Racial and Ethnic and Rural Variations in Access to Primary Care for Veterans Following the MISSION Act.

Importance: The 2018 Veterans Affairs Maintaining Internal Systems and Strengthening Integrated Outside Networks (VA MISSION) Act was implemented to increase timely access to care by expanding veterans' opportunities to receive Veterans Affairs (VA)-purchased care in the community (community care [CC]). Because health equity is a major VA priority, it is important to know whether Black and Hispanic veterans compared with White veterans experienced equitable access to primary care within the VA MISSION Act. Objective: To examine whether utilization of and wait times for primary care differed between Black and Hispanic veterans compared with White veterans in rural and urban areas after the implementation of the VA MISSION Act. Design, Setting, and Participants: This cross-sectional study used VA and CC outpatient and consult data from the VA's Corporate Data Warehouse for fiscal years 2021 to 2022 (October 1, 2020, to September 30, 2022). Separate fixed-effects multivariable models were run to predict CC utilization and wait times. Each model was run twice, once comparing Black and White veterans and then comparing Hispanic and White veterans. Adjusted risk ratios (ARRs) were calculated for Black and Hispanic veterans compared with White veterans within rurality status for both outcomes. Main Outcomes and Measures: VA and CC primary care utilization as measured by primary care visits (utilization cohort); VA and CC primary care access as measured by mean wait times (access cohort). Results: A total of 5 046 087 veterans (994 517 [19.7%] Black, 390 870 [7.7%] Hispanic, and 3 660 700 [72.6%] White individuals) used primary care from fiscal years 2021 to 2022. Utilization increased for all 3 racial and ethnicity groups, more so in CC than VA primary care. ARRs were significantly less than 1 regardless of rurality status, indicating Black and Hispanic veterans compared with White veterans were less likely to utilize CC for primary care. There were 468 246 primary care consultations during the study period. The overall mean (SD) wait time was 33.3 (32.4) days. Despite decreases in wait times over time, primary care wait times remained longer in CC than in VA. Black veterans compared with White veterans had significantly longer wait times in CC (ARRs >1) but significantly shorter wait times in VA (ARRS <1) regardless of rurality status in VA and CC. CC wait times for Hispanic veterans compared with White veterans were longer in rural areas only and in VA rural and urban areas (ARRs >1). Conclusion and Relevance: The results of this cross-sectional study suggest that additional research should explore the determinants and implications of utilization differences among Black and Hispanic veterans compared with White veterans. Efforts to promote equitable primary care access for all veterans are needed so that policy changes can be more effective in ensuring timely access to care for all veterans.

Quality improvement lessons learned from National Implementation of the "Patient Safety Events in Community Care: Reporting, Investigation, and Improvement Guidebook".

OBJECTIVE: To evaluate nationwide implementation of a Guidebook designed to standardize safety practices across VA-delivered and VA-purchased care (i.e., Community Care) and identify lessons learned and strategies to improve them. DATA SOURCES AND STUDY SETTING: Qualitative data collected from key informants at 18 geographically diverse VA facilities across 17 Veterans Integrated Services Networks (VISNs). STUDY DESIGN: We conducted semi-structured interviews from 2019 to 2022 with VISN Patient Safety Officers (PSOs) and VA facility patient safety and quality managers (PSMs and QMs) and VA Facility Community Care (CC) staff to assess lessons learned by examining organizational contextual factors affecting Guidebook implementation based on the Consolidated Framework for Implementation Research (CFIR). DATA COLLECTION/EXTRACTION METHODS: Interviews were conducted virtually with 45 facility staff and 10 VISN PSOs. Using directed content analysis, we identified CFIR factors affecting implementation. These factors were mapped to the Expert Recommendations for Implementing Change (ERIC) strategy compilation to identify lessons learned that could be useful to our operational partners in improving implementation processes. We met frequently with our partners to discuss findings and plan next steps. PRINCIPAL FINDINGS: Six CFIR constructs were identified as both facilitators and barriers to Guidebook implementation: (1) planning for implementation; (2) engaging key knowledge holders; (3) available resources; (4) networks and communications; (5) culture; and (6) external policies. The two CFIR constructs that were only barriers included: (1) cosmopolitanism and (2) executing implementation. CONCLUSIONS: Our findings suggest several important lessons: (1) engage all collaborators involved in implementation; (2) ensure end-users have opportunities to provide feedback; (3) describe collaborators' purpose and roles/responsibilities clearly at the start; (4) communicate information widely and repeatedly; and (5) identify how multiple high priorities can be synergistic. This evaluation will help our partners and key VA leadership to determine next steps and future strategies for improving Guidebook implementation through collaboration with VA staff.

