Veteran Experiences With an mHealth App to Support Measurement-Based Mental Health Care: Results From a Mixed Methods Evaluation.

BACKGROUND: Mental health conditions are highly prevalent among US veterans. The Veterans Health Administration (VHA) is committed to enhancing mental health care through the integration of measurement-based care (MBC) practices, guided by its Collect-Share-Act model. Incorporating the use of remote mobile apps may further support the implementation of MBC for mental health care. OBJECTIVE: This study aims to evaluate veteran experiences with Mental Health Checkup (MHC), a VHA mobile app to support remote MBC for mental health. METHODS: Our mixed methods sequential explanatory evaluation encompassed mailed surveys with veterans who used MHC and follow-up semistructured interviews with a subset of survey respondents. We analyzed survey data using descriptive statistics. We then compared responses between veterans who indicated having used MHC for ≥3 versus <3 months using χ2 tests. We analyzed interview data using thematic analysis. RESULTS: We received 533 surveys (533/2631, for a 20% response rate) and completed 20 interviews. Findings from these data supported one another and highlighted 4 key themes. (1) The MHC app had positive impacts on care processes for veterans: a majority of MHC users overall, and a greater proportion who had used MHC for ≥3 months (versus <3 months), agreed or strongly agreed that using MHC helped them be more engaged in their health and health care (169/262, 65%), make decisions about their treatment (157/262, 60%), and set goals related to their health and health care (156/262, 60%). Similarly, interviewees described that visualizing progress through graphs of their assessment data over time motivated them to continue therapy and increased self-awareness. (2) A majority of respondents overall, and a greater proportion who had used MHC for ≥3 months (versus <3 months), agreed/strongly agreed that using MHC enhanced their communication (112/164, 68% versus 51/98, 52%; P=.009) and rapport (95/164, 58% versus 42/98, 43%; P=.02) with their VHA providers. Likewise, interviewees described how MHC helped focus therapy time and facilitated trust. (3) However, veterans also endorsed some challenges using MHC. Among respondents overall, these included difficulty understanding graphs of their assessment data (102/245, 42%), not receiving enough training on the app (73/259, 28%), and not being able to change responses to assessment questions (72/256, 28%). (4) Interviewees offered suggestions for improving the app (eg, facilitating ease of log-in, offering additional reminder features) and for increasing adoption (eg, marketing the app and its potential advantages for veterans receiving mental health care). CONCLUSIONS: Although experiences with the MHC app varied, veterans were positive overall about its use. Veterans described associations between the use of MHC and engagement in their own care, self-management, and interactions with their VHA mental health providers. Findings support the potential of MHC as a technology capable of supporting the VHA's Collect-Share-Act model of MBC.

Defining Incidental Versus Non-incidental COVID-19 Hospitalizations.

Background Rates of COVID-19 hospitalization are an important measure of the health system burden of severe COVID-19 disease and have been closely followed throughout the pandemic. The highly transmittable, but often less severe, Omicron COVID-19 variant has led to an increase in hospitalizations with incidental COVID-19 diagnoses where COVID-19 is not the primary reason for admission. There is a strong public health need for a measure that is implementable at low cost with standard electronic health record (EHR) datasets that can separate these incidental hospitalizations from non-incidental hospitalizations where COVID-19 is the primary cause or an important contributor. Two crude metrics are in common use. The first uses in-hospital administration of dexamethasone as a marker of non-incidental COVID-19 hospitalizations. The second, used by the United States (US) CDC, relies on a limited set of COVID-19-related diagnoses (i.e., respiratory failure, pneumonia). Both measures likely undercount non-incidental COVID-19 hospitalizations. We therefore developed an improved EHR-based measure that is better able to capture the full range of COVID-19 hospitalizations. Methods We conducted a retrospective study of ED visit data from a national emergency medicine group from April 2020 to August 2023. We assessed the CDC approach, the dexamethasone-based measure, and alternative approaches that rely on co-diagnoses likely to be related to COVID-19, to determine the proportion of non-incidental COVID-19 hospitalizations. Results Of the 153,325 patients diagnosed with COVID-19 at 112 general EDs in 17 US states, and admitted or transferred, our preferred measure classified 108,243 (70.6%) as non-incidental, compared to 71,066 (46.3%) using the dexamethasone measure and 77,399 (50.5%) using the CDC measure. Conclusions Identifying non-incidental COVID-19 hospitalizations using ED administration of dexamethasone or the CDC measure provides substantially lower estimates than our preferred measure.

