Equitable resource allocation and operational efficiency evaluation

Effects of Hospital Procedure Volume and Resident Training on Clinical Outcomes and Resource Use in Radical Retropubic Prostatectomy Surgery in the Department of Veterans Affairs

BackgroundUnguided digital mental health interventions (UDMHIs) have the potential to provide low-cost and effective mental health care at scale. Controlled trials have demonstrated the efficacy of UDMHIs to address mental health symptoms and conditions. However, few previous publications have described the demographics of real-world users of UDMHIs that are freely available to the public. The US Department of Veterans Affairs has created and hosts several UDMHIs on its Veteran Training Portal website. These web-based, free-to-use, and publicly available UDMHIs include Path to Better Sleep, Anger and Irritability Management Skills, and Moving Forward, which focus on insomnia, problematic anger, and depression symptoms, respectively.ObjectiveThis study aimed to examine the user demographics of these 3 UDMHIs in the year 2021. In addition, it aimed to compare the age and gender distribution of the users of those 3 UDMHIs with one another and with the age and gender distribution of the total US veteran population.MethodsGoogle Analytics was used to collect user data for each of the 3 UDMHIs. The age and gender distribution of the users of each UDMHI was compared with that of the other UDMHIs as well as with that of the overall US veteran population using chi-square tests. Information on the total number of users, the country they were in, and the devices they used to access the UDMHIs was also collected and reported.ResultsIn 2021, the 3 UDMHIs together recorded 29,306 unique users. The estimated age range and gender were available for 24.12% (7068/29,306) of those users. Each UDMHI’s age and gender distribution significantly differed from that of the other UDMHIs and from that of the overall US veteran population (P<.001 on all chi-square tests). Women and younger age groups were overrepresented among UDMHI users compared with the overall US veteran population. The majority of devices used to access the UDMHIs were desktop or laptop devices, although a substantial proportion of devices used were mobile devices (10,199/29,752, 34.28%). Most users (27,789/29,748, 93.41%) were located in the United States, with users from Canada, the United Kingdom, and Australia accounting for another 2.61% (775/29,748) of total users.ConclusionsOur use of Google Analytics data provided useful information about the users of 3 free and publicly available UDMHIs provided by the US Department of Veterans Affairs. Although our findings should be considered in light of the limitations of autonomously collected web analytics data, they still offer useful information for health care policy makers, administrators, and UDMHI developers.

The risk is uncertain, so patients should make an informed decision.

