Background The 21st Century Cures Act has accelerated adoption of OpenNotes, providing new opportunities for patient and family engagement in their care. However, these regulations present new challenges, particularly for pediatric health systems aiming to improve information sharing while minimizing risks associated with adolescent confidentiality and safety. Objective Describe lessons learned preparing for OpenNotes across a pediatric health system during a 4-month trial period (referred to as “Learning Mode”) in which clinical notes were not shared by default but decision support was present describing the upcoming change and physicians could request feedback on complex cases from a multidisciplinary team. Methods During Learning Mode (December 3, 2020–March 9, 2021), implementation included (1) educational text at the top of commonly used note types indicating that notes would soon be shared and providing guidance, (2) a new confidential note type, and (3) a mechanism for physicians to elicit feedback from a multidisciplinary OpenNotes working group for complex cases with questions related to OpenNotes. The working group reviewed lessons learned from this period, as well as implementation of OpenNotes from March 10, 2021 to June 30, 2021. Results During Learning Mode, 779 confidential notes were written across the system. The working group provided feedback on 14 complex cases and also reviewed 7 randomly selected confidential notes. The proportion of physician notes shared with patients increased from 1.3% to 88.4% after default sharing of notes to the patient portal. Key lessons learned included (1) sensitive information was often present in autopopulated elements, differential diagnoses, and supervising physician note attestations; and (2) incorrect reasons were often selected by clinicians for withholding notes but this accuracy improved with new designs. Conclusion While OpenNotes provides an unprecedented opportunity to engage pediatric patients and their families, targeted education and electronic health record designs are needed to mitigate potential harms of inappropriate disclosures.
BACKGROUND Predictive models may help providers tailor asthma therapies to an individual’s risk of exacerbation. The effectiveness of asthma risk scores on provider behavior and pediatric asthma outcomes remains unknown. OBJECTIVE Determine the impact of an electronic health record (EHR) vendor-released model on outcomes for children with asthma. METHODS We implemented a vendor Risk of Pediatric Asthma Exacerbation model as a non-interruptive risk score visible in the patient schedule view beginning 2/24/2021 in allergy and pulmonology clinics with 6 volunteer providers. We conducted a difference-in-differences analysis from 2/24/2019 – 2/23/2022 with a control group of other providers in the same departments. Primary outcomes included asthma hospitalization, ED visit, or oral steroid course within 90 days of an outpatient encounter. Volunteer providers were also interviewed to identify barriers and facilitators to model use. RESULTS The adjusted difference-in-differences estimators for the hospitalization, ED visit, oral steroid, and composite outcomes were -0.9% (95% CI: -1.6 to -0.3), –2.4% (-3.9 to -0.8), –1.9% (-4.3 to 0.5), and –2.3% (-4.7 to 0.2). In qualitative analysis, providers generally understood the purpose of the model and felt that it was useful to flag high exacerbation risk. Trust in the model was generally calibrated against providers’ own clinical judgement. CONCLUSIONS This EHR vendor model implementation was associated with a significant decrease in asthma hospitalization and ED visits within 90 days of pediatric allergy and pulmonology clinic visits, but not oral steroid courses.
BACKGROUND Artificial intelligence (AI) and machine learning (ML) are poised to have a significant impact in the healthcare space. While a plethora of online resources exist to teach programming skills and machine learning model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty. OBJECTIVE The authors theorized that a 1-month elective for fourth year medical students, composed of high-quality existing online resources and a project-based structure, would empower students to learn about the impact of AI/ML in their chosen specialty and begin contributing to innovation in their field of interest. In this paper, we share our two year experience and publish our curriculum for other educators who may be interested in its adoption. METHODS This elective was offered in two tracks: Technical (for students who were already competent programmers) and Non-Technical (with no technical prerequisites, focusing on building a conceptual understanding of AI/ML). Students established a conceptual foundation of knowledge using curated online resources and relevant research papers, and were then tasked with completing three projects in their chosen specialty: a dataset analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student’s interest area and career goals. Students’ success was measured by self-reported confidence in AI/ML skills in pre- and post-surveys. Qualitative feedback on students’ experiences were also collected. RESULTS This virtual, self-directed elective was offered on a pass/fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, nineteen students had successfully completed the elective, representing a wide range of chosen specialties: Diagnostic Radiology (n=3), General Surgery (1), Internal Medicine (5), Neurology (2), Obstetrics/Gynecology (1), Ophthalmology (1), Orthopedic Surgery (1), Otolaryngology (2), Pathology (2), and Pediatrics (1). Students’ self-reported confidence scores for AI/ML rose by 66% after this one-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course, and commented that the self-direction and flexibility and the project-based design of the course were essential. CONCLUSIONS Course participants were successful in diving deep into applications of AI/ML in their widely ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, one-month investment in AI/ML education during medical school will empower this next generation of physicians to pave the way for AI/ML innovation in healthcare.
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