The study proposal is to deploy a wearable solution that predicts physiological perturbation comparable to invasive devices and to perform continuous remote patient monitoring; this will be connected to a structured, cascading, escalation pathway involving home health nurses, advanced practitioner providers, and heart failure specialists, and has the potential to transform heart failure management in the post-discharge period, where patients are the most vulnerable for readmission. This feasibility study will contribute to the understanding of post-discharge heart failure continuous remote patient monitoring, promote patient self-care, and has the potential of improving patient outcomes.
Heart failure is a leading cause of hospital readmission. It results in significant mortality, morbidity, and health care utilization. Effective continuous remote patient monitoring (CRPM) can reduce readmissions, but it has only been realized via invasive monitoring. The study will focus on non-invasive heart failure CRPM through a structured cascading and escalating alert system. In this feasibility study, the study team will use a wearable biosensor and collect ambulatory physiological data that are analyzed by machine learning algorithms, potentially identifying physiological perturbation in heart failure patients. Alerts from this algorithm may be cascaded with other patient status data to inform management by the home health team via a structured protocol. The escalation pathway will engage home health, advanced practitioner providers, and heart failure specialists. In the first aim, the study team will perform a soft launch on five patients with an extensive evaluation to assess feasibility for the pilot trial. In aim 2, the study team will implement the feasibility pilot study. In aim 2a, the study team will conduct surveys and semi-structured interviews with both providers and patients. The surveys and interviews will be applied at three time points (initiation, maintenance, and post-study) to evaluate perceptions, acceptance, and experience of this CRPM solution. In aim 2b, the investigators will leverage temporal data mining, feature extraction, and patient clustering methods to identify valid patterns associated with the pathophysiological events of interest, using continuous physiological data, patient reports, and electronic health record data. The study team will also compare outcome and process measures from our pilot study to a retrospective cohort matched for key demographics and disease severity. This feasibility study will provide key learning for a larger efficacy clinical trial to evaluate if this non-invasive telemonitoring solution tied to structured patient management via cascading and escalating alert pathways can improve outcomes and reduce heart failure readmission.
Continuous patient monitoring through non-invasive biosensors coupled with machine learning algorithms, with a structured escalation and communication pathway for home health providers and HF care team
Surveys and interviews with enrolled participants
NorthShore University HealthSystem Evanston Hospital
Evanston, Illinois, United States
Enrollment Rate
Enrollment rate for entire patient cohort
Time frame: Through study completion, an average 1 year
Adherence Rate
Patient adherence to electronic patient reported outcomes
Time frame: Through study completion, an average of one year
Documented Diuretic Escalation
Number of patients on escalated diuretics dosage by the clinical care team during monitoring period
Time frame: 30 days from patient discharge date
30-day Readmission Rate
30-day readmission rate from the day of discharge
Time frame: 30 days from patient discharge date
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Study Type
INTERVENTIONAL
Allocation
NA
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Enrollment
54