163 Clinical Trials for Various Conditions
This study aims to improve how lab results are communicated to older adults by refining a predictive model that uses electronic health record (EHR) data. The model was originally developed to estimate the risk of chronic kidney disease (CKD) progression. Researchers will use existing health data to test and improve the accuracy of the model and explore how it might be adapted for use in other health conditions. The study does not involve direct interaction with patients and is conducted entirely using de-identified data in a secure environment.
The goal of this experimental study is to learn whether different types of best practice advisories (BPAs) that direct clinicians to reference clinical guidelines embedded in the electronic health record (EHR) increase the delivery of evidence-based care in children presenting to the hospital with bronchiolitis. The main questions it aims to answer are: * Do BPAs improve clinicians' delivery of guideline-concordant care in bronchiolitis? * Do interruptive BPAs improve guideline-concordant care of bronchiolitis more than non-interruptive BPAs? Researchers will compare the treatment and outcomes of patients whose clinicians did not receive a BPA, to those whose clinicians received a non-interruptive BPA, to those whose clinicians received an interruptive BPA. Patients will continue to receive standard hospital care for bronchiolitis. Clinicians will: * retain access to an EHR-embedded clinical guideline for bronchiolitis care * be exposed to either no BPA, a non-interruptive BPA, or an interruptive BPA promoting the EHR-embedded clinical guideline (randomized per patient encounter)
Investigators are building an empirical evidence base for real world data through large-scale emulation of randomized controlled trials. The investigators' goal is to understand for what types of clinical questions real world data analyses can be conducted with confidence and how to implement such studies.
The overall goal of the proposed research is to refine and adapt and perform efficacy testing of a novel reinforcement learning-based approach to personalizing EHR-based tools for PCPs on deprescribing of high-risk medications for older adults. The trial will be conducted at Atrius Health, an integrated delivery network in Massachusetts, and will intervene upon primary care providers. The investigators will conduct a cluster randomized trial using reinforcement learning to adapt electronic health record (EHR) tools for deprescribing high-risk medications versus usual care. 60 PCPs will be randomized (i.e., 30 each to the reinforcement learning intervention and usual care \[no EHR tool\] in each arm) to the trial and follow them for approximately 30 weeks. The primary outcome will be discontinuation or ordering a dose taper for the high-risk medications for eligible patients by included primary care providers, using EHR data at Atrius. The primary hypothesis is that the personalized intervention using reinforcement learning will improve deprescribing compared with usual care.
Identifying eligible patients is a key process in the clinical trial enterprise. Currently, this process relies on time-intensive manual chart review, creating a rate-limiting step for trial participation. The integration of AI technology into the trial screening process has potential to improve participation rates. This study aims to assess the performance (accuracy, efficiency) of AI-augmented patient identification and inform optimal integration into clinical research screening processes.
Based on the outcomes of the initial study (NCT05348603), this optimization study will employ the most effective interventions (letters and messages) and distribute these communications to underrepresented minorities to further promote interest in research. Optimized language will be distributed in English, Spanish, Portuguese, Chinese, Arabic, or Haitian Creole, based on preferred language identified in the patient profile in an electronic medical records system.
Kaiser Permanente Northern California (KPNC) has been a national leader in implementation of HIV preexposure prophylaxis (PrEP), a daily antiretroviral pill that is more than 99% effective in preventing HIV acquisition. However, many patients at risk for HIV at KPNC are not yet using PrEP, resulting in preventable infections each year. In prior work, the study team developed and validated a prediction model that used electronic health record (EHR) data from 3.7 million KPNC members to identify patients at high risk of HIV acquisition but not using PrEP. The model substantially outperformed models based only on CDC criteria for PrEP use, particularly for Black patients, a population with high HIV incidence and lower PrEP uptake. The objective of this proposal is to evaluate the feasibility of implementing this EHR-based model at the point of care to increase PrEP referrals and uptake at KPNC. The specific aims of this project are to 1) conduct provider focus groups to identify barriers and facilitators to PrEP referrals and to optimize the delivery mechanism of our clinical decision support intervention, 2) evaluate the feasibility and acceptability of a clinical decision support intervention for primary care providers (PCPs) to increase PrEP referrals and uptake among high-risk patients, and 3) assess patient- and provider-based characteristics associated with PrEP referrals and uptake. To accomplish these aims, the study team proposes a randomized controlled trial of a clinical decision support intervention for PrEP, which involves alerting KP San Francisco (KPSF) Adult and Family Medicine PCPs about patients identified by our prediction model as being at high risk for HIV acquisition prior to in-person clinic visits. We will compare PrEP referrals and uptake among patients who are seen by PCPs randomized to intervention and usual care study arms using an intention-to-treat analysis.
