Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis.

Daniel Capurro,Josefa Santa Maria,Mario Barbe,Claudio Daza,Javier Trincado

doi:10.2196/medinform.6924

Daniel Capurro, Josefa Santa Maria + Show 3 more

Open Access

https://doi.org/10.2196/medinform.6924

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Abstract

BackgroundInclusion criteria for observational studies frequently contain temporal entities and relations. The use of digital phenotypes to create cohorts in electronic health record–based observational studies requires rich functionality to capture these temporal entities and relations. However, such functionality is not usually available or requires complex database queries and specialized expertise to build them.ObjectiveThe purpose of this study is to systematically assess observational studies reported in critical care literature to capture design requirements and functionalities for a graphical temporal abstraction-based digital phenotyping tool.MethodsWe iteratively extracted attributes describing patients, interventions, and clinical outcomes. We qualitatively synthesized studies, identifying all temporal and nontemporal entities and relations.ResultsWe extracted data from 28 primary studies and 367 temporal and nontemporal entities. We generated a synthesis of entities, relations, and design patterns.ConclusionsWe report on the observed types of clinical temporal entities and their relations as well as design requirements for a temporal abstraction-based digital phenotyping system. The results can be used to inform the development of such a system.

Highlights

The increasing costs of health care [1] and the rapid advance of new discoveries create the need for streamlining the identification of effective health interventions
We report on the observed types of clinical temporal entities and their relations as well as design requirements for a temporal abstraction-based digital phenotyping system
We systematically reviewed the critical care literature to characterize the temporal representation of inclusion criteria, interventions, and outcomes, used by clinical researchers when designing a clinical study

Summary

Introduction

The increasing costs of health care [1] and the rapid advance of new discoveries create the need for streamlining the identification of effective health interventions. When we consider the amount of resources required to conduct a randomized controlled trial [3], alternative ways to assess the effectiveness of clinical interventions become attractive. A health care system that systematically analyzes clinical data to generate and test hypotheses should be able to learn from itself, becoming a learning health care system [5]. The use of digital phenotypes to create cohorts in electronic health record–based observational studies requires rich functionality to capture these temporal entities and relations. Such functionality is not usually available or requires complex database queries and specialized expertise to build them

Methods

Results

Conclusion