An explainable machine learning framework for predicting the risk of buprenorphine treatment discontinuation for opioid use disorder among commercially insured individuals.

OBJECTIVES: Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation. METHODS: This retrospective study used United States (US) 2018-2021 MarketScan commercial claims data of insured individuals aged 18-64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within 12 months of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, extreme gradient boosting, Adaboost, and random forest-extreme gradient boosting ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. For model validation, we used several statistics to evaluate performance, such as C-statistics and precision-recall curves. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models. RESULTS: A total of 30,373 patients initiated buprenorphine and 14.98% (4551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured after one month and three months following treatment initiation significantly increased the models' discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-months PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply. CONCLUSION: ML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.

Physician selection for hospital integration: Theoretical considerations and empirical findings.

BACKGROUND: The U.S. health care system has seen an increase in hospital-physician integration, with hospitals acquiring increasing numbers of physician practices. This shift has been linked to higher costs without significant improvements in quality. PURPOSE: This study sought to identify the characteristics of physicians who transitioned from independent practice to hospital integration. METHODOLOGY/APPROACH: We used physician variables, including quality scores, medical school rankings, years of experience, experience treating socially or medically complex patients, practice style, and location, as well as health care market and county-level variables to understand these determinants using a fixed-effects logistic regression model. RESULTS: A total of 101,746 physicians representing 66 clinical specialties satisfied our inclusion criteria, of which 3,656 became hospital-integrated between 2018 and 2020. The integrating physicians were generally less experienced, had lower quality scores, and generated less revenue per Medicare patient. Their patients, on average, had higher comorbidity scores, were more likely to be dually eligible, and resided in counties with higher poverty rates. CONCLUSION: Our findings indicate that the physicians most likely to become hospital integrated are those facing reimbursement pressures due to a complex case mix and the associated challenges of performing well on the quality metrics. We also found some support for the anticompetitive aspects of hospital-physician integration. Our results suggest that hospitals are integrating with a relatively less experienced physician workforce but one that is perhaps more capable of treating clinically and socioeconomically complex patients. PRACTICE IMPLICATIONS: Hospitals interested in using physician integration strategically to improve care quality should put more emphasis on physician quality. Such an approach has the potential to increase efficiency without sacrificing quality of care.

Hospital-physician Integration and Value-based Payment: Early Results From MIPS.

BACKGROUND: Hospital-physician integration is often justified as a driver of clinical quality improvement due to joint resources covering a broad spectrum of care. Value-based programs, such as the Medicare Merit-Based Incentive Payment System (MIPS), are intended to tie financial incentives to clinical quality, which may confer an advantage on such integrated practices. OBJECTIVES: We assessed the relationship between hospital-physician integration and MIPS performance by comparing hospital-integrated practices and independent practices. RESEARCH DESIGN: This was a cross-sectional study using data from the Quality Payment Program for the performance year 2020. SUBJECTS: Physician practices with a valid MIPS composite score in performance year 2020. MEASURES: Hospital integration was based on whether at least 75% of a practice's physicians either billed most of their services using hospital outpatient department codes or billed through a hospital tax identifier. The primary outcome was the MIPS quality category score, and the secondary outcomes were the specific quality measures reported by practice groups. RESULTS: Of the 20 most frequently reported measures, 14 were common in both groups. No difference was observed in the quality category score between hospital-integrated practices and independent practices in either unadjusted comparisons or after adjusting for practice characteristics, including practice size, geography, specialty mix, and case mix. In the secondary outcome models for specific quality measures, hospital-integrated practices achieved higher scores on most overlap measures but not all. CONCLUSIONS: The findings on quality category score suggest that hospital integration does not confer much advantage in the context of MIPS quality performance.

Hospital-Physician Integration Is Associated With Greater Use Of Cardiac Catheterization And Angioplasty.

