Disparities in the use of telehealth at the onset of the COVID-19 public health emergency.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic resulted in an unprecedented expansion in telehealth, but little is known about differential use of telehealth according to demographics, rurality, or insurance status. METHODS: We performed a cross-sectional analysis of 7742 family medicine encounters at a single USA institution in the initial month of the COVID-19 public health emergency (PHE). We compared the demographics of those using telehealth during the PHE to those with face-to-face visits during the same time period; we also compared the demographics of those using full audio-video to those using audio-only. RESULTS: The likelihood of any telehealth visit in the first 30 days of telehealth expansion was higher for women, those age 65 years and older, self-pay patients, and those with Medicaid and Medicare as primary payers. The likelihood of a telehealth visit was reduced for rural residence and Black or other races. Among all telehealth visits, the likelihood of a full audio-video telehealth visit was reduced for patients who were older, Black, from urban areas, or who were self-pay, Medicaid, or Medicare payer status. DISCUSSION: Significant disparities exist in telehealth use during the COVID-19 PHE by age, race, residence and payer.

Improving the User Experience with Discount Site-Specific User Testing.

OBJECTIVES: Poor electronic health record (EHR) usability is associated with patient safety concerns, user dissatisfaction, and provider burnout. EHR certification requires vendors to perform user testing. However, there are no such requirements for site-specific implementations. Health care organizations customize EHR implementations, potentially introducing usability problems. Site-specific usability evaluations may help to identify these concerns, and "discount" usability methods afford health systems a means of doing so even without dedicated usability specialists. This report characterizes a site-specific discount user testing program launched at an academic medical center. We describe lessons learned and highlight three of the EHR features in detail to demonstrate the impact of testing on implementation decisions and on users. METHODS: Thirteen new EHR features which had already undergone heuristic evaluation and iterative design were evaluated over the course of three user test events. Each event included five to six users. Participants used think aloud technique. Measures of user efficiency, effectiveness, and satisfaction were collected. Usability concerns were characterized by the type of usability heuristic violated and by correctability. RESULTS: Usability concerns occurred at a rate of 2.5 per feature tested. Seventy percent of the usability concerns were deemed correctable prior to implementation. The first highlighted feature was moved to production despite low single ease question (SEQ) scores which may have predicted its subsequent withdrawal from production based on post implementation feedback. Another feature was rebuilt based on usability findings, and a new version was retested and moved to production. A third feature highlights an easily correctable usability concern identified in user testing. Quantitative usability metrics generally reinforced qualitative findings. CONCLUSION: Simplified user testing with a limited number of participants identifies correctable usability concerns, even after heuristic evaluation. Our discount usability approach to site-specific usability has a role in implementations and may improve the usability of the EHR for the end user.

Diabetes Control in a Student-Run Free Clinic During the COVID-19 Pandemic.

Student run free health clinics (SRFCs) provide medical care to vulnerable populations in communities throughout the United States. The COVID-19 pandemic had a significant impact on the delivery of healthcare services and demanded a rapid adjustment in care delivery methods in both resource-rich and resource-poor settings. The aim of this study is to evaluate the impact of the pandemic on the management of chronic disease, specifically diabetes. Patients with diabetes who received care continuously throughout the pre-pandemic (face-to-face) and pandemic (telehealth) study periods at MedZou Community Health Center, a SRFC located in central Missouri, were evaluated. This sample of patients (n = 29) was evaluated on six quality measures including annual eye exams, blood pressure, hemoglobin A1c, chronic kidney disease monitoring, flu vaccination, and statin therapy. Overall diabetes care, as measured by the number of quality measures met per patient, decreased by 0.37 after the onset of the pandemic. The median COVID-era ranks were not statistically significantly different than the pre-pandemic ranks (z = 1.65, P = 0.099). Fewer patients received an influenza vaccination the year following the onset of the pandemic (10.3%) compared to the year before the pandemic (37.9%; difference in proportions 0.276, 95% CI 0.079, 0.473; p = 0.005). No other individual measures of diabetes care statistically differed significantly in the year after the pandemic began. Twenty-six (90%) patients received diabetes care using telehealth after the onset of the pandemic. Diabetes care using telehealth in a SRFC may be an acceptable alternative model when face-to-face visits are not feasible. Observed decreases in diabetes-related clinical quality measure performance warrant further study.

Impact of a Vendor-Developed Opioid Clinical Decision Support Intervention on Adherence to Prescribing Guidelines, Opioid Prescribing, and Rates of Opioid-Related Encounters.

