Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers.
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ABSTRACT:
SUBMITTER: Griffith KN
PROVIDER: S-EPMC7450350 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
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Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers.
Griffith Kevin N KN Ndugga Nambi J NJ Pizer Steven D SD
JAMA network open 20200803 8
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Project description:ImportanceTimely access to medical care is an important determinant of health and well-being. The US Congress passed the Veterans Access, Choice, and Accountability Act in 2014 and the VA MISSION (Maintaining Systems and Strengthening Integrated Outside Networks) Act in 2018, both of which allow veterans to access care from community-based clinicians, but geographic variation in appointment wait times after the passage of these acts have not been studied.ObjectiveTo describe geographic variation in wait times experienced by veterans for primary care, mental health, and other specialties.Design, setting, and participantsThis is a cross-sectional study using data from the Veterans Health Administration (VHA) Corporate Data Warehouse. Participants include veterans who sought medical care from January 1, 2018, to June 30, 2021. Data analysis was performed from February to June 2022.ExposuresReferral to either VHA or community-based clinicians.Main outcomes and measuresTotal appointment wait times (in days) for 3 care categories: primary care, mental health, and all other specialties. VHA medical centers are organized into regions called Veterans Integrated Services Networks (VISNs); wait times were aggregated to the VISN level.ResultsThe final sample included 22 632 918 million appointments for 4 846 892 unique veterans (77.3% male; mean [SD] age, 61.6 [15.5] years). Among non-VHA appointments, mean (SD) VISN-level appointment wait times were 38.9 (8.2) days for primary care, 43.9 (9.0) days for mental health, and 41.9 (5.9) days for all other specialties. Among VHA appointments, mean (SD) VISN-level appointment wait times were 29.0 (5.5) days for primary care, 33.6 (4.6) days for mental health, and 35.4 (2.7) days for all other specialties. There was substantial geographic variation in appointment wait times. Among non-VHA appointments, VISN-level appointment wait times ranged from 25.4 to 52.4 days for primary care, from 29.3 to 65.7 days for mental health, and from 34.7 to 54.8 days for all other specialties. Among VHA appointments, wait times ranged from 22.4 to 43.4 days for primary care, from 24.7 to 42.0 days for mental health, and from 30.3 to 41.9 days for all other specialties. There was a correlation between wait times across care categories and setting (VHA vs community care).Conclusions and relevanceThis cross-sectional study found substantial variation in wait times across care type and geography, and VHA wait times in a majority of VISNs were lower than those for community-based clinicians, even after controlling for differences in specialty mix. These findings suggest that liberalized access to community care under the Veterans Access, Choice, and Accountability Act and the VA MISSION Act may not result in lower wait times within these regions.
| S-EPMC9412224 | biostudies-literature
Project description:ImportanceConcerns have been raised about the adequacy of health care access among patients cared for within the United States Department of Veterans Affairs (VA) health care system.ObjectivesTo determine wait times for new patients receiving care at VA medical centers and compare wait times in the VA medical centers with wait times in the private sector (PS).Design, setting, and participantsA retrospective, repeated cross-sectional study was conducted of new appointment wait times for primary care, dermatology, cardiology, or orthopedics at VA medical centers in 15 major metropolitan areas in 2014 and 2017. Comparison data from the PS came from a published survey that used a secret shopper survey approach. Secondary analyses evaluated the change in overall and unique patients seen in the entire VA system and patient satisfaction survey measures of care access between 2014 and 2017.Main outcomes and measuresThe outcome of interest was patient wait time. Wait times in the VA were determined directly from patient scheduling. Wait times in the PS were as reported in Merritt Hawkins surveys using the secret shopper method.ResultsCompared with the PS, overall mean VA wait times for new appointments in 2014 were similar (mean [SD] wait time, 18.7 [7.9] days PS vs 22.5 [7.3] days VA; P = .20). Department of Veterans Affairs wait times in 2014 were similar to those in the PS across specialties and regions. In 2017, overall wait times for new appointments in the VA were shorter than in the PS (mean [SD], 17.7 [5.9] vs 29.8 [16.6] days; P < .001). This was true in primary care (mean [SD], 20.0 [10.4] vs 40.7 [35.0] days; P = .005), dermatology (mean [SD], 15.6 [12.2] vs 32.6 [16.5] days; P < .001), and cardiology (mean [SD], 15.3 [12.6] vs 22.8 [10.1] days; P = .04). Wait times for orthopedics remained longer in the VA than the PS (mean [SD], 20.9 [13.3] vs 12.4 [5.5] days; P = .01), although wait time improved significantly between 2014 and 2017 in the VA for orthopedics while wait times in the PS did not change (change in mean wait times, increased 1.5 days vs decreased 5.4 days; P = .02). Secondary analysis demonstrated an increase in the number of unique patients seen and appointment encounters in the VA between 2014 and 2017 (4 996 564 to 5 118 446, and 16 476 461 to 17 331 538, respectively), and patient satisfaction measures of access also improved (satisfaction scores increased by 1.4%, 3.0%, and 4.0% for specialty care, routine primary care, and urgent primary care, P < .05).Conclusions and relevanceAlthough wait times in the VA and PS appeared to be similar in 2014, there have been interval improvements in VA wait times since then, while wait times in the PS appear to be static. These findings suggest that access to care within the VA has improved over time.