Standardizing Patient Safety Event Reporting between Care Delivered or Purchased by the Veterans Health Administration (VHA).

BACKGROUND: Increasing community care (CC) use by veterans has introduced new challenges in providing integrated care across the Veterans Health Administration (VHA) and CC. VHA's well-recognized patient safety program has been particularly challenging for CC staff to adopt and implement. To standardize VHA safety practices across both settings, VHA implemented the Patient Safety Guidebook in 2018. The authors compared national- and facility-level trends in VHA and CC safety event reporting post-Guidebook implementation. METHODS: In this retrospective study using patient safety event data from VHA's event reporting system (2020-2022), the research team examined trends in patient safety events, adverse events, close calls (near misses), and recovery rates (ratio of close calls to adverse events plus close calls) in VHA and CC using linear regression models to determine whether the average changes in VHA and CC safety events at the national and facility levels per quarter were significant. RESULTS: A total of 499,332 safety events were reported in VHA and CC. Although VHA patient safety event trends were not significant (p > 0.05), there was a significant negative trend for adverse events (p = 0.02) and positive trends for close calls (p = 0.003) and recovery rates (p = 0.004). In CC there were significant negative trends for patient safety events and adverse events (p = 0.02) and a significant positive trend for recovery rates (p = 0.03). There was less variation in VHA than in CC facilities with significant decreases (for example, interquartile ranges in VHA and CC were 0.03 vs. 0.05, respectively). CONCLUSION: Fluctuations in different safety events over time were likely due to the disruption of care caused by COVID-19 as well as organizational factors. Notably, the increases in recovery rates reflect less staff focus on harmful events and more attention to close calls (preventable events). Although safety practice adoption from VHA to CC was feasible, additional implementation strategies are needed to sustain standardized safety reporting across settings.

Using the Kitagawa Decomposition to Measure Overall-and Individual Facility Contributions to-Within-facility and Between-facility Differences: Analyzing Racial and Ethnic Wait Time Disparities in the Veterans Health Administration.

BACKGROUND: Identifying whether differences in health care disparities are due to within-facility or between-facility differences is key to disparity reductions. The Kitagawa decomposition divides the difference between 2 means into within-facility differences and between-facility differences that are measured on the same scale as the original disparity. It also enables the identification of facilities that contribute most to within-facility differences (based on facility-level disparities and the proportion of patient population served) and between-facility differences. OBJECTIVES: Illustrate the value of a 2-stage Kitagawa decomposition to partition a disparity into within-facility and between-facility differences and to measure the contribution of individual facilities to each type of difference. SUBJECTS: Veterans receiving a new outpatient consult for cardiology or orthopedic services during fiscal years 2019-2021. MEASURES: Wait time for a new-patient consult. METHODS: In stage 1, we predicted wait time for each Veteran from a multivariable model; in stage 2, we aggregated individual predictions to determine mean adjusted wait times for Hispanic, Black, and White Veterans and then decomposed differences in wait times between White Veterans and each of the other groups. RESULTS: Noticeably longer wait times were experienced by Hispanic Veterans for cardiology (2.32 d, 6.8% longer) and Black Veterans for orthopedics (3.49 d, 10.3% longer) in both cases due entirely to within-facility differences. The results for Hispanic Veterans using orthopedics illustrate how positive within-facility differences (0.57 d) can be offset by negative between-facility differences (-0.34 d), resulting in a smaller overall disparity (0.23 d). Selecting 10 facilities for interventions in orthopedics based on the largest contributions to within-in facility differences instead of the largest disparities resulted in a higher percentage of Veterans impacted (31% and 12% of Black and White Veterans, respectively, versus 9% and 10% of Black and White Veterans, respectively) and explained 21% of the overall within-facility difference versus 11%. CONCLUSIONS: The Kitagawa approach allows the identification of disparities that might otherwise be undetected. It also allows the targeting of interventions at those facilities where improvements will have the largest impact on the overall disparity.