BACKGROUND: Provider prescribing practices contribute to an excess of opioid-related deaths in the United States. Clinical guidelines exist to assist providers with improving prescribing practices and promoting patient safety. Clinical decision support systems (CDSS) may promote adherence to these guidelines and improve prescribing practices. The aim of this project was to improve opioid guideline adherence, prescribing practices, and rates of opioid-related encounters through the implementation of an opioid CDSS. METHODS: A vendor-developed, provider-targeted CDSS package was implemented in a multi-location academic health center. An interrupted time-series analysis was performed, evaluating 30 weeks pre- and post-implementation time periods. Outcomes were derived from vendor-supplied key performance indicators and directly from the electronic health record (EHR) database. Opioid-prescribing outcomes included count of opioid prescriptions, morphine milligram equivalents per prescription, counts of opioids with concurrent benzodiazepines, and counts of short-acting opioids in opioid-naïve patients. Encounter outcomes included rates of encounters for opioid abuse and dependence and rates of encounters for opioid poisoning and overdose. Guideline adherence outcomes included rates of provision of naloxone and documentation of opioid treatment agreements. RESULTS: The opioid CDSS generated an average of 1,637 alerts per week. Rates of provision of naloxone and opioid treatment agreements improved after CDSS implementation. Vendor-supplied prescribing outcomes were consistent with prescribing outcomes derived directly from the EHR, but all prescribing and encounter outcomes were unchanged. CONCLUSION: A vendor-developed, provider-targeted opioid CDSS did not improve opioid-prescribing practices or rates of opioid-related encounters. The CDSS improved some measures of provider adherence to opioid-prescribing guidelines. Further work is needed to determine the optimal configuration of opioid CDSS so that opioid-prescribing patterns are appropriately modified and encounter outcomes are improved.

Impact of the COVID-19 Pandemic & Telehealth Implementation in a Student Run Free Clinic.

Student run free clinics (SRFCs) fill a void in healthcare access for many communities and have been subject to unprecedented shifts in care delivery brought about by the coronavirus disease 2019 (COVID-19) pandemic. Our single-center institution serving uninsured patients in central Missouri switched from in-person visits to strictly telehealth visits with the onset of the pandemic. This study investigated the impact of the pandemic and the switch to telehealth on the clinic return rates by ethnicity, race, gender, rurality, and age. The pandemic led to a 47.4% reduction in the number of monthly patient encounters. Of the established SRFC population (N = 309), only 87 patients (28.2%) returned for a telehealth visit during the COVID-19 pandemic. Older patients (≥ 45 years old) were more likely to return (OR 1.71, 95% CI 1.02-2.85) for care via telehealth after the onset of the pandemic than younger patients (< 45 years old). No differences in the likelihood of returning for a telehealth visit were identified by race, ethnicity, gender, or rurality. Telehealth offers an effective solution to the complex problems faced by SRFCs during the COVID-19 pandemic and has not added barriers to care with regards to race, ethnicity, gender, or rurality at our SRFC.

A Comparison of Models Predicting One-Year Mortality at Time of Admission.

CONTEXT: Hospitalization provides an opportunity to address end-of-life care (EoLC) preferences if patients at risk of death can be accurately identified while in the hospital. The modified Hospital One-Year Mortality Risk (mHOMR) uses demographic and admission data in a logistic regression algorithm to identify patients at risk of death one year from admission. OBJECTIVES: This project sought to validate mHOMR and identify superior models. METHODS: The mHOMR model was validated using historical data from an academic health system. Alternative logistic regression and random forest (RF) models were developed using the same variables. Receiver operating characteristic (ROC) and precision recall curves were developed, and sensitivity, specificity, and positive and negative predictive values were compared over a range of model thresholds. RESULTS: The RF model demonstrated higher area under the ROC curve (0.950, 95% CI 0.947 - 0.954) as compared to the logistic regression models (0.818 [95% CI 0.812 - 0.825] and 0.841 [95% CI 0.836 - 0.847]). Area under the precision recall curve was higher with the random forest model compared to the logistic regression models (0.863 vs. 0.458 and 0.494, respectively). Across a range of thresholds, the RF model demonstrated superior sensitivity, equivalent specificity, and higher positive and negative predictive values. CONCLUSION: A machine learning RF model, using common demographic and utilization data available on hospital admission, identified inpatients at risk of death more effectively than logistic regression models using the same variables. Machine learning models have promise for identifying admitted patients with elevated one-year mortality risk, increasing opportunities to prompt discussion of EoLC preferences.

The Effect of Electronic Health Record Usability Redesign on Annual Screening Rates in an Ambulatory Setting.

OBJECTIVES: Improving the usability of electronic health records (EHR) continues to be a focus of clinicians, vendors, researchers, and regulatory bodies. To understand the impact of usability redesign of an existing, site-configurable feature, we evaluated the user interface (UI) used to screen for depression, alcohol and drug misuse, fall risk, and the existence of advance directive information in ambulatory settings. METHODS: As part of a quality improvement project, based on heuristic analysis, the existing UI was redesigned. Using an iterative, user-centered design process, several usability defects were corrected. Summative usability testing was performed as part of the product development and implementation cycle. Clinical quality measures reflecting rolling 12-month rates of screening were examined over 8 months prior to the implementation of the redesigned UI and 9 months after implementation. RESULTS: Summative usability testing demonstrated improvements in task time, error rates, and System Usability Scale scores. Interrupted time series analysis demonstrated significant improvements in all screening rates after implementation of the redesigned UI compared with the original implementation. CONCLUSION: User-centered redesign of an existing site-specific UI may lead to significant improvements in measures of usability and quality of patient care.