Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.

Objective: To evaluate the predictive value of clinical features in the diagnosis of endometriosis by utilizing machine learning algorithms (MLAs), aiming to develop an accurate, explainable prediction model.

Design: Retrospective case-control study from 2011 to 2022.

Setting: Tertiary referral center specializing in pelvic pain and minimally invasive gynecologic surgery.

AI in Hypertensive Disorders of Pregnancy: Review.

Background: Hypertensive disorders of pregnancy (HDP) are a leading cause of maternal and fetal mortality worldwide. Early detection and risk stratification are critical for timely intervention to prevent severe complications such as eclampsia, stroke, and preterm delivery. However, traditional clinical methods often lack the precision needed to identify high-risk individuals effectively.

Evaluating ChatGPT's moral competence in health care-related ethical problems.

Objectives: Artificial intelligence tools such as Chat Generative Pre-trained Transformer (ChatGPT) have been used for many health care-related applications; however, there is a lack of research on their capabilities for evaluating morally and/or ethically complex medical decisions. The objective of this study was to assess the moral competence of ChatGPT.

Materials And Methods: This cross-sectional study was performed between May 2023 and July 2023 using scenarios from the Moral Competence Test (MCT).

Classifying early infant feeding status from clinical notes using natural language processing and machine learning.

The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status.

Development of a Probabilistic Boolean network (PBN) to model intraoperative blood pressure management.

Background: Blood pressure is a vital sign for organ perfusion that anesthesiologists measure and modulate during surgery. However, current decision-making processes rely heavily on clinicians' experience, which can lead to variability in treatment across surgeries. With the advent of machine learning, we can now create models to predict the outcomes of interventions and guide perioperative decision-making.

Racial disparities in septic shock mortality: a retrospective cohort study.

Background: Patients with septic shock have the highest risk of death from sepsis, however, racial disparities in mortality outcomes in this cohort have not been rigorously investigated. Our objective was to describe the association between race/ethnicity and mortality in patients with septic shock.

Methods: Our study is a retrospective cohort study of adult patients in the OneFlorida Data Trust (Florida, United States of America) admitted with septic shock between January 2012 and July 2018 We identified patients as having septic shock if they received vasopressors during their hospital encounter and had either an explicit International Classification of Disease (ICD) code for sepsis, or had an infection ICD code and received intravenous antibiotics.

Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records.

Objective: This study aimed to develop and validate predictive models using electronic health records (EHR) data to determine whether hospitalized COVID-19-positive patients would be admitted to alternative medical care or discharged home.

Methods: We conducted a retrospective cohort study using deidentified data from the University of Florida Health Integrated Data Repository. The study included 1,578 adult patients (≥18 years) who tested positive for COVID-19 while hospitalized, comprising 960 (60.

Clinical Decision Support Systems for Palliative Care Management: A Scoping Review.

Context: With the expansion of palliative care services in clinical settings, clinical decision support systems (CDSSs) have become increasingly crucial for assisting bedside nurses and other clinicians in improving the quality of care to patients with life-limiting health conditions.

Objectives: To characterize palliative care CDSSs and explore end-users' actions taken, adherence recommendations, and clinical decision time.

Methods: The CINAHL, Embase, and PubMed databases were searched from inception to September 2022.

Study protocol for a type III hybrid effectiveness-implementation trial to evaluate scaling interoperable clinical decision support for patient-centered chronic pain management in primary care.

Background: The US continues to face public health crises related to both chronic pain and opioid overdoses. Thirty percent of Americans suffer from chronic noncancer pain at an estimated yearly cost of over $600 billion. Most patients with chronic pain turn to primary care clinicians who must choose from myriad treatment options based on relative risks and benefits, patient history, available resources, symptoms, and goals.

Telehealth and racial disparities in colorectal cancer screening: A pilot study of how virtual clinician characteristics influence screening intentions.

Introduction: Racial disparities in colorectal cancer (CRC) can be addressed through increased adherence to screening guidelines. In real-life encounters, patients may be more willing to follow screening recommendations delivered by a race concordant clinician. The growth of telehealth to deliver care provides an opportunity to explore whether these effects translate to a virtual setting.

A Mobile App to Support Self-management of Chronic Kidney Disease: Development Study.

Background: Chronic kidney disease (CKD) is a common and costly condition that is usually accompanied by multiple comorbidities including type 2 diabetes, hypertension, and obesity. Proper management of CKD can delay or prevent kidney failure and help mitigate cardiovascular disease risk, which increases as kidney function declines. Smart device apps hold potential to enhance patient self-management of chronic conditions including CKD.

The effects of experimentally manipulated social status and subjective social status on physical activity among Hispanic adolescents: An RCT.

Background: Low objective socioeconomic status (SES) and subjective social status (SSS), one's perceived social rank, are associated with obesity. This association may be due, in part, to social status-related differences in energy expenditure. Experimental studies are needed to assess the extent to which SES and SSS relate to energy expenditure.

Development of a Credible Virtual Clinician Promoting Colorectal Cancer Screening via Telehealth Apps for and by Black Men: Qualitative Study.

