State Policies Targeting Patient Brokering and Deceptive Marketing of Substance Use Disorder Treatment.

OBJECTIVES: To characterize state laws targeting patient brokering and deceptive marketing of substance use disorder (SUD) treatment. BACKGROUND: Patient brokering and deceptive marketing of SUD treatment leads to poor outcomes for individuals with SUD, including relapse- or overdose-related hospitalizations, ED visits, or death. In response, several states within the United States have passed laws targeting unethical practices of SUD treatment in recent years. The context in which these laws were passed has not been previously described. The extent to which states engaged in recovery residence regulation that also pass patient brokering and deceptive marketing laws is unknown. METHODS: We conducted a descriptive study and identified state laws relating to patient brokering and deceptive marketing that were enacted and effective as of December 31, 2022. Using a model state law for addressing unethical SUD treatment practices as a guide, we developed a taxonomy to describe the laws' elements, including covered entities, prohibited activities, and penalties. We used descriptive statistics to characterize variation across current laws. RESULTS: All patient brokering laws explicitly mention referrals to SUD treatment facilities, and most specify that both individuals and facilities are prohibited from paying, receiving, or soliciting referrals in exchange for fees or commissions. All deceptive marketing laws prohibit making false or misleading statements about the nature of services provided. Beyond these common features, there is wide variability in the degree to which states specifically prohibit other patient brokering and deceptive marketing activities (e.g., indirect offerings, lead generation, or kickback schemes involving laboratories). CONCLUSIONS: State policies targeting patient brokering and deceptive marketing may be useful for preventing instances of unethical SUD treatment practices. We constructed a taxonomy to characterize elements of patient brokering and deceptive marketing laws and facilitate future evaluations of their effectiveness.

Medical training program size and clinical staff productivity and turnover.

OBJECTIVE: The objective of this analysis was to evaluate the effect of resident program training size on clinician productivity and turnover in the Veterans Health Administration (VHA), the largest education and training platform for medical professionals in the United States. DATA SOURCES: We retrieved administrative data on training programs and training facilities from the VA Office of Academic Affiliations and the VHA Corporate Data Warehouse. Data on primary care physician shortage areas were retrieved from the Health Resources and Services Administration. STUDY DESIGN: We used a quasi-experimental instrumental variables 2SLS design and constructed an exogenous predicted training allocation treatment variable as a function of the total national training program allocation. The outcome was clinical staff productivity and turnover. Secondary analyses stratified results using Health Professional Shortage Areas data (HPSA). DATA COLLECTION/EXTRACTION METHODS: Data were obtained for a national dataset of 141 VHA medical facilities and 26 specialties that hosted training programs across 11 years from 2011 to 2021 (N = 132,177). PRINCIPAL FINDINGS: Instrumental variables results showed that on average, an increase of one training slot in a specialty leads to a decrease of 0.039 visits per standardized clinic day (p < 0.001) and a 0.02 percentage point increase in turnover (p < 0.001). The direction of this association varied by specialty: while psychiatry and psychology specialties saw a decline in productivity, fields such as primary care and cardiology experienced an increase in productivity. HPSA stratified results indicate that negative effects on productivity and turnover are driven by areas with little to no primary care physician shortage, whereas shortage areas experienced a small increase in productivity and no effect on turnover. CONCLUSIONS: This quasi-experimental evaluation indicates that resident training program size is associated with reduced productivity and increased turnover in specialties such as psychiatry and in facilities with high baseline productivity. However, in specialties like primary care and cardiology, as well as areas with shortages of primary care, larger training programs are associated with increased productivity.

Effect of the Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act Scribes Trial on Emergency Department Provider Productivity and Patient Throughput Times.