Disparities in Wait Times for Care Among US Veterans by Race and Ethnicity.

Importance: Prior studies indicate that Black and Hispanic vs White veterans wait longer for care. However, these studies do not capture the COVID-19 pandemic, which caused care access disruptions, nor implementation of the US Department of Veterans Affairs (VA) Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION), which is intended to improve care access by increasing veterans' options to use community clinicians. Objective: To determine whether wait times increased differentially for Black and Hispanic compared with White veterans from the pre-COVID-19 to COVID-19 periods given concurrent MISSION implementation. Design, Setting, and Participants: This cross-sectional study used data from the VA's Corporate Data Warehouse for fiscal years 2019 to 2021 (October 1, 2018, to September 30, 2021). Participants included Black, Hispanic, and White veterans with a new consultation for outpatient cardiology and/or orthopedic services during the study period. Multivariable mixed-effects models were used to estimate individual-level adjusted wait times and a likelihood ratio test of the significance of wait time disparity change over time. Main Outcomes and Measures: Overall mean wait times and facility-level adjusted relative mean wait time ratios. Results: The study included 1 162 148 veterans (mean [SD] age, 63.4 [14.4] years; 80.8% men). Significant wait time disparities were evident for orthopedic services (eg, Black veterans had wait times 2.09 [95% CI, 1.57-2.61] days longer than those for White veterans) in the pre-COVID-19 period, but not for cardiology services. Mean wait times increased from the pre-COVID-19 to COVID-19 periods for both services for all 3 racial and ethnic groups (eg, Hispanic wait times for cardiology services increased 5.09 [95% CI, 3.62-6.55] days). Wait time disparities for Black veterans (4.10 [95% CI, 2.44-5.19] days) and Hispanic veterans (4.40 [95% CI, 2.76-6.05] days) vs White veterans (3.75 [95% CI, 2.30-5.19] days) increased significantly from the pre-COVID-19 to COVID-19 periods (P < .001). During the COVID-19 period, significant disparities were evident for orthopedic services (eg, mean wait times for Hispanic vs White veterans were 1.98 [95% CI, 1.32-2.64] days longer) but not for cardiology services. Although there was variation in wait time ratios across the 140 facilities, only 6 facility wait time ratios were significant during the pre-COVID-19 period and 26 during the COVID-19 period. Conclusions and Relevance: These findings suggest that wait time disparities increased from the pre-COVID-19 to COVID-19 periods, especially for orthopedic services for both Black and Hispanic veterans, despite MISSION's goal to improve access. Facility-level analyses identified potential sites that could be targeted to reduce disparities.

Differences Between VHA-Delivered and VHA-Purchased Behavioral Health Care in Service and Patient Characteristics.

OBJECTIVE: Federal legislation has expanded Veterans Health Administration (VHA) enrollees' access to VHA-purchased "community care." This study examined differences in the amount and type of behavioral health care delivered in VHA and purchased in the community, along with patient characteristics and area supply and demand factors. METHODS: This retrospective cross-sectional study examined data for 204,094 VHA enrollees with 448,648 inpatient behavioral health stays and 3,467,010 enrollees with 55,043,607 outpatient behavioral health visits from fiscal years 2016 to 2019. Standardized mean differences (SMDs) were calculated for patient and provider characteristics at the outpatient-visit level for VHA and community care. Linear probability models assessed the association between severity of behavioral health condition and site of care. RESULTS: Twenty percent of inpatient stays were purchased through community care, with severe behavioral health conditions more likely to be treated in VHA inpatient care. In the outpatient setting, community care accounted for 3% of behavioral health care visits, with increasing use over time. For outpatient care, veterans receiving community care were more likely than those receiving VHA care to see clinicians with fewer years of training (SMD=1.06). CONCLUSIONS: With a large portion of inpatient behavioral health care occurring in the community and increased use of outpatient behavioral health care with less highly trained community providers, coordination between VHA and the community is essential to provide appropriate inpatient follow-up care and address outpatient needs. This is especially critical given VHA's expertise in providing behavioral health care to veterans and its legislative responsibility to ensure integrated care.

