Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Advertisement

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 14.12.18 in Vol 5, No 4 (2018): Oct-Dec

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/10240, first published Feb 28, 2018.

This paper is in the following e-collection/theme issue:

    Original Paper

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

    1Department of Adult Psychiatry and Medical Psychology, Sorbonne Université, Saint-Antoine Hospital, Assistance Publique Hôpitaux de Paris, Paris, France

    2Sociology and Anthropology Laboratory, University of Burgundy Franche-Comté, Besançon, France

    *these authors contributed equally

    Corresponding Author:

    Alexis Bourla, MD

    Department of Adult Psychiatry and Medical Psychology

    Sorbonne Université

    Saint-Antoine Hospital, Assistance Publique Hôpitaux de Paris

    184 rue du faubourg Saint-Antoine

    Paris, 75012

    France

    Phone: 33 149282628

    Fax:33 149282010

    Email:


    ABSTRACT

    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.

    JMIR Ment Health 2018;5(4):e10240

    doi:10.2196/10240

    KEYWORDS



    Introduction

    Recent discoveries in the fields of genetics, imaging, and biomarkers, together with the development of medical informatics, are leading us to rethink psychiatry. The practices, representations, ethics, and beliefs of practitioners could be disrupted. In science, the ability to predict [1] the occurrence of a morbid event opens up important perspectives—not only preventive or curative but also ethical. At the interface between electronic health, new technologies, and clinical observation, a large number of new tools are currently being developed for the early detection of psychotic or mood disorders and for the prediction of their course. A growing number of studies are reporting on the use of computerized assistance, especially artificial intelligence, in the form of new clinical decision support systems (CDSSs), and current changes to these systems tend to associate 2 concepts: digital phenotyping and machine learning (ML).

    Torous and Gualtieri underlined the potential usefulness of connected objects in the field of mental health [2], as many devices now include multiple sensors (accelerometer, heart rate sensor, sleep tracker, skin conductance sensor, light sensor, etc). The prospect of being able to gather real-time physiological data from fitness trackers as well as from symptom checkers in smartwatches is an attractive one, and there is increasing interest in using real-time patient data as biomarkers for illness. Their team recently developed the concept of the digital phenotype of pathology. This refers to capture by computerized measurement tools of specific characteristics of psychiatric disorders [3,4]. Some behaviors or symptoms may be objectifiable and quantifiable by computer tools, thereby constituting an e-semiotic (semiotics mediated by computerized tools). Thus, the graphorrhea observed in manic episodes can be reflected in an increase in the number of short service message text messages sent, and depressive psychomotor retardation can be assessed by an accelerometer [5].

    Sensor miniaturization and the ubiquitous use of smartphones mean that it is now possible to collect a large amount of data that psychiatrists had never previously been able to access. Models based on these new signs are emerging in the field of schizophrenia [6] and mood disorders [5]. These passive data are collected in background tasks for which no intervention is necessary. To reduce observer bias, the individual is not always aware when the data are being collected. Detection may involve a mobile phone and its onboard sensors (global positioning system, accelerometer, verbal flow detector, etc) or connected wearable objects that allow biometric monitoring to take place in the real-time. Data can also be collected actively by Ecological Momentary Assessment (EMA) on a smartphone, but the collection of live data requires action on the part of the patient. EMA involves the evaluation of symptoms from day to day in the patient’s habitual environment, and as they evaluate themselves (right then, not later; right there, not elsewhere) there can be no recall biases [7,8]. This method allows a much more individualized approach to introducing precision diagnosis in psychiatry [9], as symptoms are connected through a system of causal relations, with symptoms impacting on each other (ie, insomnia impacting on depressive symptoms, depressive symptoms impacting on anxiety symptoms, hallucinations impacting on delusions, etc). In addition, many symptoms may be context-dependent (ie, increasing alcohol craving when approaching a bar). EMA can capture dimensional variation in mental states in response to other mental states or environmental variation, resulting in a diagnosis that is both precise and contextual.

    All these data, far too copious to be analyzed manually, can be processed by computer software, allowing patients to be classified according to their illness. As we have seen, new technologies (smartphone, computers, and biomarkers) and the parallel expansion of medical informatics and artificial intelligence have brought about a paradigm shift toward a more personalized and predictive form of medicine [10]. But if some disorders can be recognized by computer models and if diseases or relapses can be detected earlier or more precisely by machines or smartphones, what role will health care providers play in the future?