BACKGROUND: To help improve access to care, section 507 of the VA MISSION (Maintaining Internal Systems and Strengthening Integrated Outside Networks) Act of 2018 mandated a 2-year trial of medical scribes in the Veterans Health Administration (VHA). OBJECTIVE: The impact of scribes on provider productivity and patient throughput time in VHA emergency departments (EDs) was evaluated. METHODS: A clustered randomized trial was designed using intent-to-treat difference-in-differences analysis. The intervention period was from June 30, 2020 to July 1, 2022. The trial included six intervention and six comparison ED clinics. Two ED providers who volunteered to participate in the trial were assigned two scribes each. Scribes assisted providers with documentation and visit-related activities. The outcomes were provider productivity and patient throughput time per clinic-pay period. RESULTS: Randomization to intervention resulted in decreased provider productivity and increased patient throughput time. In adjusted regression models, randomization to scribes was associated with a decrease of 8.4 visits per full-time equivalent (95% confidence interval [CI] 12.4-4.3; p < 0.001) and 0.5 patients per day per provider (95% CI 0.8-0.3; p < 0.001). Intervention was associated with increases in length of stay of 29.1 min (95% CI 21.2-36.9 min; p < 0.001), 6.3 min in door to doctor (95% CI 2.9-9.6 min; p < 0.001), 19.5 min in door to disposition (95% CI 13.2-25.9 min; p < 0.001), and 13.7 min in doctor to disposition (95% CI 8.8-18.6 min; p < 0.001). CONCLUSIONS: Scribes were associated with decreased provider productivity and increased patient throughput time in VHA EDs. Although scribes may have contributed to improvements in other dimensions of quality, further examination of the ways in which scribes were used is advisable before widespread adoption in VHA EDs.

Effect of mental health staffing inputs on initiation of care among recently separated Veterans.

OBJECTIVE: To estimate a causal relationship between mental health staffing and time to initiation of mental health care for new patients. DATA SOURCES AND STUDY SETTING: As the largest integrated health care delivery system in the United States, the Veterans Health Administration (VHA) provides a unique setting for isolating the effects of staffing on initiation of mental health care where demand is high and out-of-pocket costs are not a relevant confounder. We use data from the Department of Defense and VHA to obtain patient and facility characteristics and health care use. STUDY DESIGN: To isolate exogenous variation in mental health staffing, we used an instrumental variables approach-two-stage residual inclusion with a discrete time hazard model. Our outcome is time to initiation of mental health care after separation from active duty (first appointment) and our exposure is mental health staffing (standardized clinic time per 1000 VHA enrollees per pay period). DATA COLLECTION/EXTRACTION METHODS: Our cohort consists of all Veterans separating from active duty between July 2014 and September 2017, who were enrolled in the VHA, and had at least one diagnosis of post-traumatic stress disorder, major depressive disorder, and/or substance use disorder in the year prior to separation from active duty (N = 54,209). PRINCIPAL FINDINGS: An increase of 1 standard deviation in mental health staffing results in a higher likelihood of initiating mental health care (adjusted hazard ratio: 3.17, 95% confidence interval: 2.62, 3.84, p < 0.001). Models stratified by tertile of mental health staffing exhibit decreasing returns to scale. CONCLUSIONS: Increases in mental health staffing led to faster initiation of care and are especially beneficial in facilities where staffing is lower, although initiation of care appears capacity-limited everywhere.

The effects of the Veterans Health Administration's Referral Coordination Initiative on referral patterns and waiting times for specialty care.

