190 Clinical Trials for Various Conditions
The purpose of this research is to evaluate the overall use of the WounDx medical device in a clinical setting, such as a hospital. The WounDx device is experimental and not yet approved by the United States Food and Drug Administration (FDA). WounDx uses information about a patient's wound to generate a report that a surgeon may use to help determine when to close or not close the wound. The final decision to close the wound remains with the surgeon. The results from this pilot trial will inform a larger pivotal trial.
The goal of this clinical trial is to address care gaps for participants at high risk of breast and ovarian cancer (HBOC), or Lynch syndrome (LS) because of testing positive for specific genetic variants. A patient-centered clinical decision support (PC-CDS) tool will help identify participants with genetic variations and display recommendations for referrals and testing to the clinician and participant at a primary care visit. The main question the study aims to answer is: - Does clinical decision support for participants with hereditary cancer syndromes improve the use of evidence-based cancer prevention care. Participants being seen in the PC-CDS group are compared to participants being seen in usual care (UC) to see if they are up to date on guideline-based cancer prevention care and to see if participants in the PC-CDS group report more shared decision making and higher rates of self-management of their genetic cancer risks. Participants will be asked to answer survey questions.
The overarching goal of this proposal is to integrate patient social risk information into an existing electronic health record (EHR)-based clinical decision support (CDS) tool (CDSv1) to facilitate emergency department (ED)-initiated, social risk-informed opioid use disorder (OUD) medication treatment and ultimately improve treatment adherence and follow up. The investigators will evaluate the feasibility and acceptability of the social care-enhanced CDS tool, CDSv2, (compared to CDSv1) at a single study site (UCSF) as an intervention to increase medication treatment adherence and follow up for adult ED patients experiencing opioid use disorder using a mixed-methods, before-after approach.
To work best, clinical decision support tools (CDS) must be timed to provide support when healthcare decisions are made, which includes virtual visits (phone or video). Unfortunately, most CDS tools are either missing from virtual visits or not designed for the unique context of virtual visits (e.g., availability of physical assessments and labs, different workflows), which could generate new inequities for patients more likely to use virtual visits. The objective of this study is to test the reach, feasibility and acceptability of a new CDS tool for heart failure with reduced ejection fraction (HFrEF) during virtual visits. This new CDS tool was developed through an iterative design process, and will be compared to an existing HFrEF CDS tool in a randomized pilot study at outpatient cardiology clinics throughout the UCHealth system.
This study will evaluate the use of an automated process in the electronic health record (EHR) that will help providers to detect patients at risk of developing postpartum depression (PPD).
Scale-up of HIV preexposure prophylaxis (PrEP) is a key strategy of the U.S. initiative to end the HIV epidemic, but healthcare providers lack tools to support PrEP discussions and prescribing for patients likely to benefit. This research will evaluate whether integrating automated tools into electronic health records to help providers efficiently and equitably identify potential candidates for PrEP, discuss PrEP, and prescribe PrEP can improve PrEP initiation and persistence in safety-net community health centers. It will achieve this by conducting a stepped-wedge trial of a decision support tool with an embedded HIV prediction model to identify patients likely to benefit from PrEP. The intervention will be delivered to healthcare providers in 16 community health centers within the national OCHIN network.
This study will evaluate the effectiveness of SIGHT as a clinical support system to prompt provider/patient discussion and shared decision making regarding the need for genetic testing in the form of a chromosomal microarray. Identifying patients at high predicted probability of needing a test in clinical settings will be examined to determine if it decreases the duration of time to testing and increases diagnostic yield. SIGHT requires only data already collected in routine clinical encounters and is calculated prior to a clinical visit at VUMC.
Six primary care practices within a large Philadelphia pediatric care network will use an electronic Clinical Decision Support (eCDS) tool as standard care for concussion evaluation. The eCDS tool will include a prediction rule for children aged 5-18 assessed for mild traumatic brain injury (mTBI). The eCDS tool predicts risk for persistent symptoms and prompts referral to specialty care for those deemed high risk. This research proposes to analyze the clinical and process outcomes in these six practices relative to the rest of the care network, specifically, whether the eCDS tool reduces time to symptom resolution.
The research team will evaluate the effectiveness of an auto-pended bot liver ultrasound order that will prompt providers at the time of encounter to place appropriate imaging orders for hepatocellular carcinoma (HCC) screening in patients with cirrhosis.