Background: Traditionally, promotion of colorectal cancer (CRC) screening among Black men was delivered by community health workers, patient navigators, and decision aids (printed text or video media) at clinics and in the community setting. A novel approach to increase CRC screening of Black men includes developing and utilizing a patient-centered, tailored message delivered via virtual human technology in the privacy of one's home.

Objective: The objective of this study was to incorporate the perceptions of Black men in the development of a virtual clinician (VC) designed to deliver precision messages promoting the fecal immunochemical test (FIT) kit for CRC screening among Black men in a future clinical trial.

Accessing Artificial Intelligence for Clinical Decision-Making.

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry.

Key changes to improve social presence of a virtual health assistant promoting colorectal cancer screening informed by a technology acceptance model.

Background: Understanding how older, minoritized patients attend to cues when interacting with web-based health messages may provide opportunities to improve engagement with novel health technologies. We assess acceptance-promoting and acceptance-inhibiting cues of a web-based, intervention promoting colorectal cancer (CRC) screening with a home stool test among Black women.

Materials And Methods: Focus group and individual interview data informed iterative changes to a race- and gender-concordant virtual health assistant (VHA).

Planning for patient-reported outcome implementation: Development of decision tools and practical experience across four clinics.

Introduction: Many institutions are attempting to implement patient-reported outcome (PRO) measures. Because PROs often change clinical workflows significantly for patients and providers, implementation choices can have major impact. While various implementation guides exist, a stepwise list of decision points covering the full implementation process and drawing explicitly on a sociotechnical conceptual framework does not exist.

Developing and Validating a Computable Phenotype for the Identification of Transgender and Gender Nonconforming Individuals and Subgroups.

Transgender and gender nonconforming (TGNC) individuals face significant marginalization, stigma, and discrimination. Under-reporting of TGNC individuals is common since they are often unwilling to self-identify. Meanwhile, the rapid adoption of electronic health record (EHR) systems has made large-scale, longitudinal real-world clinical data available to research and provided a unique opportunity to identify TGNC individuals using their EHRs, contributing to a promising routine health surveillance approach.

A Pilot Study Examining the Efficacy of Delivering Colorectal Cancer Screening Messages via Virtual Health Assistants.

Introduction: Patients are more likely to complete colorectal cancer screening when recommended by a race-concordant healthcare provider. Leveraging virtual healthcare assistants to deliver tailored screening interventions may promote adherence to colorectal cancer screening guidelines among diverse patient populations. The purpose of this pilot study is to determine the efficacy of the Agent Leveraging Empathy for eXams virtual healthcare assistant intervention to increase patient intentions to talk to their doctor about colorectal cancer screening.

A Subjective Culture Approach to Cancer Prevention: Rural Black and White Adults' Perceptions of Using Virtual Health Assistants to Promote Colorectal Cancer Screening.

In the US, Black adults are less likely than White adults to be screened for colorectal cancer (CRC). This study uses a subjective culture approach to describe and compare perceptions of a CRC screening intervention delivered via virtual health assistants (VHAs) among rural Black and White study participants. We analyzed 28 focus groups with Black ( = 85) and White ( = 69) adults aged 50-73.

Impact of the Early Phase of the COVID-19 Pandemic on US Healthcare Workers: Results from the HERO Registry.

Background: The HERO registry was established to support research on the impact of the COVID-19 pandemic on US healthcare workers.

Objective: Describe the COVID-19 pandemic experiences of and effects on individuals participating in the HERO registry.

Design: Cross-sectional, self-administered registry enrollment survey conducted from April 10 to July 31, 2020.

mMotiv8: A smartphone-based contingency management intervention to promote smoking cessation.

Cigarette smoking is the leading preventable cause of death and illness in the United States. We tested the usability, acceptability, and efficacy of a smartphone-based contingency management treatment to promote cessation. We used a nonconcurrent multiple-baseline design.

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

Background: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these patients.

Methods: The National Surgery Quality Improvement Program database was used to identify patients undergoing appendectomy between 2005 and 2017. Logistic regression, support vector machines, random forest decision trees, and extreme gradient boosting machines were used to model the occurrence of postoperative sepsis.

Tailoring virtual human-delivered interventions: A digital intervention promoting colorectal cancer screening for Black women.

Objective: Despite efforts to reduce cancer disparities, Black women remain underrepresented in cancer research. Virtual health assistants (VHAs) are one promising digital technology for communicating health messages and promoting health behaviors to diverse populations. This study describes participant responses to a VHA-delivered intervention promoting colorectal cancer (CRC) screening with a home-stool test.

Simple tests of cardiorespiratory fitness in a pediatric population.

A progressive, treadmill-based VO2max is the gold standard of cardiorespiratory fitness determination but is rarely used in pediatric clinics due to time requirements and cost. Simpler and shorter fitness tests such as the Squat Test or Step Test may be feasible and clinically useful alternatives. However, performance comparisons of these tests to treadmill VO2max tests are lacking.