15 Clinical Trials for Various Conditions
This is a clinical study to implement and evaluate a hospital-wide, operational intervention for a real-time natural language processing (NLP)-driven clinical decision support (CDS) tool, called Substance Misuse Algorithm for Referral to Treatment Using Artificial Intelligence (SMART-AI). The SMART-AI CDS tool will be evaluated via implementation in the UW Health electronic health record (EHR). The CDS tool is meant for screening inpatient adults for opioid misuse as part of a best practice alert to nurses and providers for addiction consult service needs.
The investigators propose to use a Natural Language Processing System (NLP) to provide an initial baseline report for primary care patients at risk for diabetes and cardiovascular complications that will include: a) evidence of foot exam documentation in the previous year; b) use of aspirin for cardiovascular risk reduction; and c) tobacco use. As part of a randomized trial, we plan to use a previously validated mailed survey (NCQA Provider Recognition Program) that requests information on the last foot exam, use of aspirin and tobacco. Patients who have been identified by NLP as not having had a foot exam will be randomized into treatment and control arms. Both arms will receive an informational letter; with a second mailing to nonresponders after one month, describing the key strategies for effective patient-physician communication during the clinical encounter. The treatment arm will also receive an informational letter and patient education brochure containing key messages about the importance of regular foot examinations. NLP will be repeated after 6 months to compare the impact of the patient education materials.
This Pre-Post, open-cohort design, pragmatic trial with 150 clinicians and will evaluate the effectiveness of the use of telehealth Advanced Care Planning (ACP) Program by comparing ACP documentation among 13,000 patients over 65
The purpose of the research is to assess the impact of a natural language processing + artificial intelligence (NLP+AI)-based risk communication feedback system to improve quality of risk communication of key tradeoffs during prostate cancer consultations among physicians and to improve patient decision making. In this cluster randomized trial, an evaluable 220 patients with newly diagnosed clinically localized prostate cancer will be cluster randomized within an evaluable 22 physicians to: 1. a control arm, in which patients will receive standard of care treatment consultations along with AUA-endorsed educational materials on treatment risks and benefits (for patients) and on SDM (for physicians) or 2. an experimental arm, in which patients and participating physicians will receive NLP+AI-based feedback on what was said about key tradeoffs within approximately 72 hours of the consultation to assist with decision making. Physicians will additionally be provided with grading of their risk communication for each visit based on an a priori defined framework for quality of risk communication and recommendations for improvement. In both study arms, there will be an audio-recorded follow-up phone or video call between the physician and patient to allow for further discussion of risk and clarifying any areas of ambiguity, which will be qualitatively analyzed to see if areas of poor communication were rectified. After the follow-up phone call, patients and participating physicians will be asked to complete a very brief survey about their experience. The study plans to test whether receiving NLP+AI-based feedback improves decisional conflict, shared decision making, and appropriateness of treatment choice over the standard of care in patients undergoing treatment consultations for prostate cancer. Study staff will also test whether providing feedback and grading of risk communication to physicians affects quality of physician risk communication, since providing feedback will promote more accountability for the quality of information provided to patients. The study will also analyze data from the control arm of the randomized controlled trial to understand variation in risk communication of key tradeoffs in relevant subgroups of tumor risk (low-, intermediate-, and high-risk), provider specialty (Urology, Radiation Oncology, Medical Oncology), and patient sociodemographics.
This is a research study looking at whether the way people speak can help predict how well they'll respond to certain mental health treatments. The Main Goal: The researchers want to see if computer analysis of a person's speech patterns can predict whether they'll respond well to two specific treatments: TMS (Transcranial Magnetic Stimulation) and Spravato (a nasal spray medication). They're focusing on people with depression, bipolar disorder, OCD, anxiety, and PTSD. How It Works: 200 people with these conditions will participate in the study.Participants will record themselves speaking for about 12 minutes, responding to six different prompts.They'll do these recordings before treatment starts, daily during treatment, right after treatment ends, and again four weeks later. Doctors will track how well people are doing using various questionnaires and rating scales The researchers will look for connections between speech patterns and treatment success. The study will last 12 months. What Makes Someone a "Treatment Success": The study considers treatment successful if a person's symptoms improve significantly (specifically, a 2-point or greater reduction on a clinical rating scale (called Clinical Global Impression) and stays improved during the follow-up period (4-weeks). Why This Matters: If successful, this research could lead to a simple, non-invasive way to help doctors predict which treatments might work best for different patients. This could help people get the most effective treatment more quickly and help healthcare providers use their resources more efficiently. Safety Consideration The researchers will also check whether doing the speech assessments causes any distress to participants, making sure the evaluation process itself is safe and comfortable.