Reliability of a Measure of Admission Intensity for Emergency Physicians.

STUDY OBJECTIVE: We assess the stability of a measure of emergency department (ED) admission intensity for value-based care programs designed to reduce variation in ED admission rates. Measure stability is important to accurately assess admission rates across sites and among physicians. METHODS: We sampled data from 358 EDs in 41 states (January 2018 to December 2021), separate from sites where the measure was derived. The measure is the ED admission rate per 100 ED visits for 16 clinical conditions and 535 included International Classification of Disease 10 diagnosis codes. We used descriptive plots and multilevel linear probability models to assess stability over time across EDs and among physicians. RESULTS: Across included 3,571 ED-quarters, the average admission rate was 27.6% (95% confidence interval [CI] 26.0% to 28.2%). The between-facility standard deviation was 9.7% (95% CI 9.0% to 10.6%), and the within-facility standard deviation was 3.0% (95% CI 2.95% to 3.10%), with an intraclass correlation coefficient of 0.91. At the physician-quarter level, the average admission rate was 28.3% (95% CI 28.0% to 28.5%) among 7,002 physicians. Relative to their site's mean in each quarter, the between-physician standard deviation was 6.7% (95% CI 6.6% to 6.8%), and the within-physician standard deviation was 5.5% (95% CI 5.5% to 5.6%), with an intraclass correlation coefficient of 0.59. Moreover, 2.9% of physicians were high-admitting in 80%+ of their practice quarters relative to their peers in the same ED and in the same quarter, whereas 3.9% were low-admitting. CONCLUSION: The measure exhibits stability in characterizing ED-level admission rates and reliably identifies high- and low-admitting physicians.

Factors Associated with Veteran Self-Reported Use of Digital Health Devices.

BACKGROUND: Digital health devices (DHDs), technologies designed to gather, monitor, and sometimes share data about health-related behaviors or symptoms, can support the prevention or management of chronic conditions. DHDs range in complexity and utility, from tracking lifestyle behaviors (e.g., pedometer) to more sophisticated biometric data collection for disease self-management (e.g., glucometers). Despite these positive health benefits, supporting adoption and sustained use of DHDs remains a challenge. OBJECTIVE: This analysis examined the prevalence of, and factors associated with, DHD use within the Veterans Health Administration (VHA). DESIGN: National survey. PARTICIPANTS: Veterans who receive VHA care and are active secure messaging users. MAIN MEASURES: Demographics, access to technology, perceptions of using health technologies, and use of lifestyle monitoring and self-management DHDs. RESULTS: Among respondents, 87% were current or past users of at least one DHD, and 58% were provided a DHD by VHA. Respondents 65 + years were less likely to use a lifestyle monitoring device (AOR 0.57, 95% CI [0.39, 0.81], P = .002), but more likely to use a self-management device (AOR 1.69, 95% [1.10, 2.59], P = .016). Smartphone owners were more likely to use a lifestyle monitoring device (AOR 2.60, 95% CI [1.42, 4.75], P = .002) and a self-management device (AOR 1.83, 95% CI [1.04, 3.23], P = .037). CONCLUSIONS: The current analysis describes the types of DHDs that are being adopted by Veterans and factors associated with their adoption. Results suggest that various factors influence adoption, including age, access to technology, and health status, and that these relationships may differ based on the functionalities of the device. VHA provision of devices was frequent among device users. Providing Veterans with DHDs and the training needed to use them may be important factors in facilitating device adoption. Taken together, this knowledge can inform future implementation efforts, and next steps to support patient-team decision making about DHD use.

Variation in telehealth use for endocrine care: Patterns and predictors under the "new normal".