OBJECTIVE: To investigate whether the Veterans Health Administration's (VA) 2019 Referral Coordination Initiative (RCI) was associated with changes in the proportion of VA specialty referrals completed by community-based care (CC) providers and mean appointment waiting times for VA and CC providers. DATA SOURCES/STUDY SETTINGS: Monthly facility level VA data for 3,097,366 specialty care referrals for eight high-volume specialties (cardiology, dermatology, gastroenterology, neurology, ophthalmology, orthopedics, physical therapy, and podiatry) from October 1, 2019 to May 30, 2022. STUDY DESIGN: We employed a staggered difference-in-differences approach to evaluate RCI's effects on referral patterns and wait times. Our unit of analysis was facility-month. We dichotomized facilities into high and low RCI use based on the proportion of total referrals for a specialty. We stratified our analysis by specialty and the staffing model that high RCI users adopted: centralized, decentralized, and hybrid. DATA COLLECTION/EXTRACTION METHODS: Administrative data on referrals and waiting times were extracted from the VA's corporate data warehouse. Data on staffing models were provided by the VA's Office of Integrated Veteran Care. PRINCIPAL FINDINGS: We did not reject the null hypotheses that high RCI use do not change CC referral rates or waiting times in any of the care settings for most specialties. For example, high RCI use for physical therapy-the highest volume specialty studied-was associated with -0.054 (95% confidence interval [CI]: -0.114 to 0.006) and 2.0 days (95% CI: -4.8 to 8.8) change in CC referral rate and waiting time at CC providers, respectively, among centralized staffing model adopters. CONCLUSIONS: In the initial years of the RCI program, RCI does not have a measurable effect on waiting times or CC referral rates. Our findings do not support concerns that RCI might be impeding Veterans' access to CC providers. Future evaluations should examine whether RCI facilitates Veterans' ability to receive care in their preferred setting.

Costs and Productivity Benefits of the Department of Veterans Affairs Maintaining Internal Systems and Strengthening Integrated Outside Networks Act Scribes Trial.

OBJECTIVES: To improve access, the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act of 2018 mandated a 2-year study of medical scribes in Veterans Health Administration specialty clinics and emergency departments. Medical scribes are employed in clinical settings with the goals of increasing provider productivity and satisfaction by minimizing physicians' documentation burden. Our objective is to quantify the economic outcomes of the MISSION Act scribes trial. METHODS: A cluster-randomized trial was designed with 12 Department of Veterans Affairs (VA) medical centers randomized into the intervention. We estimated the total cost of the trial, cost per scribe-year, and projected cost of hiring additional physicians to achieve the observed scribe productivity benefits in relative value units and visits per full-time-equivalent over the 2-year intervention period (June 30, 2020 to July 1, 2022). RESULTS: The estimated cost of the trial was $4.6 million, below the Congressional Budget Office estimate of $5 million. A full-time scribe-year cost approximately $74 600 through contracting and $62 900 through VA hiring. Randomization into the trial led to an approximate 30% increase in productivity in cardiology and 20% in orthopedics. The projected incremental cost of using additional physicians instead of scribes to achieve the same productivity benefits was nearly $1.7 million more, or 75% higher, than the observed cost of scribes in cardiology and orthopedics. CONCLUSIONS: As the largest randomized trial of scribes to date, the MISSION Act scribes trial provides important evidence on the costs and benefits of scribes. Improving productivity enhances access and scribes may give VA a new tool to improve productivity in specialty care at a lower cost than hiring additional providers.

Telehealth Access and Substitution in the VHA.

