58 Clinical Trials for Various Conditions
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.
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.
The objective of this proposal is to compare clinical outcomes, implementation metrics (i.e., patient reach and clinician adoption), and clinician preferences of two designs (customized vs. commercial) of a clinical decision support (CDS). Beta blocker prescribing for patients with heart failure will be used as a test case. The best practices in CDS design, including the user-centered design will be incorporated into the customized CDS and compared to a commercially-available CDS in the electronic health record using a cluster randomized trial.
This clinical trial evaluates the use of novel decision support educational materials and services using health coaches. The study includes men newly diagnosed with low-risk prostate cancer. A 160 men will be recruited. Half of the men will receive a call from a health coach before their initial consultation visit with their urologist to review their treatment concerns and questions. The other half will receive usual care provided by the urologist, such as educational materials and services provided by the urologist.
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.
Among patients with cognitive impairment (CI) that undergo surgery, the risk for developing postoperative delirium (POD) is high (50%) and associated with further morbidity and mortality. Yet, 30-40% of POD cases are preventable with perioperative management. This randomized pragmatic clinical trial aims to assess incidence of POD in adult surgical patients with CI, as well as provider adherence to a set of 12 perioperative best practice recommendations for perioperative management. Electronic health record (EHR) data will be used to identify patients as high risk for developing POD and clinical decision support (CDS) prompts within the EHR will display best practices. Cases will be randomized to either the control group, usual care or the intervention which includes the high-risk alert and best practice prompts.
Preventing contextual errors requires heightening clinician responsiveness to clues that there are contextual factors during the clinical encounter, in real time. These clues, termed contextual red flags are evident in two sources: the medical record and from patients directly. An effective intervention would prompt clinicians to determine whether there are underlying contextual factors that could be addressed in the care plan, averting contextual error. This desirable process is termed contextual probing. While clinical decision support (CDS) has been used to provide physicians with timely biomedical information at the point of care to prevent errors and promote appropriate care, this technology also affords an opportunity to draw physician attention to both contextual red flags and contextual factors in order to avert contextual errors. This study assesses the potential of "contextualized CDS" to improve contextualization of care through a randomized controlled intervention trial, with assessment measures of both patient health care outcomes and averted costs associated with overuse and misuse of medical services. The three hypotheses are that CDS: 1. Reduces contextual error: CDS tools that inform clinicians of contextual factors and prompt them to explore contextual red flags should result in a reduction in contextual error. 2. Improve health care outcomes: Contextualized CDS predicts improved health care outcomes defined as a partial or full resolution of the contextual red flag (e.g. elevated HgB A1c) after the index visit. 3. Reduces avoidable health care costs: Contextualized CDS is associated with a reduction in misuse and overuse of inappropriate or unnecessary medical services.
The investigators propose an evaluation of an end of life patient decision aid (EOL-PtDA) developed by the Foundation for Informed Medical Decision Making using the RE-AIM (Reach Effectiveness, Adoption, Implementation, and Maintenance) framework. To evaluate the Reach and Effectiveness, the investigators propose a pilot randomized clinical trial of the EOL-PtDA among patients on the inpatient palliative care service at University Hospital in Colorado. To evaluate barriers and facilitators of Adoption, Implementation, and Maintenance of the EOL-PtDA, we propose focus groups of non-palliative care physicians as we perceive these physicians to be the largest barrier to ultimate adoption of the EOL-PtDA. Additionally we propose a focus group of the palliative care physicians who participated in the implementation of this decision guide study to evaluate the feasibility of conducting a randomized control trial within a pall. care service and to evaluate the acceptability of this decision aid as it was implemented. The investigators also propose to conduct focus groups of normal, healthy clinic patients to determine the acceptability among that population. The End-of-Life decision aid is different from other decision aids. From the vantage point of decision quality, a major difference is that its primary focus is on helping patients clarify their values rather than gain knowledge. The results from this study will provide critical preliminary data to inform a randomized clinical trial and/or widespread implementation of the EOL-PtDA. Specific Aims/Research question(s):Aim 1: To determine the Reach and Effectiveness of an end-of-life patient decision aid by conducting a pilot randomized clinical trial in an inpatient palliative care service. Aim 2: To determine physicians' attitudes towards the end-of-life patient decision aid and to gain insights into potential barriers and facilitators to Adoption, Implementation, and Maintenance by conducting a qualitative study of non-palliative care, and separately, palliative care physicians. Aim 3: To determine healthy clinic patients' attitudes towards the end-of-life patient decision aid and to gain insight into its Reach and Effectiveness.