INTRODUCTION: Use of telehealth for outpatient endocrine care remains common since onset of the COVID-19 pandemic, though the context for its use has matured. We aimed to describe the variation in telehealth use for outpatient endocrine visits under these "new normal" conditions and examine the patient, clinician-, and organization-level factors predicting use. METHODS: Retrospective cross-sectional study using data from the U.S. Department of Veterans Affairs (VA) Corporate Data Warehouse on 167,017 endocrine visits conducted between 3/9/21 and 3/8/22. We used mixed effects logistic regression models to examine 1) use of telehealth vs. in-person care among all visits and 2) use of telephone vs. video among the subsample of telehealth visits. RESULTS: Visits were in person (58%), by telephone (29%), or by video (13%). Unique variability in telehealth use at each level of the analysis was 56% patient visit, 24% clinician, 18% facility. The strongest predictors were visit type (first vs. follow up) and clinician and facility characteristics. Among telehealth visits, unique variability in telephone (vs. video) use at each level was 44% patient visit, 24% clinician, 26% facility. The strongest predictors of telephone vs. video were visit type, patient age, and percent of the facility's population that was rural. CONCLUSIONS: We found wide variation in use of telehealth for endocrinology under the "new normal". Future research should examine clinician and facility factors driving variation, as many may be amenable to influence by clinical leaders and leveraged to enhance the availability of telehealth for all clinically appropriate patients.

Emergency Department Volume, Severity, and Crowding Since the Onset of the Coronavirus Disease 2019 Pandemic.

STUDY OBJECTIVE: We describe emergency department (ED) visit volume, illness severity, and crowding metrics from the onset of the coronavirus disease 2019 (COVID-19) pandemic through mid-2022. METHODS: We tabulated monthly data from 14 million ED visits on ED volumes and measures of illness severity and crowding from March 2020 through August 2022 compared with the same months in 2019 in 111 EDs staffed by a national ED practice group in 18 states. RESULTS: Average monthly ED volumes fell in the early pandemic, partially recovered in 2022, but remained below 2019 levels (915 per ED in 2019 to 826.6 in 2022 for admitted patients; 3,026.9 to 2,478.5 for discharged patients). The proportion of visits assessed as critical care increased from 7.9% in 2019 to 11.0% in 2022, whereas the number of visits decreased (318,802 to 264,350). Visits billed as 99285 (the highest-acuity Evaluation and Management code for noncritical care visits) increased from 35.4% of visits in 2019 to 40.0% in 2022, whereas the number of visits decreased (1,434,454 to 952,422). Median and median of 90th percentile length of stay for admitted patients rose 32% (5.2 to 6.9 hours) and 47% (11.7 to 17.4 hours) in 2022 versus 2019. Patients leaving without treatment rose 86% (2.9% to 5.4%). For admitted psychiatric patients, the 90th percentile length of stay increased from 20 hours to more than 1 day. CONCLUSION: ED visit volumes fell early in the pandemic and have only partly recovered. Despite lower volumes, ED crowding has increased. This issue is magnified in psychiatric patients.

Estimating the uncertain effect of the COVID pandemic on drug overdoses.

OBJECTIVE: U.S. drug-related overdose deaths and Emergency Department (ED) visits rose in 2020 and again in 2021. Many academic studies and the news media attributed this rise primarily to increased drug use resulting from the societal disruptions related to the coronavirus (COVID-19) pandemic. A competing explanation is that higher overdose deaths and ED visits may have reflected a continuation of pre-pandemic trends in synthetic-opioid deaths, which began to rise in mid-2019. We assess the evidence on whether increases in overdose deaths and ED visits are likely to be related primarily to the COVID-19 pandemic, increased synthetic-opioid use, or some of both. METHODS: We use national data from the Centers for Disease Control and Prevention (CDC) on rolling 12-month drug-related deaths (2015-2021); CDC data on monthly ED visits (2019-September 2020) for EDs in 42 states; and ED visit data for 181 EDs in 24 states staffed by a national ED physician staffing group (January 2016-June 2022). We study drug overdose deaths per 100,000 persons during the pandemic period, and ED visits for drug overdoses, in both cases compared to predicted levels based on pre-pandemic trends. RESULTS: Mortality. National overdose mortality increased from 21/100,000 in 2019 to 26/100,000 in 2020 and 30/100,000 in 2021. The rise in mortality began in mid-to-late half of 2019, and the 2020 increase is well-predicted by models that extrapolate pre-pandemic trends for rolling 12-month mortality to the pandemic period. Placebo analyses (which assume the pandemic started earlier or later than March 2020) do not provide evidence for a change in trend in or soon after March 2020. State-level analyses of actual mortality, relative to mortality predicted based on pre-pandemic trends, show no consistent pattern. The state-level results support state heterogeneity in overdose mortality trends, and do not support the pandemic being a major driver of overdose mortality. ED visits. ED overdose visits rose during our sample period, reflecting a worsening opioid epidemic, but rose at similar rates during the pre-pandemic and pandemic periods. CONCLUSION: The reasons for rising overdose mortality in 2020 and 2021 cannot be definitely determined. We lack a control group and thus cannot assess causation. However, the observed increases can be largely explained by a continuation of pre-pandemic trends toward rising synthetic-opioid deaths, principally fentanyl, that began in mid-to-late 2019. We do not find evidence supporting the pandemic as a major driver of rising mortality. Policymakers need to directly address the synthetic opioid epidemic, and not expect a respite as the pandemic recedes.

