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14 Decision Support System Trials Near You

Power is an online platform that helps thousands of Decision Support System patients discover FDA-reviewed trials every day. Every trial we feature meets safety and ethical standards, giving patients an easy way to discover promising new treatments in the research stage.

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No Placebo
Highly Paid
Stay on Current Meds
Pivotal Trials (Near Approval)
Breakthrough Medication

Clinical Decision Support for Functional Gastrointestinal Disorders

Indianapolis, Indiana
While gastroenterologists care for many of the pediatric patients with Functional gastrointestinal disorders (FGIDs), the majority of the burden continues to be borne by general pediatricians, especially with respect to initial diagnosis. Unfortunately, FGIDs are often diagnosed incorrectly by primary care providers, and patients often wait months to years before a correct diagnosis is made, and effective treatment is begun. Furthermore, primary care providers are often unaware of recent guideline changes or the evidence base for children with FGIDs, leading to overuse of testing, inappropriate or ineffective treatment, and increased costs. Given this information, it is essential that we develop interventions that target pediatric primary care providers to improve their care for children with FGIDs. The investigators propose that using a Clinical Decision Support System (CDSS) that incorporates the Rome IV criteria for diagnosis and evidence-based care for FGIDs will improve the (1) accuracy of diagnosis and (2)_ effectiveness of clinical care. A CDSS has advantages with respect to guideline adherence and automated diagnosis, because it can provide focused, real-time, patient-specific data to the clinician. The investigators hypothesize that automation of screening, diagnosis, and management of FGIDs using the Rome IV criteria will result in improved resolution of FGIDs (primary outcome), as well as decreased utilization of medical services (secondary outcomes). This hypothesis will be tested utilizing a randomized controlled trial. The intervention clinic sites will be provided access to both the FGIDs Screening Module and the Treatment Module. The control clinics will have the FGIDs Screening Module. However, control clinics will not have access to the FGIDs Treatment Module. These clinic sites will be given access to the pre-screener form section of the module, so that providers are made aware of a positive screen.
No Placebo Group

Trial Details

Trial Status:Active Not Recruiting
Age:1 - 17

33 Participants Needed

APOL1 Genetic Testing for Kidney Disease

Chicago, Illinois
Living donor (LD) kidney transplantation is the optimal treatment for patients with end-stage kidney disease (ESKD). However, LDs take on a higher risk of future ESKD themselves. African American (AA) LDs have an even greater, 3.3-fold, risk of ESKD than white LDs post-donation. Because evidence suggests that Apolipoprotein L1 (APOL1) risk variants contribute to this greater risk, transplant nephrologists are increasingly using APOL1 testing to evaluate LD candidates of African ancestry. However, nephrologists do not consistently perform genetic counseling with LD candidates about APOL1 due to a lack of knowledge and skill in counseling about APOL1. Without proper counseling, APOL1 testing will magnify LD candidates' decisional conflict about donating, jeopardizing their informed consent. Given their elevated risk of ESRD post-donation, and AAs' widely-held cultural concerns about genetic testing, it is ethically critical to protect AA LD candidates' safety through APOL1 testing in a culturally competent manner to improve informed decisions about donating. No transplant programs have integrated APOL1 testing into LD evaluation in a culturally competent manner. Clinical "chatbots," mobile apps that use artificial intelligence to provide genetic information to patients and relieve constraints on clinicians' time, can improve informed treatment decisions and reduce decisional conflict. The chatbot "Gia," created by a medical genetics company, can be adapted to any condition. However, no chatbot on APOL1 is currently available. No counseling training programs are available for nephrologists to counsel AA LDs about APOL1 and donation in a culturally competent manner. Given the shortage of genetic counselors, increasing nephrologists' genetic literacy is critical to integrating genetic testing into practice. The objective of this study is to culturally adapt and evaluate the effectiveness of an APOL1 testing program for AA LDs at two transplant centers serving large AA LD populations (Chicago, IL, and Washington, DC). The APOL1 testing program will evaluate the effect of the culturally competent testing, chatbot, and counseling on AA LD candidates' decisional conflict about donating, preparedness for decision-making, willingness to donate, and satisfaction with informed consent. The specific aims are to: 1. Adapt Gia and transplant counseling to APOL1 for use in routine clinical practice 2. Evaluate the effectiveness of this intervention on decisional conflict, preparedness, and willingness to donate in a pre-post design 3. Evaluate the implementation of this intervention into clinical practice by using the RE-AIM framework to longitudinally evaluate nephrologist counseling practices and LDs' satisfaction with informed consent. The impact of this study will be the creation of a model for APOL1 testing of AA LDs, which can then be implemented nationally via implementation science approaches. APOL1 will serve as a model for integrating culturally competent genetic testing into transplant and other practices to improve patient informed consent.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased

