Contributions of health technologies in risk assessment for suicide behavior: an integrative review

Rocha, Daniel de Macêdo; Cavalcante, Andreia Karla de Carvalho Barbosa; Oliveira, Aline Costa de; Benício, Claudia Daniella Avelino Vasconcelos; Santos, Ana Maria Ribeiro dos; Nogueira, Lídya Tolstenko

doi:10.1590/0034-7167-2020-0205

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Revista Brasileira de Enfermagem

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REVIEW • Rev. Bras. Enferm. 74 (Suppl 3) • 2021 • https://doi.org/10.1590/0034-7167-2020-0205 copy

Contributions of health technologies in risk assessment for suicide behavior: an integrative review

Contribuciones de las tecnologías sanitarias a la evaluación de riesgos de la conducta suicida: una revisión integradora

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Objective:

to identify how health technologies contribute to risk assessment for suicidal behavior.

Methods:

an integrative review carried out on the MEDLINE, CINAHL, Web of Science, SCOPUS, LILACS and BDENF databases. The sample consisted of 12 primary studies, with no temporal or language delimitation. Analysis of results occurred descriptively.

Results:

the technologies included assistance and teaching aspects favorable to data screening, monitoring and navigation, capable of predicting individual, biological, clinical, psychological, environmental and social markers for suicidal behavior. Thus, measurement scale, software and computational algorithms constituted valid and sensitive instruments for improving performance, expanding skills and knowledge, good care practices, effective communication and assistance planning.

Conclusion:

the identified technologies proved to be effective in identifying risk states for suicidal behavior, representing effective resources for directing care and necessary interventions.

Descriptors:
Suicide; Suicide, Attempt; Risk Measurement; Technologies; Nursing

Health Sciences Descriptors
P	Controlled	Suicídio; Tentativa de Suicídio; Ideação Suicida
P	Non-controlled	Suicídios; Parassuicídio; Comportamento Suicida; Suicidio; Suicide; Ideación Suicida; Ideas Suicidas; Suicidal Ideation; Ideations, Suicidal; Intento de Suicidio; Suicide, Attempted; Attempted Suicide; Parasuicide
I	Controlled	Medição de Risco
I	Non-controlled	Análise de Risco; Avaliação de Risco; Avaliação de Risco para a Saúde; Determinação do Risco; Classificação de Risco; Medición de Riesgo; Análisis de Riesgo; Evaluación de Riesgo; Evaluación de Riesgo para la Salud; Risk Assessment; Assessment, Health Risk; Assessment, Risk; Assessments, Health Risk; Health Risk Assessment; Risk Assessments
Co	Controlled	Tecnologia
Co	Non-controlled	Sistema Tecnológico; Tecnologias e Aplicativos de Software; Tecnología; Tecnología y Aplicaciones de Software; Technological System; Technologies and Software Applications; Technologies; Technology; Technology and Software Applications
Medical Subject Headings and list CINAHL
P	Controlled	Suicide; Suicidal Ideation; Suicide, Attempted
P	Non-controlled	Suicides; Ideation, Suicidal; Ideations, Suicidal; Suicidal Ideations; Attempted Suicide; Parasuicide; Parasuicides
I	Controlled	Risk Assessment
I	Non-controlled	Assessments, Risk; Risk Assessments; Health Risk Assessment; Assessment, Health Risk; Health Risk Assessments; Risk Assessment, Health; Risk Assessments, Health; Assessment, Risk
Co	Controlled	Technology
Co	Non-controlled	Technology; Technologies; Technologies and Software Applications
P AND I AND Co
((((((((((((((“Suicide” [Mesh] OR “Suicide, Attempted” [Mesh]) OR “Suicidal Ideation” [Mesh])) OR “suicide”) OR “suicide, attempted”) OR “suicidal ideation”) OR “suicides”) OR “ideation, suicidal”) OR “suicidal ideations”) OR “attempted suicide”) OR “parasuicide”) OR “parasuicides”)) AND ((((((((“Risk Assessment” [Mesh]) OR “risk assessment”) OR “assessments, risk”) OR “risk assessments”) OR “health risk assessment”) OR “health risk assessments”) OR “risk assessment, health”) OR “assessment, risk”)) AND ((((“technology” [MeSH Terms]) OR “technology”) OR “technologies”) OR (“technologies and software applications”))