The Cost Shifting Economics of United States Emergency Department Professional Services (2016-2019).

STUDY OBJECTIVE: We estimate the economics of US emergency department (ED) professional services, which is increasingly under strain given the longstanding effect of unreimbursed care, and falling Medicare and commercial payments. METHODS: We used data from the Nationwide Emergency Department Sample (NEDS), Medicare, Medicaid, Health Care Cost Institute, and surveys to estimate national ED clinician revenue and costs from 2016 to 2019. We compare annual revenue and cost for each payor and calculate foregone revenue, the amount clinicians may have collected had uninsured patients had either Medicaid or commercial insurance. RESULTS: In 576.5 million ED visits (2016 to 2019), 12% were uninsured, 24% were Medicare-insured, 32% Medicaid-insured, 28% were commercially insured, and 4% had another insurance source. Annual ED clinician revenue averaged $23.5 billion versus costs of $22.5 billion. In 2019, ED visits covered by commercial insurance generated $14.3 billion in revenues and cost $6.5 billion. Medicare visits generated $5.3 billion and cost $5.7 billion; Medicaid visits generated $3.3 billion and cost $7 billion. Uninsured ED visits generated $0.5 billion and cost $2.9 billion. The average annual foregone revenue for ED clinicians to treat the uninsured was $2.7 billion. CONCLUSION: Large cost-shifting from commercial insurance cross-subsidizes ED professional services for other patients. This includes the Medicaid-insured, Medicare-insured, and uninsured, all of whom incur ED professional service costs that substantially exceed their revenue. Foregone revenue for treating the uninsured relative to what may have been collected if patients had health insurance is substantial.

Implementation of an emergency department back pain clinical management tool on the early diagnosis and testing of spinal epidural abscess.

BACKGROUND: Spinal epidural abscess (SEA) is a rare, catastrophic condition for which diagnostic delays are common. Our national group develops evidence-based guidelines, known as clinical management tools (CMT), to reduce high-risk misdiagnoses. We study whether implementation of our back pain CMT improved SEA diagnostic timeliness and testing rates in the emergency department (ED). METHODS: We conducted a retrospective observational study before and after implementation of a nontraumatic back pain CMT for SEA in a national group. Outcomes included diagnostic timeliness and test utilization. We used regression analysis to compare differences before (January 2016-June 2017) and after (January 2018-December 2019) with 95% confidence intervals (CIs) clustered by facility. We graphed monthly testing rates. RESULTS: In 59 EDs, pre versus post periods included 141,273 (4.8%) versus 192,244 (4.5%) back pain visits and 188 versus 369 SEA visits, respectively. After implementation, SEA visits with prior related visits were unchanged (12.2% vs. 13.3%, difference +1.0%, 95% CI -4.5% to 6.5%). Mean number of days to diagnosis decreased but not significantly (15.2 days vs. 11.9 days, difference -3.3 days, 95% CI -7.1 to 0.6 days). Back pain visits receiving CT (13.7% vs. 21.1%, difference +7.3%, 95% CI 6.1% to 8.6%) and MRI (2.9% vs. 4.4%, difference +1.4%, 95% CI 1.0% to 1.9%) increased. Spine X-rays decreased (22.6% vs. 20.5%, difference 2.1%, 95% CI -4.3% to 0.1%). Back pain visits receiving erythrocyte sedimentation rate or C-reactive protein increased (1.9% vs. 3.5%, difference +1.6%, 95% CI 1.3% to 1.9%). CONCLUSIONS: Back pain CMT implementation was associated with an increased rate of recommended imaging and laboratory testing in back pain. There was no associated reduction in the proportion of SEA cases with a related prior visit or time to SEA diagnosis.

