Impact of Medical Training Programs on Time-to-Fill Physician Vacancies at the Veterans Health Administration.

BACKGROUND: The Department of Veterans Affairs (VA) provides the largest Graduate Medical Education (GME) training platform for health professionals in the United States. Studies on the impact of VA GME programs on physician recruitment were lacking. OBJECTIVES: To examine the impact of the size of residency training programs at a VA facility on the facility's time-to-fill physician vacancies, and whether the impact differs by the socioeconomic deprivation and public school quality of the geographic area. PROJECT DESIGN: We constructed an instrumental variable for training program size by interacting the facility clinicians share with the total training allocation nationally. SUBJECTS: Our evaluation used national data on filled physician vacancies in the VA that were posted between 2020 and 2021. MEASURES: The outcome evaluated was time-to-fill physician vacancies. Our explanatory variable was the facility-year level number of physician residency slots. RESULTS: For positions posted in 2020, an increase of one training slot was significantly associated with a decrease of 1.33 days to fill physician vacancies (95% CI, 0.38-2.28) in facilities in less deprived areas, a decrease of 1.50 days (95% CI, 0.75-2.25) in facilities with better public schools, a decrease of 3.30 days (95% CI, 0.85-5.76) in facilities in both less deprived areas and better public schools. We found similar results for positions posted in 2020 and 2021 when limiting time-to-fill to <500 days. CONCLUSIONS: We found that increasing the size of the residency program at a VA facility could decrease the facility's time-to-fill vacant physician positions in places with less socioeconomic deprivation or better public schools.

Excess Mortality at Veterans Health Administration Facilities During the COVID-19 Pandemic.

IMPORTANCE: The COVID-19 pandemic resulted in excess mortality among the general US population and at Veterans Health Administration (VHA) facilities. It is critical to understand the characteristics of facilities that experienced the highest and lowest pandemic-related mortality to inform future mitigation efforts. OBJECTIVE: To identify facility-level excess mortality during the pandemic and to correlate these estimates with facility characteristics and community-wide rates of COVID-19 burden. DESIGN: We used pre-pandemic data to estimate mortality risk prediction models using 5-fold cross-validation and Poisson quasi-likelihood regression. We then estimated excess mortality and observed versus expected (O/E) mortality ratios by the VHA facility from March to December 2020. We examined facility-level characteristics by excess mortality quartile. PARTICIPANTS: Overall, there were 11.4 million VHA enrollees during 2016 and 2020. MAIN MEASURES: Facility-level O/E mortality ratios and excess all-cause mortality. RESULT: VHA-enrolled veterans experienced 52,038 excess deaths from March to December 2020, equating to 16.8% excess mortality. Facility-specific rates ranged from -5.5% to +63.7%. Facilities in the lowest quartile for excess mortality experienced fewer COVID-19 deaths (0.7-1.51, P <0.001) and cases (52.0-63.0, P =0.002) per 1,000 population compared with the highest quartile. The highest quartile facilities had more hospital beds (276.7-187.6, P =0.024) and a higher percent change in the share of visits conducted via telehealth from 2019 to 2020 (183%-133%, P <0.008). CONCLUSIONS: There was a large variation in mortality across VHA facilities during the pandemic, which was only partially explained by the local COVID-19 burden. Our work provides a framework for large health care systems to identify changes in facility-level mortality during a public health emergency.

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.

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.

Caring Letters Sent by a Clinician or Peer to At-Risk Veterans: A Randomized Clinical Trial.

Importance: Caring letters is an evidence-based suicide prevention intervention in acute care settings, but its outcomes among individuals who contact a national crisis line have not previously been evaluated. Objective: To examine the outcomes of the Veterans Crisis Line (VCL) caring letters intervention and determine whether there are differences in outcomes by signatory. Design, Setting, and Participants: This parallel randomized clinical trial compared signatories of caring letters and used an observational design to compare no receipt of caring letters with any caring letters receipt. Participants included veterans who contacted the VCL. Enrollment occurred between June 11, 2020, and June 10, 2021, with 1 year of follow-up. Analyses were completed between July 2022 and August 2023. Intervention: Veterans were randomized to receive 9 caring letters for 1 year from either a clinician or peer veteran signatory. Main Outcomes and Measures: The primary outcome measure was suicide attempt incidence in the 12 months following the index VCL contact. Incidence of Veterans Health Administration (VHA) inpatient, outpatient, and emergency health care use were secondary outcomes. All-cause mortality was an exploratory outcome. Wilcoxon rank-sum tests and χ2 tests were used to assess the differences in outcomes among the treatment and comparison groups. Results: A total of 102 709 veterans (86 942 males [84.65%]; 15 737 females [15.32%]; mean [SD] age, 53.82 [17.35] years) contacted the VCL and were randomized. No association was found among signatory and suicide attempts, secondary outcomes, or all-cause mortality. In the analysis of any receipt of caring letters, there was no evidence of an association between caring letters receipt and suicide attempt incidence. Caring letters receipt was associated with increased VHA health care use (any outpatient: hazard ratio [HR], 1.10; 95% CI, 1.08-1.13; outpatient mental health: HR, 1.19; 95% CI, 1.17-1.22; any inpatient: HR, 1.13; 95% CI, 1.08-1.18; inpatient mental health: HR, 1.14; 95% CI, 1.07-1.21). Caring letters receipt was not associated with all-cause mortality. Conclusions and Relevance: Among VHA patients who contacted the VCL, caring letters were not associated with suicide attempts, but were associated with a higher probability of health care use. No differences in outcomes were identified by signatory. Trial Registration: isrctn.org Identifier: ISRCTN27551361.

Delivery system emergency department capacity and its effect on nonsystem service utilization.