Association of Quality of Care With Where Veterans Choose to Get Knee Replacement Surgery.

Importance: Recent legislation expanded veterans' access to Veterans Health Administration (VA)-purchased care. Quality should be considered when choosing where to get total knee arthroplasty (TKA), but currently available quality metrics provide little guidance. Objective: To determine whether an association exists between the proportion of TKAs performed (vs purchased) at each VA facility and the quality of care provided (as measured by short-term complication rates). Design, Setting, and Participants: This 3-year cohort study used VA and community care data (fiscal year 2017 to fiscal year 2019) from the VA's Corporate Data Warehouse. Complications were defined following the Centers for Medicare and Medicaid Services' methodology. The setting included 140 VA health care facilities performing or purchasing TKAs. Participants included veterans who had 43 371 primary TKA procedures that were either VA-performed or VA-purchased during the study period. Exposures: Of the 43 371 primary TKA procedures, 18 964 (43.7%) were VA-purchased. Main Outcomes and Measures: The primary outcome was risk-standardized short-term complication rates of VA-performed or VA-purchased TKAs. The association between the proportion of TKAs performed at each VA facility and quality of VA-performed and VA-purchased care was examined using a regression model. Subgroups were also identified for facilities that had complication rates above or below the overall mean complication rate and for facilities that performed more or less than half of the facility's TKAs. Results: Among the study sample's 41 775 veterans who underwent 43 371 TKAs, 38 725 (89.3%) were male, 6406 (14.8%) were Black, 33 211 (76.6%) were White, and 1367 (3.2%) had other race or ethnicity (including American Indian or Alaska Native, Asian, and Native Hawaiian or other Pacific Islander); mean (SD) age was 66.9 (8.5) years. VA-performed and VA-purchased TKAs had a mean (SD) raw overall short-term complication rate of 2.97% (0.08%). There was no association between the proportion of TKAs performed in VA facilities and risk-standardized complication rates for VA-performed TKAs, and no association for VA-purchased TKAs. Conclusions and Relevance: In this cohort study, surgical quality did not have an association with where veterans had TKA, possibly because meaningful comparative data are lacking. Reporting local and community risk-standardized complication rates may inform veterans' decisions and improve care. Combining these data with the proportion of TKAs performed at each site could facilitate administrative decisions on where resources should be allocated to improve care.

Comparing Postoperative Readmission Rates Between Veterans Receiving Total Knee Arthroplasty in the Veterans Health Administration Versus Community Care.

BACKGROUND: There are growing concerns that Veterans' increased use of Veterans Health Administration (VA)-purchased care in the community may lead to lower quality of care. OBJECTIVE: We compared rates of hospital readmissions following elective total knee arthroplasties (TKAs) that were either performed in VA or purchased by VA through community care (CC) at both the national and facility levels. METHODS: Three-year cohort study using VA and CC administrative data from the VA's Corporate Data Warehouse (October 1, 2016-September 30, 2019). We obtained Medicare data to capture readmissions that were paid by Medicare. We used the Centers for Medicare and Medicaid Services (CMS) methods to identify unplanned, 30-day, all-cause readmissions. A secondary outcome, TKA-related readmissions, identified readmissions resulting from complications of the index surgery. We ran mixed-effects logistic regression models to compare the risk-adjusted odds of all-cause and TKA-related readmissions between TKAs performed in VA versus CC, adjusting for patients' sociodemographic and clinical characteristics. PRINCIPAL FINDINGS: Nationally, the odds of experiencing an all-cause or TKA-related readmission were significantly lower for TKAs performed in VA versus CC (eg, the odds of experiencing an all-cause readmission in VA were 35% of those in CC. At the facility level, most VA facilities performed similarly to their corresponding CC providers, although there were 3 VA facilities that performed worse than their corresponding CC providers. CONCLUSIONS: Given VA's history in providing high-quality surgical care to Veterans, it is important to closely monitor and track whether the shift to CC for surgical care will impact quality in both settings over time.

