Clinician Note Documentation of Parental Justice Involvement: Preliminary Evidence from Pediatric Electronic Health Records.

Little is known about clinical documentation for youth exposed to parental justiceinvolvement (e.g., parole, probation, jail, prison).

A large collection of real-world pediatric sleep studies.

Despite being crucial to health and quality of life, sleep-especially pediatric sleep-is not yet well understood. This is exacerbated by lack of access to sufficient pediatric sleep data with clinical annotation. In order to accelerate research on pediatric sleep and its connection to health, we create the Nationwide Children's Hospital (NCH) Sleep DataBank and publish it at Physionet and the National Sleep Research Resource (NSRR), which is a large sleep data common with physiological data, clinical data, and tools for analyses.

Medical Text Prediction and Suggestion Using Generative Pretrained Transformer Models with Dental Medical Notes.

Background: Generative pretrained transformer (GPT) models are one of the latest large pretrained natural language processing models that enables model training with limited datasets and reduces dependency on large datasets, which are scarce and costly to establish and maintain. There is a rising interest to explore the use of GPT models in health care.

Objective: We investigate the performance of GPT-2 and GPT-Neo models for medical text prediction using 374,787 free-text dental notes.

Prevalence of Sensitive Terms in Clinical Notes Using Natural Language Processing Techniques: Observational Study.

Background: With the increased sharing of electronic health information as required by the US 21st Century Cures Act, there is an increased risk of breaching patient, parent, or guardian confidentiality. The prevalence of sensitive terms in clinical notes is not known.

Objective: The aim of this study is to define sensitive terms that represent the documentation of content that may be private and determine the prevalence and characteristics of provider notes that contain sensitive terms.

Characterization of Electronic Health Record Use Outside Scheduled Clinic Hours Among Primary Care Pediatricians: Retrospective Descriptive Task Analysis of Electronic Health Record Access Log Data.

Background: Many of the benefits of electronic health records (EHRs) have not been achieved at expected levels because of a variety of unintended negative consequences such as documentation burden. Previous studies have characterized EHR use during and outside work hours, with many reporting that physicians spend considerable time on documentation-related tasks. These studies characterized EHR use during and outside work hours using clock time versus actual physician clinic schedules to define the outside work time.

Locating Youth Exposed to Parental Justice Involvement in the Electronic Health Record: Development of a Natural Language Processing Model.

Background: Parental justice involvement (eg, prison, jail, parole, or probation) is an unfortunately common and disruptive household adversity for many US youths, disproportionately affecting families of color and rural families. Data on this adversity has not been captured routinely in pediatric health care settings, and if it is, it is not discrete nor able to be readily analyzed for purposes of research.

Objective: In this study, we outline our process training a state-of-the-art natural language processing model using unstructured clinician notes of one large pediatric health system to identify patients who have experienced a justice-involved parent.

Pretrained transformer framework on pediatric claims data for population specific tasks.

The adoption of electronic health records (EHR) has become universal during the past decade, which has afforded in-depth data-based research. By learning from the large amount of healthcare data, various data-driven models have been built to predict future events for different medical tasks, such as auto diagnosis and heart-attack prediction. Although EHR is abundant, the population that satisfies specific criteria for learning population-specific tasks is scarce, making it challenging to train data-hungry deep learning models.

Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model.

Generative pretrained transformer models have been popular recently due to their enhanced capabilities and performance. In contrast to many existing artificial intelligence models, generative pretrained transformer models can perform with very limited training data. Generative pretrained transformer 3 (GPT-3) is one of the latest releases in this pipeline, demonstrating human-like logical and intellectual responses to prompts.

Identifying the Factors Influencing Patients' Telehealth Visit Satisfaction: Survey Validation Through a Structural Equation Modeling Approach.

The COVID-19 pandemic accelerated the adoption of telehealth as an alternative to in-person hospital visits. To understand the factors impacting the quality of telehealth services, there is a need for validated survey instruments and conceptual frameworks. The objective of this study is to validate a telehealth patient satisfaction survey by structural equation modeling (SEM) and determine the relationship between the factors in the proposed telehealth patient satisfaction model (TPSM).

Rapid Development of a Telehealth Patient Satisfaction Survey Using a Multi-Stakeholder Approach.

The COVID-19 pandemic has hastened the adoption of telehealth and the drastic shift to an unfamiliar process may impose significant impact to the quality-of-care delivery. Many providers are interested in understanding the quality of their telehealth services from the patients' experience. A telehealth patient satisfaction survey (TPSS) was developed by using an iterative stakeholder-centered design approach, incorporating elements from validated telemedicine and customer service survey instruments, and meeting the operational needs and constraints.

Measuring Telehealth Visit Length and Schedule Adherence Using Videoconferencing Data.

The ability to measure clinical visit length is critical for operational efficiency, patient experience, and accurate billing. Despite the unprecedented surge in telehealth use in 2020, studies on visit length and schedule adherence in the telehealth setting are nonexistent in the literature. This article aims to demonstrate the use of videoconferencing data to measure telehealth visit length and schedule adherence.