Investigators are building an empirical evidence base supporting the utility of real-world data through the emulation of randomized controlled trials in the oncology setting. The purpose of this work is to demonstrate whether real-world evidence studies can provide reliable conclusions on treatment effectiveness to inform further applications of real-world data in pharmaceutical product label expansion, post-marketing safety, and other purposes that are complementary to RCTs.
This is a three-phase study comprising both retrospective and prospective components, as follows: Phase I: Deployment of Rare Disease Algorithm: A diagnostic screening algorithm was developed using advanced analytical methods to identify patients who have an increased likelihood of having Gaucher disease. This tool will be applied to a health system's electronic health records (EHR). The top 50 active patients per healthcare system will be identified as "highly ranked by the RDA" and moved to Phase II. As three to four healthcare systems are expected to participate in this study, between 150 to 200 persons are expected to be identified and included in Phase II. Phase II: Retrospective review of medical records of highly ranked persons: The listing of persons highly-ranked by the RDA from phase I will be forwarded to the study team within each participating healthcare system. After reviewing the RDA reports and medical records of each highly ranked person, study site personnel will determine eligibility for Phase III based on the relevant selection criteria listed in the section below. Phase III: Prospective diagnostic testing: Eligible persons (or their parent/guardian) from Phase II will be contacted and asked to provide consent for inclusion into the study. After consent is received, blood samples will be collected and sent for Gaucher diagnostic testing. Because of overlap in clinical symptoms between Gaucher disease and acid sphingomyelinase deficiency (ASMD), patients will also receive diagnostic testing for ASMD. Results will be shared with study site personnel, who will subsequently inform the study subject (and/or their parent/guardian, where appropriate) of results. It is anticipated that participation of a typical subject will be less than 3 months.
HeartShare is a comprehensive study of heart failure, a common and serious medical condition which occurs when the heart is unable to keep up with the demands of the body, resulting in shortness of breath, fluid retention, and fatigue. HeartShare aims to better classify heart failure into subtypes to help develop more personalized treatments for patients, with the hope that this will improve the lives of heart failure patients. To do this, HeartShare is bringing together a large amount of data (including images, such as heart ultrasounds and MRIs and molecular data from the blood, such as genetics) from previously conducted studies and electronic health records, and is gathering new data through participants enrolled in the HeartShare Deep Phenotyping Study.
This study is being conducted to investigate a strategy that may improve knowledge and uptake of pre-exposure prophylaxis for HIV prevention (PrEP) among cisgender women in primary care.
The purpose of this study is to determine the effectiveness of enhanced features in an online patient portal including banners, a chatbot, and direct to patient message and traditional mailed letters on increasing interest in research among online patient portal users.
A non-interventional retrospective cohort study conducted to compare the naive sacubitril/valsartan Heart Failure with reduced Ejection Fraction (HFrEF) patient population to a matched naive ACEi/ARB HFrEF patient population.
A two-arm cluster randomized controlled trial targeting primary care providers will be conducted to evaluate the impact of a multicomponent electronic health record (EHR) intervention on hypertension management. Given the cluster trial design, randomization will be conducted at the site level, and in the intervention sites, all eligible providers will receive the intervention. The intervention consists of enhancing tools already available to primary care providers in the EHR system, including developing and implementing provider disparities dashboards, enhancing electronic decision support, and simplifying self-monitoring orders and communication materials. The intervention aims to improve blood pressure control and reduce health disparities in racial and ethnic minorities. Findings from this trial will provide important insight into whether a multicomponent intervention targeting providers and leveraging health information technology can reduce health disparities.
This study was designed to develop and test clinical decision support (CDS) tools that present clinical care team members with a given patient's social risk information and recommend care plan adaptations based on those risks. This study will test the hypothesis that providing care team members with CDS about patients' known social risks will result in improved outcomes. This study's primary outcomes are hypertension and diabetes control, but the results will have implications for a wide range of morbidities.