Four- and three-year emergency medicine residency graduates perform similarly in their first year of practice compared to experienced physicians.

INTRODUCTION: United States emergency medicine (EM) post-graduate training programs vary in training length, either 4 or 3 years. However, it is unknown if clinical care by graduates from the two curricula differs in the early post-residency period. METHODS: We performed a retrospective observational study comparing measures of clinical care and practice patterns between new graduates from 4- and 3-year EM programs with experienced new physician hires as a reference group. We included emergency department (ED) encounters from a national EM group (2016-19) between newly hired physicians from 4- and 3- year programs and experienced new hires (>2 years' experience) during their first year of practice with the group. Primary outcomes were at the physician-shift level (patients per hour and relative value units [RVUs] per hour) and encounter-level (72-h return visits with admission/transfer and discharge length of stay [LOS]). Secondary outcomes included discharge opioid prescription rates, test ordering, computer tomography (CT) use, and admission/transfer rate. We compared outcomes using multivariable linear regression models that included patient, shift, and facility-day characteristics, and a facility fixed effect. We hypothesized that experienced new hires would be most efficient, followed by new 4-year graduates and then new 3-year graduates. RESULTS: We included 1,084,085 ED encounters by 4-year graduates (n = 39), 3-year graduates (n = 70), and experienced new hires (n = 476). There were no differences in physician-level and encounter-level primary outcomes except discharge LOS was 10.60 min (2.551, 18.554) longer for 4-year graduates compared to experienced new hires. Secondary outcomes were similar among the three groups except 4- and 3-year new graduates were less likely to prescribe opioids to discharged patients, -3.70% (-5.768, -1.624) and - 3.38% (-5.136, -1.617) compared to experienced new hires. CONCLUSIONS: In this sample, measures of clinical care and practice patterns related to efficiency, safety, and flow were largely similar between the physician groups; however, experienced new hires were more likely to prescribe opioids than new graduates. These results do not support recommending a specific length of residency training in EM.

Secure Messaging for Diabetes Management: Content Analysis.

BACKGROUND: Secure messaging use is associated with improved diabetes-related outcomes. However, it is less clear how secure messaging supports diabetes management. OBJECTIVE: We examined secure message topics between patients and clinical team members in a national sample of veterans with type 2 diabetes to understand use of secure messaging for diabetes management and potential associations with glycemic control. METHODS: We surveyed and analyzed the content of secure messages between 448 US Veterans Health Administration patients with type 2 diabetes and their clinical teams. We also explored the relationship between secure messaging content and glycemic control. RESULTS: Explicit diabetes-related content was the most frequent topic (72.1% of participants), followed by blood pressure (31.7% of participants). Among diabetes-related conversations, 90.7% of patients discussed medication renewals or refills. More patients with good glycemic control engaged in 1 or more threads about blood pressure compared to those with poor control (37.5% vs 27.2%, P=.02). More patients with good glycemic control engaged in 1 more threads intended to share information with their clinical team about an aspect of their diabetes management compared to those with poor control (23.7% vs 12.4%, P=.009). CONCLUSIONS: There were few differences in secure messaging topics between patients in good versus poor glycemic control. Those in good control were more likely to engage in informational messages to their team and send messages related to blood pressure. It may be that the specific topic content of the secure messages may not be that important for glycemic control. Simply making it easier for patients to communicate with their clinical teams may be the driving influence between associations previously reported in the literature between secure messaging and positive clinical outcomes in diabetes.

Development and Internal Validation of an Emergency Department Admission Intensity Measure Using Data From a National Group.