In the US in recent years, hospital-physician integration has become a dominant form of consolidation in health care. This transition away from independent practice has raised questions about whether hospital-employed physicians may be more likely than independent physicians to refer patients to high-intensity, hospital-based services. We used Medicare claims data from the period 2013-20 to identify patients who received a new diagnosis of stable angina, a common cardiovascular condition that entails clinical discretion in treatment choice. Using linear probability models and an instrumental variables model, we found that patients whose care was managed by a hospital-integrated cardiologist were no more likely to receive stress tests (an office-based procedure) than those whose care was managed by an independent cardiologist. However, these patients were much more likely to receive high-intensity, hospital-based coronary interventions. These results suggest that hospital-physician integration is an important factor in the intensity of treatment received by patients with stable angina. Policy makers may see these findings as additional impetus for more aggressive antitrust enforcement of integrated arrangements between hospitals and physicians and for other regulatory or payment mechanisms that might deter hospitals from using such arrangements to promote high-intensity treatment unnecessarily.

Treatment Experiences for Patients Receiving Buprenorphine/Naloxone for Opioid Use Disorder: A Qualitative Study of Patients' Perceptions and Attitudes.

Background: Although buprenorphine/naloxone has been demonstrated to be an effective treatment for patients with opioid use disorder (OUD), treatment retention has been a challenge. This study extends what is presently a limited literature regarding patients' experiences with this medication and the implications for treatment retention. Methods: The study was conducted as a qualitative investigation of patients in treatment for OUD at the time of the study. Forty-three patients (27 men, 15 women, mean age 34.7) were recruited from three clinical settings, a community health center, an academically-based treatment site, and an independent substance abuse treatment facility. Most patients had returned to use in the past after attempts to become abstinent. Results: Patients generally reported positive experiences with this medication noting it helped to reduce opioid cravings quickly. As important considerations for treatment retention, patients emphasized a firm commitment to achieving abstinence when beginning treatment and a prescriber who is informed about and attentive to their emotional state. Diverging attitudes did exist regarding treatment duration as some patients were accepting of long-term treatment while others desired a relatively brief option. Among patients who had returned to use, potentially important issues emerged pertaining to the absence of patient outreach for missed medication appointments and inadequate discharge planning following stays at rehabilitation facilities. Conclusions: While results regarding the importance of patient motivation and strong patient-prescriber relationships have been noted in previous studies, other findings regarding opportunities to improve patient outreach and coordination of care have received relatively less attention and warrant further consideration.

Cost-related medication nonadherence among Medicare beneficiaries with cardiovascular disease risk factors: The role of comprehension of the Medicare programme and its prescription drug benefits.

RATIONALE, AIMS, AND OBJECTIVES: This study aims to investigate how reported comprehension of the Medicare programme and its prescription drug benefits is associated with cost-related medication nonadherence (CRN) among Medicare beneficiaries with cardiovascular disease (CVD) risk factors. METHODS: This cross-sectional study used the 2017 Medicare Current Beneficiary Survey Public Use File data and included Medicare beneficiaries aged ≥65 years who reported having at least one CVD risk factor (i.e., hypertension, hyperlipidemia, diabetes, smoking and obesity) (n = 2821). A survey-weighted logistic model was used to examine associations between perceived difficulty of understanding the Medicare programme and its prescription drug benefits and CRN, controlling for beneficiaries' demographic (e.g., age) and clinical characteristics (e.g, comorbidities). This study further analyzed five subgroups based on the type of CVD risk factors involved. RESULTS: Among Medicare beneficiaries with CVD risk factors, 14.4% reported CRN. Medicare beneficiaries with CVD risk factors who reported difficulty understanding the overall Medicare programme and its prescription drug benefits were more likely to report CRN, compared to those who reported easy understanding of the overall Medicare programme (OR = 1.50; 95% CI = 1.11-2.04; p = 0.009) and its prescription drug benefits (OR = 2.01; 95% CI = 1.52-2.66; p < 0.001). Similar results were obtained for the subgroups with obesity, hypertension or hyperlipidemia. CONCLUSIONS: Perceived difficulty of understanding the Medicare Programme and its prescription drug benefits is associated with CRN among Medicare beneficiaries with CVD risk factors, especially those with obesity, hypertension or hyperlipidemia. Monitoring and enhancing Medicare beneficiaries' overall understanding of the Medicare programme may reduce CRN.

Hospitals' uneven recovery from the COVID-19 pandemic.