Back to table of contents Previous article Next article LettersFull AccessSuicide Prevention in Patient and Nonpatient Populations: In ReplyClaire A. Hoffmire, Ph.D., Janet E. Kemp, R.N., Ph.D., Caitlin Thompson, Ph.D.Claire A. HoffmireSearch for more papers by this author, Ph.D., Janet E. KempSearch for more papers by this author, R.N., Ph.D., Caitlin ThompsonSearch for more papers by this author, Ph.D.Published Online:1 Oct 2015https://doi.org/10.1176/appi.ps.661002AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail IN REPLY: We thank Dr. Coffey and colleagues for their commentary on our recent study comparing suicide risk for veterans who did and did not use VHA services. Comparing this study with their recent analysis of suicides among Henry Ford Health System members highlights the importance of continued evaluation of suicide prevention activities.The main findings from our analysis were that veterans, particularly female veterans, were at higher and increasing risk of suicide, compared with their nonveteran peers and that use of VHA services mitigated some of this risk. By compiling death certificate records for all known suicide decedents, the Department of Veterans Affairs (VA) archive of state suicide data made direct comparisons between VHA and non-VHA veterans possible for the first time (1–3). Veteran status in this database is carefully validated by using VA and Department of Defense records (2). Validation of veteran status was shown to have an impact on effect estimates in a study evaluating the relationship between veteran status and firearm suicide (3). Death certificate indication of military history is particularly problematic for female and younger veterans (2)—two of the fastest-growing veteran subgroups for which there is an immediate need to better understand suicide risk and trends.The VA has, however, been evaluating suicide risk among VHA-utilizing veterans since implementing the Suicide Prevention Program in 2007 (1). As summarized in our report, VA’s Suicide Prevention Program is poised to reach the broad population of VHA users and provide access to an integrated network of services aimed at reducing risk. Initiatives include awareness and education campaigns such as “The Power of 1,” a 24-hour Veterans Crisis Line, universal training in risk recognition and management for VHA employees, and the placement of suicide prevention coordinators in each VA medical center to support high-risk patients. Recent efforts also include the development of predictive modeling strategies to identify VHA patients at high risk of suicide (4).This strong, multifaceted, and continually evolving program makes the VA a clear leader in suicide prevention. As such, we are pleased that our recent publication shed light on the pressing need to increase awareness of risk among female veterans. Response efforts will include continued research and program evaluation activities conducted collaboratively across the VA Suicide Prevention Program and VA’s Women Veterans Health Care. Comprehensive, high-quality women’s health care is already a top priority within the VA. Every medical center has a women veterans program manager to serve as an administrative leader for women’s health programs and an advocate for women veterans, and a Women Veterans Call Center has been established as a resource for women veterans seeking information on VA benefits and services.Similarly, we recommend that efforts to evaluate gender and age differences in the HFHS population are undertaken. The effectiveness of intervention programs is likely to vary considerably by these factors, and identifying such differences is a key step toward targeting and tailoring programs to prevent additional loss of life.References1 Kemp J, Bossarte R: Suicide Data Report, 2012. Washington, DC, US Department of Veterans Affairs, Mental Health Services, Suicide Prevention Program, Feb 2013Google Scholar2 Hoffmire CA, Piegari RI, Bossarte RM: Misclassification of veteran status on Washington State death certificates for suicides from 1999 to 2008. Annals of Epidemiology 23:298–300, 2013Crossref, Medline, Google Scholar3 Hoffmire CA, Bossarte RM: A reconsideration of the correlation between veteran status and firearm suicide in the general population. Injury Prevention 20:317–321, 2014Crossref, Medline, Google Scholar4 McCarthy JF, Bossarte RM, Katz IR, et al.: Predictive modeling and concentration of the risk of suicide: implications for preventive interventions in the US Department of Veterans Affairs. American Journal of Public Health (Epub ahead of print, June 11, 2015)Google Scholar FiguresReferencesCited byDetailsCited ByRates and Correlates of Suicidality in VA Intensive Case Management Programs4 May 2021 | Community Mental Health Journal, Vol. 58, No. 2JMIR Mental Health, Vol. 5, No. 3 Volume 66Issue 10 October 01, 2015Pages 1120-1121 Metrics PDF download History Published online 1 October 2015 Published in print 1 October 2015

Reply to: “Treatment of veterans with hepatitis C in the United States Department of Veterans Affairs”

A Comparison of Percutaneous Transluminal Coronary Angioplasty in the Department of Veterans Affairs and in the Private Sector in the State of Washington

The US Department of Veterans Affairs (VA) has over 15 years of experience in delivery of evidence-based psychotherapies (EBPs). This paper describes strategies for using clinical documentation and administrative data to understand adherence and modifications to EBPs for Posttraumatic Stress Disorder (PTSD). This study focused on two EBPs for PTSD, Cognitive Processing Therapy (CPT) and Prolonged Exposure (PE). The sample included VA therapists from across the US who provided CPT and PE and the patients they treated over a 1-year period. The data sources for this study were templated EBP chart notes and VA administrative data. We used a manual review of note content and administrative data rules to code therapy adherence and modifications in 7,297 EBP sessions for 1,257 patients seen by 182 therapists. Two trained coders rated each therapy note and resolved discrepancies through consensus. To contextualize and explain variation in adherence and modifications, we conducted brief 30-45-min semi-structured interviews with a purposive subsample of these therapists (n = 32). Combining manual chart review and administrative data allowed for identification of 11 types of modifications. Raters disagreed on adherence for 30% of notes. The disagreement stemmed from the presence of therapy modifications that were not clearly documented, necessitating the development of decision rules and strategies for modification coding. Both therapists and patients contributed to the variance in the extent to which different modifications occurred. Therapist interviews demonstrated therapist awareness of modifying the protocols in the ways identified through chart review. Healthcare systems can use data collected as part of routine care to understand how and when EBPs are modified but need to develop scalable strategies to document adaptations and modifications to EBPs in routine care.