STUDY OBJECTIVE: We develop and assess variation in an emergency department (ED) admission intensity measure intended for value-based payment models. The measure includes ED diagnoses amenable to evidence-based protocols and where admission decisions vary based on physician discretion. METHODS: Measure International Classification of Diseases (ICD)-10 codes were selected by face validity by 3 emergency physicians using expertise and administrative data. Feedback was sought from a separate technical panel. Using data from a national group (2018 to 2019), we assessed measure stability at the physician and facility level by quarter using descriptive plots, multilevel linear probability models, and intraclass correlation coefficients (ICC). RESULTS: A total of 535 ICD-10 measure codes were selected from 23,590 codes. Across 127 EDs, facility-quarter admission rates averaged 26.1% (95% confidence interval [CI] 24.5 to 27.7). Between- and within-facility standard deviations were 9.2 (95% CI 8.2 to 10.5) and 2.9 (95% CI 2.7 to 3.0), respectively, with an ICC of 0.91. Most ED-quarters (749/961) fell within 2.5% of their facility's average. Among 2,398 physicians, quarterly rates averaged 29.1% (95% CI 28.6 to 29.6). The between- and within-physician standard deviation was 6.3 (95% CI 6.1 to 6.5) and 5.3 (95% CI 5.3 to 5.4), respectively, with an ICC of 0.58; 220 physicians (9.2%) had an admission rate consistently higher than average and 193 (8.0%) consistently lower. CONCLUSION: This set of ICD-10 diagnoses demonstrates face validity and stability for quarterly admission rates at the facility and physician levels. The measure may be useful to monitor facility admission rates in value-based models and reliably identify high and low admitters within facilities to manage admission variation.

Opioid Prescription Reduction After Implementation of a Feedback Program in a National Emergency Department Group.

STUDY OBJECTIVE: Reducing excessive opioid prescribing in emergency departments (ED) may prevent opioid addiction. We evaluated the largest personalized feedback and peer comparison intervention to date on emergency clinician opioid prescription rates in a national emergency clinician group. METHODS: This interrupted time series analysis of a quality improvement intervention included data from adults discharged from 102 EDs in 17 states from January 1, 2019, to July 31, 2021. From June 16, 2020, to November 30, 2020, site-level ED directors received emails on local opioid prescription rates. From December 1, 2020, to July 31, 2021, all clinicians were granted electronic dashboard access, which showed prescription rates compared with peers, and national ED leaders sent emails to high-prescribing clinicians and engaged in one-on-one conversations. The primary outcome was opioid prescriptions per 100 discharges. RESULTS: The study included 5,328,288 ED discharges from 924 physicians and 472 advanced practice providers. Opioid prescription rates did not change meaningfully in the site-level director feedback period (mean difference = -0.3, 95% confidence interval [CI] -0.6 to -0.1). During the direct clinician feedback period, opioid prescription rates declined from 10.4 per 100 discharges to 8.4 per 100 discharges (mean difference = -2.0, 95% CI -2.4 to -1.5), a 19% relative reduction. Among prescribers in the highest initial quintile, opioid prescribing reduced by 35% among physicians and 41% among advanced practice providers in the direct feedback period. CONCLUSION: We demonstrated a large, sustained reduction in opioid prescribing by emergency clinicians using direct, personalized feedback to clinicians and an electronic dashboard for peer comparison.

Differences in Secure Messaging, Self-management, and Glycemic Control Between Rural and Urban Patients: Secondary Data Analysis.

BACKGROUND: Rural patients with diabetes have difficulty accessing care and are at higher risk for poor diabetes management. Sustained use of patient portal features such as secure messaging (SM) can provide accessible support for diabetes self-management. OBJECTIVE: This study explored whether rural patients' self-management and glycemic control was associated with the use of SM. METHODS: This secondary, cross-sectional, mixed methods analysis of 448 veterans with diabetes used stratified random sampling to recruit a diverse sample from the United States (rural vs urban and good vs poor glycemic control). Administrative, clinical, survey, and interview data were used to determine patients' rurality, use of SM, diabetes self-management behaviors, and glycemic control. Moderated mediation analyses assessed these relationships. RESULTS: The sample was 51% (n=229) rural and 49% (n=219) urban. Mean participant age was 66.4 years (SD 7.7 years). More frequent SM use was associated with better diabetes self-management (P=.007), which was associated with better glycemic control (P<.001). Among rural patients, SM use was indirectly associated with better glycemic control through improved diabetes self-management (95% CI 0.004-0.927). These effects were not observed among urban veterans with diabetes (95% CI -1.039 to 0.056). Rural patients were significantly more likely than urban patients to have diabetes-related content in their secure messages (P=.01). CONCLUSIONS: More frequent SM use is associated with engaging in diabetes self-management, which, in turn, is associated with better diabetes control. Among rural patients with diabetes, SM use is indirectly associated with better diabetes control. Frequent patient-team communication through SM about diabetes-related content may help rural patients with diabetes self-management, resulting in better glycemic control.