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.
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.
This study will compare the postoperative recovery of two patient cohorts who attended outpatient physical therapy at two clinics in the Greenville, South Carolina area. The Usual Care cohort received care according to each clinic's pre-existing care guidelines. The CDS Cohort received care informed by a new clinical decision support (CDS) tool.
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.
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.
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.
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.
Polypharmacy is common among older adults in the United States and is associated with harms such as adverse drug reactions and higher costs of care. This pilot-phase project is designed to test two electronic health record (EHR)-based behavioral economic nudges to help primary care clinicians reduce the rate of high-risk polypharmacy among their older adult patients.
More than 50% of adults treated for diabetes, hypertension, or lipid disorders have suboptimal medication adherence, a prominent barrier to continued improvement in chronic disease care in the United States. Primary care providers (PCPs) often fail to identify medication nonadherence and/or have insufficient time and training to address underlying reasons for it. In this project, we propose a patient-centered and technology-driven strategy to identify patients with adherence issues and apply a team approach to help them achieve evidence-based personalized goals for glucose, blood pressure, or lipids. This intervention extends the use of a widely available clinical decision support (CDS) infrastructure to support a model of care that, for the first time outside of a fully integrated care environment, will integrate pharmacists within the primary care team. The intervention relies on a continuous health informatics loop to do the following: (a) identify high-risk patients with adherence problems at the point of care by expanding the capability of an electronic medical record (EMR)-linked CDS to identify poor adherence to medications; (b) establish and maintain an auto-populating up-to-date registry of patients identified for proactive pharmacist outreach; (c) implement a pharmacist outreach strategy based on an information-motivation-behavioral (IMB) framework recommended by the World Health Organization (WHO) with demonstrated ability to influence adherence across a variety of clinical applications; and (d) coordinate care and adherence information by incorporating pharmacist assessment and action plans into CDS at subsequent office encounters.
The overarching goal of this project is to reduce smoking-associated morbidity and mortality by increasing the number of patients who are referred for tobacco cessation counseling. Using a stratified, group-randomized, controlled, 2-arm trial conducted in two settings, the investigators will compare smoking-related clinical decision support (CDS) to usual care.
The overarching goal of this project is to de-implement the reliance on opioid analgesics and to implement reliance on non-opioid analgesics to manage postoperative pain following dental extractions. Using a prospective, provider-level, 3-arm cluster randomized trial design, the investigators will compare different strategies to reduce the reliance on opioids and increase the use of alternative pain management approaches utilizing information support tools aimed at both providers and their patients.
The purpose of this study is to determine whether a decision support system can improve the adherence to thresholds for low blood pressure by anesthesia providers, which in turn prevents their patients from having organ injury.
The purpose of this study is to determine how automated recommendations are best presented to optimize the adherence to guidelines on prophylaxis for nausea and vomiting after surgery.
This pragmatic trial examines the uptake and effects of primary care clinician commitments to follow 3 Choosing Wisely® recommendations. The investigators hypothesize that pre-encounter invitations to clinicians to commit to the recommendations will decrease ordering of: (1) imaging tests for low back pain, (2) antibiotics for acute sinusitis, and (3) imaging tests for headaches. The study is a mixed-methods, stepped wedge cluster randomized trial in which the intervention will be sequentially introduced to 6 clinics in southeastern Michigan in a randomly assigned order.
The utilization of clinical decision support (CDS) is increasing among healthcare facilities which have implemented computerized physician order entry or electronic medical records. Formal prospective evaluation of CDS implementations occurs rarely, and misuse or flaws in system design are often unrecognized. Retrospective review can identify failures but is too late to make critical corrections or initiate redesign efforts. A real-time surveillance dashboard for high-alert medications integrates externalized CDS interactions with relevant medication ordering, administration, and therapeutic monitoring data. The surveillance view of the dashboard displays all currently admitted, eligible patients and provides brief demographics with triggering order, laboratory, and CDS failure data to allow prioritization of high-risk scenarios. The patient detail view displays a detailed timeline of orders, order administrations, laboratory values, and CDS interactions for an individual patient and allows users to understand provider actions and patient condition changes occurring in conjunction with CDS failures. Clinical pharmacists' use of the dashboard for patient monitoring and intervention aims to increase the rate and timeliness of intercepted medication errors compared to CPOE-based CDS in the setting of acute kidney injury, which affects patients at various points across all hospital units and services and has numerous opportunities for intervention.