The goal of this randomized controlled trial is to compare the effects of a clinical decision support tool consisting of a 48-hour stop order for indwelling urinary catheters versus no clinical decision support in hospitalized patients with indwelling urinary catheters. The main questions it aims to answer are: - Does the presence of an automated stop order integrated as part of a clinical decision support tool reduce dwell time of urinary catheters and the rate of catheter associated urinary tract infections? Participants who have indwelling urinary catheters ordered will be randomized to either have these orders automatically expire after 48 hours unless an action is taken or have orders without expiration. Researchers will compare the urinary catheter dwell time and the rate of catheter associated urinary tract infections between the two groups.
The investigators overarching goal is to increase the percentage of patients engaging in diabetes prevention activities to reduce the incidence of diabetes. The investigators objective is to design and pilot test a prediabetes clinical decision support (CDS) tool in the electronic health record (EHR) that will assess the patient's activation level based on responses to a questionnaire. Based on the patient's assessed level of activation, the tool will generate several communication recommendations to guide clinicians in conversations related to prediabetes/lifestyle change and tailor recommendations about available resources (e.g., care manager, health coach, DPP) to support patient activation.
The objective of this study is to evaluate the impact of a clinical decision support (CDS) alert to facilitate the co-prescribing of naloxone, an opioid overdose reversal agent, with high-risk opioid prescriptions. Prescribing naloxone with opioids is a best practice described in the 2022 US Center for Disease Control and Prevention (CDC) guidelines on opioid prescribing. The CDS can improve quality of care delivered by improving compliance with the guideline defined best practices. The project will compare CDS alert facilitated co-prescribing of naloxone with high-risk opioid prescriptions vs usual care to evaluate the effectiveness of the CDS alert for improving naloxone prescribing. The patients are not assigned to an intervention and will be receiving any changes in care as part of their routine medical care, rather than a specific intervention that is distinct from their usual medical care. The researchers hypothesize that the CDS alert will be acceptable to providers while increasing naloxone co-prescribing which will reduce the number of opioid overdoses in subsequent 6 months.
A prospective, cluster-randomized, care-as-usual controlled trial to evaluate the impact of an ECG-based artificial intelligence (ECG-AI) algorithm to detect low left ventricular ejection fraction (LVEF) on diagnosis rates of LVEF ≤ 40% in the outpatient setting. The objective of this study is to evaluate the impacts of an ECG-AI algorithm to detect low LVEF and an associated Medical Device Data System when used during routine outpatient care. The study will be conducted in 2 phases: feasibility assessment phase and clinical impact phase.
This clinical trial develops and tests how well a clinical decision support (CDS) tool works to increase human papillomavirus (HPV) vaccination of children between the age of 9-17 (adolescents) in pharmacies. HPV vaccination rate in eligible adolescents remains low even though over 90% of the cancers in adults caused by HPV can be prevented by the HPV vaccine. The National Vaccine Advisory Committee recommends HPV vaccinations to be given in pharmacies to increase access to vaccines, but pharmacy processes and lack of awareness of the service among parents impact the use of local pharmacies for HPV vaccinations. Using a focus group may be an effective method to develop a CDS tool and create a process that may be more convenient for parents to get their adolescent's vaccine at their local pharmacy. A CDS tool may make it easier to obtain HPV vaccines, and as a result increase the adolescent HPV vaccination rate and reduce the incidence of cancer caused by HPV.
The purpose of this research study is to measure the effect on of a large language model interface on the usability, attitudes, and provider trust when using a machine learning algorithm-based clinical decision support system in the setting of bleeding from the upper gastrointestinal tract (upper GIB). Specifically, the investigators are looking to assess the optimal implementation of such machine learning algorithms in simulation scenarios to best engender trust and improve usability. Participants will be randomized to either machine learning algorithm alone or algorithm with a large language model interface and exposed to simulation cases of upper GIB.
The goal of this study is to evaluate the impact of a clinical decision support system (CDSS) in children receiving mechanical ventilation (MV) after surgery for congenital heart disease (CHD). The main question it aims to answer is: -What is the impact of a CDSS designed to facilitate weaning and discontinuation of MV on the duration of MV in post-operative congenital cardiac surgery patients? Participants will be identified as eligible to initiate weaning from mechanical ventilation. Providers will decide whether or not to initiate weaning based on recommendations provided by the CDSS. Researchers will compare patients exposed to the CDSS with a historical cohort to see if the CDSS facilitated a decrease in MV duration.
