PMH53 A Machine Learning Approach to Understanding How Patient Engagement with a Prescription Digital Therapeutic Relates to Healthcare Resource Utilization in Opioid Use Disorder
نویسندگان
چکیده
To assess the relationship between patient engagement with reSET-O, an FDA-cleared prescription digital therapeutic (PDT) for treatment of opioid use disorder in adults, and change inpatient (IP) emergency department (ED) utilization using a machine learning approach. An unsupervised approach was applied to classify PDT subgroups based on two distinct categories: count lessons completed, weeks >= 1 lesson completed. IP ED resource utilization, along cost ($12,476 $536, respectively, 2020 USD), evaluated six months pre- post-treatment initiation reSET-O (index date). were assessed relative IP/ED cost. Four identified via k-means clustering: high (n=130), medium (n=88), low (n=104), non (n=26) engagement. Twenty-one percent patients subgroup had >=1 event pre-index, versus 31%, 39%, 19% medium, low, non-engagement subgroups, respectively. All engaged experienced decreases following (high: 39% vs 16% post-index; medium: 31% 13%; low: 21% 9% post-index, respectively). By contrast, increase (19% 23% post-index). The highest within-patient reduction at 34% (95% CI 25.3-43.2%), compared (26%, 18.1-36.2%) (18%, 12.1-25.2%) subgroups. Within-patient lowest (12%, 4-29%). Cost reductions 1.8 - 2.7 times greater any ($2080, $2654 $1749 high, groups, respectively) ($979). Compared non-engagers, that costs, supporting broad value PDT.
منابع مشابه
The Why, Who, What, How, and When of Patient Engagement in Healthcare Organizations: A Response to Recent Commentaries
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ژورنال
عنوان ژورنال: Value in Health
سال: 2021
ISSN: ['1098-3015', '1524-4733']
DOI: https://doi.org/10.1016/j.jval.2021.04.675