Fact Check: Assessing the Response of ChatGPT to Alzheimer's Disease Myths.

Introduction: There are many myths regarding Alzheimer's disease (AD) that have been circulated on the internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models, such as ChatGPT, may be a valuable tool to help assess these myths for veracity and inaccuracy; however, they can induce misinformation as well.

Objective: This study assesses ChatGPT's ability to identify and address AD myths with reliable information.

Using large language model to guide patients to create efficient and comprehensive clinical care message.

Objective: This study aims to investigate the feasibility of using Large Language Models (LLMs) to engage with patients at the time they are drafting a question to their healthcare providers, and generate pertinent follow-up questions that the patient can answer before sending their message, with the goal of ensuring that their healthcare provider receives all the information they need to safely and accurately answer the patient's question, eliminating back-and-forth messaging, and the associated delays and frustrations.

Methods: We collected a dataset of patient messages sent between January 1, 2022 to March 7, 2023 at Vanderbilt University Medical Center. Two internal medicine physicians identified 7 common scenarios.

Leveraging large language models for generating responses to patient messages-a subjective analysis.

Objective: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal.

Materials And Methods: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism.

Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.

Objectives: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.

Materials And Methods: We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4.

A Cost Savings Analysis of Topical Estrogen Therapy in Urinary Tract Infection Prevention Among Postmenopausal Women.

Introduction: UTIs are some of the most common infections in geriatric patients, with many women experiencing recurrent infections after menopause. In the US, annual UTI-related costs are $2 billion, with recurrent infections creating a significant economic burden. Given the data published on topical estrogen in reducing the number of infections for postmenopausal women with recurrent UTI, we sought to evaluate how this would translate to cost savings.

State-Level Adverse Outcomes Among Long-Term Services and Supports Users With Alzheimer's Disease and Related Dementias.

Home- and community-based services (HCBS) users, on average, experience hospitalizations more frequently than nursing facility residents. However, little is known about state-level variation in such adverse events among these groups. Using 2018 Medicare and Medicaid claims for dual-eligible beneficiaries with Alzheimer's disease and related dementias, we described hospitalization and emergency department (ED) visit rates among HCBS users and nursing facility residents and observed substantial state-level variation.

Fact Check: Assessing the Response of ChatGPT to Alzheimer's Disease Statements with Varying Degrees of Misinformation.

Background: There are many myths regarding Alzheimer's disease (AD) that have been circulated on the Internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models such as ChatGPT, may be a useful tool to help assess these myths for veracity and inaccuracy. However, they can induce misinformation as well.

Leveraging Large Language Models for Generating Responses to Patient Messages.

Objective: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal.

Methods: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism.

The 21st Century Cures Act Information Blocking Rule in Post-Acute Long-Term Care.

Included as part of the 21st Century Cures Act, the information blocking rule entered the first compliance phase in April 2021. Under this rule, post-acute long-term care (PALTC) facilities must not engage in any activity that interferes with accessing, using, or exchanging electronic health information. In addition, facilities must respond to information requests in a timely fashion and allow records to be readily available to patients and their delegates.

Cloud Computing Synthetic Syndromic Surveillance Systems: Opioid Epidemic in Illinois.

With an increasing number of overdose cases yearly, the city of Chicago is facing an opioid epidemic. Many of these overdose cases lead to 911 calls that necessitate timely response from our limited emergency medicine services. This paper demonstrates how data from these calls along with synthetic and geospatial data can help create a syndromic surveillance system to combat this opioid crisis.

HCV universal EHR prompt successfully increases screening, highlights potential disparities.

Background & Objectives: Screening for hepatitis C virus is the first critical decision point for preventing morbidity and mortality from HCV cirrhosis and hepatocellular carcinoma and will ultimately contribute to global elimination of a curable disease. This study aims to portray the changes over time in HCV screening rates and the screened population characteristics following the 2020 implementation of an electronic health record (EHR) alert for universal screening in the outpatient setting in a large healthcare system in the US mid-Atlantic region.

Methods: Data was abstracted from the EHR on all outpatients from 1/1/2017 through 10/31/2021, including individual demographics and their HCV antibody (Ab) screening dates.

Use of long-term services and supports among dual-eligible beneficiaries with Alzheimer's disease and related dementias.

Background: To respect people's preference for aging in place and control costs, many state Medicaid programs have enacted policies to expand home and community-based services as an alternative to nursing facility care. However, little is known about the use of Medicaid long-term services and supports (LTSS) at a national level, particularly among dual-eligible beneficiaries with Alzheimer's disease and related dementias (ADRD).

Methods: Using Medicare and Medicaid claims of 30 states from 2016, we focused on dual-eligible beneficiaries 65 years or older with ADRD and described their use of any form of LTSS and sub-types of LTSS (home-based, community-based, and nursing facility services) across states.

A New Public Corpus for Clinical Section Identification: MedSecId.

The process by which sections in a document are demarcated and labeled is known as section identification. Such sections are helpful to the reader when searching for information and contextualizing specific topics. The goal of this work is to segment the sections of clinical medical domain documentation.

A Multimodal Sepsis Quality-Improvement Initiative Including 24/7 Screening and a Dedicated Sepsis Response Team-Reduced Readmissions and Mortality.

Objectives: To evaluate if a hospitalwide sepsis performance improvement initiative improves compliance with the Centers for Medicare and Medicaid Services-mandated sepsis bundle interventions and patient outcomes.

Study Design: Retrospective analysis comparing 6 months before and 14 months after intervention.

Setting: Tertiary teaching hospital in Washington, DC.

An Initial Cost-Effectiveness Analysis of Magnetic Resonance-Guided Laser Interstitial Thermal Therapy in Pediatric Epilepsy Surgery.

Introduction: Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) is a new technology that provides a clinically efficacious and minimally invasive alternative to conventional microsurgical resection. However, little data exist on how costs compare to traditional open surgery. The goal of this paper is to investigate the cost-effectiveness of MRgLITT in the treatment of pediatric epilepsy.

Consolidation in the dental industry: a closer look at dental payers and providers.

We examine the effect of commercial dental insurance concentration on the size of dental practices, the decision of dentists to own a practice, and the choice of dentists to work at a dental management service organization-a type of corporate group practice that has become more prevalent in the United States in recent years. Using 2013-2015 dentist-level data from the American Dental Association, county-level data on firms and employment from the United States Census, and commercial dental insurance market concentration data from FAIR Health, we find a modest effect of dental insurance market concentration on the size of dental practices. We also find that a higher level of commercial dental insurance market concentration is associated with a dentist's decision not to own a practice.

The Determinants and Variation of Nursing Home Private-Pay Prices: Organizational and Market Structure.

Nursing home (NH) care is arguably the most significant financial risk faced by the elderly without long-term care insurance or Medicaid coverage. Annual out-of-pocket expenditures for NH care can easily exceed $70,000. However, our understanding of private-pay prices is limited by data availability.

Wearable Fetal Monitoring Solution for Improved Mobility During Labor & Delivery.

Electronic fetal monitoring (EFM) is used widely during labor & delivery, but existing solutions limit patient mobility, are uncomfortable, and do not consistently capture fetal heart rate (FHR) and uterine activity (UA) signals. A wireless EFM system was developed that features wearable US and tocodynamometer devices that conform to the body and do not require cables or belts. Benchtop testing demonstrated that the devices can accurately and consistently measure simulated FHRs and UAs over clinically meaningful ranges and body curvatures.