Is the Availability of Direct-Acting Antivirals Associated with Increased Access to Hepatitis C Treatment for Homeless and Unstably Housed Veterans?

OBJECTIVE: Hepatitis C virus (HCV) treatment has experienced a rapid transformation in the USA. New direct-acting antiviral (DAA) medications make treatment easier, less toxic, and more successful (90% or greater viral cure) than prior, interferon-based HCV medications. We sought to determine whether DAAs may have improved access to HCV treatment for hard-to-reach populations such as the homeless. METHODS: In a retrospective study of VA electronic medical record data, a cohort was created of 63,586 veterans with a positive HCV RNA or genotype test taken at any point from January 1, 2012, through December 31, 2016. Patient data were examined for up to 5 years using a discrete time survival model to assess the relationship between their housing status and receipt of HCV medications in 6-month time periods in both the interferon and DAA eras. RESULTS: In the interferon era, the probability of HCV treatment in a given 6-month window among housed veterans, at 6.2% (95% CI: 5.3-7.1%) was significantly higher than among veterans who were homeless or unstably housed; for example, among currently homeless veterans, the probability of treatment initiation, in a given 6-month window, was 2.6% (95% CI: 1.9-3.3%). With the arrival of DAAs, each housing category had an increased probability of treatment initiation. For housed veterans, the probability was 8.6% (95% CI: 8.3-8.9%) while for currently homeless veterans, it was 6.3% (95% CI: 5.7-6.9%). CONCLUSIONS: We found a clear indication that the likelihood of treatment initiation was greater for all veterans in the DAA era as compared to the interferon era. However, disparities in treatment initiation rates between housed and homeless veterans that were observed in the interferon era persisted in the DAA era.

Estimating the Cost of Surgical Care Purchased in the Community by the Veterans Health Administration.

Background. Veterans' access to Veterans Affairs (VA)-purchased community care expanded due to large increases in funding provided in the 2014 Veterans Choice Act. Objectives. To compare costs between VA-delivered care and VA payments for purchased care for two commonly performed surgeries: total knee arthroplasties (TKAs) and cataract surgeries. Research Design. Descriptive statistics and regressions examining costs in VA-delivered and VA-purchased care (fiscal year [FY] 2018 [October 2017 to September 2018]). Subjects. A total of 13,718 TKAs, of which 6,293 (46%) were performed in VA. A total of 91,659 cataract surgeries, of which 65,799 (72%) were performed in VA. Measures. Costs of VA-delivered care based on activity-based cost estimates; costs of VA-purchased care based on approved and paid claims. Results. Ninety-eight percent of VA-delivered TKAs occurred in inpatient hospitals, with an average cost of $28,969 (SD $10,778). The majority (86%) of VA-purchased TKAs were also performed at inpatient hospitals, with an average payment of $13,339 (SD $23,698). VA-delivered cataract surgeries were performed at hospitals as outpatient procedures, with an average cost of $4,301 (SD $2,835). VA-purchased cataract surgeries performed at hospitals averaged $1,585 (SD $629); those performed at ambulatory surgical centers cost an average of $1,346 (SD $463). We also found significantly higher Nosos risk scores for patients who used VA-delivered versus VA-purchased care. Conclusions. Costs of VA-delivered care were higher than payments for VA-purchased care, but this partly reflects legislative caps limiting VA payments to community providers to Medicare amounts. Higher patient risk scores in the VA could indicate that community providers are reluctant to accept high-risk patients because of Medicare reimbursements, or that VA providers prefer to keep the more complex patients in VA.

Comparing Complication Rates After Elective Total Knee Arthroplasty Delivered Or Purchased By The VA.

The Department of Veterans Affairs (VA) both delivers health care in its own facilities and, increasingly, purchases care for veterans in the community. Policy makers, administrators, health care providers, and veterans frequently face decisions about which services should be delivered versus purchased by the VA. Comparisons of quality across settings are essential if veterans are to receive care that is consistently accessible, patient centered, effective, and safe. We compared risk-adjusted major postoperative complication rates for total knee arthroplasties that were delivered in VA facilities versus purchased from community providers. Overall, adjusted complication rates were significantly lower for arthroplasties delivered by the VA compared with those that were purchased. However, hospital-level comparisons revealed five locations where VA-purchased care outperformed VA-delivered care. As the amount of VA-purchased care continues to increase under the Veterans Access, Choice, and Accountability Act of 2014 and the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks Act of 2018, these results support VA monitoring of overall and local comparative hospital performance to improve the quality of the care that the VA delivers while ensuring optimal outcomes in VA-purchased care.

