From eHealth to iHealth: Transition to participatory and personalized medicine in mental health

Clinical assessment in psychiatry is commonly based on findings from brief, regularly scheduled in-person appointments. Although critically important, this approach reduces assessment to cross-sectional observations that miss essential information about disease course. The mental health provider makes all medical decisions based on this limited information. Thanks to recent technological advances such as mobile phones and other personal devices, electronic health (eHealth) data collection strategies now can provide access to real-Time patient self-report data during the interval between visits. Since mobile phones are generally kept on at all times and carried everywhere, they are an ideal platform for the broad implementation of ecological momentary assessment technology. Integration of these tools into medical practice has heralded the eHealth era. Intelligent health (iHealth) further builds on and expands eHealth by adding novel built-in data analysis approaches based on (1) incorporation of new technologies into clinical practice to enhance real-Time self-monitoring, (2) extension of assessment to the patient's environment including caregivers, and (3) data processing using data mining to support medical decision making and personalized medicine. This will shift mental health care from a reactive to a proactive and personalized discipline.This research was partially support by Instituto de Salud Carlos III (PI16/01852 Grant), Plan Nacional de Drogas (20151073 Project), and American Foundation for Suicide Prevention (LSRG-1-005-16). SB’s work was supported by Fondation de l’Avenir, the French Embassy in Madrid; MMPR's work was supported by a National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award (YIA) grant and a KL2 Faculty Scholar (KL2TR001435) grant (PI: Perez-Rodriguez

An approach for data mining of electronic health record data for suicide risk management: Database analysis for clinical decision support

Background: In an electronic 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) for suicide prevention. Objective: The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. Methods: We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. Results: We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. Conclusions: Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians.This study received a Hospital Clinical Research Grant (PHRC 2009) from the French Health Ministry. None of the funding sources had any involvement in the study design; collection, analysis, or interpretation of data; writing of the report; or the decision to submit the paper for publication. This study was funded partially by Instituto de Salud Carlos III (ISCIII PI13/02200; PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), and the American Foundation for Suicide Prevention (LSRG-1-005-16)

Smartphone-based Ecological Momentary Intervention for secondary prevention of suicidal thoughts and behaviour: Protocol for the Smart Crisis V.2.0 randomised clinical trial

Introduction Suicide is one of the leading public health issues worldwide. Mobile health can help us to combat suicide through monitoring and treatment. The SmartCrisis V.2.0 randomised clinical trial aims to evaluate the effectiveness of a smartphone-based Ecological Momentary Intervention to prevent suicidal thoughts and behaviour. Methods and analysis The SmartCrisis V.2.0 study is a randomised clinical trial with two parallel groups, conducted among patients with a history of suicidal behaviour treated at five sites in France and Spain. The intervention group will be monitored using Ecological Momentary Assessment (EMA) and will receive an Ecological Momentary Intervention called SmartSafe' in addition to their treatment as usual (TAU). TAU will consist of mental health follow-up of the patient (scheduled appointments with a psychiatrist) in an outpatient Suicide Prevention programme, with predetermined clinical appointments according to the Brief Intervention Contact recommendations (1, 2, 4, 7 and 11 weeks and 4, 6, 9 and 12 months). The control group would receive TAU and be monitored using EMA. Ethics and dissemination This study has been approved by the Ethics Committee of the University Hospital Fundacion Jimenez Diaz. It is expected that, in the near future, our mobile health intervention and monitoring system can be implemented in routine clinical practice. Results will be disseminated through peer-reviewed journals and psychiatric congresses. Reference number EC005-21FJD. Participants gave informed consent to participate in the study before taking part. Trial registration number NCT04775160This work was supported by American Foundation for Suicide Prevention (LSRG-1-005-16), Instituto de Salud Carlos III (ISCIII PI13/02200; PI16/01852; CM19/00026), Ministerio de Ciencia, Innovación y Universidades (RTI2018-099655-B-I00; TEC2017-92552-EXP) and Comunidad de Madrid (Y2018/TCS-4705, PRACTICO-CM

Development of a Web-Based Clinical Decision Support System for Drug Prescription: ...

