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A Study of Workflow-Integrated Artificial Intelligence for RPM Enrollment

Pragmatic Analysis of the Impact and Utilization of Workflow-Integrated Artificial Intelligence for RPM Enrollment

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT05744180
Enrollment
10
Registered
2023-02-24
Start date
2023-06-19
Completion date
2024-01-16
Last updated
2024-10-09

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Nursing

Brief summary

The objective of this study is to evaluate effectiveness, usability and clinical utility of the remote patient monitoring (RPM) fit score when choosing patients to enter the RPM Program.

Interventions

OTHERInterventional

The FitScore is a machine learning algorithm embedded within the electronic health record that identifies patients most likely to benefit from remote patient monitoring.

Sponsors

Mayo Clinic
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

* The study participants will be nurses who are part of the RPM care team that cares for adult patients ≥18 years. * A patient's data will be included in the analysis if the patient is ≥18 years old and receives care from a participating nurse. * Patient data will only be collected if permitted (based on the use of the Minnesota Research Authorization Retrieval Tool). * Patients who will be considered for this study will be assessed based on standard RPM program inclusion and

Exclusion criteria

for the any of the chronic disease RPM programs (congestive heart failure, coronary artery disease, hypertension, type 2 diabetes, COPD, and general complex care).

Design outcomes

Primary

MeasureTime frameDescription
Evaluation of the effectiveness, usability, and clinical utility of the RPM fit score as displayed in the Acute Multipatient Viewer (AMP) and underlying AI models in the real-world setting1 yearFitScore effectiveness will determined by the patient care utilization outcomes of those who did or did not participate in RPM (for those enrolled with or without the FitScore). Usability and clinical utility will be self-reported by nursing staff collected through surveys or as directly observed by study staff (as to experience with or without the FitScore).

Secondary

MeasureTime frameDescription
Assessment of fit score overall effect on nursing efficiency and clinical workflows1 yearEfficiency will be measured by timing studies of nurse patient screening for RPM eligibility as directly observed by study staff. The effect on clinical workflows will be self-reported by nursing staff collected through surveys (as to experience with or without the FitScore).

Countries

United States

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026