BACKGROUND: In response to COVID-19, the Veterans Health Administration (VHA) expanded telehealth availability, allowing veterans to receive care at home. We explore the extent of substitution of telehealth for in-person care among medical centers (facilities), providers, and patients. We explore the extent to which patient preferences drive telehealth utilization, and compare access to care (as measured by waiting times) for telehealth and in-person visits. METHODS: We use VHA electronic health records to identify scheduled outpatient mental health (MH) appointments from January 2019 through February 2023 focusing on care delivered by social workers, psychologists, and psychiatrists. For each quarter, we compute the proportion of completed appointments that were delivered via phone or video by each facility, provider, and patient and show the changes in these proportions before, during, and after the onset of COVID-19. To explore patient preferences, we match providers of patients with high rates of telehealth utilization and examine the extent to which those providers deliver in-person care. To examine access to care, we compute waiting times for in-person, video, and phone new patient appointments. We investigate differences between urban and rural patients, and patients of different ages. KEY RESULTS: Telehealth for MH grew dramatically in the VHA after the onset of COVID-19. While some facilities provided more telehealth than others, all facilities (as of early 2023) provided some telehealth MH services. Approximately 86% of individual providers provided telehealth, with 27% scheduling MH appointments almost exclusively as telehealth appointments and 59% providing a mix. Patients exhibited more polarization, with 36% scheduling only in-person visits for almost all their MH visits and 56% of them scheduling exclusively telehealth, and only 8% of them utilizing a mix of modalities. Of those who exclusively received telehealth care, a majority of them utilized video (80%) over phone (20%). Take-up of MH among younger patients was higher relative to older patients. Urban patients used telehealth more than rural patients. Patient preferences rather than provider preferences drove utilization of patients who almost exclusively utilized telehealth. Between April 2021 and February 2023, the average difference in waiting time for in-person and video appointments was less than 1 day, with comparable appointment volumes, suggesting that the supply of and demand for in-person and video were not different enough to merit waiting longer. Telehealth was chosen over in-person more among urban and younger patients, as older and rural patients exhibited higher willingness to wait for in-person over video appointments. By contrast, appointment volumes and waiting times for phone appointments were lower across all groups, suggesting that phone may not be as substitutable for in-person visits in MH. CONCLUSIONS: We find that the VHA has made telehealth widely available, providing access to many veterans. While telehealth utilization has increased, face-to-face care persists for MH services, suggesting that one modality may not serve all purposes and preferences for care. Patient preferences drive the modality decision among those who exclusively use MH care via telehealth. For those who persist in mostly utilizing in-person care, there may be various factors influencing those preferences such as issues with limited internet connectivity, language barriers, and digital literacy, especially for older and rural patients who utilize in-person care more than those who are younger and more urban. Further investigation is required to investigate the optimal mix of modalities which may allow for potential increases in patient satisfaction, quality of care, and clinic efficiency.

Clinical effectiveness and cost-impact after 2 years of a ketogenic diet and virtual coaching intervention for patients with diabetes.

AIM: We previously evaluated the impacts at 5 months of a digitally delivered coaching intervention in which participants are instructed to adhere to a very low carbohydrate, ketogenic diet. With extended follow-up (24 months), we assessed the longer-term effects of this intervention on changes in clinical outcomes, health care utilization and costs associated with outpatient, inpatient and emergency department use in the Veterans Health Administration. MATERIALS AND METHODS: We employed a difference-in-differences model with a waiting list control group to estimate the 24-month change in glycated haemoglobin, body mass index, blood pressure, prescription medication use, health care utilization rates and associated costs. The analysis included 550 people with type 2 diabetes who were overweight or obese and enrolled in the Veterans Health Administration for health care. Data were obtained from electronic health records from 2018 to 2021. RESULTS: The virtual coaching and ketogenic diet intervention was associated with significant reductions in body mass index [-1.56 (SE 0.390)] and total monthly diabetes medication usage [-0.35 (SE 0.054)]. No statistically significant differences in glycated haemoglobin, blood pressure, outpatient visits, inpatient visits, or emergency department visits were observed. The intervention was associated with reductions in per-patient, per-month outpatient spending [-USD286.80 (SE 97.175)] and prescription drug costs (-USD105.40 (SE 30.332)]. CONCLUSIONS: A virtual coaching intervention with a ketogenic diet component offered modest effects on clinical and cost parameters in people with type 2 diabetes and with obesity or overweight. Health care systems should develop methods to assess participant progress and engagement over time if they adopt such interventions, to ensure continued patient engagement and goal achievement.

Measuring restrictiveness of Medicare Advantage networks: A claims-based approach.

