The COVID-19 national emergency has led to surging care demand and the need for unprecedented telehealth expansion. Rapid telehealth expansion can be especially complex for pediatric patients. From the experience of a large academic medical center, this report describes a pathway for efficiently increasing capacity of remote pediatric enrollment for telehealth while fulfilling privacy, security, and convenience concerns. The design and implementation of the process took 2 days. Five process requirements were identified: efficient enrollment, remote ability to establish parentage, minimal additional work for application processing, compliance with guidelines for adolescent autonomy, and compliance with institutional privacy and security policies. Weekly enrollment subsequently increased 10-fold for children (age 0–12 years) and 1.2-fold for adolescents (age 13–17 years). Weekly telehealth visits increased 200-fold for children and 90-fold for adolescents. The obstacles and solutions presented in this report can provide guidance to health systems for similar challenges during the COVID-19 response and future disasters.

Emergent policy changes related to telemedicine and the Emergency Medical Treatment and Labor Act during the novel coronavirus disease 2019 (COVID-19) pandemic have created opportunities for technology-based clinical evaluation, which serves to conserve personal protective equipment (PPE) and protect emergency providers. We define electronic PPE as an approach using telemedicine tools to perform electronic medical screening exams while satisfying the Emergency Medical Treatment and Labor Act. We discuss the safety, legal, and technical factors necessary for implementing such a pathway. This approach has the potential to conserve PPE and protect providers while maintaining safe standards for medical screening exams in the emergency department for low-risk patients in whom COVID-19 is suspected.

Objective To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions. Methods We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy. Results Five clinicians analyzed 36 AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. The suggestions generated by AI were found to offer unique perspectives and were evaluated as highly understandable and relevant, with moderate usefulness, low acceptance, bias, inversion, redundancy. Conclusion AI-generated suggestions could be an important complementary part of optimizing CDS alerts, can identify potential improvements to alert logic and support their implementation, and may even be able to assist experts in formulating their own suggestions for CDS improvement. ChatGPT shows great potential for using large language models and reinforcement learning from human feedback to improve CDS alert logic and potentially other medical areas involving complex, clinical logic, a key step in the development of an advanced learning health system.

Significant reductions occurred in neurosurgical operations, clinic visits, and inpatient consultations during COVID-19. Telehealth was increasingly used for assessments. The long-term effects of the reduced neurosurgical volume and increased telehealth usage on patient outcomes should be explored.

Background In the United States, attention has been focused on “open notes” and “open results” since the Office of the National Coordinator for Health Information Technology implemented the 21st Century Cures Act Final Rule on information blocking. Open notes is an established best practice, but open results remains controversial, especially for diseases associated with stigma, morbidity, and mortality. Coronavirus disease 2019 (COVID-19) is associated with all three of these effects and represents an ideal disease for the study of open results for sensitive test results. Objectives This study evaluates patient perspectives related to receiving COVID-19 test results via an online patient portal prior to discussion with a clinician. Methods We surveyed adults who underwent COVID-19 testing between March 1, 2020 and October 21, 2020 who agreed to be directly contacted about COVID-19-related research about their perspectives on receiving test results via a patient portal. We evaluated user roles (i.e., patient vs. care partner), demographic information, ease of use, impact of immediate release, notification of results, impact of viewing results on health management, and importance of sharing results with others. Results Users were mostly patients themselves. Users found the portal easy to use but expressed mixed preferences about the means of notification of result availability (e.g., email, text, or phone call). Users found immediate access to results useful for managing their health, employment, and family/childcare. Many users shared their results and encouraged others to get tested. Our cohort consisted mostly of non-Hispanic white, highly educated, English-speaking patients. Conclusion Overall, patients found open results useful for COVID-19 testing and few expressed increased worries from receiving their results via the patient portal. The demographics of our cohort highlight the need for further research in patient portal equity in the age of open results.

Objective: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions. Methods: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy. Results: Five clinicians analyzed 36 AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. The suggestions generated by AI were found to offer unique perspectives and were evaluated as highly understandable and relevant, with moderate usefulness, low acceptance, bias, inversion, redundancy. Conclusion: AI-generated suggestions could be an important complementary part of optimizing CDS alerts, can identify potential improvements to alert logic and support their implementation, and may even be able to assist experts in formulating their own suggestions for CDS improvement. ChatGPT shows great potential for using large language models and reinforcement learning from human feedback to improve CDS alert logic and potentially other medical areas involving complex, clinical logic, a key step in the development of an advanced learning health system.

Objective We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts. Materials and Methods We developed a 10-step Clickbusting process and implemented a program that included a curriculum, CDS alert inventory, oversight process, and gamification. We carried out two 3-month rounds of the Clickbusters program at VUMC. We completed descriptive analyses of the changes made to alerts during the process, and of alert firing rates before and after the program. Results Prior to Clickbusters, VUMC had 419 CDS alerts in production, with 488 425 firings (42 982 interruptive) each week. After 2 rounds, the Clickbusters program resulted in detailed, comprehensive reviews of 84 CDS alerts and reduced the number of weekly alert firings by more than 70 000 (15.43%). In addition to the direct improvements in CDS, the initiative also increased user engagement and involvement in CDS. Conclusions At VUMC, the Clickbusters program was successful in optimizing CDS alerts by reducing alert firings and resulting clicks. The program also involved more users in the process of evaluating and improving CDS and helped build a culture of continuous evaluation and improvement of clinical content in the electronic health record.

ImportanceThe 21st Century Cures Act Final Rule mandates the immediate electronic availability of test results to patients, likely empowering them to better manage their health. Concerns remain about unintended effects of releasing abnormal test results to patients.ObjectiveTo assess patient and caregiver attitudes and preferences related to receiving immediately released test results through an online patient portal.Design, Setting, and ParticipantsThis large, multisite survey study was conducted at 4 geographically distributed academic medical centers in the US using an instrument adapted from validated surveys. The survey was delivered in May 2022 to adult patients and care partners who had accessed test results via an online patient portal account between April 5, 2021, and April 4, 2022.ExposuresAccess to test results via a patient portal between April 5, 2021, and April 4, 2022.Main Outcomes and MeasuresResponses to questions related to demographics, test type and result, reaction to result, notification experience and future preferences, and effect on health and well-being were aggregated. To evaluate characteristics associated with patient worry, logistic regression and pooled random-effects models were used to assess level of worry as a function of whether test results were perceived by patients as normal or not normal and whether patients were precounseled.ResultsOf 43 380 surveys delivered, there were 8139 respondents (18.8%). Most respondents were female (5129 [63.0%]) and spoke English as their primary language (7690 [94.5%]). The median age was 64 years (IQR, 50-72 years). Most respondents (7520 of 7859 [95.7%]), including 2337 of 2453 individuals (95.3%) who received nonnormal results, preferred to immediately receive test results through the portal. Few respondents (411 of 5473 [7.5%]) reported that reviewing results before they were contacted by a health care practitioner increased worry, though increased worry was more common among respondents who received abnormal results (403 of 2442 [16.5%]) than those whose results were normal (294 of 5918 [5.0%]). The result of the pooled model for worry as a function of test result normality was statistically significant (odds ratio [OR], 2.71; 99% CI, 1.96-3.74), suggesting an association between worry and nonnormal results. The result of the pooled model evaluating the association between worry and precounseling was not significant (OR, 0.70; 99% CI, 0.31-1.59).Conclusions and RelevanceIn this multisite survey study of patient attitudes and preferences toward receiving immediately released test results via a patient portal, most respondents preferred to receive test results via the patient portal despite viewing results prior to discussion with a health care professional. This preference persisted among patients with nonnormal results.