Category 1 - Assistance technologies used in risk management for suicidal behavior
Technology and objective	Applicability	Main results	Conclusion	Limitation
HelPath(¹⁷17 Miranda JM, Palacios-Isaac A, López-Flores F, Martínez A, Aguilar H, Jiménez L, et al. Development of a computational platform to support the screening, surveillance, prevention and detection of suicidal behaviors. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering. Proceddings [Internet]. 2019 [cited 2019 Dec 28];288:83-101. doi: 10.1007/978-3-030-25872-6_7 https://doi.org/10.1007/978-3-030-25872-... ) (Computational platform) Subsidizing screening and directing to specialized services according to the risk situation.	Screening and monitoring	Device based on cognitive-behavioral therapy, which favored prevention from the emotional state. It enabled risk management and supported clinical decision and information recording for surveillance purposes.	Easy to use and support tool for clinical decision and monitoring of patients at risk, indication of treatment and follow-up actions.	It did not assess results of the technological implantation.
Mobile-assisted treatment(²⁵25 O'Toole MS, Arendt MB, Pedersen CM. Testing an app-assisted treatment for suicide prevention in a randomized controlled trial: effects on suicide risk and depression. Behavior Ther. 2019;50(2):421-9. doi: 10.1016/j.beth.2018.07.007 https://doi.org/10.1016/j.beth.2018.07.0... ) Assessing the impact of an application on depression and suicide risk.	Monitoring	It favored the reduction of self-reported suicide risk. There were no differences between groups regarding the symptom of depression.	The technology reduced the immediate risk of suicide, but did not influence the level of depression experienced, indicating that other therapeutic resources are needed.	The results were generalized to the adult population.
Natural language processing(²⁶26 Coppersmith G, Leary R, Crutchley P, Fine A. Natural language processing of social media as screening for suicide risk. Biomed Inform Insights. 2018;10:1-11. doi: 10.1177/1178222618792860 https://doi.org/10.1177/1178222618792860... ) OurDataHelps.org Estimating suicide risk through combined analysis of social media, demographic and mental conditions.	Screening and data navigation	Automated resource that can be performed by untrained professionals and that has shown feasibility and accuracy to predict quantifiable signs of suicidal behavior.	It was the basis for screening, crisis prevention, improvement of clinical systems and care.	Ethical and privacy implications. Application by individual consent.
Linehan Suicide Safety Net(¹⁸18 Harned MS, Lungu A, Wilks CR, Linehan MM. Evaluating a multimedia tool for suicide risk assessment and management: the Linehan Suicide Safety Net. J Clin Psychol. 2017;73(3):308-18. doi: 10.1002/jclp.22331 https://doi.org/10.1002/jclp.22331... ) (LSSN) Assessing, managing, and documenting suicide risk.	Monitoring and data navigation	Web-based multimedia tool that increased confidence in risk management, assessment and management, representing a method to improve clinical interventions.	The high acceptability shows that this tool is promising for assessing and managing suicide risk.	There is no evidence of effectiveness in different cultures.
e-PASS Suicidal Ideation Detector (eSID)(¹⁹19 Meyer D, Abbott JA, Rehm I, Bhar S, Barak A, Deng G, et al. Development of a suicidal ideation detection tool for primary healthcare settings: using open access online psychosocial data. Telemed J Educ Health. 2017;23(4):273-81. doi: 10.1089/tmj.2016.0110 https://doi.org/10.1089/tmj.2016.0110... ) Identifying suicidal ideation in Primary Health Care.	Screening and data navigation	Fast, effective, low cost method with the potential to improve the quality of care. It performed well among adults and the elderly of both genders.	ESID has been configured as a precise and favorable method for identification and assessment of risk states.	Only the medical performance was assessed, not considering the other essential categories for risk assessment.
Computational algorithm(²²22 Pestian JP, Sorter M, Connolly B, Cohen KB, McCullumsmith C, Gee JT, et al. A machine learning approach to identifying the thought markers of suicidal subjects: a prospective multicenter trial. Suicide Life-Threatening Behav. 2017;47(1):112-21. doi: 10.1111/sltb.12312 https://doi.org/10.1111/sltb.12312... ) Identifying markers of suicidal thinking in linguistic and acoustic characteristics (dynamics, frequency and vocal quality).	Screening and data navigation	Objective, precise and useful tool to determine the level of suicidal intention, directing prevention and treatment in different contexts and environments.	A technology with potential for application in school, youth and community environments was evidenced, favoring the early identification of behavior.	Technological performance has not been assessed in other populations.
Momentary ecological assessment(²⁷27 Husky M, Olie E, Guillaume S, Genty C, Swendsen J, Courtet P. Feasibility and validity of ecological momentary assessment in the investigation of suicide risk. Psychiatry Res. 2014;220(1-2):564-70. doi: 10.1016/j.psychres.2014.08.019 https://doi.org/10.1016/j.psychres.2014.... ) Classifying and recognizing situations of risk related to mood, self-harm, environment and social context.	Screening and monitoring	Instrument based on computational monitoring, considered valid, viable and with a high acceptance rate, which enabled advances in identification of predisposing factors for behavior.	Potential to support the study of suicide risk, the assessment and monitoring of cognitive states and associated psychosocial factors.	Technological accuracy may be reduced when the algorithm is applied to different navigation routes.
