Promoting Secondary Analysis of Electronic Medical Records in China: Summary of the PLAGH-MIT Critical Data Conference and Health Datathon

Li, Peiyao; Chen, Xie; Pollard, Tom; Johnson, Alistair E. W.; Cao, Desen; Kang, Hongjun; Liang, Hong; Zhang, Yuezhou; Liu, Xiaoli; Fan, Yong; Zhang, Yuan; Xue, Wanguo; Xie, Lixin; Celi, Leo Anthony; Zhang, Zhengbo

doi:10.2196/medinform.7380

Cited by 22 publications

(22 citation statements)

References 18 publications

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“…EHR data are also increasingly used for public health forecasts and surveillance [ 58 , 35 , 59 , 60 ]. The research potential of EHR data is also increasingly recognized outside the Western world [ 61 ].…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2018

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BackgroundEnormous 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.ObjectiveIn 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.MethodsThis 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.ResultsWe 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.ConclusionsThere 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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2018

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“…Heavy workload was identified as a key barrier to the quality of EHR data. Over 1.56 billion patient visits were made to hospitals in the first half of 2016 in China 26. It is not surprising that some participants considered that the quality of outpatient data might not meet the needs of clinical research.…”

Section: Discussionmentioning

confidence: 99%

“…Healthcare professionals usually conduct EHR-based clinical research independently in China 26. However, using EHR data for clinical research requires multidisciplinary involvement.…”

Section: Discussionmentioning

confidence: 99%

Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study

Chu

Zeng

et al. 2019

BMJ Open

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ObjectivesThere is an increasing trend in the use of electronic health records (EHRs) for clinical research. However, more knowledge is needed on how to assure and improve data quality. This study aimed to explore healthcare professionals’ experiences and perceptions of barriers and facilitators of data quality of EHR-based studies in the Chinese context.SettingFour tertiary hospitals in Beijing, China.ParticipantsNineteen healthcare professionals with experience in using EHR data for clinical research participated in the study.MethodsA qualitative study based on face-to-face semistructured interviews was conducted from March to July 2018. The interviews were audiorecorded and transcribed verbatim. Data analysis was performed using the inductive thematic analysis approach.ResultsThe main themes included factors related to healthcare systems, clinical documentation, EHR systems and researchers. The perceived barriers to data quality included heavy workload, staff rotations, lack of detailed information for specific research, variations in terminology, limited retrieval capabilities, large amounts of unstructured data, challenges with patient identification and matching, problems with data extraction and unfamiliar with data quality assessment. To improve data quality, suggestions from participants included: better staff training, providing monetary incentives, performing daily data verification, improving software functionality and coding structures as well as enhancing multidisciplinary cooperation.ConclusionsThese results provide a basis to begin to address current barriers and ultimately to improve validity and generalisability of research findings in China.

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“…[22][23][24] Pioneered by MIT Critical Data, a consortium founded by members of the LCP and the Computer Science and Artificial Intelligence Laboratory (CSAIL), datathons pair clinicians and data scientists to challenge them to work together to solve a clinical problem. 22,23,[25][26][27] Clinicians learn the nuances of data extraction and model development, and data scientists are provided invaluable insights into clinical data capture and decision making. 28 The success of the datathon model has relied heavily on the availability of data.…”

Section: Collaborative Data Sciencementioning

confidence: 99%

Critical Care, Critical Data

Celi

Stone

2019

Biomed Eng Comput Biol

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As big data, machine learning , and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.

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Promoting Secondary Analysis of Electronic Medical Records in China: Summary of the PLAGH-MIT Critical Data Conference and Health Datathon

Cited by 22 publications

References 18 publications

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

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

Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study

Critical Care, Critical Data

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