This study will be a multicenter clustered randomized trial of patients in hospitals in which a universal "SMART on FHIR" platform-based EHR-embedded IMPROVE DD VTE clinical prediction rules (CPRs) with electronic order entry has been incorporated into required admission and discharge EHR workflow versus hospitals following UMC for VTE risk assessment of medically ill patients. The patient population will consist of hospitalized, medically ill (non-surgical, non-obstetrical) individuals aged \> 60 years.
The objective of this research is to assess the effects of electronic health record (EHR)-based decision support tools on primary care provider (PCP) decision-making around pain treatment and opioid prescribing. The decision support tools are informed by principles of "behavioral economics," whereby clinicians are "nudged," though never forced, towards guideline-concordant care.
The objective of this research is to assess the effects of electronic health record (EHR)-based decision support tools on primary care provider (PCP) decision-making around pain treatment and opioid prescribing. The decision support tools are informed by principles of "behavioral economics," whereby clinicians are "nudged," though never forced, towards guideline-concordant care.
The objective of this research is to assess the effects of electronic health record (EHR)-based decision support tools on primary care provider (PCP) decision-making around pain treatment and opioid prescribing. The decision support tools are informed by principles of "behavioral economics," whereby clinicians are "nudged," though never forced, towards guideline-concordant care.
Prescribing of potentially unsafe medications for older adults is extremely common; benzodiazepines and sedative hypnotics are, for example, key drug classes frequently implicated in adverse health consequences for vulnerable older adults, such as confusion or sedation, leading to hospitalizations, falls, and fractures. Fortunately, most of these consequences are preventable. Physicians' lack of awareness of alternatives, ambiguous practice guidelines, and perceived pressure from patients or caregivers are among the reasons why these drugs are used more than might be optimal. Reducing inappropriate use of these drugs may be achieved through decision support tools for providers that are embedded in electronic health record (EHR) systems. While EHR strategies are widely used to support the informational needs of providers, these tools have demonstrated only modest effectiveness at improving prescribing. The effectiveness of these tools could be enhanced by leveraging principles of behavioral economics and related sciences.
The goal of this quality improvement program is to implement, evaluate, and sustain an evidence-based smoking cessation treatment program with a population-based approach so that all patients at the Siteman Cancer Center, Washington University, Barnes-Jewish Hospital, BJC Healthcare, and satellite locations receive assessment of smoking and all smokers receive treatment support.
This protocol represents a pilot randomized-controlled trial evaluating the effect of an electronic health record (EHR)-based peripheral artery disease (PAD) screening tool on rates of new non-invasive testing, diagnosis and treatment of PAD over a 6-month period. An EHR-based PAD screening tool will be applied to the Stanford EHR, which will generate a group of patients of varying risks of having undiagnosed PAD. Patients with the highest risk of having undiagnosed PAD will then be evaluated for inclusion in this study. 1:1 randomization will be performed on a consecutive basis until study enrollment is completed (25 patients per arm). Physicians of patients randomized to the intervention arm will be sent notification via an EHR message detailing the patient's risk of undiagnosed PAD and suggestions for referral to vascular medicine for risk assessment and/or non-invasive ankle brachial index (ABI) testing. The primary outcome is number of patients receiving ABI testing for PAD at 6 months, with secondary outcomes including number of new PAD diagnoses, number of new referrals to cardiovascular specialists (vascular medicine, vascular surgery, and/or cardiology) and number of patients receiving initiation of new cardiovascular medications (anti-platelet agents, statins, and/or antihypertensive agents).
Diabetes is a significant medical problem in the United States and across the world. Despite significant progress in understanding how to better manage diabetes, there is oftentimes still uncertainty in the optimal management strategy for a specific patient. As a result, providers and patients must often use a trial-and-error approach to identify an effective treatment regimen. The objective of this research is to evaluate a diabetes dashboard integrated with the electronic health record (EHR) that has been developed as a collaborative project between the University of Utah and Hitachi, Ltd. This dashboard tool provides a graphical overview of the patient's relevant data parameters as well as information on the impact of different treatment options on previous patients with similar characteristics. The different treatment options compare the predicted impact of relevant medication regimens as well as weight loss. Primary care clinics are randomized to either an intervention condition where the tool is available or to a control condition where the tool is not yet available. Patients' hemoglobin A1c levels (a measure of diabetes control) are the main outcome variable. Other secondary analyses will also be conducted. Use of the tool will be encouraged but optional. Following any suggestions made in the tool will also be optional and up to the discretion of the clinician.