OBJECTIVE: To develop and validate a measure of provider network restrictiveness in the Medicare Advantage (MA) population. DATA SOURCES: Prescription drug event data and beneficiary information for Part D enrollees from the Center for Medicare and Medicaid Services, along with prescriber identifiers; geographic variables from the Area Health Resources Files. STUDY DESIGN: A prediction model was used to predict the unique number of primary care providers that would have been seen by MA beneficiaries absent network restrictions. The model was trained and validated on Traditional Medicare (TM) beneficiaries. A pseudo-Poisson and a random forest model were evaluated. An observed-to-expected (O/E) ratio was calculated as the number of unique providers seen by MA beneficiaries divided by the number expected based the TM prediction model. Multivariable linear models were used to assess the relationship between network restrictiveness and plan and market factors. DATA COLLECTION/EXTRACTION METHODS: Prescription drug event data were obtained for a 20% random sample of beneficiaries enrolled in prescription drug coverage from 2011 to 2017. PRINCIPAL FINDINGS: Health Maintenance Organization plans were more restrictive (O/E = 55.5%; 95% CI 55.3%-55.7%) than Health Maintenance Organization-Point of Service plans (67.2%; 95% CI 66.7%-67.8%) or Preferred Provider Organization plans (74.7%; 95% CI 74.3%-75.1%), and rural areas had more restrictive networks (31.6%; 95% CI 29.0%-34.2%) than metropolitan areas (61.5%; 95% CI 61.3%-61.7%). Multivariable results confirmed these findings, and also indicated that increased provider supply was associated with less restrictive networks. CONCLUSIONS: We developed a means of estimating provider network restrictiveness in MA from claims data. Our results validate the approach, providing confidence for wider application (e.g., for other markets and specialties) and use for regulation.

Effect of a National VHA Medical Scribe Pilot on Provider Productivity, Wait Times, and Patient Satisfaction in Cardiology and Orthopedics.

BACKGROUND: Section 507 of the VA MISSION Act of 2018 mandated a 2-year pilot study of medical scribes in the Veterans Health Administration (VHA), with 12 VA Medical Centers randomly selected to receive scribes in their emergency departments or high wait time specialty clinics (cardiology and orthopedics). The pilot began on June 30, 2020, and ended on July 1, 2022. OBJECTIVE: Our objective was to evaluate the impact of medical scribes on provider productivity, wait times, and patient satisfaction in cardiology and orthopedics, as mandated by the MISSION Act. DESIGN: Cluster randomized trial, with intent-to-treat analysis using difference-in-differences regression. PATIENTS: Veterans using 18 included VA Medical Centers (12 intervention and 6 comparison sites). INTERVENTION: Randomization into MISSION 507 medical scribe pilot. MAIN MEASURES: Provider productivity, wait times, and patient satisfaction per clinic-pay period. KEY RESULTS: Randomization into the scribe pilot was associated with increases of 25.2 relative value units (RVUs) per full-time equivalent (FTE) (p < 0.001) and 8.5 visits per FTE (p = 0.002) in cardiology and increases of 17.3 RVUs per FTE (p = 0.001) and 12.5 visits per FTE (p = 0.001) in orthopedics. We found that the scribe pilot was associated with a decrease of 8.5 days in request to appointment day wait times (p < 0.001) in orthopedics, driven by a 5.7-day decrease in appointment made to appointment day wait times (p < 0.001), and observed no change in wait times in cardiology. We also observed no declines in patient satisfaction with randomization into the scribe pilot. CONCLUSIONS: Given the potential improvements in productivity and wait times with no change in patient satisfaction, our results suggest that scribes may be a useful tool to improve access to VHA care. However, participation in the pilot by sites and providers was voluntary, which could have implications for scalability and what effects could be expected if scribes were introduced to the care process without buy-in. Cost was not considered in this analysis but is an important factor for future implementation. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04154462.

Community care emergency room use and specialty care leakage from Veterans Health Administration hospitals.

BACKGROUND: Care leakage from health systems can affect quality and cost of health care delivery. Identifying modifiable predictors of care leakage may help health systems avoid adverse consequences. Out-of-system emergency department (ED) use may be one modifiable cause of care leakage. Our objective was to investigate the relationship between out-of-system ED use and subsequent specialty care leakage. METHODS: We used the Veterans Health Administration's (VA) Corporate Data Warehouse data from January 2021 to July 2021. A total of 330,547 patients who had at least one ED visit (in-house or community care [CC]) in the index period (January 2021-March 2021) were included. Outcomes were the proportions of specialty care visits in the community within 30, 60, 90, and 120 days from the index ED visit. Instrumental-variables regressions, using VA ED physician capacity as an instrument for Veterans' CC ED use, were utilized to estimate the proportions of subsequent specialty care visits in the community. Estimates were adjusted for patient and facility characteristics. RESULTS: A CC ED visit was associated with increases in the proportions of specialty care visits in the community within 30, 60, 90, and 120 days from index visit. Within 30 days from index visit, CC ED patients were estimated to have a 45-percentage-point (pp; 95% confidence interval [CI], 43-47 pp) higher proportion of CC specialty care visits than patients with an in-house ED visit (p < 0.001). We observed similar, though slightly attenuated, results over long time periods since the index visit. CONCLUSIONS: Veterans who have a CC ED visit have a greater proportion of subsequent specialty care visits in CC hospitals and clinics than Veterans with a VA ED visit. This relationship persists when we examine Veterans whose decision to go to a CC ED is influenced by VA ED physician capacity rather than general preferences for CC.