Using hospital cost report data from the Centers for Medicare and Medicaid Services, we examined the changes in hospitals' operating margins and total margins between 2019 and 2021. We found that, as of 2021, hospitals' operating margins had not fully rebounded to 2019 levels, although they had recovered from the 2020 nadirs. Conversely, average total margins increased by 140% during this period across all US hospitals, with the most significant growth occurring among rural hospitals, publicly operated hospitals, and critical access hospitals. Rural hospitals exhibited the largest gains in total margins during this time, experiencing a 1600% increase from 2019 to 2021. Our findings indicate that government relief funding tied to the COVID-19 Public Health Emergency significantly bolstered the financial health of the average hospital and had an outsize effect on lifting total margins among smaller hospitals that entered the pandemic in the most financially vulnerable position.

Long-term patient outcomes following buprenorphine/naloxone treatment for opioid use disorder: a retrospective analysis in a commercially insured population.

Background: While buprenorphine/naloxone (buprenorphine) has been demonstrated to be an effective medication for treating opioid use disorder (OUD), an important question exists about how long patients should remain in treatment.Objective: To examine the relationship between treatment duration and patient outcomes for individuals with OUD who have been prescribed buprenorphine.Methods: We conducted a retrospective, longitudinal study using the Massachusetts All Payer Claims Database, 2013 to 2017. The study comprised over 2,500 patients, approximately one-third of whom were female, who had been prescribed buprenorphine for OUD. The outcomes were hospitalizations and emergency room (ER) visits at 36 months following treatment initiation and 12 months following treatment discontinuation. Patients were classified into four groups based on treatment duration and medication adherence: poor adherence, duration <12 months; good adherence, duration <6 months; good adherence, duration 6 to 12 months, and good adherence, duration >12 months. We conducted analyses at the patient level of the relationship between duration and outcomes.Results: Better outcomes were observed for patients whose duration was greater than 12 months. Patients in the other groups had higher odds of hospitalization at 36 months following treatment initiation: poor adherence (2.71), <6 months (1.53), and 6 to 12 months (1.42). They also had higher odds of ER visits: poor adherence (1.69), <6 months (1.51), and 6 to 12 months (1.30). Similar results were observed following treatment discontinuation.Conclusions: OUD treatment with buprenorphine should be continued for at least 12 months to reduce hospitalizations and ED visits.

Regional Economic Conditions and Preventable Hospitalization Among Older Patients With Diabetes.

OBJECTIVE: The aim was to explore the relationship between changes in regional economic conditions and quality of care-preventable hospitalization or death among older patients with diabetes at Veterans Health Administration (VHA), safety-net system for veterans. SUBJECTS: VHA patients aged 65 years and older with a diabetes diagnosis between July 2012 and June 2014, who had at least 1 primary care visit in the past year. MEASURES: County-level and state-level public data were used to characterize regional health insurance coverage and affluence surrounding the VHA facilities. Each patient was associated with a VHA facility and its corresponding regional market variables, and followed up to 48 months or until they experienced diabetes-related Prevention Quality Indicators or death. RESULTS: Discrete-time Cox proportional hazards models estimated that changes in regional market variables characterizing regional health insurance coverage and affluence were significant factors associated with preventable hospitalization or death. All regional market variables were combined into a demand index, where 1 SD decrease in the demand index was associated with a 2.0-point increase in predicted survival for an average patient at an average VHA facility. For comparison, a 1 SD increase in primary care capacity was associated with 4.7-point increase. CONCLUSIONS: Downturns in regional economic conditions could increase demand for VHA care and raise the risk of diabetes-related preventable hospitalization or death among older VHA patients diagnosed with diabetes. Safety-net hospitals may be unfairly penalized for lower quality of care when experiencing higher demand for care because of an economic downturn.

Medicaid Expansion and Not-For-Profit Hospitals' Financial Status: National and State-Level Estimates Using IRS and CMS Data, 2011-2016.

