{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T00:17:07Z","timestamp":1706228227703},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684567","type":"print"},{"value":"9781643684574","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>The Cascade-HF protocol is a Continuous Remote Patient Monitoring (CRPM) study at a major health system in the United States to reduce Heart Failure (HF)-related hospitalizations and readmissions using wearable biosensors to collect physiological data over a 30-day period to determine decompensation risk among HF patients. The alerts produced, coupled with electronic patient-reported outcomes, are utilized daily by the home health team, and escalated to the heart failure team as needed, for proactive actions. Limited research has examined anticipating the implementation and workflow challenges of such complex CRPM studies such as resource planning and staffing decisions that leverage the recorded data to drive clinical preparedness and operational efficiency. This preliminary analysis applies discrete event simulation modeling to the Cascade-HF protocol using pilot data from a soft launch to assess workload of the clinical team, evaluate escalation patterns and provide decision support recommendations to enable scale-up for all post-discharge patients.<\/jats:p>","DOI":"10.3233\/shti231078","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:19Z","timestamp":1706178319000},"source":"Crossref","is-referenced-by-count":0,"title":["Continuous Remote Patient Monitoring for Post-Discharge Heart Failure Management: Workflow Modeling Using Discrete Event Simulation"],"prefix":"10.3233","author":[{"given":"Rema","family":"Padman","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, USA"}]},{"given":"Anirudh Vaidhyaa","family":"Venkatasubramanian","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, USA"}]},{"given":"Wei Ning","family":"Chi","sequence":"additional","affiliation":[{"name":"NorthShore University HealthSystem, Chicago, USA"}]},{"given":"Anthony","family":"Solomonides","sequence":"additional","affiliation":[{"name":"NorthShore University HealthSystem, Chicago, USA"}]},{"given":"Nirav","family":"Shah","sequence":"additional","affiliation":[{"name":"NorthShore University HealthSystem, Chicago, USA"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231078","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:21Z","timestamp":1706178321000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231078"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231078","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}