    The advent of these technologies calls into question psychiatrists’ professional culture. This sociological concept, derived from the sociology of professions, refers to the fact that professionals refer not only to theoretical knowledge or experience but also to a set of customs, a specific language, and a set of common values [11,12]. According to sociology, a professional activity profoundly influences the identity of the individuals who exercise it. These individuals are defined by their membership of the profession, conceived of as a fully-fledged social group and a culture bearer, sharing values and beliefs as well as a common way of expressing them [13,14]. In that aspect, some authors are already suggesting that psychiatrists are an endangered species [15]. Indeed, psychiatrists diverge from other medical specialties in terms of the predominance of clinical reasoning, the lack of specific or valid imaging techniques or biological tests, and the importance given to intuition, clinical sensitivity, and the therapeutic relationship. From this point of view, the psychotherapeutic dimension of the psychiatric interview could be challenged by these new technologies.

    To our knowledge, there has been little research in this area, and although several studies have recently focused on the acceptability of these technologies for patients or patient compliance, potential prescribers have never been questioned on the subject. The acceptability of these technologies must, therefore, be assessed at different levels, namely, usability (intention to use), utility (technology’s contribution), reliability (including accuracy, effectiveness, and efficiency), and risk, which constitute important dimensions of medical reasoning.

    The main objective of this study was to analyze psychiatrists’ perspectives on these new technologies by assessing the acceptability of 3 CDSSs: (1) smartphone-based EMA, (2) connected wristband-based digital phenotype, and (3) ML-based prediction magnetic resonance imaging (MRI) or blood test. We used a model specifically developed for this purpose with a pluridisciplinary approach (psychiatric and sociological). The secondary objective was to characterize the factors affecting this acceptability and, consequently, indirectly affecting the psychiatrists’ professional culture.


    Methods

    Study Design

    We conducted a qualitative and quantitative study via a computerized survey (Google-Form), in collaboration with the Sociology and Anthropology Laboratory of the University of Burgundy Franche-Comté (LaSA, UBFC).

    Target Population and Sample Composition

    This study focused on a population of psychiatrists working in France. They ranged from residents to senior psychiatrists, working in psychiatric facilities, general or university hospitals, or private practices. Requirement of Ethical Committee’s approval was waived.

    Survey Development

    We used an original form of assessment, with 2 researchers at Sociology and Anthropology Laboratory of the University of Burgundy Franche-Comté, based on the screenplay method, an assessment method used to expose respondents to challenging and problematic clinical cases, in order to ask them to express what should be done or what they themselves have done to act with competency in such situations. By confronting the psychiatrists with systems or devices that are still essentially restricted to the field of research, we were able to review some aspects of reality that are not captured by other types of evocation. The 3 scenarios used here allow practitioners to think about devices that are currently in the research domain and are not used (or little used) in daily routine.

    The screenplay method featured 3 clinical case presentations involving new technologies (Table 1 and Multimedia Appendix 1).

    All the questions were designed during 3 focus groups including psychiatrists and sociologists and were tested with cross-validation on a sample of psychiatrists working at Saint-Antoine Hospital in Paris, France. The first part of the survey (15 questions) collected epidemiological data: sex, job, place of practice, theoretical and practical training (neurobiological, psychoanalytic, integrative, cognitive behavioral therapy, etc), workplace, year of graduation, and practice area (adult psychiatry, child psychiatry, forensic psychiatry, etc). The second part assessed the acceptability of the support systems and the psychiatrists’ professional culture, with 15 questions per scenario (total of 60 questions). To avoid responder focus being too much on confounding factors (ie, the shortage of health personnel aspect), we asked direct questions about the devices in our questionnaire (Textbox 1). A blank field allowed us to collect qualitative data in the form of feedback at the end of the survey.

    Assessment of Acceptability

    The various technologies described above can be studied from a sociological perspective by examining the factors that prevent or, conversely, encourage their use. Several dimensions that can influence acceptability were included in an acceptability model specifically developed for the study and inspired by research on human-machine interaction and management information systems, combining the Nielsen, International Organization for Standardization, and Shackel models [21,22] (see Multimedia Appendix 2). The variables most frequently associated with acceptability are usability (ie, intention to use), supposed usefulness, and reliability [23-25]. In our model, we assessed 4 variables: usefulness, usability, reliability, and risk (Textbox 1). For each variable, participants responded to the questions on a Likert-like scale ranging from 1 to 6, depending on the item. To gauge the acceptability of each system, we calculated a composite score with 3 values (positive, intermediate, and negative).