What is the role of selection bias in quality comparisons between the Veterans Health Administration and community care? Example of elective hernia surgery.

OBJECTIVE: To investigate the relationship between community care (CC) treatment and a postoperative surgical complication in elective hernia surgery among Veterans using multiple approaches to control for potential selection bias. DATA SOURCES AND STUDY SETTING: Veterans Health Administration (VHA) data sources included Corporate Data Warehouse (VHA encounters and patient data), the Program Integrity Tool and Fee tables (CC encounters), the Planning Systems Support Group (geographic information), and the Paid file (VHA primary care providers). STUDY DESIGN: Prior works suggest patient outcomes are better in VHA than in CC settings; however, these studies may not have appropriately accounted for the selection of higher-risk cases into CC. We estimated (1) a naïve logistic regression model to calculate the effect of CC setting on the probability of a complication, controlling for facility fixed effects and patient and procedure characteristics, and (2) a 2-stage model using the hernia patient's primary care provider's 1-year prior CC referral rate as the instrument. DATA COLLECTION: We identified patients residing ≤40 miles from a VHA surgical facility with elective VHA or CC hernia surgery from 2018 to 2019. PRINCIPAL FINDINGS: Of 7991 hernia surgeries, 772 (9.7%) were in CC. The overall complication rate was 4.2%; 286/7219 (4.0%) among VHA surgeries versus 51/5772 (6.6%, p < 0.05) in CC. We observed a 2.8 percentage point increase in the probability of postoperative complication given CC surgery (95% confidence interval: 0.7, 4.8) in the naïve model. After accounting for the VHA provider's historical rate of CC referral, we no longer observed a relationship between surgery setting and risk of postoperative complication. CONCLUSIONS: After accounting for the selection of higher-risk patients to CC settings, we found no difference in hernia surgery postoperative complications between CC and VHA. Future VHA and non-VHA comparisons should account for unobserved as well as observed differences in patients seen in each setting.

Grading Evidence to Support Legislative and Budget Proposals-Veterans Health Administration's Key to Implementing the Evidence Act.

OBJECTIVES: The Foundations for Evidence-Based Policymaking Act of 2018 requires cabinet-level agencies to use evidence to justify and support budget and policy making. As investigators from the Quality Enhancement Research Initiative (QUERI) program, we were tasked with assisting Veterans Health Administration (VHA) leadership with the implementation of the Evidence-Based Policymaking Act of 2018. Through meetings with stakeholders, we identified a gap in the review process for legislative and budget proposals; no systematic process existed to evaluate the supporting evidence base for proposals. METHODS: Here, we describe the development, refinement, and use of a checklist to assess the strength of evidence included in VHA legislative and budget proposals for changes to care delivery; clinical, research, and administrative operations; and staffing and workforce issues. RESULTS: The evidence assessment checklist is now part of the regular review process for VHA legislative and budget proposals. It is also being adapted for use elsewhere within the Department of Veterans Affairs. The checklist has provided a framework for briefings and training on best practices for using evidence to guide policy and budget decisions. CONCLUSION: Including evidence reviews in the legislative and budget proposal prioritization process may be an effective institutional arrangement to promote the use of evidence to inform high-level health policy decisions and to build a "culture of evidence" within the government.