Patient portal engagement and diabetes management among new portal users in the Veterans Health Administration.

OBJECTIVE: The study sought to investigate whether consistent use of the Veterans Health Administration's My HealtheVet (MHV) online patient portal is associated with improvement in diabetes-related physiological measures among new portal users. MATERIALS AND METHODS: We conducted a retrospective cohort study of new portal users with type 2 diabetes that registered for MHV between 2012 and 2016. We used random-effect linear regression models to examine associations between months of portal use in a year (consistency) and annual means of the physiological measures (hemoglobin A1c [HbA1c], low-density lipoproteins [LDLs], and blood pressure [BP]) in the first 3 years of portal use. RESULTS: For patients with uncontrolled HbA1c, LDL, or BP at baseline, more months of portal use in a year was associated with greater improvement. Compared with 1 month of use, using the portal 12 months in a year was associated with annual declines in HbA1c of -0.41% (95% confidence interval [CI], -0.46% to -0.36%) and in LDL of -6.25 (95% CI, -7.15 to -5.36) mg/dL. Twelve months of portal use was associated with minimal improvements in BP: systolic BP of -1.01 (95% CI, -1.33 to -0.68) mm Hg and diastolic BP of -0.67 (95% CI, -0.85 to -0.49) mm Hg. All associations were smaller or not present for patients in control of these measures at baseline. CONCLUSIONS: We found consistent use of the patient portal among new portal users to be associated with modest improvements in mean HbA1c and LDL for patients at increased risk at baseline. For patients with type 2 diabetes, self-management supported by online patient portals may help control HbA1c, LDL, and BP.

The Effect of the COVID-19 Pandemic on the Economics of United States Emergency Care.

STUDY OBJECTIVE: We describe how the coronavirus disease 2019 (COVID-19) pandemic affected the economics of emergency department care (ED). METHODS: We conducted an observational study of 136 EDs from January 2019 to September 2020, using 2020-to-2019 3-week moving ratios for ED visits, complexity, revenue, and staffing expenses. We tabulated 2020-to-2019 staffing ratios and calculated hour and full-time-equivalent changes. RESULTS: Following the COVID-19 pandemic's onset, geriatric (age ≥65), adult (age 18 to 64), and pediatric (age <18) ED visits declined by 43%, 40%, and 73%, respectively, compared to 2019 visits and rose thereafter but remained below 2019 levels through September. Relative value units per visit rose by 8%, 9%, and 18%, respectively, compared to 2019, while ED admission rates rose by 32%. Both fell subsequently but remained above 2019 levels through September. Revenues dropped sharply early in the pandemic and rose gradually but remained below 2019 levels. In medium and large EDs, staffing and expenses were lowered with a lag, largely compensating for lower revenue at these sites, and barely at freestanding EDs. Staffing and expense reductions could not match revenue losses in smaller EDs. During the pandemic, emergency physician and advanced practice provider clinical hours and compensation fell 15% and 27%, respectively, corresponding to 174 lost physician and 193 lost advanced practice provider full-time-equivalent positions. CONCLUSION: The COVID-19 pandemic adversely impacted the economics of ED care, with large drops in overall and, in particular, low-acuity ED visits, necessitating reductions in clinical hours. Staffing cutbacks could not match reduced revenue at small EDs with minimum emergency physician coverage requirements.

How emergency department visits for substance use disorders have evolved during the early COVID-19 pandemic.

