Taxonomy-based prompt engineering to generate synthetic drug-related patient portal messages.

Objective: The objectives of this study were to: (1) create a corpus of synthetic drug-related patient portal messages to address the current lack of publicly available datasets for model development, (2) assess differences in language used and linguistics among the synthetic patient portal messages, and (3) assess the accuracy of patient-reported drug side effects for different racial groups.

Methods: We leveraged a taxonomy for patient- and clinician-generated content to guide prompt engineering for synthetic drug-related patient portal messages. We generated two groups of messages: the first group (200 messages) used a subset of the taxonomy relevant to a broad range of drug-related messages and the second group (250 messages) used a subset of the taxonomy relevant to a narrow range of messages focused on side effects.

Characterizing patient portal use of people with cognitive impairment and potentially inappropriate medications.

Background: People with cognitive impairment commonly use central nervous system-active potentially inappropriate medications (CNS-PIM), increasing risk of adverse outcomes. Patient portals may be a promising tool for facilitating medication-related conversations. Little is known about portal use by this population related to medications.

Usability, Engagement, and Report Usefulness of Chatbot-Based Family Health History Data Collection: Mixed Methods Analysis.

Background: Family health history (FHx) is an important predictor of a person's genetic risk but is not collected by many adults in the United States.

Objective: This study aims to test and compare the usability, engagement, and report usefulness of 2 web-based methods to collect FHx.

Methods: This mixed methods study compared FHx data collection using a flow-based chatbot (KIT; the curious interactive test) and a form-based method.

Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.

Background:  Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being.

Objectives:  This study explored using preoperative electronic health record (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use.

Methods:  The study cohort consisted of approximately 347 Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data.

Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation.

Objectives: Early discontinuation is common among breast cancer patients taking aromatase inhibitors (AIs). Although several predictors have been identified, it is unclear how to simultaneously consider multiple risk factors for an individual. We sought to develop a tool for prediction of AI discontinuation and to explore how predictive value of risk factors changes with time.

An Automated Strategy to Calculate Medication Regimen Complexity.

Understanding medication regimen complexity is important to understand what patients may benefit from pharmacist interventions. Medication Regimen Complexity Index (MRCI), a 65-item tool to quantify the complexity by incorporating the count, dosage form, frequency, and additional administration instructions of prescription medicines, provides a more nuanced way of assessing complexity. The goal of this study was to construct and validate a computational strategy to automate the calculation of MRCI.

The Effects of Single Accreditation and Pass/Fail Licensing Exams on Osteopathic Medical Students Applying to Surgical Residency Programs.

Objective: To examine the effects of single accreditation and pass/fail licensing exams on osteopathic (DO) medical students applying for surgical residency.

Design: Electronic surveys were distributed to 1509 program directors (PD) in 10 surgical specialties. PDs were separated into 2categories based on their program's accreditation status prior to single accreditation: formerly accredited by the American Osteopathic Association (AOA) or not accredited by the AOA.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.

Objective: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

Process: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

An assessment of surgery core rotation quality at osteopathic medical schools.

Context: Osteopathic medical schools have traditionally placed a heavy emphasis on the field of primary care. While graduating osteopathic students continue to pursue family medicine, internal medicine, and pediatrics at higher rates than their allopathic counterparts, it is unknown whether students feel that surgical rotations are held to similar standards.

Objectives: The purpose of this study was to assess osteopathic medical student opinions of the quality of their surgical clerkships and to determine if good or poor experiences influenced their decision to continue pursuing surgery.

Will the Doctor "See" You Now? The Development and Implementation of a Targeted Telemedicine System for Primary Care.

Objectives: The coronavirus disease 2019 (COVID-19) pandemic led to a rapid adoption of telehealth. For underserved populations lacking internet access, telemedicine was accomplished by phone rather than an audio-video connection. The latter is presumed a more effective form and better approximation of an in-person visit.

Individual- and Neighborhood-Level Disparities in Audio-Only Telemedicine Utilization Across a Large Academic Health System.

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Feasibility of the Genetic Information Assistant Chatbot to Provide Genetic Education and Study Genetic Test Adoption Among Pancreatic Cancer Patients at Johns Hopkins Hospital.