Effect of a Predictive Analytics-Targeted Program in Patients on Opioids: a Stepped-Wedge Cluster Randomized Controlled Trial.

BACKGROUND: Risk of overdose, suicide, and other adverse outcomes are elevated among sub-populations prescribed opioid analgesics. To address this, the Veterans Health Administration (VHA) developed the Stratification Tool for Opioid Risk Mitigation (STORM)-a provider-facing dashboard that utilizes predictive analytics to stratify patients prescribed opioids based on risk for overdose/suicide. OBJECTIVE: To evaluate the impact of the case review mandate on serious adverse events (SAEs) and all-cause mortality among high-risk Veterans. DESIGN: A 23-month stepped-wedge cluster randomized controlled trial in all 140 VHA medical centers between 2018 and 2020. PARTICIPANTS: A total of 44,042 patients actively prescribed opioid analgesics with high STORM risk scores (i.e., percentiles 1% to 5%) for an overdose or suicide-related event. INTERVENTION: A mandate requiring providers to perform case reviews on opioid analgesic-prescribed patients at high risk of overdose/suicide. MAIN MEASURES: Nine serious adverse events (SAEs), case review completion, number of risk mitigation strategies, and all-cause mortality. KEY RESULTS: Mandated review inclusion was associated with a significant decrease in all-cause mortality within 4 months of inclusion (OR: 0.78; 95% CI: 0.65-0.94). There was no detectable effect on SAEs. Stepped-wedge analyses found that mandated review patients were five times more likely to receive a case review than non-mandated patients with similar risk (OR: 5.1; 95% CI: 3.64-7.23) and received more risk mitigation strategies than non-mandated patients (0.498; CI: 0.39-0.61). CONCLUSIONS: Among VHA patients prescribed opioid analgesics, identifying high risk patients and mandating they receive an interdisciplinary case review was associated with a decrease in all-cause mortality. Results suggest that providers can leverage predictive analytic-targeted population health approaches and interdisciplinary collaboration to improve patient outcomes. TRIAL REGISTRATION: ISRCTN16012111.

Effect of mental health staffing inputs on suicide-related events.

OBJECTIVE: To estimate the effects of changes in Veterans Health Administration (VHA) mental health services staffing levels on suicide-related events among a cohort of Veterans. DATA SOURCES: Data were obtained from the VHA Corporate Data Warehouse, the Department of Defense and Veterans Administration Infrastructure for Clinical Intelligence, the VHA survey of enrollees, and customized VHA databases tracking suicide-related events. Geographic variables were obtained from the Area Health Resources Files and the Centers for Medicare and Medicaid Services. STUDY DESIGN: We used an instrumental variables (IV) design with a Heckman correction for non-random partial observability of the use of mental health services. The principal predictor was a measure of provider staffing per 10,000 enrollees. The outcome was the probability of a suicide-related event. DATA COLLECTION/EXTRACTION METHODS: Data were obtained for a cohort of Veterans who recently separated from active service. PRINCIPAL FINDINGS: From 2014 to 2018, the per-pay period probability of a suicide-related event among our cohort was 0.05%. We found that a 1% increase in mental health staffing led to a 1.6 percentage point reduction in suicide-related events. This was driven by the first tertile of staffing, suggesting diminishing returns to scale for mental health staffing. CONCLUSIONS: VHA facilities appear to be staffing-constrained when providing mental health care. Targeted increases in mental health staffing would be likely to reduce suicidality.

Local Supply Of Postdischarge Care Options Tied To Hospital Readmission Rates.

The extent to which patients' risk for readmission after a hospitalization is influenced by local availability of postdischarge care options is not currently known. We used national, hospital-level data to assess whether the supply of postdischarge care options in hospitals' catchment areas was associated with readmission rates for Medicare patients after hospitalizations for acute myocardial infarction, heart failure, or pneumonia. Overall, readmission rates were negatively associated with per capita supply of primary care physicians (-0.16 percentage points per standard deviation) and licensed nursing home beds (-0.09 percentage points per standard deviation). In contrast, readmission rates were positively associated with per capita supply of nurse practitioners (0.09 percentage points per standard deviation). Our results suggest potential modifications to the Hospital Readmissions Reduction Program to account for local health system characteristics when assigning penalties to hospitals.