OBJECTIVE: Higher opioid overdoses and drug use have reportedly occurred during the COVID-19 pandemic. We provide evidence on how emergency department (ED) visits for substance use disorders (SUD) changed in the early pandemic period. METHODS: Using retrospective data from January-July 2020 compared to January-July 2019, we calculated weekly 2020/2019 visit ratios for opioid-related, alcohol-related, other drug-related disorders, and all non-COVID-19 visits. We assess how this ratio as well as overall visit numbers changed after the mid-March 2020 onset of general pandemic restrictions. RESULTS: In 4.5 million ED visits in 2020 and 2019 to 108 EDs in 18 U.S. states, SUD visits were higher in early 2020 compared to 2019. During the peak-pandemic restriction period (March 13-July 31), non-COVID-19, non-SUD visits fell by approximately 45% early on, and then partly recovered with an average decline of 33% relative to 2019 levels. Visits for opioid-related, alcohol-related, and other drug-related disorders also declined, although less sharply, with an average drop of 17%, which was similar across SUD types. The visit ratios for 2020/2019 partially or fully recovered later in our sample period, depending on SUD type, but did not exceed early-2020 levels. However, substantial variation occurred across SUD types and across states. SUD visit declines were most prominent in the 65+ age group, except for alcohol-related visits where trends were similar across ages. SUD visits arriving by ambulance declined less or increased relative to self-transport visits, and ED deaths were rare. CONCLUSIONS: The 2020/2019 ratios of SUD ED visits fell substantially early in the COVID-19 pandemic, yet less than non-SUD, non-COVID ED visits. SUD ED visit ratios partly or fully recovered to 2019 levels by early June 2020, but did not exceed early 2020 ratios.

The effect of the COVID-19 pandemic on emergency department visits for serious cardiovascular conditions.

OBJECTIVE: We examine how emergency department (ED) visits for serious cardiovascular conditions evolved in the coronavirus (COVID-19) pandemic over January-October 2020, compared to 2019, in a large sample of U.S. EDs. METHODS: We compared 2020 ED visits before and during the COVID-19 pandemic, relative to 2019 visits in 108 EDs in 18 states in 115,716 adult ED visits with diagnoses for five serious cardiovascular conditions: ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), ischemic stroke (IS), hemorrhagic stroke (HS), and heart failure (HF). We calculated weekly ratios of ED visits in 2020 to visits in 2019 in the pre-pandemic (Jan 1-March 10), early-pandemic (March 11-April 21), and later-pandemic (April 22-October 31) periods. RESULTS: ED visit ratios show that NSTEMI, IS, and HF visits dropped to lows of 56%, 64%, and 61% of 2019 levels, respectively, in the early-pandemic and gradually returned to 2019 levels over the next several months. HS visits also dropped early pandemic period to 60% of 2019 levels, but quickly rebounded. We find mixed evidence on whether STEMI visits fell, relative to pre-pandemic rates. Total adult ED visits nadired at 57% of 2019 volume during the early-pandemic period and have only party recovered since, to approximately 84% of 2019 by the end of October 2020. CONCLUSION: We confirm prior studies that ED visits for serious cardiovascular conditions declined early in the COVID-19 pandemic for NSTEMI, IS, HS, and HF, but not for STEMI. Delays or non-receipt in ED care may have led to worse outcomes.

Prescription Drug Shortages: Pediatric Emergency and Critical Care Medications.

OBJECTIVES: Drug shortages have been increasing over the past 2 decades. There are limited data on drug shortages and their effect on pediatric emergency and critical care. Our objective was to describe pediatric emergency and critical care drug shortages. METHODS: Drug shortage data from January 2001 to December 2015 were obtained from the University of Utah Drug Information Services. Shortages were reviewed, identifying agents used in pediatric emergency and critical care. Shortage data were analyzed for the type of drug, formulation, shortage reason, duration, marketing status (generic vs brand name), or if it was a pediatric-friendly formulation, used for a high-acuity condition, or a single-source product. The availability of a substitute was also described. RESULTS: Of 1883 products on shortage, 779 were used in pediatric emergency or critical care. The annual number of shortages decreased from 2001 to 2004, but then increased, reaching a high in 2011. The median duration for resolved shortages was 7.6 months (interquartile range, 3.0-17.6 months). The most common category affected was infectious disease drugs. High-acuity agents were involved in 27% of shortages and in 11% of pediatric-friendly formulations. An alternative agent was available for 95% of drugs, yet 43% of alternatives were also affected at some time during the study period. The most common reported reason for a shortage was manufacturing problems. CONCLUSIONS: From 2001 to 2015, drug shortages affected a substantial number of agents used in pediatric emergency and critical care. This has had implications to the medications available for use and may impact patient outcomes. Providers must be aware of current shortages and implement mitigation strategies to optimize patient care.