RESUMEN
BACKGROUND: One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients. METHODS: Historical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as "session dosing": 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients. RESULTS: The developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified. CONCLUSIONS: It is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.
RESUMEN
Study Objectives: We sought to develop behavioral sleep measures from passively sensed human-smartphone interactions and retrospectively evaluate their associations with sleep disturbance, anxiety, and depressive symptoms in a large cohort of real-world patients receiving virtual behavioral medicine care. Methods: Behavioral sleep measures from smartphone data were developed: daily longest period of smartphone inactivity (inferred sleep period [ISP]); 30-day expected period of inactivity (expected sleep period [ESP]); regularity of the daily ISP compared to the ESP (overlap percentage); and smartphone usage during inferred sleep (disruptions, wakefulness during sleep period). These measures were compared to symptoms of sleep disturbance, anxiety, and depression using linear mixed-effects modeling. More than 2300 patients receiving standard-of-care virtual mental healthcare across more than 111 000 days were retrospectively analyzed. Results: Mean ESP duration was 8.4 h (SD = 2.3), overlap percentage 75% (SD = 18%) and disrupted time windows 4.85 (SD = 3). There were significant associations between overlap percentage (p < 0.001) and disruptions (p < 0.001) with sleep disturbance symptoms after accounting for demographics. Overlap percentage and disruptions were similarly associated with anxiety and depression symptoms (all p < 0.001). Conclusions: Smartphone behavioral measures appear useful to longitudinally monitor sleep and benchmark depressive and anxiety symptoms in patients receiving virtual behavioral medicine care. Patterns consistent with better sleep practices (i.e. greater regularity of ISP, fewer disruptions) were associated with lower levels of reported sleep disturbances, anxiety, and depression.
RESUMEN
BACKGROUND: While medical criteria have been well established for each end-organ system, psychosocial listing criteria are less standardized. To address this limitation, we developed and tested a new assessment tool: the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT). METHODS: The SIPAT was developed from a comprehensive review of the literature on the psychosocial factors that impact transplant outcomes. Five examiners blindly applied the SIPAT to 102 randomly selected transplant cases, including liver, heart, and lung patients. After all subject's files had been rated by the examiners, the respective transplant teams provided the research team with the patient's outcome data. RESULTS: Univariate logistic regression models were fit in order to predict the transplant psychosocial outcome (positive or negative) using each rater's SIPAT scores. These results show that SIPAT scores are highly predictive of the transplant psychosocial outcome (P < 0.0001). The instrument has excellent inter-rater reliability (Pearson's correlation coefficient = 0.853), even among novice raters. CONCLUSIONS: The SIPAT is a comprehensive screening tool to assist in the psychosocial assessment of organ transplant candidates. Its strengths includes the standardization of the evaluation process and its ability to identify subjects who are at risk for negative outcomes after the transplant, in order to allow for the development of interventions directed at improving the patient's candidacy. Our goal is that the SIPAT, in addition to a set of agreed upon minimal psychosocial listing criteria, would be used in combination with organ-specific medical listing criteria in order to establish standardized criteria for the selection of transplant recipients.