390 Participants Needed

EHR Nudges for Prescribing Errors

Chicago, Illinois
The goal of this clinical trial is to learn if electronic health record (EHR) nudges (changes to the EHR that do not restrict freedom of choice or alter incentives) can reduce Z-drug prescribing in primary care clinics for patients with insomnia. The main questions it aims to answer are: 1. Can Z-drug prescribing be reduced by setting the dispense quantity default of new Z-drug orders in the EHR to 10 pills with 0 refills? 2. Can Z-drug prescribing be reduced by an EHR alert that suggests clinicians remove a Z-drug and/or add an evidence-based behavioral treatment for insomnia, followed by a request to justify their reasoning if the suggestion is not followed? 3. Does combining these two nudges reduce Z-drug prescribing? Researchers will compare each nudge individually and in combination to an guideline education control group to see if each nudge (separately and in combination) can reduce Z-drug prescribing. Clinician-participants will: 1. Complete an introductory educational module about treating insomnia and relevant EHR changes. 2. Complete their routine patient visits. 3. Either experience EHR changes when prescribing Z-drugs, including a Z-drug dispense quantity default of 10 pills for new orders, a prompt to remove or justify Z-drug orders, both, or neither.
No Placebo Group

Trial Details

Trial Status:Enrolling By Invitation
Trial Phase:Unphased

443 Participants Needed

Electronic Medical Record Support for Diabetes Management

Hershey, Pennsylvania
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.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased

15732 Participants Needed

SEPSys and RESCUE Scores for Sepsis Prediction

Largo, Maryland
This study is designed to test two new risk scores - one designed to predict a patient's four-hour risk of developing sepsis and one designed to predict a patient's four-hour risk of deterioration (cardiac arrest, death, unplanned ICU transfer, or rapid response team call). The goal of this study is to improve provider awareness of a patient's risk of these two negative outcomes by providing them with new risk scores. The primary outcome will be the time from when the risk score becomes elevated to when vital signs such as heart rate or blood pressure are measured, suggesting an increased awareness.
No Placebo Group

Trial Details

Trial Status:Not Yet Recruiting
Trial Phase:Unphased

150000 Participants Needed

EHR Alerts for Aspirin Use in High-Risk Pregnancies

Danville, Pennsylvania
The goal of this study is to assess the effect of an electronic health record (EHR) clinical decision support tool, also known as a best practice alert (BPA), on healthcare provider recommendations for low dose aspirin use in a high-risk pregnant patient population. The investigators hypothesize that the implementation of the EHR BPA tool will increase the healthcare provider's recommendation for low dose aspirin compared to current standard care.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased
Sex:Female

640 Participants Needed

Education + Decision Support for Blood Clots After Cancer Surgery

Charleston, South Carolina
While blood clots after major cancer surgery are common and harmful to patients, the medications to decrease blood clot risk are seldom used after patients leave the hospital despite the recommendation of multiple professional medical societies. The reason why these medications are seldom prescribed is not well understood. The main questions this study aims to answer are: * Does surgeon education paired with an electronic medical record based decision support tool improve the guideline concordant prescription of pharmacologic venous thromboembolism after abdominopelvic cancer surgery? * Does dedicated patient education regarding blood clots at the time of hospital discharge after abdominopelvic cancer surgery improve understanding of the risk of venous thromboembolism and adherence to pharmacologic prophylaxis? The investigators will study these questions using a stepped-wedge randomized trial where groups of surgeons will use a tool integrated to the electronic medical record to educate them on the individualized patient risks of blood clots after major cancer surgery and inform them regarding guidelines for preventative medicines. Utilization of the medications before and after using the tool will be compared. Patients will be administered a questionnaire assessing their awareness of blood clots as a risk after cancer surgery. For those prescribed medications to reduce blood clot risk after leaving the hospital, the questionnaire will evaluate whether they took the medications as prescribed. Survey results will be evaluated before and after implementation of education on blood clot risk at the time of hospital discharge.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased

30 Participants Needed

AI Decision Support for Gastrointestinal Bleeding

New Haven, Connecticut
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.
No Placebo Group