The Department of Veterans Affairs (VA) health care system currently includes 152 medical centers, with at least one in each state, Puerto Rico, and the District of Columbia. VA operates more than 1,400 sites of care, including 909 ambulatory care and community-based outpatient clinics, 135 community living centers (nursing homes), 47 residential rehabilitation treatment programs, 232 Veterans Centers and 108 comprehensive home-care programs. In 2010, the system supported 75.6 million outpatient visits and 679,000 inpatient admissions.1, 2In 1999, the VA National Center for Patient Safety (NCPS) was established to lead the VA’s patient safety efforts and to develop a culture of healthcare safety throughout the Veterans Health Administration. The NCPS program promotes the use of human factors engineering methods that focus on how users interact with technology. Within the Department of Veterans Affairs various organizations have expanded the use of human factors engineering methods as a key element in addressing patient safety from a systems-based perspective. These entities include a range of groups that work in operational and research domains to identify and mitigate root causes of error with traditional medical devices and healthcare information technology to reduce the likelihood of patient harm while continuing to enhance and advance the design of healthcare tools and environments.The expertise of the panel members includes human factors and biomedical engineering, cognitive psychology, information science, healthcare information technology and informatics, and clinical knowledge of medical technology and nursing. Each panelist will briefly introduce the organization they work in, provide an overview of their human factors activities, and briefly describe example(s) of specific projects, with emphasis on the benefit or lessons learned via these activities. Attendees will learn strategies to apply human factors engineering in healthcare and deepen their understanding of human performance challenges in this domain.

Racial and Ethnic Disparities in Chronic Pain-related Mental Health Care in a Cohort of U.S. Military Veterans

Reduced In-Person and Increased Telehealth Outpatient Visits During the COVID-19 Pandemic.

Ongoing federal efforts support equalizing access to experienced educators for low-income students and students of color, thereby narrowing the “teacher experience gap.” I show that while high-poverty and high-minority schools have larger class sizes and receive less funding nationally, school districts allocate resource equitably, on average, across schools. However, the least experienced teachers are still concentrated in high-poverty and high-minority schools, both across and within districts. I then show that additional state and local funding is associated with more equitable district resource allocation. The study offers recommendations for state and federal education policy related to the Every Student Succeeds Act.