Several studies have shown that Medicaid expansion has improved hospital financial performance. All of these studies have either used data from the Internal Revenue Service (IRS) or the Centers for Medicare and Medicaid Services (CMS), and none of them has examined the state-level impact of expansion on hospital finances. Using data for not-for-profit hospitals from both IRS and CMS for 2011-2016, we described the difference in costs related to uncompensated care and Medicaid shortfalls. We then estimated the impact of Medicaid expansion on hospitals' financial status nationally and by state. Nationally, the estimated net effect of expansion reduced not-for-profit hospital costs by 2 percentage points based on IRS data and 0.83 percentage points based on CMS data. Across expansion states, the estimated net effects varied widely with approximately a 10-fold difference for hospitals based on IRS data and a 2-fold difference based on CMS data. Future studies should further explore the differences across IRS and CMS data.

A machine learning based two-stage clinical decision support system for predicting patients' discontinuation from opioid use disorder treatment: retrospective observational study.

BACKGROUND: Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences. OBJECTIVE: To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will discontinue OUD treatment within less than a year. The proposed framework performs such prediction in two stages: (i) at the time of initiating the treatment, and (ii) after two/three months following treatment initiation. METHODS: For this retrospective observational analysis, we utilized Massachusetts All Payer Claims Data (MA APCD) from the year 2013 to 2015. Study sample included 5190 patients who were commercially insured, initiated buprenorphine treatment between January and December 2014, and did not have any buprenorphine prescription at least one year prior to the date of treatment initiation in 2014. Treatment discontinuation was defined as at least two consecutive months without a prescription for buprenorphine. Six machine learning models (i.e., logistic regression, decision tree, random forest, extreme-gradient boosting, support vector machine, and artificial neural network) were tested using a five-fold cross validation on the input data. The first-stage models used patients' demographic information. The second-stage models included information on medication adherence during the early phase of treatment based on the proportion of days covered (PDC) measure. RESULTS: A substantial percentage of patients (48.7%) who started on buprenorphine discontinued the treatment within one year. The area under receiving operating characteristic curve (C-statistic) for the first stage models varied within a range of 0.55 to 0.59. The inclusion of knowledge regarding patients' adherence at the early treatment phase in terms of two-months and three-months PDC resulted in a statistically significant increase in the models' discriminative power (p-value < 0.001) based on the C-statistic. We also constructed interpretable decision classification rules using the decision tree model. CONCLUSION: Machine learning models can predict which patients are most at-risk of premature treatment discontinuation with reasonable discriminative power. The proposed machine learning framework can be used as a tool to help inform a clinical decision support system following further validation. This can potentially help prescribers allocate limited healthcare resources optimally among different groups of patients based on their vulnerability to treatment discontinuation and design personalized support systems for improving patients' long-term adherence to OUD treatment.

Patterns of patient discontinuation from buprenorphine/naloxone treatment for opioid use disorder: A study of a commercially insured population in Massachusetts.

BACKGROUND: Research has shown buprenorphine/naloxone to be an effective medication for treating individuals with opioid use disorder. At the same time, treatment discontinuation rates are reportedly high though much of the extant evidence comes from studies of the Medicaid population. OBJECTIVES: To examine the pattern and determinants of buprenorphine/naloxone treatment discontinuation in a population of commercially insured individuals. RESEARCH DESIGN: We performed a retrospective observational analysis of Massachusetts All Payer Claims Data (MA APCD) covering years 2013 through 2017. We defined treatment discontinuation as a gap of 60 consecutive days without a prescription for buprenorphine/naloxone within a time frame of 24 months from the initiation of treatment. A mixed-effect Cox proportional hazard model examined the associated risk of discontinuing treatment with baseline predictors. SUBJECTS: A total of 5134 individuals who were commercially insured during the study period. MEASURES: Buprenorphine/naloxone treatment discontinuation. RESULTS: Overall 75% of individuals had discontinued treatment within two years of initiating treatment, and median time to discontinuation was 300 days. Patients aged between 18 and 24 years (HR = 1.436, 95%, CI = 1.240-1.663) and receiving treatment from prescribers with high panel-size (HR = 1.278, 95% CI = 1.112-1.468) had higher risk of discontinuing treatment. On the contrary, patients receiving treatment from multiple prescribers had lower associated risk of treatment discontinuation. CONCLUSIONS: A substantial percentage of patients discontinue treatment well before they can typically meet criteria for sustained remission. Further investigations should assess the clinical outcomes following premature discontinuation and identify strategies for retaining patients in treatment.