Invasive mechanical ventilation is one of the most important and life-saving therapies in the intensive care unit (ICU). In most severe cases, extracorporeal lung support is initiated when mechanical ventilation is insufficient. However, mechanical ventilation is recognised as potentially harmful, because inappropriate mechanical ventilation settings in ICU patients are associated with organ damage, contributing to disease burden. Studies revealed that mechanical ventilation is often not provided adequately despite clear evidence and guidelines. Variables at the ventilator and extracorporeal lung support device can be set automatically using optimization functions and clinical recommendations, but the handling of experts may still deviate from those settings depending upon the clinical characteristics of individual patients. Artificial intelligence can be used to learn from those deviations as well as the patient's condition in an attempt to improve the combination of settings and accomplish lung support with reduced risk of damage.
Determine whether clinical decision support (best practice advisory) improves provider adherence to transfusion guidelines for all four major blood components (red blood cells, plasma, platelets, and cryoprecipitate) using a randomized study design to reduce risk of bias. Alerts will be visible to the experimental ordering provider group, while they will not be visible to the control. Both groups still have access to information about best practices: local clinical transfusion guidelines are available and education on blood transfusion best practices will continue regardless of randomization assignment.
The overarching goal of this project is to leverage health information technology (HIT) to integrate available digital information on social needs to improve care for racial and ethnic minorities and socially disadvantaged populations with chronic diseases. In the previous phases of this project the investigators developed a social risk score to identify social needs among medically under-served patients with special emphasis on application among African American patients with low income and chronic diseases who face social determinants, risk factors, and needs (SDRN) challenges. The investigators also developed a clinical decision support (CDS) tool to present the social risk score to clinical providers and sought feedback from different users on the face and content validity of the CDS tool. In the current project the investigators will run a randomized clinical trial (RCT) study to pilot test the new risk score and CDS tool in selected primary care clinics at Johns Hopkins Health System (JHHS) and in collaboration with selected community-based organizations (CBOs). This system will help identify, manage, and refer patients with both high levels of disease burden and modifiable SDRN challenges.
The purpose of this study is to determine the impact of an electronic medical record clinical decision support tool on rates of dysglycemia in the hospital, and its clinical and economical outcomes. The study also evaluates the perspectives of providers regarding the tool's usefulness on disease management support, knowledge, and practice performance.
The purpose of the study is to examine the feasibility, acceptability, and initial outcomes of clinical decision support (CDS) and a Sleep Navigation program to enhance primary to specialty care management of pediatric sleep-disordered breathing (SDB).
Atrial fibrillation (AF) is the most common arrhythmia in the world, with significant morbidity and mortality. With appropriate oral anticoagulation, the risk of stroke due to atrial fibrillation decreases by 64%. Although atrial fibrillation is commonly diagnosed and treated in the Emergency Department (ED), oral anticoagulation is significantly underprescribed. Underprescribing has been attributed to a lack of empowerment and deferral of prescribing to longitudinal care clinicians. Using a convergent parallel quantitative-qualitative design (mixed-methods), we propose a stepped-wedge cluster randomized trial design with the implementation of a clinical decision support (CDS) tool in adults with new-onset AF that are OAC-naïve and at significant risk for stroke. In parallel, we will use qualitative approaches to evaluate clinician facilitators and barriers to tool utilization as well as patient satisfaction and engagement with the tool.
The primary objective of this study is to validate the use of an electronic clinical decision support (CDS) tool, TriageGO with Monocyte Distribution Width (TriageGO-MDW), in the emergency department (ED). TriageGO-MDW is non-device CDS designed to support emergency clinicians (nurses, physicians and advanced practice providers) in performing risk-based assessment and prioritization of patients during their ED visit. This study will follow an effectiveness-implementation hybrid design via the following three aims (phases), to be executed sequentially: (Aim 1) Validate the TriageGO-MDW algorithm locally using retrospective data at ED study sites. (Aim 2) Deploy TriageGO-MDW integrated with the electronic medical record (EMR) and perform user assessment. (Aim 3) Evaluate TriageGO-MDW in steady state with respect to clinical, process, and perceived utility outcomes.
Suicide kills 132 Americans every day. The first step of suicide prevention is risk identification and prognostication. Researchers like this study team have developed and validated predictive models that use routinely collected Electronic Health Record (EHR) data like past diagnoses and medications to predict future suicide attempt risk. The study team's model based in machine learning is known as the Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL). VSAIL has been validated prospectively and externally to predict suicide attempt risk with a number needed to screen (NNS) of 271 for suicide attempt and 23 for suicidal ideation. NNS is the number of people who need to receive a test result to prevent one outcome - lower NNS is better. This study will evaluate the effectiveness of a Clinical Decision Support System called Vanderbilt Safecourse using VSAIL to prompt a novel Best Practice Advisory (BPA) to prompt face-to-face screening with a validated suicide screening instrument like the Columbia Suicide Severity Rating Scale (CSSRS).
