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Feasibility and impact of data-driven learning within the suicide prevention action network of thirteen specialist mental healthcare institutions (SUPRANET Care) in the Netherlands: a study protocol.

ABSTRACT: INTRODUCTION:Improvement of the quality and safety of care is associated with lower suicide rates among mental healthcare patients. In The Netherlands, about 40% of all people that die by suicide is in specialist mental healthcare. Unfortunately, the degree of implementation of suicide prevention policies and best practices within Dutch mental healthcare services is variable. Sharing and comparing outcome and performance data in confidential networks of professionals working in different organisations can be effective in reducing practice variability within and across organisations and improving quality of care. METHODS AND ANALYSIS:Using formats of professional networks to improve surgical care (Dutch Initiative for Clinical Auditing) and somatic intensive care (National Intensive Care Evaluation), 113 Suicide Prevention has taken the lead in the formation of a Suicide Prevention Action Network (SUPRANET Care), with at present 13 large Dutch specialist mental health institutions. Data on suicide, suicide attempts and their determinants as well as consumer care policies and practices are collected biannually, after consensus rounds in which key professionals define what data are relevant to collect, how it is operationalised, retrieved and will be analysed. To evaluate the impact of SUPRANET Care, standardised suicide rates will be calculated adjusted for confounding factors. Second, the extent to which suicide attempts are being registered will be analysed with the suicide attempt data. Finally, professionals' knowledge, attitude and adherence to suicide prevention guidelines will be measured with an extended version of the Professionals In Training to STOP suicide survey. ETHICS AND DISSEMINATION:This study has been approved by the Central Committee on Research Involving Human Subjects, The Netherlands. This study does not fall under the scope of the Medical Research Involving Human Subjects Act (WMO) or the General Data Protection Regulation as stated by the Dutch Data Protection Authority because data are collected on an aggregated level.

SUBMITTER: Setkowski K

PROVIDER: S-EPMC6104797 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Feasibility and impact of data-driven learning within the suicide prevention action network of thirteen specialist mental healthcare institutions (SUPRANET Care) in the Netherlands: a study protocol.

Setkowski Kim K Mokkenstorm Jan J van Balkom Anton Jlm AJ Franx Gerdien G Verbeek-van Noord Inge I Dongelmans Dave A DA Eikelenboom Merijn M Gilissen Renske R

BMJ open 20180820 8

<h4>Introduction</h4>Improvement of the quality and safety of care is associated with lower suicide rates among mental healthcare patients. In The Netherlands, about 40% of all people that die by suicide is in specialist mental healthcare. Unfortunately, the degree of implementation of suicide prevention policies and best practices within Dutch mental healthcare services is variable. Sharing and comparing outcome and performance data in confidential networks of professionals working in different ...[more]

PMID: 30127055

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Project description:BACKGROUND:The Delphi technique is a proven and reliable method to create common definitions and to achieve convergence of opinion. This study aimed to prioritize suicide prevention guideline recommendations and to develop a set of quality indicators (QIs) for suicide prevention in specialist mental healthcare. METHODS:This study selected 12 key recommendations from the guideline to modify them into QIs. After feedback from two face-to-face workgroup sessions, 11 recommendations were rephrased and selected to serve as QIs. Next, a Delphi study with the 11 QIs was performed to achieve convergence of opinion among a panel of 90 participants (23 suicide experts, 23 members of patients' advisory boards or experts with experiences in suicidal behavior and 44 mental healthcare professionals). The participants scored the 11 QIs on two selection criteria: relevance (it affects the number of suicides in the institution) and action orientation (institutions or professionals themselves can influence it) using a 5-point Likert scale. Also, data analysts working in mental healthcare institutions (MHIs) rated each QI on feasibility (is it feasible to monitor and extract from existing systems). Consensus was defined as 70% agreement with priority scores of four or five. RESULTS:Out of the 11 recommendations, participants prioritized five recommendations as relevant and action-oriented in optimizing the quality of care for suicide prevention: 1) screening for suicidal thoughts and behavior, 2) safety plan, 3) early follow-up on discharge, 4) continuity of care and 5) involving family or significant others. Only one of the 11 recommendations early follow-up on discharge reached consensus on all three selection criteria (relevance, action orientation, and feasibility). CONCLUSIONS:The prioritization of relevant and action-oriented suicide prevention guideline recommendations is an important step towards the improvement of quality of care in specialist mental healthcare.

Project description:BackgroundOxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records.MethodsIn study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure).ResultsIn study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61).ConclusionsIt is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges.

