patients with upper respiratory tract infections and coprescribing of opioids and benzodiazepines for patients with painful conditions. These findings suggest opportunities for additional research and operational improvements to visit scheduling and quality of prescribing decisions in primary care.</p>

As of January 1, 2021, most U.S. hospitals are required to publish pricing information on their website to promote more informed decision making by consumers regarding their care. In a nationally representative sample of 470 hospitals, we analyzed whether hospitals met price transparency information reporting requirements and the extent to which complete reporting was associated with ownership status, bed size category, system affiliation, and location in a metropolitan area. Fewer than one quarter of sampled hospitals met the price transparency information requirements of the new rule, which include five types of standard charges in machine-readable form and the consumer-shoppable display of 300 shoppable services. Our analyses of hospital reporting by organizational and market attributes revealed limited differences, with some exceptions for nonprofit and system-member hospitals demonstrating greater responsiveness with respect to the consumer-shoppable aspects of the rule.

Background: Primary care is a frequent source of pain treatment and opioid prescribing. The objective of the Prescribing Interventions for Chronic Pain using the Electronic health record (PRINCE) study is to assess the effects of two behavioral economics-informed interventions embedded within the electronic health record (EHR) on guideline-concordant pain treatment and opioid prescribing decisions in primary care settings. Methods: Setting: The setting for this study is 43 primary care clinics in Minnesota. Design: The PRINCE study uses a cluster-randomized 2 × 2 factorial design to test the effects of two interventions. An adaptive design allows for the possibility of secondary randomization to test if interventions can be titrated while maintaining efficacy. Interventions: One intervention alters the "choice architecture" within the EHR to nudge clinicians toward non-opioid treatments for opioid-naïve patients and toward tapering for patients currently receiving a "high risk" opioid. The other intervention integrates the prescription drug monitoring program (PDMP) directly within the EHR. Outcome: The primary outcome for opioid-naïve patients is whether an opioid is prescribed in a primary care visit without a non-opioid alternative pain treatment. The primary outcome for current opioid-using patients is whether opioid prescriptions were tapered with a documented rationale. Discussion: The PRINCE study will provide real-world evidence on two approaches to improving pain treatment in primary care using the EHR. The adaptive study design strikes a balance between establishing intervention efficacy and testing whether efficacy varies with intervention intensity.

Background: Primary care is a frequent source of pain treatment and opioid prescribing. The objective of the Prescribing Interventions for Chronic Pain using the Electronic health record (PRINCE) study is to assess the effects of two behavioral economics-informed interventions embedded within the electronic health record (EHR) on guideline-concordant pain treatment and opioid prescribing decisions in primary care settings. Methods: Setting: The setting for this study is 43 primary care clinics in Minnesota. Design: The PRINCE study uses a cluster-randomized 2 × 2 factorial design to test the effects of two interventions. An adaptive design allows for the possibility of secondary randomization to test if interventions can be titrated while maintaining efficacy. Interventions: One intervention alters the "choice architecture" within the EHR to nudge clinicians toward non-opioid treatments for opioid-naïve patients and toward tapering for patients currently receiving a "high risk" opioid. The other intervention integrates the prescription drug monitoring program (PDMP) directly within the EHR. Outcome: The primary outcome for opioid-naïve patients is whether an opioid is prescribed in a primary care visit without a non-opioid alternative pain treatment. The primary outcome for current opioid-using patients is whether opioid prescriptions were tapered with a documented rationale. Discussion: The PRINCE study will provide real-world evidence on two approaches to improving pain treatment in primary care using the EHR. The adaptive study design strikes a balance between establishing intervention efficacy and testing whether efficacy varies with intervention intensity.