With increasing rates of opioid use disorder (OUD) and overdose deaths in the US, increased access to medications for OUD (MOUD) is paramount. Rigorous effectiveness evaluations of large-scale implementation initiatives using quasi-experimental designs are needed to inform expansion efforts. To evaluate a US Department of Veterans Affairs (VA) initiative to increase MOUD use in nonaddiction clinics. This quality improvement initiative used interrupted time series design to compare trends in MOUD receipt. Primary care, pain, and mental health clinics in the VA health care system (n = 35) located at 18 intervention facilities and nonintervention comparison clinics (n = 35) were matched on preimplementation MOUD prescribing trends, clinic size, and facility complexity. The cohort of patients with OUD who received care in intervention or comparison clinics in the year after September 1, 2018, were evaluated. The preimplementation period extended from September 1, 2017, through August 31, 2018, and the postimplementation period from September 1, 2018, through August 31, 2019. The multifaceted implementation intervention included education, external facilitation, and quarterly reports. The main outcomes were the proportion of patients receiving MOUD and the number of patients per clinician prescribing MOUD. Segmented logistic regression evaluated monthly proportions of MOUD receipt 1 year before and after initiative launch, adjusting for demographic and clinical covariates. Poisson regression models examined yearly changes in clinician prescribing over the same time frame. Overall, 7488 patients were seen in intervention clinics (mean [SD] age, 53.3 [14.2] years; 6858 [91.6%] male; 1476 [19.7%] Black, 417 [5.6%] Hispanic; 5162 [68.9%] White; 239 [3.2%] other race [including American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, and multiple races]; and 194 [2.6%] unknown) and 7558 in comparison clinics (mean [SD] age, 53.4 [14.0] years; 6943 [91.9%] male; 1463 [19.4%] Black; 405 [5.4%] Hispanic; 5196 [68.9%] White; 244 [3.2%] other race; 250 [3.3%] unknown). During the preimplementation year, the proportion of patients receiving MOUD in intervention clinics increased monthly by 5.0% (adjusted odds ratio [AOR], 1.05; 95% CI, 1.03-1.07). Accounting for this preimplementation trend, the proportion of patients receiving MOUD increased monthly by an additional 2.3% (AOR, 1.02; 95% CI, 1.00-1.04) during the implementation year. Comparison clinics increased by 2.6% monthly before implementation (AOR, 1.03; 95% CI, 1.01-1.04), with no changes detected after implementation. Although preimplementation-year trends in monthly MOUD receipt were similar in intervention and comparison clinics, greater increases were seen in intervention clinics after implementation (AOR, 1.04; 95% CI, 1.01-1.08). Patients treated with MOUD per clinician in intervention clinics saw greater increases from before to after implementation compared with comparison clinics (incidence rate ratio, 1.50; 95% CI, 1.28-1.77). A multifaceted implementation initiative in nonaddiction clinics was associated with increased MOUD prescribing. Findings suggest that engagement of clinicians in general clinical settings may increase MOUD access.

A significant proportion of SARS-CoV-2 infected individuals experience post-COVID-19 condition months after initial infection. To determine the rates, clinical setting, risk factors, and symptoms associated with the documentation of International Statistical Classification of Diseases Tenth Revision (ICD-10), code U09.9 for post-COVID-19 condition after acute infection. This retrospective cohort study was performed within the US Department of Veterans Affairs (VA) health care system. Veterans with a positive SARS-CoV-2 test result between October 1, 2021, the date ICD-10 code U09.9 was introduced, and January 31, 2023 (n = 388 980), and a randomly selected subsample of patients with the U09.9 code (n = 350) whose symptom prevalence was assessed by systematic medical record review, were included in the analysis. Positive SARS-CoV-2 test result. Rates, clinical setting, risk factors, and symptoms associated with ICD-10 code U09.9 in the medical record. Among the 388 980 persons with a positive SARS-CoV-2 test, the mean (SD) age was 61.4 (16.1) years; 87.3% were men. In terms of race and ethnicity, 0.8% were American Indian or Alaska Native, 1.4% were Asian, 20.7% were Black, 9.3% were Hispanic or Latino, 1.0% were Native Hawaiian or Other Pacific Islander; and 67.8% were White. Cumulative incidence of U09.9 documentation was 4.79% (95% CI, 4.73%-4.87%) at 6 months and 5.28% (95% CI, 5.21%-5.36%) at 12 months after infection. Factors independently associated with U09.9 documentation included older age, female sex, Hispanic or Latino ethnicity, comorbidity burden, and severe acute infection manifesting by symptoms, hospitalization, or ventilation. Primary vaccination (adjusted hazard ratio [AHR], 0.80 [95% CI, 0.78-0.83]) and booster vaccination (AHR, 0.66 [95% CI, 0.64-0.69]) were associated with a lower likelihood of U09.9 documentation. Marked differences by geographic region and facility in U09.9 code documentation may reflect local screening and care practices. Among the 350 patients undergoing systematic medical record review, the most common symptoms documented in the medical records among patients with the U09.9 code were shortness of breath (130 [37.1%]), fatigue or exhaustion (78 [22.3%]), cough (63 [18.0%]), reduced cognitive function or brain fog (22 [6.3%]), and change in smell and/or taste (20 [5.7%]). In this cohort study of 388 980 veterans, documentation of ICD-10 code U09.9 had marked regional and facility-level variability. Strong risk factors for U09.9 documentation were identified, while vaccination appeared to be protective. Accurate and consistent documentation of U09.9 is needed to maximize its utility in tracking patients for clinical care and research. Future studies should examine the long-term trajectory of individuals with U09.9 documentation.