Momentary ecological assessment(²³23 Thompson WK, Gershon A, O'Hara R, Bernert RA, Depp CA. The prediction of study-emergent suicidal ideation in bipolar disorder: a pilot study using ecological momentary assessment data. Bipolar Disord. 2014;16(7):669-77. doi: 10.1111/bdi.12218 https://doi.org/10.1111/bdi.12218... ) Predicting individual changes, clinical factors and risk states.	Screening and monitoring	Predictive model, with high sensitivity and accuracy to identify ideation, leading to the planning of preventive interventions.	It made it possible to improve identification, assessment and intervention in high-risk states, in addition to management of chronic and recurrent disorders.	It allows assessment in a single condition related to suicidal behavior (ideation).
Suicide Ideation Scale(²⁰20 Luxton DD, Rudd MD, Reger MA, Gahm GA. A psychometric study of the Suicide Ideation Scale. Arch Suicide Res. 2011;15(3):250-8. doi: 10.1080/13811118.2011.589720 https://doi.org/10.1080/13811118.2011.58... ) Identifying and classifying suicide risk.	Screening	Specificity, sensitivity of measurement, simplicity of administration, clinical and research applications.	The scale showed adequate psychometric properties to identify suicidal thoughts.	It does not include variables related to individual suicide history.
Software – Lifenet(²¹21 Ferns WJ. Lifenet: a knowledge-based decision support tool for the risk assessment of adolescent suicide. Expert Syst Applic. 1995;9(2):165-76. doi: 10.1016/0957-4174(94)00059-5 https://doi.org/10.1016/0957-4174(94)000... ) Determining suicide risk in adolescents, identify predictors, and point out interventions.	Screening	Accuracy in determining risk situations, reducing service time, improving data collection and recording.	It presented organizational, clinical and legal impacts when assessing risks using psychological and behavioral indicators.	There is no evidence of validity and external reliability.
Category 2 - Teaching technologies in risk assessment for suicidal behavior
Electronic health education system(²⁴24 Ryan K, Tindall C, Strudwick G. Enhancing key competencies of health professionals in the assessment and care of adults at risk of suicide through education and technology. Clin Nurse Spec. 2017;31(5):268-75. doi: 10.1097/NUR.0000000000000322 https://doi.org/10.1097/NUR.000000000000... ) Improving skills for assessment and care for adults at risk of suicide.	Screening, data navigation and storage	Increased confidence and knowledge from the multiple approach involving education and technology. It favored good practices, documentation and care planning.	It resulted in the best performance for assessing suicide and was part of the team at all levels of performance.	No limitation has been described.
Clinical simulation(²⁸28 Foster A, Chaudhary N, Murphy J, Lok B, Waller J, Buckley PF. The use of simulation to teach suicide risk assessment to health profession trainees: rationale, methodology, and a proof of concept demonstration with a virtual patient. Acad Psychiatry. 2015;39(6):620-9. doi: 10.1007/s40596-014-0185-9 https://doi.org/10.1007/s40596-014-0185-... ) Developing and applying interactive virtual patient for risk assessment for suicide.	Screening	The contributions expressed an improvement in knowledge, communication skills and performance for risk assessment.	Resource with potential for higher education and permanent education activities.	It requires material and financial resources to guarantee the realism of the clinical scenario.
Software – Lifenet(²¹21 Ferns WJ. Lifenet: a knowledge-based decision support tool for the risk assessment of adolescent suicide. Expert Syst Applic. 1995;9(2):165-76. doi: 10.1016/0957-4174(94)00059-5 https://doi.org/10.1016/0957-4174(94)000... ) Determining suicide risk by identifying predictors.	Screening, data navigation and storage	Improved knowledge and skills as well as constituting a data repository and clinical assessments.	It presented properties favorable to the consistent assessment of patients at risk.	There is no evidence of external validity.

Quality assessment							Quality
N	Design	Bias risk	Inconsistency	Uncertainty if evidence is indirect	Inaccuracy	Other considerations
Assistance technologies used in risk management for suicidal behavior
10	Randomized clinical trial (3) Observational (7)	High^* * Risk of bias in randomized controlled trials related to blinding absence and allocation of participants.	No serious inconsistency	No important indirect evidence	No serious inaccuracy	None	Moderate
Teaching technologies in risk assessment for suicidal behavior
3	Randomized clinical trial (1) Observational (2)	High^* * Risk of bias in randomized controlled trials related to blinding absence and allocation of participants.	No serious inconsistency	No important indirect evidence	No serious inaccuracy	None	Moderate

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[1] Corresponding author: Daniel de Macêdo Rocha E-mail: daniel_m.rocha@outlook.com.br