A patient education intervention improved rates of successful video visits during rapid implementation of telehealth.

INTRODUCTION: The need to rapidly implement telehealth at large scale during the COVID-19 pandemic led to many patients using telehealth for the first time. We assessed the effect of structured pre-visit preparatory telephone calls on success of telehealth visits and examined risk factors for unsuccessful visits. METHODS: A retrospective cohort study was carried out of 45,803 adult patients scheduled for a total of 64,447 telehealth appointments between March and July 2020 at an academic medical center. A subset of patients received a structured pre-visit phone call. Demographic factors and inclusion of a pre-visit call were analysed by logistic regression. Primary outcomes were non-completion of any visit and completion of phone-only versus audio-visual telehealth visits. RESULTS: A pre-visit telephone call to a subset of patients significantly increased the likelihood of a successful telehealth visit (OR 0.54; 95% CI: 0.48-0.60). Patients aged 18-30 years, those with non-commercial insurance or those of Black race were more likely to have incomplete visits. Compared to age 18-30, increasing age increased likelihood of a failed video visit: 31-50 years (OR 1.31; 95% CI: 1.13-1.51), 51-70 years (OR 2.98; 2.60-3.42) and >70 years (OR 4.16; 3.58-4.82). Those with non-commercial insurance and those of Black race (OR 1.8; 95% CI 1.67-1.92) were more likely to have a failed video visit. DISCUSSION: A structured pre-call to patients improved the likelihood of a successful video visit during widespread adoption of telehealth. Structured pre-calls to patients may be an important tool to help reduce gaps in utilization among groups.

Demographic Factors Associated With Successful Telehealth Visits in Inflammatory Bowel Disease Patients.

BACKGROUND: This study evaluated synchronous audiovisual telehealth and audio-only visits for patients with inflammatory bowel disease (IBD) to determine frequency of successful telehealth visits and determine what factors increase the likelihood of completion. METHODS: Data were collected from March to July 2020 in a tertiary care adult IBD clinic that was transitioned to a fully telehealth model. A protocol for telehealth was implemented. A retrospective analysis was performed using electronic medical record (EMR) data. All patients were scheduled for video telehealth. If this failed, providers attempted to conduct the visit as audio only. RESULTS: Between March and July 2020, 2571 telehealth visits were scheduled for adult patients with IBD. Of these, 2498 (99%) were successfully completed by video or phone. Sixty percent were female, and the median age was 41 years. Eighty six percent of the population was white, 8% black, 2% other, and 4% were missing. Seventy-five percent had commercial insurance, 15% had Medicare, 5% had Medicaid, and 5% had other insurance. No significant factors were found for an attempted but completely failed visit. Using a multivariate logistic regression model, increasing age (odds ratio, 1.80; 95% CI, 1.55-2.08; P < 0.05), noncommercial insurance status (odds ratio, 1.89; 95% CI, 1.61-2.21; P < 0.05), and black race (odds ratio, 2.07; 95% CI, 1.38-3.08; P < 0.05) increased the likelihood of a video encounter failure. CONCLUSIONS: There is a high success rate for telehealth within an IBD population with defined clinic protocols. Certain patient characteristics such as age, race, and health insurance type increase the risk of failure of a video visit.

Health Care Price Transparency in Ophthalmology.

IMPORTANCE: Health care price transparency legislation is intended to reduce the ambiguity of hospital charges and the resultant financial stress faced by patients. OBJECTIVE: To evaluate the availability, usability, and variability of standard reported prices for ophthalmologic procedures at academic hospitals. DESIGN, SETTING, AND PARTICIPANTS: In this multicenter economic evaluation study, publicly available price transparency web pages from Association of American Medical Colleges affiliate hospitals were parsed for standard charges and usability metrics. Price transparency data were collected from hospital web pages that met the inclusion criteria. Geographic practice cost indices for work, practice expense, and malpractice were sourced from the Centers for Medicare & Medicaid Services. Data were sourced from February 1 to April 30, 2021. Multiple regression was used to study the geographic influence on standard charges and assess the correlation between standard charges. MAIN OUTCOMES AND MEASURES: Availability and variability of standard prices for Current Procedural Terminology (CPT) codes 66984 (removal of cataract with insertion of lens) and 66821 (removal of recurring cataract in lens capsule using laser). RESULTS: Of 247 hospitals included, 191 (77.3%) provided consumer-friendly shoppable services, most commonly in the form of a price estimator or online tool. For CPT code 66984, 102 hospital (53.4%) provided discount cash pay estimates with a mean (SD) price of $7818.86 ($5407.91). For CPT code 66821, 71 hospital (37.2%) provided discount cash pay estimates with a mean (SD) price of $2041.72 ($2106.44). The top quartile of hospitals, prices wise, listed included prices higher than $10 400 for CPT code 66984 and $2324 for CPT code 66821. Usability issues were noted for 36 hospitals (18.8%), including requirements for personal information or web page navigability barriers. Multiple regression analysis found minimal explanatory value for geographic practice cost indices for cash discount prices for CPT codes 66984 (adjusted R2 = 0.54; 95% CI, 0.41-0.67; P < .001) and 66821 (adjusted R2 = 0.64; 95% CI, 0.51-0.77; P < .001). CONCLUSIONS AND RELEVANCE: Despite recent legislature that codified price transparency requirements, some current standard charges remain ambiguous, with substantial interhospital variability not explained by geographic variability in costs. Given the potential for ambiguous pricing to burden vulnerable, uninsured patients, additional legislation might consider allowing hospitals to defer price estimates or rigorously define standards for actionable cash discount percentages with provisions for displaying relevant benchmark prices.

Quiz-Based Microlearning at Scale: a Rapid Educational Response to COVID-19.

Virtual quizzing is a viable model for continuing education at a large scale, particularly during the COVID-19 pandemic. By leveraging technology, microlearning encourages mobile education that is engaging, flexible, and accessible. Learners reported that this format was effective and preferable to traditional methods of education, suggesting further opportunity for innovation.

Predicting Length of Stay of Coronary Artery Bypass Grafting Patients Using Machine Learning.

BACKGROUND: There is a growing need to identify which bits of information are most valuable for healthcare providers. The aim of this study was to search for the highest impact variables in predicting postsurgery length of stay (LOS) for patients who undergo coronary artery bypass grafting (CABG). MATERIALS AND METHODS: Using a single institution's Society of Thoracic Surgeons (STS) Registry data, 2121 patients with elective or urgent, isolated CABG were analyzed across 116 variables. Two machine learning techniques of random forest and artificial neural networks (ANNs) were used to search for the highest impact variables in predicting LOS, and results were compared against multiple linear regression. Out-of-sample validation of the models was performed on 105 patients. RESULTS: Of the 10 highest impact variables identified in predicting LOS, four of the most impactful variables were duration intubated, last preoperative creatinine, age, and number of intraoperative packed red blood cell transfusions. The best performing model was an ANN using the ten highest impact variables (testing sample mean absolute error (MAE) = 1.685 d, R2 = 0.232), which performed consistently in the out-of-sample validation (MAE = 1.612 d, R2 = 0.150). CONCLUSION: Using machine learning, this study identified several novel predictors of postsurgery LOS and reinforced certain known risk factors. Out of the entire STS database, only a few variables carry most of the predictive value for LOS in this population. With this knowledge, a simpler linear regression model has been shared and could be used elsewhere after further validation.