ArticleApplied clinical informatics2021
Continuous Remote Patient Monitoring: Evaluation of the Heart Failure Cascade Soft Launch.
Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Wearable Devices for Remote Monitoring of Chronic Diseases: Systematic Review.JMIR mHealth and uHealth · 2026Pooled it
- Wearable technologies to predict and prevent and heart failure hospitalizations: a systematic review.European heart journal. Digital health · 2025Review
- Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Perspectives on AI use in medicine: views of the Italian Society of Artificial Intelligence in Medicine.Journal of preventive medicine and hygiene · 2024Article
- Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review.Biosensors · 2024Review
- Prospective Cohort Study of Remote Patient Monitoring with and without Care Coordination for Hypertension in Primary Care.Applied clinical informatics · 2023Observational
- Continuous Remote Patient Monitoring in Patients With Heart Failure (Cascade Study): Protocol for a Mixed Methods Feasibility Study.JMIR research protocols · 2022Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe report on our experience of deploying a continuous remote patient monitoring (CRPM) study soft launch with structured cascading and escalation pathways on heart failure (HF) patients post-discharge. The lessons learned from the soft launch are used to modify and fine-tune the workflow process and study protocol.
methodsThis soft launch was conducted at NorthShore University HealthSystem's Evanston Hospital from December 2020 to March 2021. Patients were provided with non-invasive wearable biosensors that continuously collect ambulatory physiological data, and a study phone that collects patient-reported outcomes. The physiological data are analyzed by machine learning algorithms, potentially identifying physiological perturbation in HF patients. Alerts from this algorithm may be cascaded with other patient status data to inform home health nurses' (HHNs') management via a structured protocol. HHNs review the monitoring platform daily. If the patient's status meets specific criteria, HHNs perform assessments and escalate patient cases to the HF team for further guidance on early intervention.
resultsWe enrolled five patients into the soft launch. Four participants adhered to study activities. Two out of five patients were readmitted, one due to HF, one due to infection. Observed miscommunication and protocol gaps were noted for protocol amendment. The study team adopted an organizational development method from change management theory to reconfigure the study protocol.
conclusionWe sought to automate the monitoring aspects of post-discharge care by aligning a new technology that generates streaming data from a wearable device with a complex, multi-provider workflow into a novel protocol using iterative design, implementation, and evaluation methods to monitor post-discharge HF patients. CRPM with structured escalation and telemonitoring protocol shows potential to maintain patients in their home environment and reduce HF-related readmissions. Our results suggest that further education to engage and empower frontline workers using advanced technology is essential to scale up the approach.
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