Déjà Vu? How Might Lessons Learned from Electronic Health Record Implementation Apply to Artificial Intelligence?

Background: The US healthcare system is currently facing significant challenges in quality, affordability, and labor shortages. Artificial intelligence (AI) promises to transform healthcare delivery by making it safer, more effective, less wasteful, and more patient-centered. With more than $30 billion invested in healthcare AI companies in the past three years, the proliferation of AI solutions is expected to bring much-needed relief to the strained healthcare industry.

Synthetic medical education in dermatology leveraging generative artificial intelligence.

The advent of large language models (LLMs) represents an enormous opportunity to revolutionize medical education. Via "synthetic education," LLMs can be harnessed to generate novel content for medical education purposes, offering potentially unlimited resources for physicians in training. Utilizing OpenAI's GPT-4, we generated clinical vignettes and accompanying explanations for 20 skin and soft tissue diseases tested on the United States Medical Licensing Examination.

A Future of Self-Directed Patient Internet Research: Large Language Model-Based Tools Versus Standard Search Engines.

Purpose: As generalist large language models (LLMs) become more commonplace, patients will inevitably increasingly turn to these tools instead of traditional search engines. Here, we evaluate publicly available LLM-based chatbots as tools for patient education through physician review of responses provided by Google, Bard, GPT-3.5 and GPT-4 to commonly searched queries about prevalent chronic health conditions in the United States.

Risk stratification of potential drug interactions involving common over-the-counter medications and herbal supplements by a large language model.

Background: As polypharmacy, the use of over-the-counter (OTC) drugs, and herbal supplements becomes increasingly prevalent, the potential for adverse drug-drug interactions (DDIs) poses significant challenges to patient safety and health care outcomes.

Objective: This study evaluates the capacity of Generative Pre-trained Transformer (GPT) models to accurately assess DDIs involving prescription drugs (Rx) with OTC medications and herbal supplements.

Methods: Leveraging a popular subscription-based tool (Lexicomp), we compared the risk ratings assigned by these models to 43 Rx-OTC and 30 Rx-herbal supplement pairs.

A Pilot of Digital Whiteboards for Improving Patient Satisfaction in the Emergency Department: Nonrandomized Controlled Trial.

Background: Electronic paper (E-paper) screens use electrophoretic ink to provide paper-like low-power displays with advanced networking capabilities that may potentially serve as an alternative to traditional whiteboards and television display screens in hospital settings. E-paper may be leveraged in the emergency department (ED) to facilitate communication. Providing ED patient status updates on E-paper screens could improve patient satisfaction and overall experience and provide more equitable access to their health information.

Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff.

Objectives: Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital.

Design: Single-site, observational cohort study.

Development and Testing of an Electronic Health Record-Integrated Patient-Reported Outcome Application and Intervention to Improve Efficiency of Rheumatoid Arthritis Care.

Objective: Many patients with rheumatoid arthritis (RA) have difficulty finding clinicians to treat them because of workforce shortages. We developed an app to address this problem by improving care efficiency. The app collects patient-reported outcomes (PROs) and can be used to inform visit timing, potentially reducing the volume of low-value visits.

Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation.

In recent years, the number of digital health tools with the potential to significantly improve delivery of healthcare services has grown tremendously. However, the use of these tools in large, complex health systems remains comparatively limited. The adoption and implementation of digital health tools at an enterprise level is a challenge; few strategies exist to help tools cross the chasm from clinical validation to integration within the workflows of a large health system.

Real-World Analysis of Off-Label Use of Molecularly Targeted Therapy in a Large Academic Medical Center Cohort.

Purpose: The primary objective of this study is to quantify the use of off-label molecularly targeted therapy and describe the clinical situations in which off-label targeted therapy are used. A key secondary objective is to report the outcomes of patients treated with off-label use of targeted therapy.

Patients And Methods: We searched the electronic health record between 2000 and 2020 at our center to characterize the volume, clinical settings, and outcomes associated with off-label use of targeted therapies in different types of solid tumors.

