Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

Robert A. Verheij (Lead / Corresponding author), Vasa Curcin, Brendan C. Delaney, Mark M. McGilchrist

Research output: Contribution to journalArticle

14 Citations (Scopus)
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Abstract

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 these 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 are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are 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 use and reuse of these type of data.

Methods: This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.

Results: We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are 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 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.

Conclusions: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were 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 health care 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.

Original languageEnglish
Article numbere185
Pages (from-to)1-13
Number of pages13
JournalJournal of Medical Internet Research
Volume20
Issue number5
DOIs
Publication statusPublished - 29 May 2018

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Electronic Health Records
Primary Health Care
Delivery of Health Care
Health
Research
Research Personnel
Learning
Informatics
Translational Medical Research
Health Care Quality Indicators
Patient Safety
General Practitioners
Health Services
Patient Care
Data Accuracy

Keywords

  • electronic health record
  • data accuracy
  • data sharing
  • health information interoperability
  • health care systems
  • health information systems
  • medical informatics

Cite this

Verheij, Robert A. ; Curcin, Vasa ; Delaney, Brendan C. ; McGilchrist, Mark M. / Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse. In: Journal of Medical Internet Research. 2018 ; Vol. 20, No. 5. pp. 1-13.
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Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse. / Verheij, Robert A. (Lead / Corresponding author); Curcin, Vasa; Delaney, Brendan C.; McGilchrist, Mark M.

In: Journal of Medical Internet Research, Vol. 20, No. 5, e185, 29.05.2018, p. 1-13.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

AU - Verheij, Robert A.

AU - Curcin, Vasa

AU - Delaney, Brendan C.

AU - McGilchrist, Mark M.

N1 - This project received funding from the European Commission’s 7th Framework Programme for research, technological development, and demonstration under grant agreement number 247787 (Translational Research and Patient Safety in Europe, TRANSFoRm).

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Y1 - 2018/5/29

N2 - 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 these 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 are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are 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 use and reuse of these type of data.Methods: This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.Results: We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are 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 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.Conclusions: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were 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 health care 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.

AB - 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 these 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 are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are 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 use and reuse of these type of data.Methods: This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.Results: We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are 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 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.Conclusions: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were 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 health care 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.

KW - electronic health record

KW - data accuracy

KW - data sharing

KW - health information interoperability

KW - health care systems

KW - health information systems

KW - medical informatics

U2 - 10.2196/jmir.9134

DO - 10.2196/jmir.9134

M3 - Article

C2 - 29844010

VL - 20

SP - 1

EP - 13

JO - Journal of Medical Internet Research

JF - Journal of Medical Internet Research

SN - 1439-4456

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M1 - e185

ER -