Randomized Policy Evaluation of the Veterans Health Administration Stratification Tool for Opioid Risk Mitigation (STORM).

BACKGROUND: The Veterans Health Administration (VHA) developed a dashboard Stratification Tool for Opioid Risk Mitigation (STROM) to guide clinical practice interventions. VHA released a policy mandating that high-risk patients of an adverse event based on the STORM dashboard are to be reviewed by an interdisciplinary team of clinicians. AIM: Randomized program evaluation to evaluate if patients in the oversight arm had a lower risk of opioid-related serious adverse events (SAEs) or death compared to those in the non-oversight arm. SETTING AND PARTICIPANTS: One-hundred and forty VHA facilities (aka medical centers) were randomly assigned to two groups: oversight and non-oversight arms. VHA patients who were prescribed opioids between April 18, 2018, and November 8, 2019, were included in the cohort. PROGRAM DESCRIPTION: We hypothesized that patients cared for by VHA facilities that received the policy with the oversight accountability language would achieve lower opioid-related SAEs or death. PROGRAM EVALUATION: We did not observe a relationship between the oversight arm and opioid-related SAEs or death. Patients in the non-oversight arm had a significantly higher chance of receiving a case review compared to those in the oversight arm. DISCUSSION: Even though our findings were unexpected, the STORM policy overall was likely successful in focusing the provider's attention on very high-risk patients.

Provider supply and access to primary care.

Resource-constrained delivery systems often have access issues, causing patients to wait a long time to see a provider. We develop theoretical and empirical models of wait times and apply them to primary care delivery by the U.S. Veterans Health Administration (VHA). Using instrumental variables to handle simultaneity issues, we estimate the effect of clinician supply on new patient wait times. We find that it has a sizable impact. A 10% increase in capacity reduces wait times by 2.1%. Wait times are also associated with clinician productivity, scheduling protocols, and patient access to alternative sources of care. The VHA has adopted our models to identify underserved areas as specified by the MISSION Act of 2018.

Dually-enrolled patients choose providers with lower wait times: Budgetary implications for the VHA.

OBJECTIVE: To estimate the effect of wait times on patients' choice of provider and simulate changes in choice of provider due to compliance with VA MISSION Act wait time targets. DATA SOURCES: We use nationwide administrative data (2014-2017) on Veterans who are enrolled in Medicare and the Veterans Health Administration (VHA), the Survey of VHA Enrollees, Area Health Resource Files, and other data provided by the Centers for Medicare & Medicaid Services. STUDY DESIGN: We use an instrumental variables approach to identify the effect of VHA wait times on the proportion of total (Medicare and VHA) services that are paid for by the VHA ("reliance"). We exploit shocks to VHA provider supply to isolate supply-driven changes in wait times and estimate the effect on VHA reliance. We control for market and time fixed effects and local demand factors. DATA COLLECTION/EXTRACTION METHODS: We use monthly aggregated data on 140 markets (groups of counties). VHA reliance is computed among patients aged 65 years or older who are dually enrolled in VHA and Medicare. VHA wait times and reliance are calculated for multiple specialties: cardiology, gastroenterology, orthopedics, urology, dermatology, and ophthalmology/optometry. PRINCIPAL FINDINGS: A 10% increase in the mean wait time (+2.8 days) reduces VHA reliance by 2.3 percentage points (95% CI: 2.3, 2.7), or 7.9% of the sample mean. This implies that meeting the MISSION Act wait time targets may have multi-billion-dollar budgetary impacts. Effects vary across specialties. For example, a 10% increase in the mean wait time for cardiology services (+2.0 days) reduces reliance by 1.8 percentage points (95% CI: 1.6, 2.1), or 6.3% of the sample mean for cardiology services. CONCLUSIONS: Meeting statutory wait time targets may have substantial unforeseen impacts on federal health care spending as patients sort to providers who have lower wait times.