Objective: To test whether there were fewer missed medical appointments (“no-shows”) for patients and clinics affected by a significant public transportation expansion. Study setting: A new light rail line was opened in a major metropolitan area in June 2014. We obtained electronic health records data from an integrated health delivery system in the area with over three million appointments at 97 clinics between 2013 and 2016. Study design: We used a difference-in-differences research design to compare whether no-show appointment rates differentially changed among patients and clinics located near versus far from the new light rail line after it opened. Models included fixed effects to account for underlying differences across clinics, patient zip codes, and time. Data extraction methods: We obtained data from an electronic health records system representing all appointments scheduled at 97 outpatient clinics in this system. We excluded same-day, urgent care, and canceled appointments. Principal findings: The probability of no-show visits differentially declined by 0.5 percentage points (95% confidence interval [CI]: −0.9 to −0.1), or 4.5% relative to baseline, for patients living near the new light rail compared to those living far from it, after the light rail opened. The effects were stronger among patients covered by Medicaid (−1.6 percentage points [95% CI: −2.4 to −0.8] or 9.5% relative to baseline). Conclusions: Improvements to public transit may improve access to health care, especially for people with low incomes.

Objective: Insurance status and continuity may affect access to and quality of care. The authors characterized patterns of and changes in insurance status over 1 year among people with first-episod...

As of January 1, 2021, most U.S. hospitals are required to publish pricing information on their website to promote more informed decision making by consumers regarding their care. In a nationally r...

The recent coronavirus disease 2019 (COVID-19) global pandemic has resulted in unprecedented job losses, disrupting health insurance coverage for millions of Americans. Several models predict large increases in Medicaid enrollment among those who have lost jobs, yet the number of Americans who have gained coverage since the pandemic is unknown. We compile early Medicaid enrollment reports covering the period of March 1 through June 1, 2020 for 26 states. We find that, in these 26 states, Medicaid has covered over an additional 1.7 million Americans in roughly a three-month period. Relative changes in Medicaid enrollment differed significantly across states although enrollment growth was not systemically related to job losses. Our results point to the importance of state policy differences in the response to COVID-19. [Editor's Note: This Fast Track Ahead Of Print article is the accepted version of the peer-reviewed manuscript. The final edited version will appear in an upcoming issue of Health Affairs.].

Objective:This study sought to characterize recent trends in mental health visits of adult outpatients to primary care physicians (PCPs), specialty mental health providers (SMHPs), and other provid...

Objective:This study evaluated the effects of the federal Mental Health Parity and Addiction Equity Act (MHPAEA) of 2008 on the use of outpatient and clinic-based mental health services and spending on those services.Methods:Data came from the 2005–2013 Medical Expenditure Panel Survey. The analytic sample included adults ages 26–64 who were continuously enrolled in employer-sponsored insurance for a calendar year (N=66,602 person-year observations). A difference-in-differences study design was used to compare changes in outcomes before and after implementation of the MHPAEA between people whose insurance plan was or was not affected by the law.Results:The federal parity law was not significantly associated with changes in the likelihood of using mental health services, the amount of mental health services used, or total or out of-pocket spending for mental health services. The law was marginally significantly associated with a shift toward more use of mental health specialty services rather than primary ...

Our health care coverage, access to medical providers, and prices of care regularly change. Sometimes the changes are unwelcome – such as when a favorite doctor is no longer covered by an insurance network. Change can also be good, however, when it delivers higher quality care at a lower price. Health care innovations may bring welcome changes, but their full impacts are not understood. Harvard Business School Professor Ariel Stern will discuss the risks and potential of innovation in the medical device and pharmaceutical industries. University of Minnesota Professor Ezra Golberstein will moderate.

<p>States have considerable leeway in the options they are pursuing to design a buy-in plan that meets these goals. Buy-in plans need to make key decisions on four major issues: 1) whether plans are sold on or off exchange, 2) whether plans are run by the state or a Medicaid Managed Care Organization, 3) who is eligible to purchase plans, and 4) which benefits and providers will be covered and at what payment rates. Our goal is to clarify the implications of these choices with respect to the goals and with respect to the tradeoffs each choice would involve.</p>