    Psychiatrists’ Professional Culture

    The purpose of each item was to bring out the characteristics of the sociological concept known as psychiatrists’ professional culture: what made them psychiatrists, with which technologies they would refuse to compromise, and how they saw themselves in relation to other specialists. We took 2 major areas of professional culture into account. First, we investigated the psychiatrists’ scientificity level, reflected by the use of biometric data, MRI, blood tests, and physical examinations (assuming that the more scientifically-minded the psychiatrists are, the more willing they are to use complementary examinations). Second, we probed the psychiatrists’ specific relationship with technology by analyzing the hopes and fears generated by these new tools (ie, did they think that these technologies would help them or replace them?; see Textbox 2).

    Table 1. Screenplay method.
    View this table

    Textbox 1. Acceptability assessment.
    View this box

    Textbox 2. Professional culture assessment.
    View this box

    Data Collection

    The survey was created with Google Forms and sent by email to the relevant professionals via several mailing lists (residents’ association, private practice associations, clinical facilities, personal social networks, etc). Respondents could answer via an internet browser. After a short introductory text, the scenarios appeared one after the other, each followed by the corresponding questions. The survey was anonymous and took about 10-15 minutes to complete.

    Data Analysis

    We performed an initial descriptive analysis of the population using multiple regression analysis. Comparisons of proportions were carried out using a z test with Bonferroni correction. Pearson correlation coefficients were used to analyze correlations between variables. The variables were compared with nonparametric chi-square tests or with Fisher’s test when the conditions for chi-square application were not met, using Microsoft Excel, SPSS v24, and R statistical software. The significance level was set at 5%, such that differences with a P value <.05 were deemed to be significant. In order to achieve 95% statistical power with an alpha risk of .05, the bibliographic analysis indicated that 374 participants were required (bearing in mind that there were 12,591 psychiatrists in France in 2016) [26]. Qualitative variables were partially analyzed by LaSA using Modalisa (in press).


    Results

    Survey Implementation

    The Web-based survey was available between June 30 and August 8, 2016. A total of 528 responses were received. We excluded 5 empty surveys, 5 duplicates, and 3 incomplete surveys (no responses to at least 1 whole scenario) such that 515 surveys were included in the analysis.

    Demographics

    The study population was predominantly female (299/515, 58.1%), mainly composed of young psychiatrists who had already graduated or were set to do so between 2010 and 2020 (342/515, 66.4%), and the majority of practitioners worked in adult psychiatry (270/515, 52.4%). Residents made up a large proportion of the sample (241/515, 46.8%), followed by hospital practitioners (148/515, 28.7%) and private practitioners (49/515, 9.5%). The 2 most common theoretical approaches were “several approaches focusing on neurobiology or cognitive behavioral therapy” and “integrative practice” (see Multimedia Appendix 3).

    Primary Outcome: Acceptability of Support Systems

    Quantitative Analysis

    The overall acceptability was moderate (Table 2). Positive scores only outweighed negative scores for ML. They did not differ significantly for computerized adaptive testing (CAT) or EMA, and the fewest positive scores were for the connected wristband (Figure 1 and Table 2). MRI coupled with ML was considered to be the most useful system, although when asked about reliability, participants gave CAT most positive scores. All the systems were deemed to be potentially risky (211/515, 41.1%) or risky (198/515, 38.5%). MRI and blood tests had the most favorable risk profile (ie, fewest negative scores). For those who responded that there was a risk (potential or real), the main risks were medical (regardless of the technology), then ethical (especially regarding MRI and blood tests), and finally legal (mainly with regard to the connected wristband).

    Qualitative Analysis

    Qualitative analysis explored the obstacles to the acceptability of these new technologies. There were 3 major issues emerging from the analysis, with a variable distribution according to different scenarios: medical, ethical, and forensic (Textbox 3).

    Subgroup Analysis

    A cross-analysis of the epidemiological data and acceptability profiles was performed (Table 3).