This study investigates whether AI-driven analysis of speech can accurately predict clinical diagnoses and assess risk for various mental or behavioral health conditions, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, generalized anxiety disorder, major depressive disorder, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and schizophrenia. We aim to develop tools that can support clinicians in making more accurate and efficient diagnoses.
The overarching goal of this study is to establish initial proof of mechanism for precision interventions in an adult population.
Including patient perspectives when developing new therapy interventions is crucial because it can help to understand response heterogeneity and promote engagement. Yet, analyzing patient interview data is difficult and time-consuming. This study aims to explore the potential for natural language processing and deep learning to analyze patient interviews and identify potential ways in which therapy leads to psychological change. This study will recruit participants from an existing clinical service that offers a 16-week online group therapy model (and adjunct individual therapy sessions) called Program for Alleviating and Resolving Trauma and Stress (PARTS) based on a therapy called Internal Family Systems (IFS). The investigators will use a mixed methods approach, applying natural language processing and deep learning to develop models that identify potential mechanisms of change. These models will be based on patient perspectives of psychological change, as expressed in interviews, and be compared to models based on clinical measures.
Cross-sectional observational study of the relationship between speech patterns and psychiatric symptoms and disorders.
The COVID-19 pandemic puts individuals recovering from opioid use disorders (OUDs), an already vulnerable population, at increased risk of overdose due to decreased access to treatment, decreased social support, and increased psychosocial stress. This proposal will test the efficacy of a promising mobile app-based peer support program, compared to usual care, in increasing recovery capital, improving retention in treatment, and reducing psychosocial adverse effects, among a national sample of people in recovery from OUD. If effective, it would provide an accessible, personalized, and scalable approach to OUD recovery increasingly needed during the COVID-19 pandemic.
The primary objective of this grant is to develop and evaluate an Artificial Intelligence-based clinical training tool--CBTpro--to support high-quality skills training in CBT for psychosis (CBTp). CBTpro will provide a rapid means of scaling and sustaining high-quality CBTp in routine care settings across the US.
Primary Objective: Conduct pilot study to assess effects of brief negotiation interview (BNI) Chatbot among individuals involved in the Connecticut criminal justice system with opioid use disorder (OUD). Study Duration: Approximately 2 years (1 year for study activities, 1 year for data analysis) Study Design: This is a prospective, randomized study to evaluate the effectiveness of a BNI Chatbot on patients with OUD compared with Standard Care (SC). Number of Study Sites: The offices of the Center for Progressive Recovery, LLC and the New Haven Police Department Detention Center (NHPD). Study Population: The study population includes adult individuals with OUD who are involved in the Connecticut criminal justice system and not currently receiving medication-assisted treatment for their OUD. Number of Participants: Sixty participants Primary Outcome Variable: Attendance at participants first treatment appointment within four weeks of referral among participants in the BNI Chatbot vs. Standard Care (SC) groups. Secondary Outcome and Exploratory Outcome Variables: Secondary outcomes include readiness and intention to engage in buprenorphine (bup) treatment, and urine toxicology test-confirmed drug use at four weeks among participants in BNI Chatbot vs. SC groups. Exploratory outcomes include ratings of feasibility, acceptability and satisfaction between study groups, and a comparison of study findings to engagement data from previous in-person studies, including BNI+bup, and other digital programs, such as reSET-O, and DynamiCare.
The investigators propose to develop an open-source, publicly available machine learning model that health systems could download and apply to their electronic health record data marts to screen for substance misuse in their patients. The investigators hypothesize that the natural language processing algorithm can provide a standardized and interoperable approach for an automated daily screen on all hospitalized patients and provide better implementation fidelity for screening, brief intervention, and referral to treatment.
Asthma is the most common chronic condition in children and one of the five most burdensome diseases in the United States. Despite this, research and care for childhood asthma are limited by inefficient utilization of electronic medical records (EMRs) to facilitate large-scale studies and care. The primary goal of this clinical trial is to implement the asthma-Guidance and Prediction System (a-GPS) on the Asthma Management Program (AMP, a current care coordination program for asthma care of children aged 5-17 years at Mayo Clinic). Primary hypothesis: The implementation of a-GPS in the current care is logistically feasible.
This multisite pragmatic clinical trial was designed to assess the effectiveness of a single scripted telephone call to diabetes patients who (a) were currently above recommended clinical goals for glucose, blood pressure, or lipids, and (b) had recently been prescribed a new medication for that specific clinical domain. The goals of the intervention were to improve primary adherence and persistence to the newly prescribed medication and to improve control of glucose, blood pressure, and lipids.