Purpose The emergence of electronic prescribing devices with clinical decision support systems (CDSS) is able to significantly improve management pharmacological treatments. We developed a web application available on smartphones in order to help clinicians monitor prescription and further propose CDSS. Method A web application (www.MEmind.net) was developed to assess patients and collect data regarding gender, age, diagnosis and treatment. We analyzed antipsychotic prescriptions in 4345 patients attended in five Psychiatric Community Mental Health Centers from June 2014 to October 2014. The web-application reported average daily dose prescribed for antipsychotics, prescribed daily dose (PDD), and the PDD to defined daily dose (DDD) ratio. Results The MEmind web-application reported that antipsychotics were used in 1116 patients out of the total sample, mostly in 486 (44%) patients with schizophrenia related disorders but also in other diagnoses. Second generation antipsychotics (quetiapine, aripiprazole and long-acting paliperidone) were preferably employed. Low doses were more frequently used than high doses. Long acting paliperidone and ziprasidone however, were the only two antipsychotics used at excessive dosing. Antipsychotic polypharmacy was used in 287 (26%) patients with classic depot drugs, clotiapine, amisulpride and clozapine. Conclusions In this study we describe the first step of the development of a web application that is able to make polypharmacy, high dose usage and off label usage of antipsychotics visible to clinicians. Current development of the MEmind web application may help to improve prescription security via momentary feedback of prescription and clinical decision support system

Development of a Web-Based Clinical Decision Support System for Drug Prescription: ...

Purpose The emergence of electronic prescribing devices with clinical decision support systems (CDSS) is able to significantly improve management pharmacological treatments. We developed a web application available on smartphones in order to help clinicians monitor prescription and further propose CDSS. Method A web application (www.MEmind.net) was developed to assess patients and collect data regarding gender, age, diagnosis and treatment. We analyzed antipsychotic prescriptions in 4345 patients attended in five Psychiatric Community Mental Health Centers from June 2014 to October 2014. The web-application reported average daily dose prescribed for antipsychotics, prescribed daily dose (PDD), and the PDD to defined daily dose (DDD) ratio. Results The MEmind web-application reported that antipsychotics were used in 1116 patients out of the total sample, mostly in 486 (44%) patients with schizophrenia related disorders but also in other diagnoses. Second generation antipsychotics (quetiapine, aripiprazole and long-acting paliperidone) were preferably employed. Low doses were more frequently used than high doses. Long acting paliperidone and ziprasidone however, were the only two antipsychotics used at excessive dosing. Antipsychotic polypharmacy was used in 287 (26%) patients with classic depot drugs, clotiapine, amisulpride and clozapine. Conclusions In this study we describe the first step of the development of a web application that is able to make polypharmacy, high dose usage and off label usage of antipsychotics visible to clinicians. Current development of the MEmind web application may help to improve prescription security via momentary feedback of prescription and clinical decision support system

A Mobile Text Message Intervention to Reduce Repeat Suicidal Episodes: Design and Development of Reconnecting After a Suicide Attempt (RAFT)

Background Suicide is a leading cause of death, particularly among young people. Continuity of care following discharge from hospital is critical, yet this is a time when individuals often lose contact with health care services. Offline brief contact interventions following a suicide attempt can reduce the number of repeat attempts, and text message (short message service, SMS) interventions are currently being evaluated. Objective The aim of this study was to extend postattempt caring contacts by designing a brief Web-based intervention targeting proximal risk factors and the needs of this population during the postattempt period. This paper details the development process and describes the realized system. Methods To inform the design of the intervention, a lived experience design group was established. Participants were asked about their experiences of support following their suicide attempt, their needs during this time, and how these could be addressed in a brief contact eHealth intervention. The intervention design was also informed by consultation with lived experience panels external to the project and a clinical design group. Results Prompt outreach following discharge, initial distraction activities with low cognitive demands, and ongoing support over an extended period were identified as structural requirements of the intervention. Key content areas identified included coping with distressing feelings, safety planning, emotional regulation and acceptance, coping with suicidal thoughts, connecting with others and interpersonal relationships, and managing alcohol consumption. Conclusions The RAFT (Reconnecting AFTer a suicide attempt) text message brief contact intervention combines SMS contacts with additional Web-based brief therapeutic content targeting key risk factors. It has the potential to reduce the number of repeat suicidal episodes and to provide accessible, acceptable, and cost-effective support for individuals who may not otherwise seek face-to-face treatment. A pilot study to test the feasibility and acceptability of the RAFT intervention is underway.the Australian National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Suicide Prevention Lived Experience Committee; the Black Dog Institute Lived Experience Advisory Panel, Dr Bridi O’Dea and Dr Aliza Werner-Seidler for their support in the design of this project. This study is supported by the Australian Foundation for Mental Health Research, the Ottomin Foundation, and the NHMRC Centre for Research Excellence in Suicide Prevention (APP1042580). ML was supported by a Society of Mental Health 2015 Early Career Research Award and HC by an NHMRC Fellowship (APP1056964)