Did Access to Care Improve Since Passage of the Veterans Choice Act?: Differences Between Rural and Urban Veterans.

BACKGROUND: The 2014 Veterans Choice Program aimed to improve care access for Veterans through expanded availability of community care (CC). Increased access to CC could particularly benefit rural Veterans, who often face obstacles in obtaining medical care at the Veterans Health Administration (VHA). However, whether Veterans Choice Program improved timely access to care for this vulnerable population is understudied. OBJECTIVES: To examine wait times among rural and urban Veterans for 5 outpatient specialty care services representing the top requests for CC services among rural Veterans. RESEARCH DESIGN: Retrospective study using VHA and CC outpatient consult data from VHA's Corporate Data Warehouse in Fiscal Year (FY) 2015 (October 1, 2014 to September 30, 2015) and FY2018 (October 1, 2017 to September 30, 2018). SUBJECTS: All Veterans who received a new patient consult for physical therapy, cardiology, optometry, orthopedics, and/or dental services in VHA and/or CC. MEASURES: Wait time, care setting (VHA/CC), rural/urban status, sociodemographics, and comorbidities. RESULTS: Our sample included 1,112,876 Veterans. Between FY2015 and FY2018, mean wait times decreased for all services for both rural and urban Veterans; declines were greatest in VHA (eg, mean optometry wait times for rural Veterans in VHA vs. CC declined 8.3 vs. 6.4 d, respectively, P<0.0001). By FY2018, for both rural and urban Veterans, CC mean wait times for most services were longer than VHA wait times. CONCLUSIONS: Timely care access for all Veterans improved between FY15 and FY18, particularly in VHA. As expansion of CC continues under the MISSION Act, more research is needed to evaluate quality of care across VHA and CC and what role, if any, wait times play.

Rural Veterans' Experiences With Outpatient Care in the Veterans Health Administration Versus Community Care.

BACKGROUND: The 2014 Veterans Access, Choice and Accountability Act was intended to improve Veterans' access to timely health care by expanding their options to receive community care (CC) paid for by the Veterans Health Administration (VA). Although CC could particularly benefit rural Veterans, we know little about rural Veterans' experiences with CC. OBJECTIVE: The objective of this study was to compare rural Veterans' experiences with CC and VA outpatient health care services to those of urban Veterans and examine changes over time. RESEARCH DESIGN: Retrospective, cross-sectional study using data from the Survey of Healthcare Experiences of Patients (SHEP) and VA Corporate Data Warehouse. Subjects: All Veterans who responded to the SHEP survey in Fiscal Year (FY) 16 or FY19. MEASURES: Outcomes were 4 measures of care experience (Access, Communication, Coordination, and Provider Rating). Independent variables included care setting (CC/VA), rural/urban status, and demographic and clinical characteristics. RESULTS: Compared with urban Veterans, rural Veterans rated CC the same (for specialty care) or better (for primary care). Rural Veterans reported worse experiences in CC versus VA, except for specialty care Access. Rural Veterans' care experiences improved between FY16 and FY19 in both CC and VA, with greater improvements in CC. CONCLUSIONS: Rural Veterans' reported comparable or better experiences in CC compared with urban Veterans, but rural Veterans' CC experiences still lagged behind their experiences in VA for primary care. As growing numbers of Veterans use CC, VA should ensure that rural and urban Veterans' experiences with CC are at least comparable to their experiences with VA care.

Comparing Driving Miles for Department of Veterans Affairs-delivered Versus Department of Veterans Affairs-purchased Cataract Surgery.