Trial Details

Trial Status:Recruiting

102 Participants Needed

Collaborative Care Model for Depression

Lebanon, New Hampshire
The goal of this clinical trial is to learn if adding patients' goals and concerns to measurement-based collaborative care can tailor care and provide a more holistic view of treatment, thereby improving engagement in care among adult patients receiving collaborative care. The main questions it aims to answer are: * Does using a clinical decision support system (which includes an enhanced pre-visit questionnaire and patient-level dashboard) improve patient engagement in the collaborative care model? * Does using a clinical decision support system improve patient and clinician satisfaction with care? Researchers will compare the enhanced collaborative care with traditional collaborative care. Patient participants will complete pre-visit questionnaires before their collaborative care appointments. Responses will be viewed by the clinician and/or patient in a visual dashboard inside the electronic health record.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased

2448 Participants Needed

Machine Learning Tool for Opioid Overdose

Gainesville, Florida
This clinical trial aims to evaluate the pilot implementation of a machine-learning (ML)-driven clinical decision support (CDS) tool designed to predict opioid overdose risk within the electronic health record (EHR) system at UF Health Internal Medicine and Family Medicine clinics in Gainesville, Florida. The study will use a pre- versus post-implementation design to compare outcomes within clinics, focusing on measures such as naloxone prescribing rates and opioid overdose occurrences. Researchers will also assess the usability, acceptability, and feasibility of the CDS tool through qualitative interviews with primary care clinicians (PCPs) in the participating clinics.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased

2000 Participants Needed

Clinical Decision Support Tool for Heart Failure

Aurora, Colorado
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.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased

500 Participants Needed

Personalized CDS for Heart Failure

Aurora, Colorado
Clinical decision support (CDS) tools can 'nudge' clinicians to make the best decisions easy. Although required by "meaningful use" regulations, more than 40% of CDS lead to no change and the remaining lead to improvements that are modest at best. This is because CDS tools often ignore contextual factors and present irrelevant information. Although many tools have undergone patient-specific optimization, 'traditional CDS' are rarely clinician-specific. For example, a traditional CDS tool for beta blockers and heart failure with reduced ejection fraction (HFrEF) addresses common prescribing misconceptions by stating asthma is not a contraindication and providing a safe threshold for blood pressure. For clinicians without these misconceptions, these statements are irrelevant and distract from key information. A 'personalized CDS' would evaluate clinician past prescribing patterns to determine whether prescribing misconceptions might exist and then conditionally present information to address those misconceptions. The objective of this research is to create personalized clinician-specific CDS that overcome shortcomings of traditional CDS. The central hypothesis is a personalized CDS that minimizes irrelevant information will lead to a higher rate of prescribing guideline-directed management and therapy (GDMT) for HFrEF compared to a traditional CDS.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased

1075 Participants Needed

AFM System for Postoperative Recovery in High-Risk Abdominal Surgery

Irvine, California
Goal directed fluid therapy (GDFT) or "Personalized fluid therapy" may benefit high-risk surgical patients but these strategies are infrequently implemented. It has also been shown that without any goal or protocol for fluid resuscitation, large inter- and intra-provider variability exist that have been correlated with poor patient outcomes. Recently, an "Assisted Fluid Management" (AFM) system has been developed to help ease some of the work associated with GDFT protocol implementation. The AFM system may help increase GDFT protocol adherence while leaving direction and guidance in the hands of the care providers. This artificial intelligence-based system can suggest administration of fluid boluses, analyse the hemodynamic effects of the bolus, and continually re-assess the patient for further fluid requirements. To date, there are no large outcome study using this AFM system. The primary objective of this trial is thus to evaluate the impact of this AFM system to guide fluid bolus administration on a composite of major postoperative complications in high-risk patients undergoing high-risk abdominal surgery.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased

2000 Participants Needed

CirrhosisRx CDS for Liver Cirrhosis

San Francisco, California
The aim of the study is to compare the effect of CirrhosisRx, a novel clinical decision support (CDS) system for inpatient cirrhosis care, versus "usual care" on adherence to national quality measures and clinical outcomes for hospitalized patients with cirrhosis.
No Placebo Group

Trial Details

Trial Status:Recruiting
Trial Phase:Unphased

2106 Participants Needed

Why Other Patients Applied

"I was diagnosed with stage 4 pancreatic cancer three months ago, metastatic to my liver, and I have been receiving and responding well to chemotherapy. My blood work revealed that my tumor markers have gone from 2600 in the beginning to 173 as of now, even with the delay in treatment, they are not going up. CT Scans reveal they have been shrinking as well. However, chemo is seriously deteriorating my body. I have 4 more treatments to go in this 12 treatment cycle. I am just interested in learning about my other options, if any are available to me."