Santé connectée et prévention du suicide : vers une aide à la décision

Suicide prevention research faces specific challenges related to characteristics of suicide attempts and attempters. The design of powerful suicide prevention studies is especially challenging. Suicide attempters have been described as poorly adhering to long term treatment, and organizing such interventions from the emergency department can be difficult. While approximately one third of those who attempt suicide seek treatment for their injuries from hospital emergency department, a previous SA is a strong precursor of suicide-related premature death. The post-discharge period constitutes a critical challenge for emergency and mental health care services both in the short- and long-terms. Given these issues, there has been growing interest in assessing the efficacy of interventions that focus on maintaining post-discharge contact and offering re-engagement with health care services to suicide attempters. 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.La recherche en prévention du suicide fait face à des défis spécifiques liés aux caractéristiques des sujets à risque. La conception d’interventions de prévention efficaces est particulièrement difficile. Les sujets suicidants sont accueillis aux urgences qui assurent les soins immédiats et organisent la prise en charge au long cours. Un antécédent de passage à l’acte suicidaire est un puissant prédicteur de décès prématuré par au suicide. La prise en charge suivant un passage aux urgences pour un geste suicidaire constitue un défi critique pour les urgences et services de santé mentale. Compte tenu de ces enjeux, il y a eu un intérêt majeur à évaluer l’efficacité des interventions visant le maintien du contact des sujets à risque avec les services de soins. L’évaluation ponctuelle du risque suicidaire habituellement conduite aux urgences, après un geste suicidaire, ne rend pas compte son évolution après la sortie des soins, alors même que le risque de récidive reste important plusieurs mois après. Dans ces conditions, les possibilités d’identification, et donc de prise en charge, des patients à risque suicidaire sont limitées. Le développement de la santé connectée (eHealth) donne désormais accès en temps réel à des informations sur l’état de santé d’un patient entre deux séjours en centre de soins. Cette extension de l’évaluation clinique à l’environnement du patient permet de développer des outils d’aide à la décision face à la gestion du risque suicidaire

eHealth and suicide prevention : towards clinical decision support systems

La recherche en prévention du suicide fait face à des défis spécifiques liés aux caractéristiques des sujets à risque. La conception d’interventions de prévention efficaces est particulièrement difficile. Les sujets suicidants sont accueillis aux urgences qui assurent les soins immédiats et organisent la prise en charge au long cours. Un antécédent de passage à l’acte suicidaire est un puissant prédicteur de décès prématuré par au suicide. La prise en charge suivant un passage aux urgences pour un geste suicidaire constitue un défi critique pour les urgences et services de santé mentale. Compte tenu de ces enjeux, il y a eu un intérêt majeur à évaluer l’efficacité des interventions visant le maintien du contact des sujets à risque avec les services de soins. L’évaluation ponctuelle du risque suicidaire habituellement conduite aux urgences, après un geste suicidaire, ne rend pas compte son évolution après la sortie des soins, alors même que le risque de récidive reste important plusieurs mois après. Dans ces conditions, les possibilités d’identification, et donc de prise en charge, des patients à risque suicidaire sont limitées. Le développement de la santé connectée (eHealth) donne désormais accès en temps réel à des informations sur l’état de santé d’un patient entre deux séjours en centre de soins. Cette extension de l’évaluation clinique à l’environnement du patient permet de développer des outils d’aide à la décision face à la gestion du risque suicidaire.Suicide prevention research faces specific challenges related to characteristics of suicide attempts and attempters. The design of powerful suicide prevention studies is especially challenging. Suicide attempters have been described as poorly adhering to long term treatment, and organizing such interventions from the emergency department can be difficult. While approximately one third of those who attempt suicide seek treatment for their injuries from hospital emergency department, a previous SA is a strong precursor of suicide-related premature death. The post-discharge period constitutes a critical challenge for emergency and mental health care services both in the short- and long-terms. Given these issues, there has been growing interest in assessing the efficacy of interventions that focus on maintaining post-discharge contact and offering re-engagement with health care services to suicide attempters. 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

Droit et intelligence artificielle en psychiatrie

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