A mixed-methods feasibility study of a novel AI-enabled, web-based, clinical decision support system for the treatment of major depression in adults

Background:- The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship of a novel, artificial-intelligence (AI) enabled clinical decision support system (CDSS) for use in treating adults with major depression. Methods:- A single arm, naturalistic follow-up study aimed at assessing the acceptability and useability of the software. Patients had a baseline appointment, followed by a minimum of two appointments with the CDSS. Study exit questionnaires and interviews were conducted to assess perceived clinical utility, impact on patient-physician relationship, and understanding and trust. 7 physicians and 17 patients, of which 14 completed, consented to participate. Results:- 86 % of physicians (6/7) felt the information provided by the CDSS provided more comprehensive understanding of patient situations. 71 % (5/7) felt the information was helpful. 86 % of physicians (6/7) reported the AI/predictive model was useful when deciding treatment. 62 % of patients (8/13) reported improved care due to the tool, and 46 %(6/13) reported a significantly or somewhat improved physician-patient relationship 54 % reported no change. 71 % of physicians (5/7) and 62 % of patients (8/13) rated trusting the tool. Limitations:- Small sample size and treatment changes prior to CDSS introduction limits ability to verify impact on outcomes. Conclusions:- Qualitative results from 12 patient exit interviews are analyzed and presented. Findings suggest physicians perceived the tool as useful in conducting appointments and used it while deciding treatment. Physicians and patients generally found the tool trustworthy, and it may have positive effects on physician-patient relationships. (Study identifier: NCT04061642)

Evaluating the clinical feasibility of an artificial intelligence–powered, web-based clinical decision support system for the treatment of depression in adults: longitudinal feasibility study

Background:- Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence–powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. Objective:- This study aims to examine the feasibility of an artificial intelligence–powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network–based individualized treatment remission prediction. Methods:- Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. Results:- Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change. Conclusions:- Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies

Evidence for UHP anatexis in the Shuanghe UHP paragneiss from inclusions in clinozoisite, garnet, and zircon

The Shuanghe garnet‐bearing paragneiss from the Dabie ultra‐high–pressure (UHP) orogen occurs as an interlayer within partially retrogressed eclogite. A first UHP metamorphic stage at 680°C, 3.8–4.1 GPa is documented by Zr‐in‐rutile temperatures coupled with phengite inclusions (Si = 3.55) in clinozoisite and grossular‐rich garnet. Relic matrix phengite and phengite inclusions in zircon rims display lower Si of 3.42. Combined with garnet compositions and Ti‐in‐zircon temperatures, they provide evidence for a second UHP metamorphic stage at 800–850°C, ~3.8 GPa. Such isobaric heating at UHP conditions has not been documented so far from the adjacent eclogites and other rock types in the Dabie orogen and indicates proximity to the hot, convecting mantle wedge. The dominant mineral assemblage consisting of plagioclase, epidote, biotite, and amphibole provides evidence for widespread retrogression during the exhumation of the UHP paragneiss. Several types of polyphase mineral inclusions were identified. Phengite inclusions hosted by clinozoisite are partially replaced by kyanite and K‐feldspar, whereas inclusions in host garnet consist of relic phengite, K‐feldspar, and garnet, indicating limited sub‐solidus dehydration of phengite by the reaction Ph→Kfs+Ky±Grt+fluid. Tightly intergrown K‐feldspar and quartz are preserved as inclusions with sharp boundaries and radial cracks in garnet. Analyses of whole inclusions also show small enrichments in light rare earth elements. These inclusions are interpreted to be derived from melting of an inclusion assemblage consisting of Ph+Coe±Czo. A third type of polyphase inclusion consists of typical nanogranite (Ab+Kfs+Qz±Ep) inclusions in recrystallized metamorphic zircon. Ti‐in‐zircon thermometry and the Si content of phengite included in these zircon domains indicate that melting occurred at 800–850°C and 3.8–4.0 GPa during isobaric heating at UHP conditions. The partial melting event led to an equilibration of trace elements in garnet, phengite, and apatite. Using published partition coefficients between these minerals and hydrous granitic melt, the trace element composition of the UHP anatectic melt can be constrained. The melts are characterized by high LILE contents and pronounced relative enrichments of U over Th and Ta over Nb. The REE are below primitive mantle values, likely due to the presence of residual clinozoisite and garnet during partial melting. So far, no major granitic bodies have been found that share the same trace element pattern as the partial melts from the UHP anatexis of the Shuanghe paragneiss