| S-EPMC6484544 | biostudies-literature
Project description:BackgroundCurrently, in Canada, existing health administrative data and hospital-inputted portal systems are used to measure the wait times to receiving a procedure or therapy after a specialist visit. However, due to missing and inconsistent labelling, estimating the wait time prior to seeing a specialist physician requires costly manual coding to label primary care referral notes.MethodsIn this work, we represent the notes using word-count vectors and develop a logistic regression machine learning model to automatically label the target specialist physician from a primary care referral note. These labels are not available in the administrative system. We also study the effects of note length (measured in number of tokens) and dataset size (measured in number of notes per target specialty) on model performance to help other researchers determine if such an approach may be feasible for them. We then calculate the wait time by linking the specialist type from a primary care referral to a full consultation visit held in Ontario, Canada health administrative data.ResultsFor many target specialties, we can reliably (F1Score ≥ 0.70) predict the target specialist type. Doing so enables the automated measurement of wait time from family physician referral to specialist physician visit. Of the six specialties with wait times estimated using both 2008 and 2015 data, two had a substantial increase (defined as a change such that the original value lay outside the 95% confidence interval) in both median and 75th percentile wait times, one had a substantial decrease in both median and 75th percentile wait times, and three has non-substantial increases.ConclusionsAutomating these wait time measurements, which had previously been too time consuming and costly to evaluate at a population level, can be useful for health policy researchers studying the effects of policy decisions on patient access to care.
| S-EPMC9098074 | biostudies-literature
Project description:Reporting of quality indicators (QIs) in Veterans Health Administration Medical Centers is complicated by estimation error caused by small numbers of eligible patients per facility. We applied multilevel modeling and empirical Bayes (EB) estimation in addressing this issue in performance reporting of stroke care quality in the Medical Centers.We studied a retrospective cohort of 3812 veterans admitted to 106 Medical Centers with ischemic stroke during fiscal year 2007. The median number of study patients per facility was 34 (range, 12-105). Inpatient stroke care quality was measured with 13 evidence-based QIs. Eligible patients could either pass or fail each indicator. Multilevel modeling of a patient's pass/fail on individual QIs was used to produce facility-level EB-estimated QI pass rates and confidence intervals. The EB estimation reduced interfacility variation in QI rates. Small facilities and those with exceptionally high or low rates were most affected. We recommended 8 of the 13 QIs for performance reporting: dysphagia screening, National Institutes of Health Stroke Scale documentation, early ambulation, fall risk assessment, pressure ulcer risk assessment, Functional Independence Measure documentation, lipid management, and deep vein thrombosis prophylaxis. These QIs displayed sufficient variation across facilities, had room for improvement, and identified sites with performance that was significantly above or below the population average. The remaining 5 QIs were not recommended because of too few eligible patients or high pass rates with little variation.Considerations of statistical uncertainty should inform the choice of QIs and their application to performance reporting.