    Secondary Outcome: Characterization of Psychiatrists’ Professional Culture

    We asked 3 questions that allowed us to study the place of psychiatrists compared with other medical specialists in terms of scientificity by probing the importance they gave to physical signs (ie, biometric data), technology, and algorithmic thinking. These questions made it possible to distinguish between psychiatrists who:

    1. make extensive use of biometric data in their practice,
    2. give a prominent place to predictability based on algorithms,
    3. view technology not as replacing them but as playing a complementary role.

    They also allowed us to highlight the degree of opposition to these dimensions, including affirming the primacy of the human relationship and the refusal or rejection of technology. We were able to place psychiatrists on a continuum running from a medical to psychological subtype and conducted an analysis in which these profiles were crossed with the epidemiological data. This analysis revealed a perfect match between acceptability scores and psychiatrists’ professional culture: the medical profile had the highest acceptability score.

    Table 2. Acceptability score.
    View this table
    Figure 1. Acceptability and score classification of the 3 scenarios. CAT: computerized adaptive testing; EMA: Ecological Momentary Assessment; ML: machine learning; MRI: magnetic resonance imaging.
    View this figure

    Textbox 3. Major issues raised by psychiatrists.
    View this box
    Table 3. Cross-analysis of the epidemiological data and acceptability profiles.
    View this table

    Discussion

    Principal Findings

    We achieved our primary objective of determining the acceptability of new technologies by psychiatrists, and to our knowledge, this is the first study to assess psychiatrists’ views on computerized CDSSs. CDSSs clearly had different acceptability profiles. The connected wristband seemed to have the lowest acceptability profile; CAT and EMA smartphone- based assessment were rated as the most reliable but with the least usability; and ML-based MRI or blood tests were rated as having the greatest usefulness and usability but with the greatest risk. Furthermore, approximately half of the psychiatrists claimed to wait for (and expected to receive) more scientifically validated arguments before reaching any conclusions. This could be a valid argument as the wait-and-see position could be underlying a healthy skepticism. In our opinion, what appears above all in this matter is the lack of knowledge of these techniques among practitioners. Many scientific studies clearly demonstrate the interest in these devices. A recent review of the literature by Faurholt-Jepsen et al [27] regarding the comparative validity of electronic self-assessment techniques over scale or classical clinic assessment found no significant differences between all evaluation methods (smartphone, computer software, internet, and standard clinical scales). According to a review conducted by a team from the Black Dog Institute between 2008 and 2013 [28], reliability and patient acceptability of smartphone-based symptom monitoring is high, with good agreement between electronic measurements and standard diagnostic scales. Similarly, Torous et al [17] demonstrated a good agreement between paper and smartphone versions of the Patient Health Questionnaire, 9th Revision, Beck Depression Inventory, and Quick Inventory of Depressive Symptomatology, with a preference for the smartphone media. Regarding the digital phenotype, the capture of passive data concerns objective criteria of depression recognized by all practitioners (ie, psychomotor retardation in depressive disorder, manic graphorrhea, etc), so it is more a doubt cast on the ability of these devices to “capture” these elements than a rejection of the principle behind it. However, again, several studies scientifically show the validity of this type of data collection. It should also be noted that all the scripts had bibliographic references at the end, allowing responders to learn about these studies.

    The subgroup analysis showed that acceptability was strongly mediated by a psychiatrist’s profile and, more specifically, by sex, theoretical approach, mode of practice, role, and experience. One may wonder why the youngest (residents) have a more negative acceptability profile than the others, while intuitively we could think that they would be more favorable to new technologies given their different usage profile. The qualitative sociological analysis of the data shows that it could be a counter-reaction in relation to the feeling of being dispossessed of a recently hard-won knowledge (ie, “If a machine is able to do better than me, then why did I spend 4 years in training?”). This effect diminishes with age.

    The questions probing the psychiatrists’ professional culture indicated that a certain type of professional culture corresponded to a certain acceptability profile. Our analysis suggests that there were several subgroups, which differed in almost every aspect, with psychiatrists whose professional culture could be defined as medical at one extreme and those who had a more psychodynamic culture at the other extreme. The question of prediction [29] seems more sensitive for certain categories, especially child psychiatrists and forensics practitioners who are the most reluctant in this area, which illustrates the important current debate on neuroprediction [30,31].