Genetic testing is a valuable tool to guide care of pancreatic cancer patients, yet personal and family uncertainty about the benefits of genetic testing (i.e., decisional conflict) may lead to low adoption.

Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning.

There have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus.

A pilot study to predict cardiac arrest in the pediatric intensive care unit.

Background: Cardiac arrest is a leading cause of mortality prior to discharge for children admitted to the pediatric intensive care unit. To address this problem, we used machine learning to predict cardiac arrest up to three hours in advance.

Methods: Our data consists of 240 Hz ECG waveform data, 0.

The Impact of Alcohol Level on Elderly Trauma Patients Presenting to the Emergency Department.

Alcohol is a chemical substance that alters cognitive ability and judgment. We looked at our elderly patients that arrived at the Emergency Department (ED) following trauma and evaluated the factors that may influence outcome. Retrospective analysis of patients seen in ED with positive alcohol was performed.

Deep Learning Model for Static Ocular Torsion Detection Using Synthetically Generated Fundus Images.

Purpose: The objective of the study is to develop deep learning models using synthetic fundus images to assess the direction (intorsion versus extorsion) and amount (physiologic versus pathologic) of static ocular torsion. Static ocular torsion assessment is an important clinical tool for classifying vertical ocular misalignment; however, current methods are time-intensive with steep learning curves for frontline providers.

Methods: We used a dataset (n = 276) of right eye fundus images.

Risk for Poor Post-Operative Quality of Life Among Wearable Use Subgroups in an All of Us Research Cohort.

The objective of this research was to build and assess the performance of a prediction model for post-operative recovery status measured by quality of life among individuals experiencing a variety of surgery types. In addition, we assessed the performance of the model for two subgroups (high and moderately consistent wearable device users). Study variables were derived from the electronic health records, questionnaires, and wearable devices of a cohort of individuals with one of 8 surgery types and that were part of the NIH All of Us research program.

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study.

Background: Crowdsourcing is a useful way to rapidly collect information on COVID-19 symptoms. However, there are potential biases and data quality issues given the population that chooses to participate in crowdsourcing activities and the common strategies used to screen participants based on their previous experience.

Objective: The study aimed to (1) build a pipeline to enable data quality and population representation checks in a pilot setting prior to deploying a final survey to a crowdsourcing platform, (2) assess COVID-19 symptomology among survey respondents who report a previous positive COVID-19 result, and (3) assess associations of symptomology groups and underlying chronic conditions with adverse outcomes due to COVID-19.

Partnerships, Lawsuits, and Competing Accountabilities in CCAA Agreements.

Private lands are often critical for successful species conservation, and the US Fish and Wildlife Service has increasingly utilized voluntary Candidate Conservation Agreements with Assurances (CCAAs) as a strategy for promoting private land conservation. CCAAs, however, present a challenge where the FWS, with its history as a regulatory entity, must now engage landowners as conservation partners. There is a deep culture of distrust among landowners, who are often suspicious of engaging with the agency, making it necessary for the FWS to build trusting relationships.

Model for Integration of Monogenic Diabetes Diagnosis Into Routine Care: The Personalized Diabetes Medicine Program.

Objective: To implement, disseminate, and evaluate a sustainable method for identifying, diagnosing, and promoting individualized therapy for monogenic diabetes.

Research Design And Methods: Patients were recruited into the implementation study through a screening questionnaire completed in the waiting room or through the patient portal, physician recognition, or self-referral. Patients suspected of having monogenic diabetes based on the processing of their questionnaire and other data through an algorithm underwent next-generation sequencing for 40 genes implicated in monogenic diabetes and related conditions.

Photoinduced C(sp)-H Chalcogenation of Amide Derivatives and Ethers via Ligand-to-Metal Charge-Transfer.

A photoinduced, iron(III) chloride-catalyzed C-H activation of -methyl amides and ethers leads to the formation of C-S and C-Se bonds via a ligand-to-metal charge transfer (LMCT) process. This methodology converts secondary and tertiary amides, sulfonamides, and carbamates into the corresponding amido--acetal derivatives in good yields. Mechanistic work revealed that this transformation proceeds through a hydrogen atom transfer (HAT) involving chlorine radical intermediates.