Quality of Big Data in health care

Sukumar, Sreenivas R.; Natarajan, Ram; Ferrell, Regina

doi:10.1108/ijhcqa-07-2014-0080

Cited by 77 publications

(58 citation statements)

References 11 publications

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“…Data quality is often talked about in terms of data warehouses [33], connecting multiple systems [40], and big data [41]. This study also highlights that data quality is a limiter when applying computational interventions that are designed to support improvements in care for individual patients.…”

Section: Discussionmentioning

confidence: 94%

Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality

Price¹,

Davies²,

Rusk³

et al. 2017

JMIR Med Inform

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BackgroundPotentially Inappropriate Prescriptions (PIPs) are a common cause of morbidity, particularly in the elderly.ObjectiveWe sought to understand how the Screening Tool of Older People’s Prescriptions (STOPP) prescribing criteria, implemented in a routinely used primary care Electronic Medical Record (EMR), could impact PIP rates in community (non-academic) primary care practices.MethodsWe conducted a mixed-method, pragmatic, cluster, randomized control trial in research naïve primary care practices. Phase 1: In the randomized controlled trial, 40 fully automated STOPP rules were implemented as EMR alerts during a 16-week intervention period. The control group did not receive the 40 STOPP rules (but received other alerts). Participants were recruited through the OSCAR EMR user group mailing list and in person at user group meetings. Results were assessed by querying EMR data PIPs. EMR data quality probes were included. Phase 2: physicians were invited to participate in 1-hour semi-structured interviews to discuss the results.ResultsIn the EMR, 40 STOPP rules were successfully implemented. Phase 1: A total of 28 physicians from 8 practices were recruited (16 in intervention and 12 in control groups). The calculated PIP rate was 2.6% (138/5308) (control) and 4.11% (768/18,668) (intervention) at baseline. No change in PIPs was observed through the intervention (P=.80). Data quality probes generally showed low use of problem list and medication list. Phase 2: A total of 5 physicians participated. All the participants felt that they were aware of the alerts but commented on workflow and presentation challenges.ConclusionsThe calculated PIP rate was markedly less than the expected rate found in literature (2.6% and 4.0% vs 20% in literature). Data quality probes highlighted issues related to completeness of data in areas of the EMR used for PIP reporting and by the decision support such as problem and medication lists. Users also highlighted areas for better integration of STOPP guidelines with prescribing workflows. Many of the STOPP criteria can be implemented in EMRs using simple logic. However, data quality in EMRs continues to be a challenge and was a limiting step in the effectiveness of the decision support in this study. This is important as decision makers continue to fund implementation and adoption of EMRs with the expectation of the use of advanced tools (such as decision support) without ongoing review of data quality and improvement.Trial RegistrationClinicaltrials.gov NCT02130895; https://clinicaltrials.gov/ct2/show/NCT02130895 (Archived by WebCite at http://www.webcitation.org/6qyFigSYT)

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Section: Discussionmentioning

confidence: 94%

Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality

Price¹,

Davies²,

Rusk³

et al. 2017

JMIR Med Inform

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“…Similarly, methods used and decisions made by Members’ staff when data sets were created as part of the extract, transform, load process involved potential sources of error [10], any errors of this type not generating discernible discrepancies on data checking would go undetected using our methods. Examples of procedures to mitigate this type of error at the data-submitter level have been published for other HVHC projects [11].…”

Section: Discussionmentioning

confidence: 99%

“…There is an extensive literature on the importance of evaluating and reporting data quality [313]. Examples specifically targeted at comparative effectiveness research [14] as well as more abstract discussions [10] are also quite prevalent. From a research policy perspective, Benchimol et al [15] proposed reporting recommendations called REporting of studies Conducted using Observational Routinely-collected Data (RECORD) that extend the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines [16].…”

Section: Discussionmentioning

confidence: 99%

Data Cleaning in the Evaluation of a Multi-Site Intervention Project

Welch

Recklinghausen

Taenzer

et al. 2017

eGEMs

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Context:The High Value Healthcare Collaborative (HVHC) sepsis project was a two-year multi-site project where Member health care delivery systems worked on improving sepsis care using a dissemination & implementation framework designed by HVHC. As part of the project evaluation, participating Members provided 5 data submissions over the project period. Members created data files using a uniform specification, but the data sources and methods used to create the data sets differed. Extensive data cleaning was necessary to get a data set usable for the evaluation analysis.Case Description:HVHC was the coordinating center for the project and received and cleaned all data submissions. Submissions received 3 sequentially more detailed levels of checking by HVHC. The most detailed level evaluated validity by comparing values within-Member over time and between Member. For a subset of episodes Member-submitted data were compared to matched Medicare claims data.Findings:Inconsistencies in data submissions, particularly for length-of-stay variables were common in early submissions and decreased with subsequent submissions. Multiple resubmissions were sometimes required to get clean data. Data checking also uncovered a systematic difference in the way Medicare and some members defined intensive care unit stay.Conclusions:Data checking is a critical for ensuring valid analytic results for projects using electronic health record data. It is important to budget sufficient resources for data checking. Interim data submissions and checks help find anomalies early. Data resubmissions should be checked as fixes can introduce new errors. Communicating with those responsible for creating the data set provides critical information.

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“…Additional characteristics have also been proposed: variability, visualization, and value [10][11][12]. These terms are sometimes collectively referred to as the "V's of big data."…”

Section: Discussionmentioning

confidence: 99%

“…These terms are sometimes collectively referred to as the "V's of big data." Examples of healthcare related big data include electronic medical records, images, and diagnostic reports from radiology and pathology, national utilization databases, social media, and biologic "-omics" [10,13,14].…”

Section: Discussionmentioning

confidence: 99%

Toward Data-Driven Radiology Education—Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA)

et al. 2016

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The residency review committee of the Accreditation Council of Graduate Medical Education (ACGME) collects data on resident exam volume and sets minimum requirements. However, this data is not made readily available, and the ACGME does not share their tools or methodology. It is therefore difficult to assess the integrity of the data and determine if it truly reflects relevant aspects of the resident experience. This manuscript describes our experience creating a multi-institutional case log, incorporating data from three American diagnostic radiology residency programs. Each of the three sites independently established automated query pipelines from the various radiology information systems in their respective hospital groups, thereby creating a residentspecific database. Then, the three institutional resident case log databases were aggregated into a single centralized database schema. Three hundred thirty residents and 2,905,923 radiologic examinations over a 4-year span were catalogued using 11 ACGME categories. Our experience highlights big data challenges including internal data heterogeneity and external data discrepancies faced by informatics researchers.

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Quality of Big Data in health care

Abstract: Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

Cited by 77 publications

References 11 publications

Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality

Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality

Data Cleaning in the Evaluation of a Multi-Site Intervention Project

Toward Data-Driven Radiology Education—Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA)

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