    The number of usable responses we collected (N=515) allowed us to have a representative sample of a good size and a geographical distribution that did not influence the data (no significant differences between the regions). Compared with other survey-based studies, this was a large sample as most studies on psychiatrists collect between 50 and 150 responses [32]. The questions were developed in collaboration with sociologists to ensure the relevance of the data we collected and allow for the construction of a sociological hypothesis. Our analysis allowed us to identify a typology of French psychiatrists that featured 2 contrasting schools of thought. The use of quantitative data, including ratings on 3- or 6-point scales, allowed us to undertake a relatively fine-tuned analysis. Furthermore, the questions were developed from a model specifically adapted to medical technology acceptance and inspired by several valid theoretical models [21,22].

    Qualitative analysis allows us to explore the different fields of acceptability, raising several constraints. The main obstacle was the psychiatrists’ fear that they would do more harm than good either by generating anxious counter-reactions (especially with regard to the EMA smartphone app and connected wristband) or by creating a risk of overtreatment by diagnosing problems that did not exist. This evokes the notion of self-fulfilling prophecies developed by Robert K Merton [33], which consists, from a false starting hypothesis, of provoking a behavior that makes this initially false hypothesis become true. An anxious reaction may be one of the consequences, which may increase the risk of recurrence, but we could argue that this could also apply to any form of care (including medication). Much of the feedback focused on the third scenario (which had the highest acceptability profile), with questions about which course of action to pursue if the MRI or the blood tests predicted a transition to psychosis and pointing out the risk of jumping to diagnostic conclusions. Several respondents indicated that they would refuse to introduce a preemptive antipsychotic treatment based on a prediction made in this way. Several commented that there was no point in predicting an incurable disease. In fact, this is an interesting result itself, as it shows that the vast majority of psychiatrists interviewed have no idea what to do if there is a risk of psychotic transition. Recent works highlight the importance of a number of measures to limit this risk (fatty acids, low-doses of atypical antipsychotic, active surveillance, open dialogue, etc), and clearly, many psychiatrists are not aware of this [34]. Feedback on the second scenario raised the same questions, with the idea that it is not ethically acceptable for a psychiatrist to delegate a monitoring task to a machine. This technology elicited particular ethical and political considerations based on the notion that these connected wristbands are associated in people’s minds with the electronic ankle tagging of prisoners and that they are part of a political agenda used to compensate for increasing shortages of health professionals in some areas. From our point of view, although there is indeed an incentive for the development of telemedicine given the current medical demography in France, when we evaluate the impact of a depressive or psychotic recurrence on the life of a patient, it is clear that technologies that allow better monitoring of their condition should not cause a rejection but rather an interest, especially since these technologies have been developed by doctors (and in a number of cases with patients) and not by politicians. Regarding the first scenario, some respondents argued that this technology could result in the loss of opportunity, especially if it prevented practitioners from diagnosing other problems because the focus was on the system’s diagnosis, and some mentioned the risk of practitioners losing their clinical sensitivity through lack of practice. This type of reasoning applied to other fields of medicine when additional tests or new investigation tools were developed. We do not think that the development of the stethoscope made cardiologists lose their clinical sense, we think that it improved them. These technologies must become complementary psychiatric examinations and complement the currently subjective approach employed by psychiatrists in diagnosis or prediction.

    Assumptions and Recommendations

    Significant disparities between devices highlight varying degrees of acceptability, both technology-dependent (previously known technology such as MRI seems better accepted, while connected objects are less well-known) and the underlying theoretical presupposition. Thus, the prediction of a psychotic transition remains subject to many fears, whereas the computerized questionnaires are not. Paradoxically, it is the MRI coupled with ML that psychiatrists find most useful. There are 2 issues very clearly raised here: the psychiatrists questioned in this survey are very minimally informed, notably on the scientificity of these devices, although many studies have already been published and the practitioners do not necessarily know what to do with the new data that these machines can obtain, that is, they have very little knowledge about what to do if there is a risk of psychotic transition, they are not familiar with the concepts of precision medicine, and so on. In this context, it seems very important to increase training measures on new technologies, particularly by integrating this into the resident teaching program. For the past 3 years in France, most psychiatric congresses have offered an innovation session or a new technology session; this seems to be an interesting way to promote these new tools but needs to be expanded.

