Desarrollo e impacto de los sistemas informatizados de apoyo a las decisiones en el manejo clínico de la depresión: revisión sistemática

Triñanes, Yolanda; Atienza, Gerardo; González, Arturo Louro; Liñero, Elena de las Heras; Álvarez-Ariza, María; Palao, Diego

doi:10.1016/j.rpsm.2014.10.004

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…These types of statistical procedures can ultimately result in the development of decision support systems in the context of health interventions. In the field of depression treatment, these systems often lead to positive effects and even a reduction of symptoms in various situations [11]. …”

Section: Introductionmentioning

confidence: 99%

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Bremer¹,

Becker²,

Kolovos³

et al. 2018

J Med Internet Res

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BackgroundDifferent treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level.ObjectiveThis study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation.MethodsOutcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment.ResultsPredicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%).ConclusionsOur results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs.

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Section: Introductionmentioning

confidence: 99%

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Bremer¹,

Becker²,

Kolovos³

et al. 2018

J Med Internet Res

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“…Para facilitar su implementación, se ha procedido a su transformación en sistemas informatizados de apoyo a las decisiones clínicas. Con estos métodos los procesos asistenciales son más coherentes con las recomendaciones, aunque el efecto en los resultados en la salud a menudo es poco evidente 5 .…”

Section: Introductionunclassified

Un sistema informatizado de apoyo a las decisiones clínicas para el manejo de la depresión en atención primaria

et al. 2017

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Despite its clinical relevance and its importance as a public health problem, there are major gaps in the management of depression. Evidence-based clinical guidelines are useful to improve processes and clinical outcomes. In order to make their implementation easier these guidelines have been transformed into computerised clinical decision support systems. In this article, a description is presented on the basics and characteristics of a new computerised clinical guideline for the management of major depression, developed in the public health system in Catalonia. This tool helps the clinician to establish reliable and accurate diagnoses of depression, to choose the best treatment a priori according to the disease and the patient characteristics. It also emphasises the importance of systematic monitoring to assess the clinical course, and to adjust therapeutic interventions to the patient's needs at all times.

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The impact of electronic health record functions on patterns of depression treatment in primary care

Matthew

Akincigil

2021

Informatics for Health and Social Care

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Desarrollo e impacto de los sistemas informatizados de apoyo a las decisiones en el manejo clínico de la depresión: revisión sistemática

Cited by 7 publications

References 31 publications

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Un sistema informatizado de apoyo a las decisiones clínicas para el manejo de la depresión en atención primaria

The impact of electronic health record functions on patterns of depression treatment in primary care

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