scholarly journals Identifying Military Veterans in a Clinical Research Database using Natural Language Processing

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Daniel Leightley ◽  
Katharine M Mark ◽  
David Pernet ◽  
Dominic Murphy ◽  
Nicola T Fear ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
National Health Service ◽  
Mental Health Care ◽  
Health Care Services ◽  
Automatic Machine ◽  
Machine Learning Method ◽  
Learning Method ◽  
Care Services

BackgroundThere is a lack of quantitative evidence concerning United Kingdom veterans who access secondary mental health care. This is mainly due to a person’s veteran status not being routinely collected when they enter the health care system. Main AimThe study aimed to develop an NLP approach for identifying veterans accessing secondary mental health care services using National Health Service electronic health records. MethodsVeterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians. ResultsThis study showed that it was possible to identify veterans using the NLP and machine learning approach on a sub-set of 4,200 patients. The automatic machine learning method was able to identify 270 veterans representing an accuracy of 97.2%. It is estimated to take between 6 to 16 minutes to manually search patient history statements whereas the automatic machine learning method took only one minute to run. ConclusionWe have shown that it is possible to identify veterans using NLP combined with machine learning. This work contributes towards the development of a more comprehensive picture of veterans who are accessing secondary mental health care services in the UK. It represents a first step in identifying veterans from one dataset and we hope that future research can inform the possibility of deploying the methods nationally. Despite our success in the current work, the tools are tailored to the SLaM dataset and future work is needed to develop a more agnostic framework. FundingForces in Mind Trust

scholarly journals An Integrated Blueprint for Digital Mental Health Services Amidst COVID-19 (Preprint)

2020 ◽  
Author(s):  
Luke Balcombe ◽  
Diego De Leo
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
Mental Health Services ◽  
Health Services ◽  
Service Delivery ◽  
Mental Health Care ◽  
Real Time ◽  
Health Care Services ◽  
Care Services

UNSTRUCTURED In-person traditional approaches to mental health care services are facing difficulties amidst the coronavirus disease (COVID-19) crisis. The recent implementation of social distancing has redirected attention to nontraditional mental health care delivery to overcome hindrances to essential services. Telehealth has been established for several decades but has only been able to play a small role in health service delivery. Mobile and teledigital health solutions for mental health are well poised to respond to the upsurge in COVID-19 cases. Screening and tracking with real-time automation and machine learning are useful for both assisting psychological first-aid resources and targeting interventions. However, rigorous evaluation of these new opportunities is needed in terms of quality of interventions, effectiveness, and confidentiality. Service delivery could be broadened to include trained, unlicensed professionals, who may help health care services in delivering evidence-based strategies. Digital mental health services emerged during the pandemic as complementary ways of assisting community members with stress and transitioning to new ways of living and working. As part of a hybrid model of care, technologies (mobile and online platforms) require consolidated and consistent guidelines as well as consensus, expert, and position statements on the screening and tracking (with real-time automation and machine learning) of mental health in general populations as well as considerations and initiatives for underserved and vulnerable subpopulations.

scholarly journals An Integrated Blueprint for Digital Mental Health Services Amidst COVID-19

10.2196/21718 ◽  
2020 ◽  
Vol 7 (7) ◽  
pp. e21718 ◽  
Author(s):  
Luke Balcombe ◽  
Diego De Leo
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
Mental Health Services ◽  
Health Services ◽  
Service Delivery ◽  
Mental Health Care ◽  
Real Time ◽  
Health Care Services ◽  
Care Services

In-person traditional approaches to mental health care services are facing difficulties amidst the coronavirus disease (COVID-19) crisis. The recent implementation of social distancing has redirected attention to nontraditional mental health care delivery to overcome hindrances to essential services. Telehealth has been established for several decades but has only been able to play a small role in health service delivery. Mobile and teledigital health solutions for mental health are well poised to respond to the upsurge in COVID-19 cases. Screening and tracking with real-time automation and machine learning are useful for both assisting psychological first-aid resources and targeting interventions. However, rigorous evaluation of these new opportunities is needed in terms of quality of interventions, effectiveness, and confidentiality. Service delivery could be broadened to include trained, unlicensed professionals, who may help health care services in delivering evidence-based strategies. Digital mental health services emerged during the pandemic as complementary ways of assisting community members with stress and transitioning to new ways of living and working. As part of a hybrid model of care, technologies (mobile and online platforms) require consolidated and consistent guidelines as well as consensus, expert, and position statements on the screening and tracking (with real-time automation and machine learning) of mental health in general populations as well as considerations and initiatives for underserved and vulnerable subpopulations.

Mental health economics: current methodological issues

2011 ◽  
Vol 20 (3) ◽  
pp. 239-243 ◽  
Author(s):  
P. McCrone
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Health Care Services ◽  
Self Report ◽  
Economic Evaluations ◽  
Methodological Issues ◽  
Care Services ◽  
Life Years ◽  
Mental Health Economics

Background:Investment in innovative mental health care services requires the use of scarce resources that could be used in alternative ways. Economic evaluation is essential to ensure that such an investment is appropriately compared with investment elsewhere.Method:A non-systematic review of mental health evaluations identifies key methodological issues pertaining to economic studies.Results:Economic evaluations require the measurement and combination of costs and outcomes, and clarity about how this measurement is undertaken is required. Regarding costs, important considerations relate to the perspective to be taken (e.g., health service or societal), method of measurement (patient self-report or use of databases) and valuation (actual costs, fees or expenditure). Decision makers frequently need to compare evidence both within and between clinical areas and therefore there is a tension between the use of condition specific and generic outcome measures. Quality-adjusted life years are frequently used in economic evaluations, but their appropriateness in mental health care studies is still debated.Conclusions:Economic evaluations in the area of mental health care are increasing in number and it is essential that researchers continue to develop and improve methods used to conduct such studies.

scholarly journals A Socio-ecological Framing of the Philippine Mental Health Act of 2017

Spectrum ◽  
2018 ◽  
Author(s):  
Josiah Michael Villareal De Los Santos ◽  
Sonya Jakubec
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Rural Areas ◽  
Health Care Services ◽  
Population Level ◽  
Case Review ◽  
Care Services ◽  
Barriers To Implementation ◽  
Sphere Of Influence

Filipinos experience numerous barriers to mental health care in their country, such as stigmatization ofillness and behaviours, lack of mental health care services, and resource deficits. The Philippine MentalHealth Act of 2017 was formed to resolve these issues and is in its early stages of implementation.Legislation and policy interventions of this nature are but one level of many interventions that can addresshealth care at a population level. The influence of this legislation for different levels of society is analyzed inorder to understand the different barriers and alternatives to its implementation. Solutions suggested in thelegislation, such as addressing lack of accessibility in rural areas, creating liaisons between different levelsof mental health care, and educating the population regarding mental health, are explored for their effects ondifferent spheres, or levels, of influence. The comprehensiveness of the legislation to address the needs ofmental health service users are highlighted, as are barriers to implementation that inhibit the realization ofpractical strategies. This policy case review and analysis informs program development by highlighting thestrengths and weaknesses aligned to the legislative articles’ target sphere of influence and the population.

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