[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100470475":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":24,"centralContacts":29,"locations":12,"responsibleParty":34,"collaborators":12,"id":36,"slug":37,"hasResults":38,"nctId":39,"briefTitle":40,"officialTitle":40,"acronym":12,"eligibilityCriteria":41,"healthyVolunteers":42,"sex":43,"minAge":44,"maxAge":12,"enrollmentInfo":45,"targetDuration":12,"studyType":48,"phases":49,"briefSummary":51,"conditions":52,"keywords":54,"overallStatus":60,"whyStopped":12,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":65,"completionDateStruct":67,"leadSponsor":69,"locationsCount":12},{"fullName":5,"class":6},"Temple University","OTHER",[8,13],{"label":9,"type":10,"description":11,"interventionNames":12},"No Information from Machine Learning Algorithm","NO_INTERVENTION","The investigators will create case vignettes to assess clinician hypertension management behavior, specifically antihypertensive medication intensification among individuals with uncontrolled blood pressure (BP). This arm will not include information from a machine learning algorithm designed to predict uncontrolled BP at a follow up visit.",null,{"label":14,"type":15,"description":16,"interventionNames":17},"Information from Machine Learning Algorithm","EXPERIMENTAL","The investigators will create case vignettes to assess clinician hypertension management behavior, specifically antihypertensive medication intensification among individuals with uncontrolled blood pressure (BP). This arm will include information from a machine learning algorithm designed to predict uncontrolled BP at a follow up visit about whether the algorithm predicts that the patient will have uncontrolled BP at the next visit.",[18],"Other: Predicted uncontrolled BP status (yes\u002Fno) at follow up visit, derived using a machine learning algorithm",[20],{"type":6,"name":21,"description":22,"armGroupLabels":23,"otherNames":12},"Predicted uncontrolled BP status (yes\u002Fno) at follow up visit, derived using a machine learning algorithm","The investigators have created a machine learning algorithm to predict uncontrolled blood pressure (BP) status (yes\u002Fno) at a follow up visit among adults with uncontrolled BP at their current visit. The investigators will determine whether adding this information to a vignette describing a patient will increase the likelihood that a clinician will intensify antihypertensive medication treatment.",[14],[25],{"name":26,"affiliation":27,"role":28},"Gabriel Tajeu, DrPH","University of Alabama at Birmingham","PRINCIPAL_INVESTIGATOR",[30],{"name":26,"role":31,"phone":32,"phoneExt":12,"email":33},"CONTACT","2055312258","gtajeu@uab.edu",{"type":35,"investigatorFullName":12,"investigatorTitle":12,"investigatorAffiliation":12,"oldNameTitle":12,"oldOrganization":12},"SPONSOR","100470475","machine-learning-to-reduce-hypertension-treatment-clinical-inertia-100470475",false,"NCT05406336","Machine Learning to Reduce Hypertension Treatment Clinical Inertia","Inclusion Criteria: practicing primary care clinicians who see patients (i.e., internal medicine, family medicine, attending physicians, nurse practitioners) will be eligible to participate -\n\nExclusion Criteria:\n\n\\-",true,"ALL","20 Years",{"count":46,"type":47},50,"ESTIMATED","INTERVENTIONAL",[50],"NA","Among individuals with an uncontrolled BP at the current visit, the objective of this study is to compare clinical management of hypertension with and without information from a machine learning algorithm on whether a patient will have uncontrolled blood pressure at their next follow up visit through a case-vignette study.",[53],"Hypertension",[55,56,57,58,59],"Uncontrolled blood pressure","Antihypertensive medication","Hypertension treatment","Clinical inertia","Machine learning","NOT_YET_RECRUITING","2025-04-07",{"date":63,"type":64},"2025-04-10","ACTUAL",{"date":66,"type":47},"2025-04-25",{"date":68,"type":47},"2025-07-31",{"name":5,"class":6}]