Project description:ObjectiveDespite available effective treatments for mental health disorders, few patients in need receive even the most basic care. Integrated telepsychiatry services may be a viable option to increase access to mental healthcare. The aim of this qualitative process evaluation embedded in a randomised controlled feasibility trial was to explore health providers' experiences with a mental healthcare model integrating mental health specialist video consultations (MHSVC) and primary care.MethodsA qualitative process evaluation focusing on MHSVC in primary care was conducted. In 13 semistructured interviews, we assessed the experience of all mental health specialists, primary care physicians and medical assistants who participated in the trial. A thematic analysis, focusing on the implementation, mechanisms of impact and context, was applied to investigate the data.ResultsConsidering (1) the implementation, participants evaluated the consultations as feasible, easy to use and time saving. Concerning (2) the mechanisms of impact, the consultations were regarded as effective for patients. Providers attributed the patients' improvements to two key aspects: the familiarity of the primary care practice and the fast access to specialist mental healthcare. Mental health specialists observed trustful therapeutic alliances emerging and described their experience as comparable to same-room care. However, compared with same-room care, specialists perceived the video consultations as more challenging and sometimes more exhausting due to the additional effort required for establishing therapeutic alliances. Regarding (3) the intervention's context, shorter travel distances for patients positively affected the implementation, while technical failures, that is, poor Internet connectivity, emerged as the main barrier.ConclusionsMHSVCs in primary care are feasible and successful in improving access to mental healthcare for patients. To optimise engagement and comfort of both patients and health providers, future work should focus on empirical determinants for establishing robust therapeutic alliances with patients receiving MHSVC (eg, leveraging non-verbal cues for therapeutic purposes).Trial registration numberDRKS00015812; Results.

Project description:ObjectiveTo evaluate the feasibility of conducting a large clinical trial within the Rwandan mental healthcare system that would establish the safety, efficacy and benefit of paliperidone palmitate once-monthly (PP1M) and once-every-3-months (PP3M) long-acting injectable formulations in adults with schizophrenia.Study designAn open-label, prospective feasibility study.Setting/participants33 adult patients with schizophrenia were enrolled at 3 sites across Rwanda.InterventionsThe study design included 3 phases of treatment: an oral run-in to establish tolerability to risperidone (1 week), lead-in treatment with flexibly dosed PP1M to identify a stable dose (17 weeks) and maintenance treatment with PP3M (24 weeks).Primary and secondary outcome measuresFeasibility endpoints included compliance with governmental and institutional requirements, acceptable supply chain delivery and proper onsite administration of risperidone/PP1M/PP3M, adequate site infrastructure, adequate training of clinical staff and successful completion of study procedures and scales. A variety of study scales were administered to assess outcomes relevant to patients, caregivers, clinicians and payers in Rwanda and other resource-limited settings.ResultsThis study was terminated early by the sponsor because certain aspects of study conduct needed to be addressed to maintain Good Clinical Practice requirements and meet regulatory standards. Results identified areas for improvement in study execution, including study governance, site infrastructure, study preparation and conduct of procedures, study budget and study assessments. Despite the identification of areas in need of adjustment, none of these limitations were considered insurmountable.ConclusionsThis work was designed to strengthen global research in schizophrenia by building the capacity of researchers to prepare and conduct pharmaceutical trials in resource-limited settings. Although the study was ended early, modifications motivated by the results will facilitate the successful design and completion of more comprehensive studies, including an ongoing, follow-up interventional trial of PP1M/PP3M in a larger population of patients in Rwanda.Trial registration numberNCT03713658.

Project description:IntroductionMost people suffering from depression and anxiety disorders are entirely treated in primary care. Due to growing challenges in ageing societies, for example, patients' immobility and multimorbidity, the transition to specialised care becomes increasingly difficult. Although the co-location of general practitioners and mental health specialists improves the access to psychosocial care, integrated in-person approaches are not practical for rural and single-doctor practices with limited personnel and financial resources. Treating primary care patients via internet-based video consultations by remotely located mental health specialists bears the potential to overcome structural barriers and provide low-threshold care. The aim of this randomised controlled feasibility trial is to investigate the feasibility of implementing of mental health specialist video consultations in primary care practices.Methods and analysisFifty primary care patients with significant depression and/or anxiety symptomatology will be randomised in two groups receiving either the treatment as usual as provided by their general practitioner or up to five video consultations conducted by a mental health specialist. The video consultations focus on (1) systematic diagnosis plus proactive monitoring using validated clinical rating scales, (2) the establishment of an effective working alliance and (3) a stepped-care algorithm within integrated care adjusting treatments based on clinical outcomes. We will investigate the following outcomes: effectiveness of the recruitment strategies, patient acceptance of randomisation, practicability of the technical and logistical processes related to implementing video consultations in the practices' workflows, feasibility of the data collection and clinical parameters.Ethics and disseminationThis trial has undergone ethical scrutiny and has been approved by the Medical Faculty of the University of Heidelberg Ethics Committee (S-634/2018). The findings will be disseminated to the research community through presentations at conferences and publications in scientific journals. This feasibility trial will prepare the ground for a large-scale, fully powered randomised controlled trial.Trial registration numberDRKS00015812.

Dataset Information

Feasibility and impact of data-driven learning within the suicide prevention action network of thirteen specialist mental healthcare institutions (SUPRANET Care) in the Netherlands: a study protocol.

Publications

Feasibility and impact of data-driven learning within the suicide prevention action network of thirteen specialist mental healthcare institutions (SUPRANET Care) in the Netherlands: a study protocol.

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