ID
Pancreatic Cancer PatientAge: 40

"As a healthy volunteer, I like to participate in as many trials as I'm able to. It's a good way to help research and earn money."

IZ
Healthy Volunteer PatientAge: 38

"I changed my diet in 2020 and I’ve lost 95 pounds from my highest weight (283). I am 5’3”, female, and now 188. I still have a 33 BMI. I've been doing research on alternative approaches to continue my progress, which brought me here to consider clinical trials."

WR
Obesity PatientAge: 58

"I've tried several different SSRIs over the past 23 years with no luck. Some of these new treatments seem interesting... haven't tried anything like them before. I really hope that one could work."

ZS
Depression PatientAge: 51

"I've been struggling with ADHD and anxiety since I was 9 years old. I'm currently 30. I really don't like how numb the medications make me feel. And especially now, that I've lost my grandma and my aunt 8 days apart, my anxiety has been even worse. So I'm trying to find something new."

FF
ADHD PatientAge: 31

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We started Power when my dad was diagnosed with multiple myeloma, and I struggled to help him access the latest immunotherapy. Hopefully Power makes it simpler for you to explore promising new treatments, during what is probably a difficult time.

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Learn More About Trials
How Do Clinical Trials Work?Are Clinical Trials Safe?What Can I Expect During a Clinical Trial?

Frequently Asked Questions

How much do Decision Support System clinical trials pay?

Each trial will compensate patients a different amount, but $50-100 for each visit is a fairly common range for Phase 2–4 trials (Phase 1 trials often pay substantially more). Further, most trials will cover the costs of a travel to-and-from the clinic.

How do Decision Support System clinical trials work?

After a researcher reviews your profile, they may choose to invite you in to a screening appointment, where they'll determine if you meet 100% of the eligibility requirements. If you do, you'll be sorted into one of the treatment groups, and receive your study drug. For some trials, there is a chance you'll receive a placebo. Across Decision Support System trials 30% of clinical trials have a placebo. Typically, you'll be required to check-in with the clinic every month or so. The average trial length for Decision Support System is 12 months.

How do I participate in a study as a "healthy volunteer"?

Not all studies recruit healthy volunteers: usually, Phase 1 studies do. Participating as a healthy volunteer means you will go to a research facility several times over a few days or weeks to receive a dose of either the test treatment or a "placebo," which is a harmless substance that helps researchers compare results. You will have routine tests during these visits, and you'll be compensated for your time and travel, with the number of appointments and details varying by study.

What does the "phase" of a clinical trial mean?

The phase of a trial reveals what stage the drug is in to get approval for a specific condition. Phase 1 trials are the trials to collect safety data in humans. Phase 2 trials are those where the drug has some data showing safety in humans, but where further human data is needed on drug effectiveness. Phase 3 trials are in the final step before approval. The drug already has data showing both safety and effectiveness. As a general rule, Phase 3 trials are more promising than Phase 2, and Phase 2 trials are more promising than phase 1.

Do I need to be insured to participate in a Decision Support System medical study?

Clinical trials are almost always free to participants, and so do not require insurance. The only exception here are trials focused on cancer, because only a small part of the typical treatment plan is actually experimental. For these cancer trials, participants typically need insurance to cover all the non-experimental components.

What are the newest Decision Support System clinical trials?

Most recently, we added SEPSys and RESCUE Scores for Sepsis Prediction, Machine Learning Tool for Opioid Overdose and Collaborative Care Model for Depression to the Power online platform.

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By Trial

Lifestyle Physical Activity for Metabolic Diseases

REGN5837 + Odronextamab for Aggressive B-Cell Lymphoma

Computerized Education for Kidney Disease

Atezolizumab + Chemotherapy for Bladder Cancer

Coding Training for Developmental Disabilities

GET73 for Alcoholism

Antibiotics for Gum Disease

Health Screening and Referral for Premature Birth

Probiotic vs Saline Wash for Preventing UTIs in Neurogenic Bladders · Recruiting Participants for Phase Phase 2 Clinical Trial 2025 | Power | Power

Estradiol for Perimenopause

Molecularly Targeted Therapy for Advanced Cancer

Immunotherapy + Chemotherapy for Small Cell Lung Cancer

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