BACKGROUND: Emergency department (ED) use is often seen as a source of excess health care spending, prompting managers to limit ED capacity in their health systems. However, if limited ED capacity in a delivery system leads patients to seek emergency care elsewhere, then health care quality and efficient management may be compromised within the system. OBJECTIVE: The objective of this study was to explore the effect of the Veterans Health Administration (VHA) in-house ED clinician capacity on VHA community care (CC) ED claims. METHODS: We used administrative data from the VHA to identify CC ED claims and Department of Veterans Affairs emergency physician (EP) capacity for 2014-2019. We used quasi-experimental instrumental variables approach with two different instruments: percent weekday federal holidays and VHA EP full-time equivalents (FTEs). We controlled for VHA ED variables such as ED wait times (door to triage, door to doctor, and door to admission) and demand variables such as alternative insurance coverage, driving time to VHA care, and demographic variables (employment, age, household income, race, gender, and VHA priority status). RESULTS: After instrumenting for capacity with percent weekday federal holidays, we found that one clinic-day capacity (one 8-h ED shift) per 10,000 enrollees increase at the VHA ED will result in a reduction of 61 CC ED claims per 10,000 enrollees. After instrumenting for capacity with EP FTE, we found that one clinic-day capacity (one 8-h ED shift) per 10,000 enrollees increase at the VHA ED will result in a reduction of 48 CC ED claims per 10,000 enrollees. Both of these results are statistically significant at p < 0.001. CONCLUSIONS: Our findings imply that offering more in-house ED care, in the form of clinician capacity, can substantially reduce out-of-system ED use. The results may be of interest to integrated health care system managers who prefer their patients to stay within network.

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.

County-Level Impact of the COVID-19 Pandemic on Excess Mortality Among U.S. Veterans: A Population-Based Study.

BACKGROUND: As the novel coronavirus (COVID-19) continues to impact the world at large, Veterans of the US Armed Forces are experiencing increases in both COVID-19 and non-COVID-19 mortality. Veterans may be more susceptible to the pandemic than the general population due to their higher comorbidity burdens and older age, but no research has examined if trends in excess mortality differ between these groups. Additionally, individual-level data on demographics, comorbidities, and deaths are provided in near-real time for all enrolees of the Veterans Health Administration (VHA). These data provide a unique opportunity to identify excess mortality throughout 2020 at a subnational level, and to validate these estimates against local COVID-19 burden. METHODS: We queried VHA administrative data on demographics and comorbidities for 11.4 million enrolees during 2016-2020. Pre-pandemic data was used to develop and cross-validate eight mortality prediction models at the county-level including Poisson, Poisson quasi-likelihood, negative binomial, and generalized estimating equations. We then estimated county-level excess Veteran mortality during 2020 and correlated these estimates with local rates of COVID-19 confirmed cases and deaths. FINDINGS: All models demonstrated excellent agreement between observed and predicted mortality during 2016-2019; a Poisson quasi-likelihood with county fixed effects minimized median squared error with a calibration slope of 1.00. Veterans of the U.S. Armed Forces faced an excess mortality rate of 13% in 2020, which corresponds to 50,299 excess deaths. County-level estimates of excess mortality were correlated with both COVID-19 cases (R2=0.77) and deaths per 1,000 population (R2=0.59). INTERPRETATION: We developed sub-national estimates of excess mortality associated with the pandemic and shared our data as a resource for researchers and data journalists. Despite Veterans' greater likelihood of risk factors associated with severe COVID-19 illness, their excess mortality rate was slightly lower than the general population. Consistent access to health care and the rapid expansion of VHA telemedicine during the pandemic may explain this divergence. FUNDING: This work was supported by grants from the Department of Veterans Affairs Quality Enhancement Research Initiative [PEC 16-001]. Dr. Griffith's effort was supported in part by the Agency for Healthcare Research & Quality [K12 HS026395].

Associations between neighborhood-level factors and opioid-related mortality: A multi-level analysis using death certificate data.

AIM: To identify associations between opioid-related mortality and neighborhood-level risk factors. DESIGN: Cross-sectional study. SETTING: Massachusetts, USA. PARTICIPANTS: Using 2011-14 Massachusetts death certificate data, we identified opioid-related (n = 3089) and non-opioid-related premature deaths (n = 8729). MEASUREMENTS: The independent variables consisted of four sets of neighborhood-level factors: (1) psychosocial, (2) economic, (3) built environment and (4) health-related. At the individual level we included the following compositional factors: age at death, sex, race/ethnicity, marital status, education, veteran status and nativity. The primary outcome of interest was opioid-related mortality. FINDINGS: Multi-level models identified number of social associations per 10 000 [odds ratio (OR) = 0.84, P = 0.002, 95% confidence interval (CI) = 0.75-0.94] and number of hospital beds per 10 000 (OR = 0.78, P < 0.001, 95% CI = 0.68-0.88) to be inversely associated with opioid-related mortality, whereas the percentage living in poverty (OR = 1.01, P = 0.008, 95% CI = 1.00-1.01), food insecurity rate (OR = 1.21, P = 0.002, 95% CI = 1.07-1.37), number of federally qualified health centers (OR = 1.02, P = 0.028, 95% CI = 1.02-1.08) and per-capita morphine milligram equivalents of hydromorphone (OR = 1.05, P = 0.003, 95% CI = 1.01-1.08) were positively associated with opioid-related mortality. CONCLUSIONS: Opioid-related deaths between 2011 and 2014 in the state of Massachusetts appear to be positively associated with the percentage living in poverty, food insecurity rate, number of federally qualified health centers and per-capita morphine milligram equivalents of hydromorphone, but inversely associated with number of social associations per 10 000 and number of hospital beds per 10 000.