BACKGROUND: The Veterans Choice Act of 2014 increased the number of Veterans eligible for Department of Veterans Affairs (VA)-purchased care delivered in non-VA community care (CC) facilities. Driving >40 miles from home to a VA facility is a key eligibility criterion for CC. It remains unclear whether this policy change improved geographical access by reducing drive distance for Veterans. OBJECTIVES: Describe the driving distance for Veterans receiving cataract surgery in VA and CC facilities, and if they visited the closest-to-home facility or if they drove to farther facilities. SUBJECTS: Veterans who had cataract surgery in federal fiscal year 2015. MEASURES: We calculated driving miles to the Closest VA and CC facilities that performed cataract surgeries, and to the location where Veterans received care. RESULTS: A total of 61,746 Veterans received 83,875 cataract surgeries. More than 50% of CC surgeries occurred farther than the Closest CC facility providing cataract surgery (median Closest CC facility 8.7 miles vs. Actual CC facility, 19.7 miles). Most (57%) Veterans receiving cataract surgery at a VA facility used the Closest VA facility (median Closest VA facility 28.1 miles vs. Actual VA facility at 31.2 miles). In all, 26.1% of CC procedures occurred in facilities farther away than the Closest VA facility. CONCLUSIONS: Although many Veterans drove farther than needed to get cataract surgery in CC, this was not true for obtaining care in the VA. Our findings suggest that there may be additional reasons, besides driving distance, that affect whether Veterans choose CC and, if they do, where they seek CC.

Veterans' Experiences With Outpatient Care: Comparing The Veterans Affairs System With Community-Based Care.

Timely access to outpatient care was a primary driver behind the Department of Veterans Affairs' (VA's) increased purchase of community-based care under the Veterans Access, Choice, and Accountability Act of 2014, known as the Choice Act. To compare veterans' experiences in VA-delivered and community-based outpatient care after implementation of the act, we assessed veterans' scores on four dimensions of experience-access, communication, coordination, and provider rating-for outpatient specialty, primary, and mental health care received during 2016-17. Patient experiences were better for VA than for community care in all respects except access. For specialty care, access scores were better in the community; for primary and mental health care, access scores were similar in the two settings. Although all specialty care scores and the primary care coordination score improved over time, the gaps between settings did not shrink. As purchased care further expands under the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks Act of 2018, which replaced the Choice Act in 2019, monitoring of meaningful differences between settings should continue, with the results used to inform both VA purchasing decisions and patients' care choices.

Comparing cataract surgery complication rates in veterans receiving VA and community care.

OBJECTIVES: To compare 90-day postoperative complication rates between Veterans receiving cataract surgery in VA vs Community Care (CC) during the first year of implementation of the Veterans Choice Act. DATA SOURCES: Fiscal Year (FY) 2015 VA and CC outpatient data from VA's Corporate Data Warehouse (CDW) 10/01/14-9/30/15). FY14 data were used to obtain baseline clinical information prior to surgery. STUDY DESIGN: Retrospective one-year study using secondary data to compare 90-day complication rates following cataract surgery (measured using National Quality Forum (NQF) criteria) in VA vs CC. NQF defines major complications from a specified list of Current Procedural Terminology (CPT) codes. We ran a series of logistic regression models to predict 90-day complication rates, adjusting for Veterans' sociodemographic characteristics, comorbidities, preoperative ocular conditions, eye risk group, and type of cataract surgery (classified as routine vs complex). DATA COLLECTION: We linked VA and CC users through patient identifiers obtained from the CDW files. Our sample included all enrolled Veterans who received outpatient cataract surgery either in the VA or through CC during FY15. Cataract surgeries were identified through CPT codes 66 984 (routine) and 66 982 (complex). PRINCIPAL FINDINGS: Of the 83,879 cataract surgeries performed in FY15, 31 percent occurred through CC. Undergoing complex surgery and having a high-risk eye (based on preoperative ocular conditions) were the strongest clinical predictors of 90-day postoperative complications. Overall, we found low complication rates, ranging from 1.1 percent in low-risk eyes to 3.6 percent in high-risk eyes. After adjustment for important confounders (eg, race, rurality, and preoperative ocular conditions), there were no statistically significant differences in 90-day complication rates between Veterans receiving cataract surgery in VA vs CC. CONCLUSIONS: As more Veterans seek care through CC, future studies should continue to monitor quality of care across the two care settings to help inform VA's "make vs buy decisions."

