Background - Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use this data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems.
Objective - In this paper, we discuss whether data that is recorded routinely as part of the health care process in primary care is actually fit to use for these other purposes, how the original purpose may affect the extent to which the data is fit for another purpose and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the (re-)use of this type of data.
Methods –This discussion paper is based on the authors’ experience as users of electronic health records data, as a general practitioner, health informatics experts, and health services researchers. It is a product of the discussions they had during the TRANSFoRm project, which was funded by the EU and sought to develop, pilot and evaluate a core information architecture for the Learning Health System (LHS) in Europe, based on primary care electronic health records.
Results – We first describe the different stages in the processing of EHR data, as well as the different purposes for which this data is used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step, that can generate biased outcomes. We identified thirteen possible sources of bias. Four of them are related to the organization of a health care system, some are of a more technical nature.
Conclusions - There are a substantial number of possible sources of bias, and very little is known about the size and direction of their impact. However, any (re-)user of data that was recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor and purpose oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven healthcare will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.