The purpose of this study is to determine the value of shared health information on care quality and costs when this information is used to notify care providers about concerning health events for patients cared for by a community-based network of providers.
Employing a physician-directed case management system, utilizing a Certified Registered Nurse Practitioner (CRNP) in conjunction with computer-based decision support technology (CDST) will result in significantly lower total cholesterol and a lower low density lipoprotein cholesterol in a group of subjects enrolled in a general medical clinic compared to subjects managed by primary care providers in the usual care group.
The investigators propose a three-arm, pragmatic, cluster-randomized clinical trial based in primary care. Participating Primary Care Providers (PCPs) will be randomized to usual care or one of two intervention arms. The first intervention will evaluate the efficacy of an automated CDSS that utilizes the electronic health record (EHR) to facilitate triple marker test ordering, guideline implementation and BP management, compared with usual care, among patients with previous documentation of eGFRcreat \<60 ml/min/1.73m2. The second intervention goes a step further, and will evaluate whether a CDSS plus a follow-up telephone call from a pharmacist (CDSS PLUS) can improve BP management and patient CKD and NSAID toxicity knowledge among the patients with CKD, compared with CDSS alone. The primary clinical outcome is BP level, with secondary outcomes related to processes of care and patient knowledge.
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.
Chronic kidney disease (CKD) is a highly prevalent, poorly recognized and undertreated and increases risk of atherosclerotic cardiovascular disease (ASCVD) and mortality. ASCVD risk interventions such as statin medications are not effective if initiated when kidney disease is advanced. Thus, early recognition of CKD is important for effective ASCVD risk management. Patient centered medical homes (PCMH)s (clinics which include nurse educators, dietitians, pharmacists and social workers) were designed to address gaps in care for complex chronic diseases such as CKD by increasing availability of ancillary services for patients. However, PCMH models have not been shown to improve the recognition and treatment of CKD and its associated ASCVD risk. The E DYNAMIC CDS retrieves real-time patient data from the electronic health record (EHR) every 24 hours to help primary care providers (PCP) identify patients with CKD and assess ASCVD risk and provide appropriate treatment. E-DYNAMIC also delegates CKD care with utilization of an opt-out approach for nurse education and dietitian referral. The overall objective of this pragmatic trial is to examine whether the E-DYNAMIC CDS increases PCP recognition of CKD and use of ASCVD risk management interventions when implemented within a PCMH. This pragmatic trial will be conducted within the Hines VA Hospital and community-based outpatient clinics designed as PCMH called teamlets. Teamlets include several PCPs, a nurse educator, a dietitian, a pharmacist, and a social worker. We will randomize 51 teamlets to the E-DYNAMIC CDS or to standard care. This pragmatic trial will address the following aims: 1) Determine the difference in PCP diagnosis of CKD stage 3-5 non-dialysis dependent CKD by allocation to the E-DYNAMIC CDS; 2) Determine the difference in PCPs ASCVD risk management of patients with stage 3-5 non-dialysis dependent CKD by teamlet allocation to the E-DYNAMIC CDS; 3) Determine the difference in patient use of ASCVD risk interventions and patient activation measures by their teamlet allocation to the E-DYNAMIC CDS. The primary outcomes of the pragmatic trial will be ascertained from the EHR. The E-DYNAMIC CDS tool may be transferred into other health systems that utilize an EHR and improve the diagnosis and management of CKD.
The investigators plan to further develop a prototype, evidence-based, electronic clinical decision support system (CDSS) for pneumonia care (ePneumonia) with interoperability across Electronic Health Records in order to improve clinical outcomes and reduce healthcare resource utilization. The specific aims of this study are to evaluate the usability of ePneumonia adapted for Cerner and its impact on clinical, patient-centered and healthcare resource utilization outcomes in a stepped-wedge implementation study in 16 hospital emergency departments (EDs) across the Intermountain Healthcare integrated health system.
The investigators propose to study the feasibility, acceptability, usability and outcomes of a new clinical decision support system for clinicians of opioid therapy in the context of specialist palliative care for serious chronic illness. The system provides clinicians with patient-specific genetic information on opioid responsiveness and multi-drug interactions. This clinical decision support system should improve the clinician's ability to identify the optimal pain medication and dosage, and reduce risks associated with multi-drug treatment. Investigators will conduct clinician survey's to collection information about the clinical decision support system. Investigators will also conduct patient related questionnaires to determine any benefits or improvements in quality of life and symptom management from the clinical decision support system.