Information sharing and risk: a survey of psychiatrists

2007 ◽  
Vol 24 (3) ◽  
pp. 94-98 ◽  
Author(s):  
Larkin Feeney ◽  
Paul Moran
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Medical Model ◽  
Clinical Information ◽  
Data Set ◽  
Emergency Mental Health ◽  
Alert Message ◽  
Health Assessments ◽  
Multidisciplinary Model

AbstractObjectives: Historical information is central to decision making in mental health care. Clinical information in the Irish mental health services is currently mostly paper based. Mental health care in Ireland has moved from an institutional medical model towards a community based multidisciplinary model in recent years. This change has resulted in a dispersal of information between multiple sites and professionals, rendering it less accessible, particularly in emergency settings.This study sought to find out if psychiatrists working in Ireland were experiencing information problems, their ideas about and attitudes towards electronic solutions to these problems, and their views as to what particular pieces of information are indispensable in emergency mental health assessments.Method: A questionnaire was designed to answer these questions and sent to a representative sample of 150 psychiatrists working in Ireland.Results: One hundred and nineteen questionnaires (79.3%) were returned complete. Of the 119 respondents 98(82.4%) stated that they had performed emergency mental health assessments within the past year without access to key information and 79(66.4%) said they would have made different decisions in some cases had they had all the available information. Information deficits were particularly apparent in liaison and forensic psychiatry.Of the respondents 110(92.4%) stated that they would welcome an electronic database designed to support emergency mental health assessments. Misgivings were expressed regarding forms of consent, data quality, breach of confidentiality, resources and much more. Risk factors (ie. self-harm potential), a high alert message and medication details were the data items thought to be most critical.Conclusions: A shareable set of essential pieces of information (a minimum data set) would offer a balance between patient safety, confidentiality and shareability. A wider debate about solutions to the information deficits in mental health care in Ireland needs to take place among all stakeholders so that this idea can be moved forward.

The Effect of a Dedicated Psychiatric Team to Pediatric Emergency Mental Health Care

2016 ◽  
Vol 50 (3) ◽  
pp. e121-e128 ◽  
Author(s):  
David C. Sheridan ◽  
John Sheridan ◽  
Kyle P. Johnson ◽  
Amber Laurie ◽  
Allyson Knapper ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Pediatric Emergency ◽  
Emergency Mental Health

Emergency Mental Health Care in an HMO

1986 ◽  
Vol 3 (2-3) ◽  
pp. 137-155
Author(s):  
Geraldine Koppenaal ◽  
Jane Ellis
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Emergency Mental Health

scholarly journals CHRONOSIG: Digital Triage for Secondary Mental Healthcare using Natural Language Processing - rationale and protocol

2021 ◽  
Author(s):  
Dan W Joyce ◽  
Andrey Kormilitzin ◽  
Julia Hamer-Hunt ◽  
Anthony James ◽  
Alejo Nevado-Holgado ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Decision Making ◽  
Natural Language Processing ◽  
Natural Language ◽  
Mental Health Care ◽  
Language Processing ◽  
Clinical Decision ◽  
Free Text ◽  
Data Set

ABSTRACTBackgroundAccessing specialist secondary mental health care in the NHS in England requires a referral, usually from primary or acute care. Community mental health teams triage these referrals deciding on the most appropriate team to meet patients’ needs. Referrals require resource-intensive review by clinicians and often, collation and review of the patient’s history with services captured in their electronic health records (EHR). Triage processes are, however, opaque and often result in patients not receiving appropriate and timely access to care that is a particular concern for some minority and under-represented groups. Our project, funded by the National Institute of Health Research (NIHR) will develop a clinical decision support tool (CDST) to deliver accurate, explainable and justified triage recommendations to assist clinicians and expedite access to secondary mental health care.MethodsOur proposed CDST will be trained on narrative free-text data combining referral documentation and historical EHR records for patients in the UK-CRIS database. This high-volume data set will enable training of end-to-end neural network natural language processing (NLP) to extract ‘signatures’ of patients who were (historically) triaged to different treatment teams. The resulting algorithm will be externally validated using data from different NHS trusts (Nottinghamshire Healthcare, Southern Health, West London and Oxford Health). We will use an explicit algorithmic fairness framework to mitigate risk of unintended harm evident in some artificial intelligence (AI) healthcare applications. Consequently, the performance of the CDST will be explicitly evaluated in simulated triage team scenarios where the tool augments clinician’s decision making, in contrast to traditional “human versus AI” performance metrics.DiscussionThe proposed CDST represents an important test-case for AI applied to real-world process improvement in mental health. The project leverages recent advances in NLP while emphasizing the risks and benefits for patients of AI-augmented clinical decision making. The project’s ambition is to deliver a CDST that is scalable and can be deployed to any mental health trust in England to assist with digital triage.

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