Hospital Employment Of Physicians In Massachusetts Is Associated With Inappropriate Diagnostic Imaging.

The transition among many US physicians from independent practice to hospital employment has raised concerns about whether employed physicians will be more inclined to refer patients for hospital-based services that are unnecessary or inappropriate. Using claims data for 2009-16, we conducted a difference-in-differences analysis to investigate whether this form of hospital-physician integration is associated with inappropriate referrals for magnetic resonance imaging (MRI), a widely used mode of diagnostic imaging, for three common medical conditions: lower back pain, knee pain, and shoulder pain. Study findings indicate that the odds of a patient receiving an inappropriate MRI referral increased by more than 20 percent after a physician transitioned to hospital employment. Most patients who received an MRI referral by an employed physician obtained the procedure at the hospital where the referring physician was employed. These results point to hospital-physician integration as a potential driver of low-value care.

Differences in Uptake of Low-Dose CT Scan for Lung Cancer among White and Black Adult Smokers in the United States-2017.

To study racial/ethnic differences in the utilization of low-dose computerized tomography (LDCT) scan for lung cancer among adult smokers. Cross-sectional data (n=2,640) of adults aged 55-74, were from the 2017 Behavioral Risk Factor Surveillance System, Lung Cancer Screening module. Weighted, multivariable logistic regression was conducted. Most, 70.9%, were White and 52.2% male. About 16.0 % reported receiving LDCT scan in the past 12 months, 12.0% of Blacks and 17.4% of Whites. More Whites (55.0%) had ≥30 pack-years smoking history than Blacks (20%). Blacks had lower odds, .52 (CI: 0.28-0.96) of receiving LDCT scan than Whites. The odds of receiving LDCT scan were higher for those who were male, who tried to quit smoking in the past year, and for those with more education, health insurance, high blood pressure, lung disease, or cancer history (other than skin or lung cancer). This study suggests racial differences in the use of LDCT scan.

Association between daily and non-daily cannabis use and depression among United States adult cancer survivors.

BACKGROUND: Cancer survivors are vulnerable to Cannabis Use (CU) and at increased risk for depression. Yet, the relationship between CU and depression among cancer survivors is unknown. PURPOSE: The purpose of this study was to estimate the prevalence of daily/non-daily CU, investigate the association between CU and depression and evaluate CU reasons and methods of administration among cancer survivors. METHOD: Population-based, nationally representative sample of cancer survivors aged ≥18 (n = 10,799) from 2018 Behavioral Risk Factor Surveillance System Survey was used. Weighted descriptive statistics and multivariate logistic regression were conducted. FINDINGS: Overall, 4.2% reported daily and 4.1% non-daily CU. Those who self-reported depression had higher prevalence of daily and non-daily CU than those not reporting depression. Daily CU was associated with 120% increased odds of depression (odds ratio = 2.2, 95% confidence interval [1.3, 3.7]) compared with none-users. DISCUSSION: Efforts to improve open communications and evidence-informed discussions regarding benefits and risks of CU and reasons for using cannabis between clinicians and cancer survivors are imperative.

Evidence that collaborative action between local health departments and nonprofit hospitals helps foster healthy behaviors in communities: a multilevel study.

