Dataset Information

Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets.

ABSTRACT:

Introduction

Data quality and fitness for analysis are crucial if outputs of analyses of electronic health record data or administrative claims data should be trusted by the public and the research community.

Methods

We describe a data quality analysis tool (called Achilles Heel) developed by the Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare outputs from this tool as it was applied to 24 large healthcare datasets across seven different organizations.

Results

We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is a freely available software that provides a useful starter set of data quality rules with the ability to add additional rules. We also present results of a structured email-based interview of all participating sites that collected qualitative comments about the value of Achilles Heel for data quality evaluation.

Discussion

Our analysis represents the first comparison of outputs from a data quality tool that implements a fixed (but extensible) set of data quality rules. Thanks to a common data model, we were able to compare quickly multiple datasets originating from several countries in America, Europe and Asia.

SUBMITTER: Huser V

PROVIDER: S-EPMC5226382 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets.

Huser Vojtech V DeFalco Frank J FJ Schuemie Martijn M Ryan Patrick B PB Shang Ning N Velez Mark M Park Rae Woong RW Boyce Richard D RD Duke Jon J Khare Ritu R Utidjian Levon L Bailey Charles C

EGEMS (Washington, DC) 20161130 1

<h4>Introduction</h4>Data quality and fitness for analysis are crucial if outputs of analyses of electronic health record data or administrative claims data should be trusted by the public and the research community.<h4>Methods</h4>We describe a data quality analysis tool (called Achilles Heel) developed by the Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare outputs from this tool as it was applied to 24 large healthcare datasets across seven different organi ...[more]

PMID: 28154833

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Project description:BackgroundThere is a growing interest in aggregating more biomedical and patient data into large health data sets for research and public benefits. However, collecting and processing patient data raises new ethical issues regarding patient's rights, social justice and trust in public institutions. The aim of this empirical study is to gain an in-depth understanding of the awareness of possible ethical risks and corresponding obligations among those who are involved in projects using patient data, i.e. healthcare professionals, regulators and policy makers.MethodsWe used a qualitative design to examine Swiss healthcare stakeholders' experiences and perceptions of ethical challenges with regard to patient data in real-life settings where clinical registries are sponsored, created and/or used. A semi-structured interview was carried out with 22 participants (11 physicians, 7 policy-makers, 4 ethical committee members) between July 2014 and January 2015. The interviews were audio-recorded, transcribed, coded and analysed using a thematic method derived from Grounded Theory.ResultsAll interviewees were concerned as a matter of priority with the needs of legal and operating norms for the collection and use of data, whereas less interest was shown in issues regarding patient agency, the need for reciprocity, and shared governance in the management and use of clinical registries' patient data. This observed asymmetry highlights a possible tension between public and research interests on the one hand, and the recognition of patients' rights and citizens' involvement on the other.ConclusionsThe advocation of further health-related data sharing on the grounds of research and public interest, without due regard for the perspective of patients and donors, could run the risk of fostering distrust towards healthcare data collections. Ultimately, this could diminish the expected social benefits. However, rather than setting patient rights against public interest, new ethical approaches could strengthen both concurrently. On a normative level, this study thus provides material from which to develop further ethical reflection towards a more cooperative approach involving patients and citizens in the governance of their health-related big data.

Dataset Information

Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets.

Introduction

Methods

Results

Discussion

Publications

Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets.

Similar Datasets

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