Achieving veracity: A study of the development and use of an information system for data analysis in preventive healthcare

Abstract

Within healthcare, information systems are increasingly developed to enable automatic analysis of the large amounts of data that are accumulated. A prerequisite for the practical use of such data analysis is the veracity of the output, that is, that the analysis is clinically valid. Whereas most research focuses on the technical configuration and clinical precision of data analysis systems, the purpose of this article is to investigate how veracity is achieved in practice. Based on a study of a project in Denmark aimed at developing an algorithm for stratification of citizens in preventive healthcare, this article confirms that achieving veracity requires close attention to the clinical validity of the algorithm. It also concludes, however, that the veracity in practice hinges critically on the citizens' ability to report high-quality data and the ability of the health professionals to interpret the outcome in the context of existing care practices.

Data work in healthcare: An Introduction

“…These include data-driven management, accountability, and governance based on performance measurements, and secondary clinical research. While data infrastructures are built to enable monitoring of populations and early prevention measures, 20 and to monitor the quality of services, 21,22 healthcare workers increasingly learn to maneuver playfully within these environments. 23 Thus, the papers collected here discuss the data work of different groups: doctors struggling to clarify data ambiguity and use predictive algorithms for personalized medicine; 24 coming to terms with data variability-varying data on the same phenomena; 25 clinicians' response to patients' increasing data literacy; 26 nurses' interpretation of patient-generated data as a means of inclusion in their own care; 27,28 and patients generating data about their health.…”

“…These include data-driven management, accountability, and governance based on performance measurements, and secondary clinical research. While data infrastructures are built to enable monitoring of populations and early prevention measures, 20 and to monitor the quality of services, 21,22 healthcare workers increasingly learn to maneuver playfully within these environments. 23…”

PD and The Challenge of AI in Health-Care

“…The notion of veracity assumes that the meaning or semantics of the data is shared by all producers. This is, however, often not the case as categories sometimes produces different meanings for different groups of health professionals as well as for patients [14]. As a practical consequence, different health professionals may document identical activities in different ways.…”

A novel framework for bringing smart big data to proactive decision making in healthcare

“…5 The tedious and redundant work makes analysis lose its meanings of application practices, thereby hindering the development of high-level big data driven applications, such as establishing a clinical decision support system. 6,7 Currently, the analysis of medical and health big data results in several problems. 8 First, common big data-related problems, including large data volume, diverse data formats and forms, and high data dimension, still exist.…”

“…5 The tedious and redundant work makes analysis lose its meanings of application practices, thereby hindering the development of high-level big data driven applications, such as establishing a clinical decision support system. 6,7…”