    Furthermore, our results assume that new technologies are challenging the psychological subtype of psychiatrist while consolidating the medical subtype. Neurobiological psychiatrists are not challenged by these technologies, which they regard rather as tools that extend or complement their practice. Regardless of the system, psychiatrists with a psychoanalytic orientation are clearly reluctant, and we suggest that it is both the use of scales and the technological dimension that account for their negative stance. This assumption is reinforced by the large number of comments that evoked the technology’s impact on the therapeutic relationship. Special measures should be taken to reassure that specific subtype of psychiatrist by demonstrating that these systems are part of a global care that does not negate the psychological aspect of the disease and that it allows an improvement in patient care.

    Many ethical issues were raised by this study, and data security, data storage, privacy, and hacking risk have yet to be resolved. Disease detection or risk prediction, whether in the case of depressive relapse or transition to psychosis, is necessarily stressful for patients and brings the risk of excessive focus and an anxious counter-reaction. To offset these risks, it is essential for psychiatrists to be involved in the development of new technologies, to prevent their loss of control over them. Developers have a major interest in communicating better about the design of these tools and the algorithms they want to be used in the future. In order to complete our research, a comparative study using the same methodology is being developed to better understand the acceptability of these technologies by patients, nurses, and general practitioners.

    Limitations of the Study

    The main limitation could be tautological in that the differences we found between various acceptability profiles may simply have reflected a difference in theoretical approach (neurobiological psychiatrists vs the rest). This suggests that the technologies we studied were based on a theoretical presupposition that was purely neurobiological. While it is true that the third scenario had a clear neurobiological emphasis, the same cannot be said for the other two. In the first scenario, the use of smartphone-based EMA raised the question of scales, but scales presuppose nothing of the etiopathogenesis of the disorder being assessed. Furthermore, CAT does not use scales, as it simulates the psychiatrist’s way of thinking by choosing a specific question from a database made up of more than 500 items. The same applies to the connected wristband as biometric data capture only means that some depression symptoms can be objectified (eg, psychomotor retardation)—a fact that no psychiatrist can refute. It was, therefore, not the opposition between neurobiological and psychodynamic issues that were assessed in our study but the relationship with technology exhibited by different subtypes of psychiatrists.

    Conclusion

    The type of professional culture (theoretical background and practice) appeared to exert a strong influence on the acceptability of the technologies we studied. Overall acceptability was moderate, and respondents expressed many reservations and raised many ethical and ideological questions. Indeed, a probability derived from the analyzed data cannot systematically be transformed into diagnostic or therapeutic certainty. Some of their concerns are relevant and their skepticism can be understood although many of the issues raised are in fact devoid of reality and reflect a great lack of knowledge of the current state of research on these new technologies. It is surely necessary for psychiatrists to adopt a clear stance with regard to these radical changes that are upsetting traditional practice, and for them to be able to do this, they must be informed, interested, and allowed to contribute to the development of these new technologies [35,36], going as far as joining the “disruptors of health sciences” [37]. The acceptability model we developed, using a complex sociological methodology featuring clinical case scenarios intended to elicit emotional responses, needs to be replicated.

    Acknowledgments

    The authors gratefully thank all participating psychiatrists for their help.

    Authors' Contributions

    AB and SM designed the study and performed the analytic calculations. AB and FF wrote the manuscript. LO and CG performed the qualitative analysis, and both contributed to the theoretical sociological background. CSP helped supervise the project and contributed to the editing. SM conceived the original idea and was in charge of overall direction and planning. All authors discussed the results and contributed to the final manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Screenplay method for the 3 scenarios.

    PDF File (Adobe PDF File), 31KB

    Multimedia Appendix 2

    Acceptability model.

    PDF File (Adobe PDF File), 118KB

    Multimedia Appendix 3

    Sociodemographic characteristics of respondents and psychiatrists' professional culture.