Assessing Patient Experience and Orientation in the Emergency Department with Virtual Windows.

Patients have benefitted from increasingly sophisticated diagnostic and therapeutic innovations over the years. However, the design of the physical hospital environment has garnered less attention. This may negatively impact a patient's experience and health.

Coronavirus disease 2019 (COVID-19) screening system utilizing daily symptom attestation helps identify hospital employees who should be tested to protect patients and coworkers.

Objective: To investigate the effectiveness of a daily attestation system used by employees of a multi-institutional academic medical center, which comprised of symptom-screening, self-referrals to the Occupational Health Services team, and/or a severe acute respiratory coronavirus virus 2 (SARS-CoV-2) test.

Design: We conducted a retrospective cohort study of all employee attestations and SARS-CoV-2 tests performed between March and June 2020.

Setting: A large multi-institutional academic medical center, including both inpatient and ambulatory settings.

Symptom monitoring after coronavirus disease 2019 (COVID-19) vaccination in a large integrated healthcare system: Separating symptoms from severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection.

Objective: To describe the incidence of systemic overlap and typical coronavirus disease 2019 (COVID-19) symptoms in healthcare personnel (HCP) following COVID-19 vaccination and association of reported symptoms with diagnosis of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in the context of public health recommendations regarding work exclusion.

Design: This prospective cohort study was conducted between December 16, 2020, and March 14, 2021, with HCP who had received at least 1 dose of either the Pfizer-BioNTech or Moderna COVID-19 vaccine.

Setting: Large healthcare system in New England.

Association of Self-reported High-Risk Allergy History With Allergy Symptoms After COVID-19 Vaccination.

Importance: Allergic history in individuals with confirmed anaphylaxis to a messenger RNA (mRNA) COVID-19 vaccine is common. However, the risk factors for allergy symptoms after receiving the vaccine are unknown.

Objective: To assess the association between self-reported history of high-risk allergy and self-reported allergic reactions after mRNA COVID-19 vaccination of health care employees.

Incidence of Cutaneous Reactions After Messenger RNA COVID-19 Vaccines.

This cohort study examines cutaneous reactions in hospital employees who received messenger RNA COVID-19 vaccination.

Virtual Team Rounding: A Cross-Specialty Inpatient Care Staffing Program to Manage COVID-19 Surges.

Problem: The SARS-CoV-2 (COVID-19) pandemic presented numerous challenges to inpatient care, including overtaxed inpatient medicine services, surges in patient censuses, disrupted patient care and educational activities for trainees, underused providers in certain specialties, and personal protective equipment shortages and new requirements for physical distancing. In March 2020, as the COVID-19 surge began, an interdisciplinary group of administrators, providers, and trainees at Brigham and Women's Hospital created an inpatient virtual staffing model called the Virtual Team Rounding Program (VTRP).

Approach: The conceptual framework guiding VTRP development was rapid-cycle innovation.

Landscape Analysis of Oncology Mobile Health Applications.

Purpose: More than 325,000 mobile health (mhealth) applications (apps) have been developed. We sought to describe the state of oncology-specific apps and to highlight areas of strength and opportunities for future development.

Methods: We searched for oncology apps in the Apple iOS and Google Play app stores in January 2020.

Transmitting Device Identifiers of Implants From the Point of Care to Insurers: A Demonstration Project.

Background: For implanted devices, an effective postmarket surveillance system does not exist. For medications, the Food and Drug Administration's Sentinel Initiative plays that role, relying mainly on drug codes in insurance claims. Unique device identifiers (UDIs) could play an analogous role for implants, but there is no mandate for providers to include UDIs in claims or for payers to record them.

Acute Allergic Reactions to mRNA COVID-19 Vaccines.

This study examines the incidence of acute allergic reactions to mRNA COVID-19 vaccine administrations in health care employees in Massachusetts.