121 research outputs found
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). SBâs work was supported by Fondation de lâAvenir,
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),
DelegaciĂłn del Gobierno para el Plan Nacional de Drogas (20151073), and the American Foundation for Suicide Prevention (LSRG-1-005-16)
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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
Recommended from our members
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 OâDea 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)
Psychosocial interventions for self-harm in adults
Background: Self-harm (SH; intentional self-poisoning or self-injury) is common, often repeated, and associated with suicide. This is an update of a broader Cochrane review first published in 1998, previously updated in 1999, and now split into three separate reviews. This review focuses on psychosocial interventions in adults who engage in self-harm.
Objectives: To assess the effects of specific psychosocial treatments versus treatment as usual, enhanced usual care or other forms of psychological therapy, in adults following SH.
Search methods: The Cochrane Depression, Anxiety and Neurosis Group (CCDAN) trials coordinator searched the CCDAN Clinical Trials Register (to 29 April 2015). This register includes relevant randomised controlled trials (RCTs) from: the Cochrane Library (all years), MEDLINE (1950 to date), EMBASE (1974 to date), and PsycINFO (1967 to date).
Selection criteria: We included RCTs comparing psychosocial treatments with treatment as usual (TAU), enhanced usual care (EUC) or alternative treatments in adults with a recent (within six months) episode of SH resulting in presentation to clinical services.
Data collection and analysis: We used Cochrane's standard methodological procedures
an introduction to personalized ehealth
Personalized medicine can be defined as the adaptation of medical treatments to the specific characteristics of patients. This approach allows health providers to develop therapies and interventions by taking into account the heterogeneity of illnesses and external factors such as the environment, patients' needs, and lifestyle. Technology could play an important role to achieve this new approach to medicine. An example of technology's utility regards real-time monitoring of individual well-being (subjective and objective), in order to improve disease management through data-driven personalized treatment recommendations. Another important example is an interface designed based on patient's capabilities and preferences. These could improve patient-doctor communication: on one hand, patients have the possibility to improve health decision-making; on the other hand, health providers could coordinate care services more easily, because of continual access to patient's data. This contribution deepens these technologies and related opportunities for health, as well as recommendation for successful development and implementation
SanteÌ connecteÌe et preÌvention du suicide : vers une aide aÌ la deÌcision
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 preÌvention du suicide fait face aÌ des deÌfis speÌcifiques lieÌs aux caracteÌristiques des sujets aÌ risque. La conception dâinterventions de preÌvention efficaces est particulieÌrement difficile. Les sujets suicidants sont accueillis aux urgences qui assurent les soins immeÌdiats et organisent la prise en charge au long cours. Un anteÌceÌdent de passage aÌ lâacte suicidaire est un puissant preÌdicteur de deÌceÌs preÌmatureÌ par au suicide. La prise en charge suivant un passage aux urgences pour un geste suicidaire constitue un deÌfi critique pour les urgences et services de santeÌ mentale. Compte tenu de ces enjeux, il y a eu un inteÌreÌt majeur aÌ eÌvaluer lâefficaciteÌ des interventions visant le maintien du contact des sujets aÌ risque avec les services de soins. LâeÌvaluation ponctuelle du risque suicidaire habituellement conduite aux urgences, apreÌs un geste suicidaire, ne rend pas compte son eÌvolution apreÌs la sortie des soins, alors meÌme que le risque de reÌcidive reste important plusieurs mois apreÌs. Dans ces conditions, les possibiliteÌs dâidentification, et donc de prise en charge, des patients aÌ risque suicidaire sont limiteÌes. Le deÌveloppement de la santeÌ connecteÌe (eHealth) donne deÌsormais acceÌs en temps reÌel aÌ des informations sur lâeÌtat de santeÌ dâun patient entre deux seÌjours en centre de soins. Cette extension de lâeÌvaluation clinique aÌ lâenvironnement du patient permet de deÌvelopper des outils dâaide aÌ la deÌcision face aÌ la gestion du risque suicidaire
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