Are student nurses ready for new technologies in mental health? Mixed-methods study.

BACKGROUND: Technical innovations such as ecological momentary assessment (EMA), machine learning (ML), computerized adaptive testing (CAT), Digital Phenotyping, Clinical Decision Support Systems (CDSS), Algorithms, and Biomarkers have caused a paradigm shift in psychiatric care. The aim of the present study was to explore how student nurses view this paradigm shift, by assessing the acceptability of smartphone-based EMA, CAT, and biosensor-based Digital Phenotyping. We also investigated the factors affecting this acceptability. METHOD: Student nurses recruited via nursing schools participated in a quantitative study involving the screenplay method, in which they were exposed to two scenarios about depression care, one featuring EMA and CAT, the other featuring a connected wristband (CW) for Digital Phenotyping. Four acceptability domains (usefulness, usability, reliability, risk) were investigated. RESULTS: We recorded 1216 observations for the first scenario and 1106 for the second. Regarding overall acceptability, the CW was viewed less positively than CAT and EMA. Regarding reliability, whereas respondents believed that the CW could correctly detect depressive relapse, they did not think that EMA and CAT were sufficiently reliable for the accurate diagnosis of depressive disorder. More than 70% of respondents stated that they would nevertheless be interested in offering EMA, CAT or CW to their patients, but more than 60% feared that these devices might hinder the therapeutic relationship. CONCLUSION: This was the first study assessing student nurses' views of EMA, CAT and CW-based digital phenotyping. Respondents were interested in these new technologies and keen to offer them to their patients. However, our study highlighted several issues, as respondents doubted the reliability of these devices and feared that they would hinder the therapeutic relationship. Subgroup analysis revealed correspondences between acceptability profiles and demographic profiles. It is therefore essential for nurses and student nurses to receive training and become involved in the development of this new technologies.

Psychiatrists' Attitudes Toward Disruptive New Technologies: Mixed-Methods Study.

BACKGROUND: Recent discoveries in the fields of machine learning (ML), Ecological Momentary Assessment (EMA), computerized adaptive testing (CAT), digital phenotype, imaging, and biomarkers have brought about a new paradigm shift in medicine. OBJECTIVE: The aim of this study was to explore psychiatrists' perspectives on this paradigm through the prism of new clinical decision support systems (CDSSs). Our primary objective was to assess the acceptability of these new technologies. Our secondary objective was to characterize the factors affecting their acceptability. METHODS: A sample of psychiatrists was recruited through a mailing list. Respondents completed a Web-based survey. A quantitative study with an original form of assessment involving the screenplay method was implemented involving 3 scenarios, each featuring 1 of the 3 support systems, namely, EMA and CAT, biosensors comprising a connected wristband-based digital phenotype, and an ML-based blood test or magnetic resonance imaging (MRI). We investigated 4 acceptability domains based on International Organization for Standardization and Nielsen models (usefulness, usability, reliability, and risk). RESULTS: We recorded 515 observations. Regarding our primary objective, overall acceptability was moderate. MRI coupled with ML was considered to be the most useful system, and the connected wristband was considered the least. All the systems were described as risky (410/515, 79.6%). Regarding our secondary objective, acceptability was strongly influenced by socioepidemiological variables (professional culture), such as gender, age, and theoretical approach. CONCLUSIONS: This is the first study to assess psychiatrists' views on new CDSSs. Data revealed moderate acceptability, but our analysis shows that this is more the result of the lack of knowledge about these new technologies rather than a strong rejection. Furthermore, we found strong correspondences between acceptability profiles and professional culture profiles. Many medical, forensics, and ethical issues were raised, including therapeutic relationship, data security, data storage, and privacy risk. It is essential for psychiatrists to receive training and become involved in the development of new technologies.