Evaluation of data quality of multisite electronic health record data for secondary analysis

Nobles, Alicia L.; Vilankar, Ketki; Wu, Hao; Barnes, Laura E.

doi:10.1109/bigdata.2015.7364060

Cited by 16 publications

(4 citation statements)

References 14 publications

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“…Our study also reinforces previous literature suggesting that it is important to thoroughly and prospectively examine data to assure accuracy and reliability when using the EHR to collect data [13]. If we had made the assumption that providers would accurately document the reason for CT/GC testing, we would have misclassified patients, and our baseline data would have been inaccurate.…”

Section: Discussionsupporting

confidence: 77%

The Reliability of Computerized Physician Order Entry Data for Research Studies

Dexheimer¹,

Taylor²,

Kachelmeyer³

et al. 2019

Pediatr Emer Care

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Objectives: In the United States, adolescents account for nearly half of the newly diagnosed sexually transmitted infections (STI) annually, and many of these infections are asymptomatic. Adolescents often seek care in pediatric emergency departments (PED), thus the ED is an important setting to implement adolescent STI screening. Before implementation, baseline data reflecting current screening rates of symptomatic and asymptomatic patients was needed. This study aimed to evaluate the accuracy of provider reported rates of symptomatic and asymptomatic chlamydia (CT) and gonorrhea (GC) testing in adolescents overall and pre-and post-electronic health record (EHR) order modification in preparation for a research intervention.Methods: This was a one-year prospective, observational study. Provider reason for CT/GC testing was added to the existing EHR order. Chart reviews were performed to assure the accuracy of clinician CT/GC testing choices (symptomatic vs. asymptomatic). Frequencies of testing choices were obtained. Order modifications were made to further clarify the definitions. A student's t-test was used to compare data pre-and post-order modification.Results: When relying on providers to report reasons for CT/GC testing (symptomatic vs. asymptomatic), many patients were misclassified based on a priori defined testing reasons. After order modification, rates of provider reported symptomatic testing remained unchanged (p=0.16). Provider reported asymptomatic testing significantly declined (p=0.004), however, 23.2% of those tested continued to be misclassified. Conclusions:Provider entered EHR data is increasingly being used in research studies, thus it is important to assure its accuracy and reliability prior to study implementation.

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Section: Discussionsupporting

confidence: 77%

The Reliability of Computerized Physician Order Entry Data for Research Studies

Dexheimer¹,

Taylor²,

Kachelmeyer³

et al. 2019

Pediatr Emer Care

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“…Recent work in the field of DQ for EHR data reuse has also aimed at enabling systematic assessments in two ways. On one hand, other DQ assessment processes have been published [6263] but they do not provide a methodology that is systematic yet purpose-specific. For example, Reimer et al describe a six-step process to assess clinical data based on the dimensions of DQ, focusing on issues such as patient matching across databases and evaluating record completeness rather than testing fitness for a specific purpose.…”

Section: Discussionmentioning

confidence: 99%

DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data

Diaz-Garelli

Bernstam

Lee

et al. 2019

eGEMs

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The well-known hazards of repurposing data make Data Quality (DQ) assessment a vital step towards ensuring valid results regardless of analytical methods. However, there is no systematic process to implement DQ assessments for secondary uses of clinical data. This paper presents DataGauge, a systematic process for designing and implementing DQ assessments to evaluate repurposed data for a specific secondary use. DataGauge is composed of five steps: (1) Define information needs, (2) Develop a formal Data Needs Model (DNM), (3) Use the DNM and DQ theory to develop goal-specific DQ assessment requirements, (4) Extract DNM-specified data, and (5) Evaluate according to DQ requirements. DataGauge’s main contribution is integrating general DQ theory and DQ assessment methods into a systematic process. This process supports the integration and practical implementation of existing Electronic Health Record-specific DQ assessment guidelines. DataGauge also provides an initial theory-based guidance framework that ties the DNM to DQ testing methods for each DQ dimension to aid the design of DQ assessments. This framework can be augmented with existing DQ guidelines to enable systematic assessment. DataGauge sets the stage for future systematic DQ assessment research by defining an assessment process, capable of adapting to a broad range of clinical datasets and secondary uses. Defining DataGauge sets the stage for new research directions such as DQ theory integration, DQ requirements portability research, DQ assessment tool development and DQ assessment tool usability.

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“…al., 2003;. Particularly, data completeness is one of the most frequently assessed dimensions for data quality in the existing healthcare literature , and is considered as the major impediment to the availability of data for secondary use (Nobles et al, 2015), because data incompleteness could lead to significant uncertainty in health indicators such as tuberculosis incidence, prevalence and mortality rates (WHO, 2016).…”

Section: Introductionmentioning

confidence: 99%

Data Completeness in Healthcare: A Literature Survey

Liu

Talaei-Khoei

Zowghi

et al. 2017

PAJAIS

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As the adoption of eHealth has made it easier to access and aggregate healthcare data, there has been growing application for clinical decisions, health services planning, and public health monitoring with daily collected data in clinical care. Reliable data quality is a precursor of the aforementioned tasks. There is a body of research on data quality in healthcare, however, a clear picture of data completeness in this field is missing. This research aims to identify and classify current research themes related to data completeness in healthcare. In addition, the paper presents problems with data completeness in the reviewed literature and identifies methods that have been adopted to address those problems. This study has reviewed 24 papers (January 2011-April 2016) published in information and computing sciences, biomedical engineering, and medicine and health sciences journals. The paper uncovers three main research themes, including design and development, evaluation, and determinants. In conclusion, this paper improves our understanding of the current state of the art of data completeness in healthcare records and indicates future research directions.

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Evaluation of data quality of multisite electronic health record data for secondary analysis

Cited by 16 publications

References 14 publications

The Reliability of Computerized Physician Order Entry Data for Research Studies

The Reliability of Computerized Physician Order Entry Data for Research Studies

DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data

Data Completeness in Healthcare: A Literature Survey

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