A method for interoperable knowledge-based data quality assessment

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Abstract

Background

Assessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results.

Objectives

To present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations.

Methods

We describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool—openCQA—that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models.

Results

Applying the method on the study’s dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment.

Conclusions

The main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01458-1.

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Most cited references 36

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Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

Nicole Weiskopf, Chunhua Weng (2013)

Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.

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Secondary Use of EHR: Data Quality Issues and Informatics Opportunities

Taxiarchis Botsis, Gunnar Hartvigsen, FEI-FEI CHEN … (2010)

Given the large-scale deployment of Electronic Health Records (EHR), secondary use of EHR data will be increasingly needed in all kinds of health services or clinical research. This paper reports some data quality issues we encountered in a survival analysis of pancreatic cancer patients. Using the clinical data warehouse at Columbia University Medical Center in the City of New York, we mined EHR data elements collected between 1999 and 2009 for a cohort of pancreatic cancer patients. Of the 3068 patients who had ICD-9-CM diagnoses for pancreatic cancer, only 1589 had corresponding disease documentation in pathology reports. Incompleteness was the leading data quality issue; many study variables had missing values to various degrees. Inaccuracy and inconsistency were the next common problems. In this paper, we present the manifestations of these data quality issues and discuss some strategies for using emerging informatics technologies to solve these problems.

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German Medical Informatics Initiative

Sebastian C. Semler, Frank Wissing, Ralf Heyder (2018)

Summary This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. The Medical Informatics Initiative (MII) was launched within the scope of the German Federal Ministry of Education and Research’s (BMBF) Medical Informatics Funding Scheme, with the goal of developing infrastructure for the integration of clinical data from patient care and medical research in Germany. Its work is to be performed over the course of a decade (2016–2025) across three funding phases, with the first two concentrating on university hospitals. During the conceptual phase (now concluded), a central supporting project ensured coordination – and laid the ground for standardised solutions for all the initiative’s sites and scientific consortia that will enable effective data use and exchange, both for health care as well as research. The conceptual phase focused on the following: a) interoperability, through the consistent use of international standards (from an early stage, i.e. primary IT systems in patient care); b) standardised templates for patient consent and harmonised data protection; and c) standard rules for data use and access (monitoring and safeguarding access to data). On this basis, the initiative aims in the long term to improve medical research (particularly health care research, using data from treatments), to accelerate the transfer of knowledge from research to patient care – and to provide important impetus for the digitalization of medicine in Germany.

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Author and article information

Contributors

Erik Tute: Erik.Tute@plri.de

Irina Scheffner: Scheffner.Irina@mh-hannover.de

Michael Marschollek: Michael.Marschollek@plri.de

Journal

Journal ID (nlm-ta): BMC Med Inform Decis Mak

Journal ID (iso-abbrev): BMC Med Inform Decis Mak

Title: BMC Medical Informatics and Decision Making

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6947

Publication date (Electronic): 9 March 2021

Publication date PMC-release: 9 March 2021

Publication date Collection: 2021

Volume: 21

Electronic Location Identifier: 93

Affiliations

[1 ]GRID grid.10423.34, ISNI 0000 0000 9529 9877, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, , Hannover Medical School, ; Carl-Neuberg-Str. 1, 30625 Hannover, Germany

[2 ]GRID grid.10423.34, ISNI 0000 0000 9529 9877, Department of Nephrology, , Hannover Medical School, ; Hannover, Germany

Article

Publisher ID: 1458

DOI: 10.1186/s12911-021-01458-1

PMC ID: 7942002

PubMed ID: 33750371

SO-VID: 3565db17-8b22-4965-bfed-d8125c972b3a

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 28 October 2020

Date accepted : 26 February 2021

Funding

Funded by: Medizinische Hochschule Hannover (MHH) (3118)

Open Access :

Open Access funding enabled and organized by Projekt DEAL.

Custom metadata

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: information science,data quality,data aggregation,health information interoperability,knowledge bases

Data availability:

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: information science, data quality, data aggregation, health information interoperability, knowledge bases

A method for interoperable knowledge-based data quality assessment

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Abstract

Background

Objectives

Methods

Results

Conclusions

Supplementary Information

Related collections

Semantic Knowledge Base

Most cited references 36

Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

Secondary Use of EHR: Data Quality Issues and Informatics Opportunities

German Medical Informatics Initiative

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Contributors

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