Atrial Fibrillation, Cardiovascular Diseases, Genetic Disease, Heart Failure, Hypertension, Metabolic Syndrome
Conditions
Keywords
digital health, handheld ultrasound, smartphone ECG, mobile health, point of care genomics, big data, artificial intelligence, learning health system
Brief summary
The need for new models of integrated care that can improve the efficiency of healthcare and reduce the costs are key priorities for health systems across the United States. Treatment costs for patients with at least one chronic medical or cardiovascular condition make up over 4-trillion dollars in spending on healthcare, with estimations of a population prevalence of 100-million affected individuals within the next decade. Therefore, the management of chronic conditions requires innovative and new implementation methods that improve outcomes, reduce costs, and increase healthcare efficiencies. Digital health, the use of mobile computing and communication technologies as an integral new models of care is seen as one potential solution. Despite the potential applications, there is limited data to support that new technologies improve healthcare outcomes. To do so requires; 1) robust methods to determine the impact of new technologies on healthcare outcomes and costs; and 2) evaluative mechanisms for how new devices are integrated into patient care. In this regard, the proposed clinical trial aims to advance the investigator's knowledge and to demonstrate the pragmatic utilization of new technologies within a learning healthcare system providing services to high-risk patient populations.
Detailed description
Objective #1: Determine the effectiveness of handheld imaging and digital health devices on long term health and patient-reported outcomes through pragmatic and randomized clinical trial designs. Objective #2: Assess the impact of digital health devices and remote patient monitoring (RPM) on measures of healthcare efficiency. Measures of healthcare efficiency directly related to digital health technologies and RPM include: identify which interventions can improve care; define the variations in care and; demonstrate within which patient populations digital health technologies are most effective. Objective #3: Apply integration methods for handheld imaging and digital health devices used for clinical decisions. Achieving integration and interoperability-the ability of different information technology systems and software applications to communicate and exchange data with each other-requires identification for precisely how new innovations merge into systems of care and are applied to various practice settings.
Interventions
DIAGNOSTIC_TESTDigital Health Device Diagnostics
Technology-enabled visitations with digital health will include the following devices used at the time of a patient-physician encounter. These findings will be available to the treating physician at the time the visitation and to be used for clinical decisions. * Handheld imaging - focused echocardiographic examination (Butterfly IQ) * Smartphone iECG for cardiac rhythm assessments (Alivecor) * Blood Pressure (CloudDX) * Oxygen Saturation (CloudDX) * Weight (CloudDX) * Point-of-Care Genetic Testing (Phosphorous)
Sponsors
West Virginia University
Scripps Health
Study design
Allocation
RANDOMIZED
Intervention model
SEQUENTIAL
Primary purpose
SCREENING
Masking
TRIPLE (Caregiver, Investigator, Outcomes Assessor)
Masking description
Treating physicians and clinical practitioners will not be concealed to the randomized allocation of individual clinics or the patients that are seen in these encounters. Physicians in the interventional group will participate in conducting technology-enabled visitations before and after a patient encounter and therefore are not blinded to the assessment. For the standard care group handheld imaging and remote patient monitoring will not be performed after the patient-physician encounter. Principal investigators, outcome adjudicators, and statisticians are blinded to randomization, device findings, and treatment decisions.
Intervention model description
Pragmatic, multisite, cluster randomized trial comparing point of care and remote patient monitoring with digital health devices and handheld imaging to standard-care
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* All participants of the ASE 2018 Outreach Event who are at least 18 years old who are referred for a cardiac evaluation
Exclusion criteria
* Those not willing to consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Health Economic Outcomes | 180 days | Economic difference between the total costs of care between randomized arms including; clinic visitations, hospitalizations, emergency room visitations, and diagnostic testing. Collected as cumulative diagnosis-related group (DRG) and current procedural terminology (CPT) amounts in United States Dollars |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Health Economic Outcomes | 30 days | Economic difference between the total costs of care between randomized arms including; clinic visitations, hospitalizations, emergency room visitations, and diagnostic testing. Collected as cumulative diagnosis-related group (DRG) and current procedural terminology (CPT) amounts in United States Dollars |
| Patient-Reported Outcome Measures | 30 days | Veterans Research and Development Corporation-12 Patient Reported Outcomes (mean total score 50 +/- 10) where higher values are associated with greater mental and physical debility |
| Patient-Reported Experience Measures | 30 days | Agency for Healthcare Research and Quality Consumer Assessment of Healthcare Providers and Systems (average scores and difference between randomized arms) where higher scores are associated with greater patient satisfaction and patient experience |
| Diagnostic Imaging | 180 days | Number of referrals for diagnostic imaging with transthoracic echocardiography between randomized arms |
| Heart Failure | 180 days | Incidence of heart failure diagnosed between randomized arms |
| Mobile Cardiac Telemetry | 180 days | Number of referrals for mobile cardiac telemetry monitoring between randomized arms |
| Emergency Department Visitations | 180 days | Percentage of patients presenting to the emergency department for a cardiac condition (example; myocardial infarction, heart failure, atrial fibrillation, and stroke) between randomized arms |
| Hospitalization | 180 days | Percentage of patients hospitalized for a cardiac condition (example; myocardial infarction, heart failure, atrial fibrillation, and stroke) between randomized arms |
| Clinic Visitations | 180 days | Percentage of patients presenting for a clinical visitation for a cardiac condition (example; myocardial infarction, heart failure, atrial fibrillation, and stroke) between randomized arms |
| Medical Therapy | 180 days | Percentage of patients initiating medical therapy for a cardiac condition including: heart failure, coronary artery disease, atrial fibrillation, and/or hypertension between randomized arms |
| Atrial Fibrillation | 180 days | Incidence of atrial fibrillation diagnosed between randomized arms |
Countries
United States
Outcome results
None listed