{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T06:59:20Z","timestamp":1785135560533,"version":"3.55.0"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Spinal cord stimulation (SCS) is an established therapy for chronic pain, but post-implant care depends on timely identification of evolving patient needs. Remote monitoring may help detect therapy-use changes that scheduled follow-up and patient-initiated contact can miss. We evaluated Proactive Intelligence, a workflow-integrated, human-in-the-loop AI recommender using passively collected SCS device-interaction data to support care without added patients\u2019 data-entry burden.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a prospective, observational real-world pilot among research-consented patients implanted with Prospera SCS devices. Proactive Intelligence used a hybrid neural network incorporating longitudinal therapy-use patterns, operational variables, and patient history\/demography to identify patient-days with therapy-adjustment-associated patterns. Model outputs were translated into a prioritized daily review list for the Embrace Care Team (ECT), which reviewed cases, performed outreach when appropriate, adjudicated behavioral-change reasons, and recorded downstream patient care actions. Outcomes included model enrichment, field adjudication yield, intervention distribution, and pain-score change after outreach when follow-up pain scores were available.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Model development used data from 747 permanently implanted patients with sufficient telemetry. Confirmed reprogramming events were rare, occurring at 0.77% per patient-day. At the high-specificity threshold, the model achieved a positive predictive value of 28.8% and sensitivity of 8.6% on a held-out test set, corresponding to approximately 37-fold enrichment over baseline prevalence. During the pilot, predictions were generated across 101,042 patient-days. Across 91 operational days, the ECT reviewed 188 cases involving 175 unique patients. Among reviewed cases, 152 patients responded within three outreach attempts, yielding an 80.9% response rate. Of all reviewed cases, 66.0% were adjudicated as \u201ctherapy-related.\u201d Among reachable patients, the therapy-related yield was 81.6%, and \u201cAddress therapy\u201d actions occurred in 67.8% of cases, including reprogramming and therapy adjustment\/discussion. Among cases with follow-up pain scores, therapy-relevant cases showed significant mean NRS reduction after outreach, with improvement after both reprogramming and non-reprogramming therapy adjustment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>To our knowledge, this pilot represents the first real-world evaluation of an ECT driven, human-in-the-loop care workflow supported by passively collected SCS device interaction data, without added patient data-entry burden. The findings suggest that machine learning can assist longitudinal SCS care by surfacing behaviorally meaningful changes for timely human review, contextual interpretation, and coordinated support.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1887889","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T06:04:44Z","timestamp":1785132284000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From behavioral signals to therapy management: a real-world pilot implementation of human-in-the-loop AI workflow in proactive patient care"],"prefix":"10.3389","volume":"8","author":[{"given":"David","family":"Dickerson","sequence":"first","affiliation":[{"name":"Endeavor Health","place":["Chicago, IL, United States"]},{"name":"Pritzker School of Medicine, University of Chicago","place":["Chicago, IL, United 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