This protocol will collect real-world data retrospectively from the electronic health record (EHR) as data obtained from the delivery of routine medical care to develop a machine learning (ML)-based Clinical Decision Support (CDS) system for severe sepsis prediction and detection.
Scale-up of HIV preexposure prophylaxis (PrEP) is a key strategy of the federal initiative to end the HIV epidemic. However, healthcare providers lack tools to identify patients who are at increased risk for HIV infection and thus likely to benefit from PrEP. This pilot study will test the hypothesis that an electronic health record (EHR)-based clinical decision support system that incorporates an HIV risk prediction model can help providers identify patients at increased risk for HIV infection and improve PrEP prescribing in safety-net community health centers. The clinical decision support system will be implemented in the EHR at 2-3intervention clinics, while 2 control clinics will receive standard of care. The primary outcome is PrEP prescriptions. Other key metrics of PrEP-related care to be assessed include medication persistence, adherence to monitoring guidelines for PrEP, and rates of HIV/STI testing and diagnoses. The expected outcome is the foundation for a large-scale cluster randomized trial to test whether EHR-based clinical decision support tools for PrEP can improve PrEP prescribing and prevent new HIV infections in a national network of community health centers.
The central hypothesis of this proposal is that the addition of a theory-informed "nudge" to a clinical decision support (CDS) tool will address identified behavioral barriers to use and significantly improve adoption by providers. Nudges are applications of behavioral science, defined as positive reinforcement and indirect suggestions that have a non-forced effect on decision making. This study will use a behavioral theory-informed process to develop a new CDS tool that includes a nudge that addresses barriers to adoption.
Despite steady increases in obesity prevalence, the more than 12 million obese U.S. adults with type 2 diabetes (T2DM) and severe obesity encounter a number of barriers to adopting effective surgical and pharmaceutical treatments, including: (a) both patients and primary care clinicians frequently underestimate the effectiveness and potential benefits of obesity treatments; and (b) both patients and clinicians typically lack access to evidence-based estimates of the patient-specific potential benefits and risks of appropriate obesity treatment options. This project addresses these important obstacles to evidence-based obesity care by providing accurate, patient-specific estimates of benefits and risks of various obesity treatment options to inform shared decision making about obesity treatment. In this project the study team will implement a scalable, web-based point-of-care decision-support intervention in primary care that provides patient-specific estimates of obesity treatment benefits and risks in a randomized trial in 40 primary care clinics with 15,810 eligible patients, and assess intervention impact on (i) appropriate active management of obesity in eligible patients, (ii) weight trajectories, and (iii) patient and clinician satisfaction with the decision support intervention.
The goal of the PedsBP CDS research project is to adapt a previously tested web-based clinical decision support tool that appropriately identifies high blood pressure in youth for use in a primarily rural health system and compare approaches to CDS implementation in 45 primary care clinics treating children in 3 upper Midwest states. This project will advance implementation science and address a critical need for youth at risk for cardiovascular disease and with limited access to pediatric subspecialty care.
Atrial fibrillation (AF) is a major public health problem: it impairs quality of life and independently heightens the risks of ischemic stroke, heart failure and all-cause mortality. AF is a common reason for presenting to emergency departments (ED) in Kaiser Permanente Northern California (KPNC) and is associated with frequent hospitalization. Additionally, inter-facility hospitalization rates for AF vary across KPNC. Improvements in modifiable components of ED AF care could potentially reduce low-yield hospitalizations and the associated costs, patient inconveniences, and complications that can ensue. Real-time clinical decision support systems (CDSS) can transform entrenched physician practices and improve patient outcomes. The investigators will conduct a stepped-wedge cluster randomized trial of a CDSS intervention across 13 KPNC EDs for the comprehensive management of acute AF with the following three aims: 1) To evaluate the impact of the CDSS intervention on index hospitalization rates; 2) To evaluate the impact of the CDSS intervention on ED AF rate and rhythm control process-of-care metrics; and 3) To evaluate the impact of the CDSS intervention on AF stroke prevention actions for eligible participants at the time of ED discharge. The investigators hypothesize that the CDSS intervention will safely reduce index hospitalization rates, improve rate and rhythm control process-of-care metrics, and increase stroke prevention actions for eligible participants at ED discharge and within 30 days.