BACKGROUND: The Patient Protection and Affordable Care Act of 2010 (ACA) encouraged nonprofit hospitals to collaborate with local public health experts in the conduct of community health needs assessments (CHNAs) for the larger goal of improving community health. Yet, little is known about whether collaborations between local health departments and hospitals may be beneficial to community health. In this study, we investigated whether individuals residing in communities with stronger collaboration between nonprofit hospitals and local public health departments (LHDs) reported healthier behaviors. We further explored whether social capital acts as a moderating factor of these relationships. METHODS: We used multilevel cross-sectional models, controlling for both individual and community-level factors to explore LHD-hospital collaboration (measured in the National Association of County and City Health Officials (NACCHO) Forces of Change Survey), in relation to individual-level health behaviors in 56,826 adults living in 32 metropolitan and micropolitan statistical areas, captured through the 2015 Behavioral Risk Factor Surveillance System (BRFSS) SMART dataset. Nine health behaviors were examined including vigorous exercise, eating fruits and vegetables, smoking and binge drinking. Social capital, measured using an index developed by the Northeast Regional Center for Rural Development, was also explored as an effect modifier of these relationships. RESULTS: Stronger collaboration between nonprofit hospitals and LHDs was associated with not smoking (odds ratio, OR 1.32, 95% CI 1.11 to 1.58), eating vegetables daily (OR 1.29; 95% CI 1.06 to 1.57), and vigorous exercise (OR 1.17; 95% CI 1.05 to 1.30). The presence of higher social capital also strengthened the relationships between LHD-hospital collaborations and wearing a seatbelt (p for interaction = 0.01) and general exercise (p for interaction = 0.03). CONCLUSIONS: Stronger collaboration between nonprofit hospitals and LHDs was positively associated with healthier individual-level behaviors. Social capital may also play a moderating role in improving individual and population health.

On the relations between focus, experience, and hospital performance.

BACKGROUND: In the United States, a long-standing debate has existed over advantages/disadvantages of general versus specialty hospitals. A recent stream of research has investigated whether general hospitals accrue performance benefits from a focus strategy; a strategy of specializing in certain clinical conditions while remaining a multiproduct firm. In contrast, a substantial and long-standing body of research on hospitals has been concerned with the absolute volume of cases in a service area as an indication of experience based largely on the idea that absolute volume confers learning opportunities. PURPOSE: We investigated whether hospital focus and experience in a service area have complementary effects or are largely substitutive for hospital performance. METHODOLOGY/APPROACH: Key data sources were patient discharge records and hospital discharge profiles from California's Office of Statewide Health Policy and Development for years 2010-2014. We specified hospital focus as the proportion of total cardiology-related discharges and hospital experience as the cumulative volume of cardiology-related discharges for each hospital. Performance was specified using quality (inpatient mortality and 30-day readmission) and efficiency (length of stay and cost) patient-level performance metrics. We analyzed the data using logistic and log-linear ordinary least squares regression models. RESULTS: Study results generally supported our hypotheses that focus and experience are related to better quality and efficiency performance and that the effects are largely substitutive for hospitals. CONCLUSION: Our study extends the literature by finding that hospitals exhibit distinct and stable patterns regarding their positioning on focus and experience and that these patterns have important implications for hospitals' performance in terms of quality and efficiency. PRACTICE IMPLICATIONS: Many general hospitals in the United States may be stretched too thin across service areas for which they lack necessary patient volumes for clinical proficiency. A viable alternative is to select a limited set of service areas on which to focus.

Regional market factors and patient experience in primary care.

OBJECTIVES: To evaluate the association between regional market factors and experience with patient-provider communication in primary care services of safety net hospitals. STUDY DESIGN: A retrospective cohort study with 933,407 patient experience survey respondents from 128 Veterans Health Administration (VHA) hospitals between fiscal years 2013 and 2016. METHODS: Patient responses on 5 patient-provider communication questions were used to evaluate quality of care. Six regional market factors were used to characterize veterans' health care insurance coverage and affluence. A logistic regression was used to examine changes in individual-level patient-provider communication experience when regional market factors increase or decrease the demand for VHA primary care services. RESULTS: Our findings supported our hypothesis that changes in regional market factors shift patient demand for VHA care and affect patient-provider communication measured by patient experience surveys. The adjusted odds ratio (AOR) of positive patient-provider communication was associated with a regional increase (first to third quartile) of employer-sponsored insurance (AOR, 1.028; 95% CI, 1.001-1.055) and a decrease (third to first quartile) in the veterans' unemployment rate (AOR, 0.966; 95% CI, 0.944-0.990). Higher primary care capacity (first to third quartile) was also associated with positive patient-provider communication (AOR, 1.050; 95% CI, 1.018-1.082). CONCLUSIONS: Findings from this study raise concerns that safety net hospitals could be unfairly penalized by value-based payment programs and Medicare Hospital Compare. Such policies and programs could improve resource allocation by accounting for regional market factors before acting on quality of care measures.