Bias Associated with Mining Electronic Health Records

Hripcsak, George; Knirsch, Charles; Zhou, Li; Wilcox, Adam B.; Melton, Genevieve B.

doi:10.5210/disco.v6i0.3581

Cited by 68 publications

(51 citation statements)

References 5 publications

(10 reference statements)

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“…At the same time, we are better defining potential limitations of EHRderived data [20] and developing ways to address them [21] [22]. Sharing of data from disparate EHR systems to enable populationbased research [9] is a logical and widely anticipated extension.…”

Section: Introductionmentioning

confidence: 99%

Is Childhood Obesity Associated with Bone Density and Strength in Adulthood?

Ahima¹

2013

Childhood Obesity

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Objective: To evaluate the validity of multi-institutional electronic health record (EHR) data sharing for surveillance and study of childhood obesity. Methods:We conducted a non-concurrent cohort study of 528,340 children with outpatient visits to six pediatric academic medical centers during 2007-08, with sufficient data in the EHR for body mass index (BMI) assessment. EHR data were compared with data from the 2007-08 National Health and Nutrition Examination Survey (NHANES).Results: Among children 2-17 years, BMI was evaluable for 1,398,655 visits (56%). The EHR dataset contained over 6,000 BMI measurements per month of age up to 16 years, yielding precise estimates of BMI. In the EHR dataset, 18% of children were obese versus 18% in NHANES, while 35% were obese or overweight versus 34% in NHANES. BMI for an individual was highly reliable over time (intraclass correlation coefficient 0.90 for obese children and 0.97 for all children). Only 14% of visits with measured obesity (BMI $95%) had a diagnosis of obesity recorded, and only 20% of children with measured obesity had the diagnosis documented during the study period. Obese children had higher primary care (4.8 versus 4.0 visits, p,0.001) and specialty care (3.7 versus 2.7 visits, p,0.001) utilization than non-obese counterparts, and higher prevalence of diverse co-morbidities. The cohort size in the EHR dataset permitted detection of associations with rare diagnoses. Data sharing did not require investment of extensive institutional resources, yet yielded high data quality.Conclusions: Multi-institutional EHR data sharing is a promising, feasible, and valid approach for population health surveillance. It provides a valuable complement to more resource-intensive national surveys, particularly for iterative surveillance and quality improvement. Low rates of obesity diagnosis present a significant obstacle to surveillance and quality improvement for care of children with obesity.

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Section: Introductionmentioning

confidence: 99%

Is Childhood Obesity Associated with Bone Density and Strength in Adulthood?

Ahima¹

2013

Childhood Obesity

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“…As Hripcsak et al (2011) note, overlooking data errors or omissions may lead to misleading results as extreme outliers, for instance, may have a disproportionate influence on findings. Traditionally in retrospective research, the researcher will manually inspect cases for accuracy and completeness and will correct data where necessary.…”

Section: Methodological Issuesmentioning

confidence: 96%

“…However, in order to capitalize on these opportunities, a number of challenges must be surmounted. A major challenge in secondary research of EHRs is data quality, namely completeness and accuracy (Hripcsak, Knirsch, Zhou, Wilcox, & Melton, 2011;Orueta et al, 2012;Spooner & Kirkendall, 2012;Teasdale, Bates, Kmetik, Suzewits, & Bainbridge, 2007;Tolar & Balka, 2012;Weiner, Lyman, Murphy, & Weiner, 2007;Weiskopf & Weng, 2012), discrepancies/inconsistencies in data (Orueta et al, 2012;Weiner et al, 2007) and interoperability (Jensen et al, 2012;Spooner & Kirkendall, 2012). There are also significant ethical issues, such as privacy and informed consent, that emerge in secondary research of EHRs (see, e.g.…”

Section: Methodological Issuesmentioning

confidence: 99%

Increasing knowledge of mental illness through secondary research of electronic health records: opportunities and challenges

Spiranovic

Matthews

Scanlan

et al. 2015

Advances in Mental Health

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Aim/Purpose: The primary use of electronic health records (EHRs) is in the care of the individual patient. Secondary research uses employ information in EHRs for purposes beyond that of care of the individual. Secondary research uses may broadly be divided into studies which focus on improving care and treatment of individuals and those which aim to increase knowledge about disease causes, associations and prevalence at a population level. This paper provides a review of studies that have used EHRs to increase knowledge at a population level. It examines the methods used, types of research conducted, difficulties and challenges faced and implications for future research and mental health research in particular. Method: A review was undertaken based on a search for peer-reviewed and recently (i.e. since 2005) published articles with full-text available online. Findings/Results: The studies which have used EHRs to increase knowledge have predominantly involved; (1) data mining to identify biomarkers and gene-disease associations, (2) epidemiological research using linked/merged health records and (3) surveillance, prediction and alerts for diseases/illnesses. The principal methodological challenges identified were data quality, discrepancies/inconsistencies in data and interoperability of EHRs. Conclusions: Despite the challenges faced in secondary usage of EHRs, significant research has been undertaken and researchers have proposed and tested various approaches to address methodological issues. The study methods employed in other fields of medical research can be extrapolated to study issues of significance to mental health using EHRs.

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“…These observational data (rather than experimental data) are more process-related and frequently lack outcome data needed for effective research [21]. Clinical data are also biased by the incentives for clinicians to "upcode", by the non-random assignment of treatments, by systematic differences between patients and the general population, by the healthcare system complexity causing multiple confounders, and the large variability of measurement instruments and methods [40,41]. The quality of data is often problematic or insufficient for research applications [42][43][44].…”

Section: B Motivations and Challenges For Clinical Data Reusementioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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Bias Associated with Mining Electronic Health Records

Cited by 68 publications

References 5 publications

Is Childhood Obesity Associated with Bone Density and Strength in Adulthood?

Is Childhood Obesity Associated with Bone Density and Strength in Adulthood?

Increasing knowledge of mental illness through secondary research of electronic health records: opportunities and challenges

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

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