Data Quality of General Practice Electronic Health Records: The Impact of a Program of Assessments, Feedback, and Training

ObjectivesThere is an increasing trend in the use of electronic health records (EHRs) for clinical research. However, more knowledge is needed on how to assure and improve data quality. This study aimed to explore healthcare professionals’ experiences and perceptions of barriers and facilitators of data quality of EHR-based studies in the Chinese context.SettingFour tertiary hospitals in Beijing, China.ParticipantsNineteen healthcare professionals with experience in using EHR data for clinical research participated in the study.MethodsA qualitative study based on face-to-face semistructured interviews was conducted from March to July 2018. The interviews were audiorecorded and transcribed verbatim. Data analysis was performed using the inductive thematic analysis approach.ResultsThe main themes included factors related to healthcare systems, clinical documentation, EHR systems and researchers. The perceived barriers to data quality included heavy workload, staff rotations, lack of detailed information for specific research, variations in terminology, limited retrieval capabilities, large amounts of unstructured data, challenges with patient identification and matching, problems with data extraction and unfamiliar with data quality assessment. To improve data quality, suggestions from participants included: better staff training, providing monetary incentives, performing daily data verification, improving software functionality and coding structures as well as enhancing multidisciplinary cooperation.ConclusionsThese results provide a basis to begin to address current barriers and ultimately to improve validity and generalisability of research findings in China.

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Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance

Health Informatics Journal ◽

10.1177/1460458215604348 ◽

2016 ◽

Vol 22 (4) ◽

pp. 1017-1029 ◽

Cited By ~ 10

Author(s):

Lua Perimal-Lewis ◽

David Teubner ◽

Paul Hakendorf ◽

Chris Horwood

Keyword(s):

Electronic Health Records ◽

Data Quality ◽

Performance Metrics ◽

Process Mining ◽

Hospital Performance ◽

Process Models ◽

Patient Journey ◽

Health Records ◽

Electronic Health

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

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What is the Impact of Electronic Health Records on the Quality of Health Data?

Health Information Management Journal ◽

10.1177/183335831404300106 ◽

2014 ◽

Vol 43 (1) ◽

pp. 42-43 ◽

Cited By ~ 6

Author(s):

Joanne Callen

Keyword(s):

Electronic Health Records ◽

Health Data ◽

Health Records ◽

Electronic Health ◽

The Impact

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THE IMPACT OF MEETING ACCREDITATION STANDARDS ON THE QUALITY OF THE ELECTRONIC HEALTH RECORDS

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2020.v13i5.37013 ◽

2020 ◽

pp. 114-117

Author(s):

MOHAMED HOSSAM ATTIA ◽

ABDELNASSER IBRAHIM

Keyword(s):

Electronic Health Records ◽

Patient Information ◽

Medical Records ◽

Simple Random Sample ◽

Health Records ◽

Entire Study ◽

Before And After ◽

Electronic Health ◽

The Impact

Objective: Electronic health records (EHRs) are considered a way to make the management of patient information easier, improve efficiency, and decrease costs related to medical information management. Compliance with requirements from accreditation bodies on quality of documentation ensures the complete and accurate patient information in the EHR. The purpose of this study is to measure the effect of quality accreditation on the quality of documentation in the EHR. Methods: A simple random sample of 18% of patient records was manually selected each month during the entire study period from the population of discharged patients. The auditing process included 18 months starting from January 2014 until June 2015. The data collection was performed by a quality management unit using a modified medical record completeness checklist adapted from Joint Commission International (JCI) criteria. Results: The results of the study show the improvement in compliance with complete medical records’ documentation after the JCI accreditation. However, after the accreditation, the compliance suffers a dramatic fall which could be referred to the post-accreditation slump. The compliance then improved again to reach higher levels of compliance. Using paired t-test, the mean of total compliance with complete and accurate medical records in October 2014 was less than in May 2015. Conclusion: This study highlighted the performance of one process before and after the first accreditation of the organization showing the real difference between the performance before and after the accreditation and explaining the drop that happened just after the accreditation.