| S-EPMC3261327 | biostudies-literature
Project description:ObjectiveWe explored longitudinal trends in sociodemographic characteristics, reported symptoms, laboratory findings, pharmacological and non-pharmacological treatment, comorbidities, and 30-day in-hospital mortality among hospitalized patients with coronavirus disease 2019 (COVID-19).MethodsThis retrospective cohort study included patients diagnosed with COVID-19 in the United States Veterans Health Administration between 03/01/20 and 08/31/20 and followed until 09/30/20. We focused our analysis on patients that were subsequently hospitalized, and categorized them into groups based on the month of hospitalization. We summarized our findings through descriptive statistics. We used Cuzick's Trend Test to examine any differences in the distribution of our study variables across the six months.ResultsDuring our study period, we identified 43,267 patients with COVID-19. A total of 8,240 patients were hospitalized, and 13.1% (N = 1,081) died within 30 days of admission. Hospitalizations increased over time, but the proportion of patients that died consistently declined from 24.8% (N = 221/890) in March to 8.0% (N = 111/1,396) in August. Patients hospitalized in March compared to August were younger on average, mostly black, urban-dwelling, febrile and dyspneic. They also had a higher frequency of baseline comorbidities, including hypertension and diabetes, and were more likely to present with abnormal laboratory findings including low lymphocyte counts and elevated creatinine. Lastly, there was a decline from March to August in receipt of mechanical ventilation (31.4% to 13.1%) and hydroxychloroquine (55.3% to <1.0%), while treatment with dexamethasone (3.7% to 52.4%) and remdesivir (1.1% to 38.9%) increased.ConclusionAmong hospitalized patients with COVID-19, we observed a trend towards decreased disease severity and mortality over time.
| S-EPMC8321215 | biostudies-literature
Project description:Secondary stroke prevention is championed by the stroke guidelines; however, it is rarely systematically delivered. We sought to develop a locally tailored, evidence-based secondary stroke prevention program. The purpose of this paper was to apply intervention mapping (IM) to develop our locally tailored stroke prevention program and implementation plan. We completed a needs assessment and the five Steps of IM. The needs assessment included semi-structured interviews of 45 providers; 26 in Indianapolis and 19 in Houston. We queried frontline clinical providers of stroke care using structured interviews on the following topics: current provider practices in secondary stroke risk factor management; barriers and needs to support risk factor management; and suggestions on how to enhance secondary stroke risk factor management throughout the continuum of care. We then describe how we incorporated each of the five Steps of IM to develop locally tailored programs at two sites that will be evaluated through surveys for patient outcomes, and medical records chart abstraction for processes of care.
| S-EPMC3057184 | biostudies-literature
Project description:BackgroundNo-shows are a persistent and costly problem in all healthcare systems. Because forgetting is a common cause of no-shows, appointment reminders are widely used. However, qualitative research examining appointment reminders and how to improve them is lacking.ObjectiveTo understand how patients experience appointment reminders as part of intervention development for a pragmatic trial of enhanced appointment reminders.DesignQualitative content analysis PARTICIPANTS: Twenty-seven patients at a single Department of Veterans Affairs hospital and its satellite clinics APPROACH: We conducted five waves of interviews using rapid qualitative analysis, in each wave continuing to ask veterans about their experience of reminders. We double-coded all interviews, used deductive and inductive content analysis to identify themes, and selected quotations that exemplified three themes (limitations, strategies, recommendations).Key resultsInterviews showed four limitations on the usability of current appointment reminders which may contribute to no-shows: (1) excessive information within reminders; (2) frustrating telephone systems when calling in response to an appointment reminder; (3) missing or cryptic information about clinic logistics; and (4) reminder fatigue. Patients who were successful at keeping appointments often used specific strategies to optimize the usability of reminders, including (1) using a calendar; (2) heightening visibility; (3) piggybacking; and (4) combining strategies. Our recommendations to enhance reminders are as follows: (1) mix up their content and format; (2) keep them short and simple; (3) add a personal touch; (4) include specifics on clinic location and contact information; (5) time reminders based on the mode of delivery; and (6) hand over control of reminders to patients.ConclusionsAppointment reminders are vital to prevent no-shows, but their usability is not optimized for patients. There is potential for healthcare systems to modify several aspects of the content, timing, and delivery of appointment reminders to be more effective and patient-centered.
