Ecological Assessment of Clinicians’ Antipsychotic Prescription Habits in Psychiatric Inpatients: A Novel Web- and Mobile Phone–Based Prototype for a Dynamic Clinical Decision Support System

Berrouiguet, Sofian; Barrigón, María Luisa; Brandt, Sara A.; Nitzburg, George C.; Ovejero, Santiago; Álvarez-García, Raquel; Carballo, Juan J.; Walter, Michel; Billot, Romain; Lenca, Philippe; Gómez, David; Ropars, Juliette; Gonzalez, Ivan de la Calle; Courtet, Philippe; Baca‐García, Enrique

doi:10.2196/jmir.5954

Cited by 19 publications

(15 citation statements)

References 30 publications

(36 reference statements)

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“…Data were captured using paper based formularies of the actual MeMind online EHR (www.memind.net). These data could have been captured by our MeMind web application (www.memind.net) designed to gather observational data trough an HER interface and to perform ecological momentary assessment (EMA) [20]. This online software has 2 distinct views: the EHR view (for clinicians) and the EMA view (for patients).…”

Section: Main Results and Integration Into A Dynamic Cdssmentioning

confidence: 99%

“…Such a web application may represent the future of suicide risk assessment, as it allows for the data mining of static data (EHR data provided by a single clinical assessment) and dynamic data (Smartphone data provided by EMA techniques). These data can be processed in order to build a dynamic CDSS [20]. Limitations of the study 1.…”

Section: Main Results and Integration Into A Dynamic Cdssmentioning

confidence: 99%

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An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support (Preprint)

Berrouiguet¹,

Billot²,

Larsen³

et al. 2018

Preprint

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In an e-health context, combining traditional structured clinical assessment methods and routine electronic health based data capture may be a reliable method to build a dynamic clinical decision support system (CDSS) in suicide prevention. Our objective was to describe the data mining module of a web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. A database of 2802 suicide attempters has been analysed. Clustering methods were used to identify groups of similar patients while regression trees are applied to estimate the number of suicide attempts among these patients. Three groups of patients are identified by clustering methods. Relevant risk factors explaining the number of suicide attempts are highlighted by regression trees. Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. These results can be combined with web-based and smartphone based data to improve dynamic decision making for clinicians.

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Section: Main Results and Integration Into A Dynamic Cdssmentioning

confidence: 99%

Section: Main Results and Integration Into A Dynamic Cdssmentioning

confidence: 99%

An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support (Preprint)

Berrouiguet¹,

Billot²,

Larsen³

et al. 2018

Preprint

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“…This allows clinicians to monitor the evolution of the symptoms in their naturalistic setting (Ecological Momentary Assessment EMA) (9,17). The use of standardized questionnaires allows a homogeneous collection of clinical data for an informed medical decision.…”

Section: Discussionmentioning

confidence: 99%

“…The tested tool is based on an online software that allows clinicians to collect clinical information in real time (9). Data is entered into the database by clinic health professionals and related staff during consultations and after the various assessments that the patient undergoes through the early detection consultation.…”

Section: Introductionmentioning

confidence: 99%

Clinicians’ Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App (Preprint)

Lemey¹,

Larsen²,

DeVylder³

et al. 2018

Preprint

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Purpose : Many mental disorders are mostly preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic Health Records (EHR) are generally used to capture these symptoms during medical consultation. Internet-based and mobile technologies provide the opportunity to monitor symptoms emerging in patients' environment using ecological momentary assessment techniques to support preventative therapeutic decision making.Method :. We assessed the clinical acceptability of a web-based application designed to collect medical data during appointments and provide EMA features. Clinicians at four community psychiatry departments were recruited to participate. They used the application to assess patients and to collect data after viewing a video of a young patient's emerging psychiatric consultation. They were then asked to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the application, and their habit of using new technologies.Results : Of 24 practitioners solicited, 88% agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly use new technologies, and 90% connect daily to the internet, with 67% connecting three times a day or more. However, only 50% report feeling comfortable with computers. 86% (n=18) of the clinicians would recommend the tool to their colleagues and 67% consider that a daily use would be interesting. Most of the clinicians (76%) found the interface easy to use and useful. However, several clinicians regret the lack of readability (38%) and point out the necessity of improving ergonomy (19%) in particular to facilitate browsing through various subsections. Part of them (24%) are concerned about the storage of medical data and most of them (52%) seem to be uncomfortable with this. Conclusion :In this study we describe the first step of the development of a web application combining an EHR and ecological momentary assessment (EMA) features. This online tool offers the possibility to assess patients and to integrate medical data easily into face to face conditions. The acceptability of this application supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups.Introduction :

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“…L'application Web MEmind 1 [16] (Figure 2). L'interface a été testée dans des environnements de suivi en consultation [14], ainsi qu'en hospitalisation [22]. Les premières analyses portant sur 13 811 patients montrent que 20 % d'entre eux ont utilisé MEmind entre deux consultations, et que, parmi ceux-ci, 24 % ont exprimé des idées de suicide au travers de l'interface.…”

Section: Vigilans Et Data Mining : Vers Une Aide à La Décision En Préunclassified

Prévention du suicide et santé connectée

Berrouiguet¹,

Moal

Guillodo

et al. 2018

Med Sci (Paris)

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Suicide risk assessment usually rely on brief medical visit and does not report the evolution of this risk after the patient discharge. However, the reattempt risk is still high several months after the initial attempt. In these setting, long term suicide prevention of at risk subjects are challenging. Thanks to recent technological advances, electronic health (eHealth) data collection strategies now can provide access to real-time patient self-report data during the interval between visits. The extension of the clinical assessment to the patient environment and data processing using data mining will support medical decision making.

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Ecological Assessment of Clinicians’ Antipsychotic Prescription Habits in Psychiatric Inpatients: A Novel Web- and Mobile Phone–Based Prototype for a Dynamic Clinical Decision Support System

Cited by 19 publications

References 30 publications

An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support (Preprint)

An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support (Preprint)

Clinicians’ Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App (Preprint)

Prévention du suicide et santé connectée

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