    PDF File (Adobe PDF File), 49KB

    References

    1. Schleidgen S, Fernau S, Fleischer H, Schickhardt C, Oßa AK, Winkler EC. Applying systems biology to biomedical research and health care: a précising definition of systems medicine. BMC Health Serv Res 2017 Nov 21;17(1):761 [FREE Full text] [CrossRef] [Medline]
    2. Torous J, Gualtieri L. Wearable Devices for Mental Health: Knowns and Unknowns, Psychiatric Times. 2016.   URL: http://www.psychiatrictimes.com/special-reports/wearable-devices-mental-health-knowns-and-unknowns [accessed 2018-11-13] [WebCite Cache]
    3. Torous J, Staples P, Onnela J. Realizing the potential of mobile mental health: new methods for new data in psychiatry. Curr Psychiatry Rep 2015 Aug;17(8):602 [FREE Full text] [CrossRef] [Medline]
    4. Onnela J, Rauch SL. Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health. Neuropsychopharmacology 2016 Dec;41(7):1691-1696 [FREE Full text] [CrossRef] [Medline]
    5. Bourla A, Ferreri F, Ogorzelec L, Guinchard C, Mouchabac S. [Assessment of mood disorders by passive data gathering: The concept of digital phenotype versus psychiatrist's professional culture]. Encephale 2018 Apr;44(2):168-175. [CrossRef] [Medline]
    6. Torous J, Staples P, Sandoval L, Barnett I, Pekka Onnela J, Keshavan M. 323. Utilizing Smartphones to Collect Longitudinal Digital Phenotypes in Patients with Schizophrenia. Biological Psychiatry 2017 May;81(10):S132-S133. [CrossRef]
    7. Csikszentmihalyi M, Larson R. Validity and reliability of the Experience-Sampling Method. J Nerv Ment Dis 1987 Sep;175(9):526-536. [Medline]
    8. Firth J, Torous J, Yung AR. Ecological momentary assessment and beyond: The rising interest in e-mental health research. J Psychiatr Res 2016 Dec;80:3-4 [FREE Full text] [CrossRef] [Medline]
    9. van Os J, Delespaul P, Wigman J, Myin-Germeys I, Wichers M. Beyond DSM and ICD: introducing “precision diagnosis” for psychiatry using momentary assessment technology. World Psychiatry 2013 Jun;12(2):113-117 [FREE Full text] [CrossRef] [Medline]
    10. Swendsen J, Salamon R. Mobile technologies in psychiatry: providing new perspectives from biology to culture. World Psychiatry 2012 Oct;11(3):196-198 [FREE Full text] [Medline]
    11. Dubar C. Les identités professionnelles. In: Le monde du travail - Paris. Paris: La découverte; 1998:66-74.
    12. Sainsaulieu R. La construction des identités au travail. Sciences Humaines - Hors série N° 20, 1998:293-300.
    13. Trice HM. Occupational subcultures in the workplace. Ithaca, New York: ILR PRESS 1993.
    14. Leonardi P, Jackson M, Waite W, Diwan A. Occupational Work Styles and organizational change: a constitutive perspective on engineering culture. 2005 Presented at: 65th Academy of Management meeting; 2005; Honolulu.
    15. Katschnig H. Are psychiatrists an endangered species? Observations on internal and external challenges to the profession. World Psychiatry 2010 Feb;9(1):21-28 [FREE Full text] [Medline]
    16. Gibbons RD, Weiss DJ, Pilkonis PA, Frank E, Moore T, Kim JB, et al. Development of a computerized adaptive test for depression. Arch Gen Psychiatry 2012 Nov;69(11):1104-1112 [FREE Full text] [CrossRef] [Medline]
    17. Torous J, Staples P, Shanahan M, Lin C, Peck P, Keshavan M, et al. Utilizing a Personal Smartphone Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients With Major Depressive Disorder. JMIR Ment Health 2015;2(1):e8 [FREE Full text] [CrossRef] [Medline]
    18. Burton C, McKinstry B, Szentagotai Tătar A, Serrano-Blanco A, Pagliari C, Wolters M. Activity monitoring in patients with depression: a systematic review. J Affect Disord 2013 Feb 15;145(1):21-28. [CrossRef] [Medline]
    19. Koutsouleris N, Borgwardt S, Meisenzahl EM, Bottlender R, Möller HJ, Riecher-Rössler A. Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophr Bull 2012 Nov;38(6):1234-1246 [FREE Full text] [CrossRef] [Medline]