The Department of Veterans Affairs (VA) operates one of the largest health care systems in the nation; more than 2.5 million veterans receive care annually. Among the special foci of care within VA is the Dental Service. The Department of Veterans Affairs Dental Service is the largest dental care system in the nation and the largest hospital-based dental care system in the world, receiving more than 1.2 million visits annually. The authors describe the VA dental care system and the larger health care system in which it is embedded. How the system is organized, who is eligible for services, who uses care, and what types of services are used are detailed. Compared with medical care, eligibility for VA dental care is more complex and differs for inpatients and outpatients. Outpatients account for 65% of patient visits and 76% of treatment provided in VA dental clinics. The two largest groups of users are inpatients with compelling medical needs (17%) and outpatients who are totally disabled (24%). A wide variety of services is provided, ranging from diagnostic and preventive care to insertion of crowns, bridges, and removable prostheses. Changes in the nation's health care system mandate introspection by all health agencies. The goal of the VA Dental Service is to become veterans' first choice for dental care. Information needed by VA to best respond to the needs of veterans include the following: (1) reasons for why eligible veterans do not use VA dental care; (2) veterans' oral health needs; (3) definition of optimum care and whether it varies as a person moves from functional independence to dependence; (4) whether VA is providing the most cost-efficient care possible and is best utilizing allied health professions; and (5) whether this care is best provided in a hospital setting. Modifications of data gathering systems are required as a first step to providing the needed information.

Screening for hepatitis D virus (HDV) is recommended for all individuals with hepatitis B virus (HBV) infection. Coinfected individuals experience more severe liver-related outcomes. We determined the HDV testing and coinfection rates and all-cause mortality among those infected with HBV. We used the US Department of Veterans Affairs (VA) healthcare system's national databases to identify individuals with HBV infection. We determined the proportion of individuals referred to gastroenterologists/hepatologists, or infectious diseases providers, and the proportion screened and tested positive for HDV. We calculated the HBV treatment rates, defined as ≥ 3 months of continuous prescription with an approved drug. Finally, we calculated all-cause mortality stratified by HDV coinfection and HBV treatment status. Among 44,951 individuals with at least one positive HBsAg, HBeAg or HBV DNA test, 5964 (13.3%) were screened for HDV (180 [3.0%] tested positive), and 28,291 (62.9%) were referred to gastroenterology/hepatology or infectious diseases. Treatment for HBV was prescribed for 73 (40.5%) of HDV-coinfected and 2425 (41.9%) HDV-uninfected individuals. All-cause mortality rate per 100 person-years was lower among those without HDV coinfection (2.98 for untreated HBV, 2.53 for treated HBV; p < 0.001) compared with those with HDV coinfection (5.14 for untreated HBV, 3.0 for treated HBV; p = 0.02). Kaplan-Meier curves demonstrated a significantly higher mortality among HDV-coinfected individuals who were not treated for HBV (log-rank p < 0.0001). Screening rates for HDV among HBV-infected individuals are suboptimal. While HDV coinfection is associated with higher all-cause mortality, HBV treatment may confer a survival benefit.