Abstract
Personalised evidence-based-medicine aims to use stored health data to prevent future illnesses. This implies that data should be stored in a readable and understandable form, at least until the death of the person in question. The aim of this paper is to discuss the challenges that arise from the existing pressure to maintain health data in electronic format for many decades. Today clinical databases are filled with heterogeneous data regarding who has collected it, protocols used, detail, precision, and subjectivity. Some data elements are typically more exposed to these problems (e.g. diagnosis) than others (e.g. laboratory results). It is critical that data scientists fully understand how data were collected. Also, it is very important to store context information, protocols used and accuracy/precision information in clinical databases to ensure future understanding of such data.
Highlights
Personalised evidence-based-medicine (EbM) uses stored health data, namely of patient diagnoses, laboratory work, insure claims, and demographic information among other. This information allows to move beyond the reactive approach of treating illness, allowing healthcare providers to predict and prevent future illnesses [14] and become a promising application area for data science as a discipline. This area has some specific challenges as the use NORTE-01-0145-FEDER-000016 (NanoSTIMA) is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF)
Despite many efforts along the years for improving normalisation and standardization of clinical data, concerns regarding these aspects are still present in recent initiatives intending to push forward personalised medicine
Projects such as FP7 MyHealthAvatar [38] and DISCIPULUS [37] embody the relevance of having digital clinical information for pursuing personalised medicine reinforcing the importance on guarantying completeness regarding patient data allowing a complete view and integrated analysis of the patient health: to this end the methods used for the acquisition of information must be such that information is given as a standardised set of data and preferably provided with uncertainty ranges
Summary
Personalised evidence-based-medicine (EbM) uses stored health data, namely of patient diagnoses, laboratory work, insure claims, and demographic information among other. Despite many efforts along the years for improving normalisation and standardization of clinical data, concerns regarding these aspects are still present in recent initiatives intending to push forward personalised medicine Projects such as FP7 MyHealthAvatar [38] and DISCIPULUS [37] embody the relevance of having digital clinical information for pursuing personalised medicine reinforcing the importance on guarantying completeness regarding patient data allowing a complete view and integrated analysis of the patient health: to this end the methods used for the acquisition of information must be such that information is given as a standardised set of data and preferably provided with uncertainty ranges. The aim of this paper is to discuss the difficulties and possible solutions to problems that rise from the existing pressure to maintain health data in electronic format for many decades
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