    20. Chan MK, Krebs M, Cox D, Guest PC, Yolken RH, Rahmoune H, et al. Development of a blood-based molecular biomarker test for identification of schizophrenia before disease onset. Transl Psychiatry 2015 Jul 14;5:e601 [FREE Full text] [CrossRef] [Medline]
    21. Shackel B. Human Factors for Informatics Usability. In: Richardson B, editor. Human Factors. Cambridge: Cambridge University Press; 1991:21-28.
    22. Nielsen J. Usability engineering. In: Usability engineering. Boston: Academic Press; 1993.
    23. Oliver R. Measurement and evaluation of satisfaction processes in retail setting. Journal of retailing 1981:25-48.
    24. Bobillier Chaumon M. L’acceptation située des technologies dans et par l’activité : premiers étayages pour une clinique de l’usage. Psychologie du Travail et des Organisations 2016 Mar;22(1):4-21. [CrossRef]
    25. Thompson B, Higgins C, Howell J. Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly 1991:125-144.
    26. Atlas de la démographie médicale 2016. Conseil National de l’Ordre des Médecins CNOM   URL: https:/​/www.​conseil-national.medecin.fr/​sites/​default/​files/​atlas_de_la_demographie_medicale_2016.​pdf
    27. Faurholt-Jepsen M, Munkholm K, Frost M, Bardram JE, Kessing LV. Electronic self-monitoring of mood using IT platforms in adult patients with bipolar disorder: A systematic review of the validity and evidence. BMC Psychiatry 2016 Jan 15;16:7 [FREE Full text] [CrossRef] [Medline]
    28. Donker T, Petrie K, Proudfoot J, Clarke J, Birch MR, Christensen H. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res 2013 Nov 15;15(11):e247 [FREE Full text] [CrossRef] [Medline]
    29. Poldrack RA, Monahan J, Imrey PB, Reyna V, Raichle ME, Faigman D, et al. Predicting Violent Behavior: What Can Neuroscience Add? Trends Cogn Sci 2018 Feb;22(2):111-123. [CrossRef] [Medline]
    30. Aharoni E, Vincent GM, Harenski CL, Calhoun VD, Sinnott-Armstrong W, Gazzaniga MS, et al. Neuroprediction of future rearrest. Proc Natl Acad Sci U S A 2013 Apr 09;110(15):6223-6228 [FREE Full text] [CrossRef] [Medline]
    31. Gkotsi G, Moulin V, Gasser J. [Neuroscience in the Courtroom: From responsibility to dangerousness, ethical issues raised by the new French law]. Encephale 2015 Oct;41(5):385-393. [CrossRef] [Medline]
    32. Norfleet KR, Ratzliff ADH, Chan Y, Raney LE, Unützer J. The Role of the Integrated Care Psychiatrist in Community Settings: A Survey of Psychiatrists' Perspectives. Psychiatr Serv 2016 Mar;67(3):346-349. [CrossRef] [Medline]
    33. Merton RK. The Self Fulfilling Prophecy. Antioch Review 1948;8(2):193-220. [CrossRef]
    34. Millan MJ, Andrieux A, Bartzokis G, Cadenhead K, Dazzan P, Fusar-Poli P, et al. Altering the course of schizophrenia: progress and perspectives. Nat Rev Drug Discov 2016 Dec;15(7):485-515. [CrossRef] [Medline]
    35. Ferreri F, Bourla A, Mouchabac S, Karila L. e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors. Front Psychiatry 2018;9:51 [FREE Full text] [CrossRef] [Medline]
    36. Bourla A, Mouchabac S, El Hage W, Ferreri F. e-PTSD: an overview on how new technologies can improve prediction and assessment of Posttraumatic Stress Disorder (PTSD). Eur J Psychotraumatol 2018;9(sup1):1424448 [FREE Full text] [CrossRef] [Medline]
    37. Insel TR. Join the disruptors of health science. Nature 2017 Dec 01;551(7678):23-26. [CrossRef] [Medline]


    Abbreviations

    CAT: computerized adaptive testing
    CDSS: clinical decision support system
    EMA: Ecological Momentary Assessment
    ML: machine learning
    MRI: magnetic resonance imaging


    Edited by G Eysenbach; submitted 28.02.18; peer-reviewed by S Moor, E Winkler; comments to author 02.08.18; revised version received 10.09.18; accepted 14.09.18; published 14.12.18

    ©Alexis Bourla, Florian Ferreri, Laetitia Ogorzelec, Charles-Siegfried Peretti, Christian Guinchard, Stephane Mouchabac. Originally published in JMIR Mental Health (http://mental.jmir.org), 14.12.2018.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.