Dense Breast Notification Laws: Impact on Downstream Imaging After Screening Mammography.

Dense breast tissue is a common finding that decreases the sensitivity of mammography in detecting cancer. Many states have recently enacted dense breast notification (DBN) laws to provide patients with information to help them make better-informed decisions about their health. To test whether DBN legislation affected the probability of screening mammography follow-up by ultrasound and magnetic resonance imaging (MRI), we examined the proportion of times screening mammography was followed by ultrasound or MRI for a series of months pre- and post-legislation. The subjects were women aged 40 to 64 years, covered by private health insurance, undergoing screening mammography from 2007 to 2014. Except for Hawaii, Maryland, and New York, DBN legislation significantly increased the probability of ultrasound follow-up in all states that implemented DBN legislation before December 2014. It also increased the probability of MRI follow-up in California, North Carolina, Pennsylvania, and Texas. The financial and access consequences merit further study.

How hospitals select their patient safety priorities: An exploratory study of four Veterans Health Administration hospitals.

BACKGROUND: Hospitals face ongoing pressure to reduce patient safety events. However, given resource constraints, hospitals must prioritize their safety improvements. There is limited literature on how hospitals select their safety priorities. PURPOSE: The aim of this research was to describe and compare the approaches used by Veterans Health Administration (VA) hospitals to select their safety priorities. METHODOLOGY: Semistructured telephone interviews with key informants (n = 16) were used to collect data on safety priorities in four VA hospitals from May to December 2016. We conducted a directed content analysis of the interview notes using an organizational learning perspective. We coded for descriptive data on the approaches (e.g., set of cues, circumstances, and activities) used to select safety priorities, a priori organizational learning capabilities (learning processes, learning environment, and learning-oriented leadership), and emergent domains. For cross-site comparisons, we examined the coded data for patterns. RESULTS: All hospitals used multiple approaches to select their safety priorities; these approaches used varied across hospitals. Although no single approach was reported as particularly influential, all hospitals used approaches that addressed system level or national requirements (i.e., externally required activities). Additional approaches used by hospitals (e.g., responding to staff concerns of patient safety issues, conducting a multidisciplinary team investigation) were less connected to externally required activities and demonstrated organizational learning capabilities in learning processes (e.g., performance monitoring), learning environment (e.g., staff's psychological safety), and learning-oriented leadership (e.g., establishing a nonpunitive culture). PRACTICE IMPLICATIONS: Leaders should examine the approaches used to select safety priorities and the role of organizational learning in these selection approaches. Exclusively relying on approaches focused on externally required activities may fail to identify safety priorities that are locally relevant but not established as significant at the system or national levels. Organizational learning may promote hospitals' use of varied approaches to guide their selection of safety priorities and thereby benefit hospital safety improvement efforts.

Advantages of Continuous-Valued Risk Scores for Predicting Long-Term Costs: The Framingham Coronary Heart Disease 10-Year Risk Score.

BACKGROUND: The few studies that have examined the relationship between midlife cardiovascular disease risk and longer-term costs have differentiated risk using a small number of risk categories. In this paper, we illustrate the advantages of a continuous-valued score to examine the relationship between risk and longer-term costs: the Framingham 10-year coronary heart disease risk score. METHODS: Our study cohort consisted of 1333 Second Generation Framingham Heart Study participants enrolled in fee-for-service Medicare for at least 8 quarters and who had a risk score assessment between age 40 and 50 years. We used generalized linear models to examine the relationships between quarterly Medicare costs and risk scores. RESULTS: Using risk categories defined by the Framingham score, the cost differences between a low and high risk group were 40% to over 200% greater than differences in comparable studies using a small number of risk categories. A continuous-valued score facilitates comparison of the cost consequences of impacting risk score changes. For example, an intervention that is able to reduce a person's score change between midlife and later-life from the 75th percentile to the 25th percentile would result in almost a 20% reduction in longer-term costs. In contrast, an intervention that is able to reduce a person's midlife score from the 75th percentile to the 25th percentile would result in a 38% reduction in costs. CONCLUSIONS: A continuous-valued risk score has advantages compared to defining risk based on a small number of risk categories.