Literature DB >> 26480872

Structured data quality reports to improve EHR data quality.

Jane Taggart1, Siaw-Teng Liaw2, Hairong Yu1.   

Abstract

OBJECTIVE: To examine whether a structured data quality report (SDQR) and feedback sessions with practice principals and managers improve the quality of routinely collected data in EHRs.
METHODS: The intervention was conducted in four general practices participating in the Fairfield neighborhood electronic Practice Based Research Network (ePBRN). Data were extracted from their clinical information systems and summarised as a SDQR to guide feedback to practice principals and managers at 0, 4, 8 and 12 months. Data quality (DQ) metrics included completeness, correctness, consistency and duplication of patient records. Information on data recording practices, data quality improvement, and utility of SDQRs was collected at the feedback sessions at the practices. The main outcome measure was change in the recording of clinical information and level of meeting Royal Australian College of General Practice (RACGP) targets.
RESULTS: Birth date was 100% and gender 99% complete at baseline and maintained. DQ of all variables measured improved significantly (p<0.01) over 12 months, but was not sufficient to comply with RACGP standards. Improvement was greatest with allergies. There was no significant change in duplicate records.
CONCLUSIONS: SDQRs and feedback sessions support general practitioners and practice managers to focus on improving the recording of patient information. However, improved practice DQ, was not sufficient to meet RACGP targets. Randomised controlled studies are required to evaluate strategies to improve data quality and any associated improved safety and quality of care.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Data quality; Electronic health records; Feedback; Quality improvement; Quality of care; Structured reports

Mesh:

Year:  2015        PMID: 26480872     DOI: 10.1016/j.ijmedinf.2015.09.008

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  8 in total

1.  Data quality assessment framework to assess electronic medical record data for use in research.

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3.  Comparison of the cohort selection performance of Australian Medicines Terminology to Anatomical Therapeutic Chemical mappings.

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Review 4.  Quality assessment of real-world data repositories across the data life cycle: A literature review.

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Journal:  J Am Med Inform Assoc       Date:  2021-07-14       Impact factor: 4.497

5.  Do GPs know their patients with cancer? Assessing the quality of cancer registration in Dutch primary care: a cross-sectional validation study.

Authors:  Annet Sollie; Jessika Roskam; Rolf H Sijmons; Mattijs E Numans; Charles W Helsper
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6.  The impact of routine data quality assessments on electronic medical record data quality in Kenya.

Authors:  Veronica Muthee; Aaron F Bochner; Allison Osterman; Nzisa Liku; Willis Akhwale; James Kwach; Mehta Prachi; Joyce Wamicwe; Jacob Odhiambo; Fredrick Onyango; Nancy Puttkammer
Journal:  PLoS One       Date:  2018-04-18       Impact factor: 3.240

7.  Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use.

Authors:  Hanieh Razzaghi; Jane Greenberg; L Charles Bailey
Journal:  Learn Health Syst       Date:  2021-05-03

8.  Using Clinical Data Standards to Measure Quality: A New Approach.

Authors:  John D D'Amore; Chun Li; Laura McCrary; Jonathan M Niloff; Dean F Sittig; Allison B McCoy; Adam Wright
Journal:  Appl Clin Inform       Date:  2018-06-13       Impact factor: 2.342

  8 in total

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