| S-EPMC7859164 | biostudies-literature
Project description:OBJECTIVE: To determine whether safety net and non-safety net hospitals influence inpatient breast cancer care in insured and uninsured women and in white and African American women. DATA SOURCES: Six years of Virginia Cancer Registry and Virginia Health Information discharge data were linked and supplemented with American Hospital Association data. STUDY DESIGN: Hierarchical generalized linear models and linear probability regression models were used to estimate the relationship between hospital safety net status, the explanatory variables, and the days from diagnosis to mastectomy and the likelihood of breast reconstruction. PRINCIPAL FINDINGS: The time between diagnosis and surgery was longer in safety net hospitals for all patients, regardless of insurance source. Medicaid insured and uninsured women were approximately 20 percent less likely to receive reconstruction than privately insured women. African American women were less likely to receive reconstruction than white women. CONCLUSIONS: Following the implementation of health reform, disparities may potentially worsen if safety net hospitals' burden of care increases without commensurate increases in reimbursement and staffing levels. This study also suggests that Medicaid expansions may not improve outcomes in inpatient breast cancer care within the safety net system.
| S-EPMC3419883 | biostudies-literature
Project description:The United States Veterans Health Administration (VHA) serves more than 9 million enrolled Veterans each year. Although most of the care that the VHA sponsors is delivered within its own facilities, there has been a call for "privatizing" some or all of these services. Under such an arrangement, the Department of Veterans Affairs would pay non-VHA providers to deliver care in facilities open to the general public. Privatization is hotly contested on political grounds and is not resolved. Yet the question whether the VHA should be privatized cannot be resolved without first establishing that this policy change is even feasible. One potential obstacle to privatization would be the lack of nearby alternative facilities to deliver care. To assess for the presence of this impediment, we used Google Maps to measure the travel time between 167 VA hospitals and the teaching hospital nearest to each of them. We determined that the mean travel time between VA hospitals and their nearest teaching hospital was approximately 18 minutes with a median of 10 minutes. All but nine VA facilities were within two hours' travel, and these nine within ten minutes' travel to a tertiary care, nonteaching hospital. These data do not definitively resolve the privatization debate, of course, but do refute the assertion that inpatient VA services cannot be privatized because replacement hospitals are too far away. As shown, that is simply not the case.
| S-EPMC6033449 | biostudies-other
Project description:BackgroundHypertension, hyperlipidemia, diabetes, and obesity in middle adulthood each elevate the long-term risk of cardiovascular disease (CVD). The prevalence of these conditions among women veterans is incompletely described.ObjectiveTo describe the prevalence of CVD risk factors among women veterans in middle adulthood.DesignSerial cross-sectional studies of data from the Diabetes Epidemiologic Cohorts (DEpiC), a national, longitudinal data set including information on all patients in the Veterans Health Administration (VA).ParticipantsWomen veterans (n = 255,891) and men veterans (n = 2,271,605) aged 35-64 receiving VA care in fiscal year (FY) 2010.Main measuresPrevalence of CVD risk factors in FY2010 by age and, for those aged 45-54 years, by race, region, period of military service, priority status, and mental illness or substance abuse; prevalence by year from 2000 to 2010 in women veterans receiving VA care in both 2000 and 2010 who were free of the factor in 2000.Key resultsHypertension, hyperlipidemia, and diabetes were common among women and men, although more so among men. Hypertension was present in 13 % of women aged 35-44 years, 28 % of women aged 45-54, and 42 % of women aged 55-64. Hyperlipidemia prevalence was similar. Diabetes affected 4 % of women aged 35-44, and increased more than four-fold in prevalence to 18 % by age 55-64. The prevalence of obesity increased from 14 % to 18 % with age among women and was similarly prevalent in men. The relative rate of having two or more CVD risk factors in women compared to men increased progressively with age, from 0.55 (35-44 years) to 0.71 (45-54) to 0.73 (55-64). Most of the women with a factor present in 2010 were first diagnosed with the condition in the 10 years between 2000 and 2010.ConclusionsCVD risk factors are common among women veterans aged 35-64. Future research should investigate which interventions would most effectively reduce risk in this population.
| S-EPMC3695262 | biostudies-literature