This research is being done to find out if individualized feedback provided to parents on safe infant sleep can improve safety. This will be accomplished by having parents send photographs of their baby sleeping through the patient portal of the electronic health record (EHR).
Weight loss is normal for healthy newborns in the first few days, especially for those exclusively breastfed, who may have low enteral intake for several days. Although most newborns tolerate this early period of weight loss well, those with pronounced weight loss become at increased risk of feeding problems and hyperbilirubinemia, which are the two most common causes of neonatal readmission. To facilitate the assessment of risk for an individual newborn, the Newborn Weight Tool (NEWT) has been developed to categorize each infant's weight loss according to population norms, so that formula can be administered when weight loss is pronounced and avoided when weight loss is normal. The Healthy Start study will be a randomized, controlled trial testing whether displaying NEWT to clinicians providing newborn care can improve neonatal health outcomes including formula use, weight loss and readmission. Newborns will be randomly assigned either to display weight with NEWT weight categorization to their providers in the electronic health record (EHR) or to usual care (weight displayed without NEWT categorization).
There is a well-documented need for effective interventions that can help patients understand and safely adhere to prescribed medications, particularly those with greater potential for harm if not taken correctly. The investigators will leverage health and consumer technologies with their EHR-based Medication Complete Communication (EMC2) Strategy to: 1) inform patients about medication risks and safe use, 2) promote provider education and counseling about prescribed drugs and 3) monitor patient adherence outside of visits. The EMC2 Strategy could be feasible, sustainable, and readily available to ambulatory care practices.
This is a single center randomized trial that seeks to determine if the use of an automated real-time electronic medical record Acute Kidney Injury (AKI) risk score can improve patient outcomes through the use of an early standardized nephrology focused intervention.
This multi-site randomized tral aims to test methods of increasing adoption and integration of blood glucose monitoring into electronic medical records, and to measure the impact of wide-scale adoption on health status of patients with diabetes. To investigate determinants of adoption, the research will combine and test doctor and patient focused approaches to encouraging patient use of blood glucose flow sheets through the online patient portal, MyChart. Adoption will be measured on both the extensive and intensive margin: the number of patients who enter data into the flowsheets at all during the study period, and the mean number of entries per patient during the study period. Conditional on statistically significant increases in adoption, the study will examine corresponding intent-to-treat effects on patient A1c, and consider other indicators of possible mechanisms through which A1c improves or does not improve.
Problem Solving Therapy for Primary Care (PST-PC) is an evidence based psychosocial intervention (EBPI) for use in primary care settings, with more than 100 clinical trials. Despite its proven efficacy we have found that implementation of PST-PC is complicated, resulting in rapid program drift (deviation from protocol with associated loss of efficacy), among practitioners following completion of training. Many studied have shown that program drift is not uncommon in the implementation of EBPIs and can be mitigated through on-going decision support and supervision. Unfortunately, decision support and supervisors of EBPIs are not widely available in low-resourced primary care clinics. We will address this problem by creating decision support tools to be integrated into electronic health records. Because these tools are deemed by many practitioners in other fields to be burdensome, we will explicitly involve active input on the content, design and function of these support tools. Outcomes may include electronic dashboards for panel management, automated suggestions for application of PST-PC elements based on patient reported outcomes or integration of automated patient tracking, and support of patient engagement. We hypothesize that enhanced decision support (target mechanism) will sustain quality delivery of PST-PC, which in turn will improve patient reported outcomes.
This study will develop a new electronic health record module to improve guideline-compliant care of older adults with diabetes. The module will incorporate effective behavioral economics (BE) principles to improve the degree to which care of older adults is compliant with Choosing Wisely guidelines; this generally involves less aggressive targets for HbA1c, and reductions of medications other than metformin. The implementation of the module will ultimately be triggered by medication prescribing in EPIC. The BE principles include suggesting alternatives to medications, requiring justification, setting of appropriate default order sets, and incorporation of anchoring and checklists to guide behavior. The study will involve provider workflow analysis based on observation, module user testing, and live usability testing with direct observation and semi-structure interviews.