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Consistency of denominator data in electronic health records in Australian primary healthcare services: enhancing data quality

Australian Journal of Primary Health ◽

10.1071/py14071 ◽

2015 ◽

Vol 21 (4) ◽

pp. 450 ◽

Cited By ~ 9

Author(s):

Ross Bailie ◽

Jodie Bailie ◽

Amal Chakraborty ◽

Kevin Swift

Keyword(s):

Quality Of Care ◽

Electronic Health Records ◽

Data Quality ◽

Primary Healthcare ◽

Service Planning ◽

Health Records ◽

General Practices ◽

Electronic Health ◽

Access And Quality

The quality of data derived from primary healthcare electronic systems has been subjected to little critical systematic analysis, especially in relation to the purported benefits and substantial investment in electronic information systems in primary care. Many indicators of quality of care are based on numbers of certain types of patients as denominators. Consistency of denominator data is vital for comparison of indicators over time and between services. This paper examines the consistency of denominator data extracted from electronic health records (EHRs) for monitoring of access and quality of primary health care. Data collection and analysis were conducted as part of a prospective mixed-methods formative evaluation of the Commonwealth Government’s Indigenous Chronic Disease Package. Twenty-six general practices and 14 Aboriginal Health Services (AHSs) located in all Australian States and Territories and in urban, regional and remote locations were purposively selected within geographically defined locations. Percentage change in reported number of regular patients in general practices ranged between –50% and 453% (average 37%). The corresponding figure for AHSs was 1% to 217% (average 31%). In approximately half of general practices and AHSs, the change was ≥20%. There were similarly large changes in reported numbers of patients with a diagnosis of diabetes or coronary heart disease (CHD), and Indigenous patients. Inconsistencies in reported numbers were due primarily to limited capability of staff in many general practices and AHSs to accurately enter, manage, and extract data from EHRs. The inconsistencies in data required for the calculation of many key indicators of access and quality of care places serious constraints on the meaningful use of data extracted from EHRs. There is a need for greater attention to quality of denominator data in order to realise the potential benefits of EHRs for patient care, service planning, improvement, and policy. We propose a quality improvement approach for enhancing data quality.

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Impact of sharing electronic health records with patients on the quality and safety of care: a systematic review and narrative synthesis protocol

BMJ Open ◽

10.1136/bmjopen-2017-020387 ◽

2018 ◽

Vol 8 (8) ◽

pp. e020387 ◽

Cited By ~ 4

Author(s):

Ana Luisa Neves ◽

Alexander W Carter ◽

Lisa Freise ◽

Liliana Laranjo ◽

Ara Darzi ◽

...

Keyword(s):

Systematic Review ◽

Quality Of Care ◽

Electronic Health Records ◽

Analytical Framework ◽

The Body ◽

Narrative Synthesis ◽

Health Records ◽

Electronic Health ◽

The Impact

IntroductionProviding patients with access to electronic health records (EHRs) has emerged as a promising solution to improve quality of care and safety. As the efforts to develop and implement EHR-based data sharing platforms mature and scale up worldwide, there is a need to evaluate the impact of these interventions and to weigh their relative risks and benefits, in order to inform evidence-based health policies. The aim of this work is to systematically characterise and appraise the demonstrated benefits and risks of sharing EHR with patients, by mapping them across the six domains of quality of care of the Institute of Medicine (IOM) analytical framework (ie, patient-centredness, effectiveness, efficiency, timeliness, equity and safety).Methods and analysisCINAHL, Cochrane, Embase, HMIC, Medline/PubMed and PsycINFO databases will be searched from January 1997 to August 2017. Primary outcomes will include measures related with the six domains of quality of care of the IOM analytical framework. The quality of the studies will be assessed using the Cochrane Risk of Bias Tool, the ROBINS-I Tool and the Drummond’s checklist. A narrative synthesis will be conducted for all included studies. Subgroup analysis will be performed by domain of quality of care domain and by time scale (ie, short-term, medium-term or long-term impact). The body of evidence will be summarised in a Summary of Findings table and its strength assessed according to the GRADE criteria.Ethics and disseminationThis review does not require ethical approval as it will summarise published studies with non-identifiable data. This protocol complies with the Preferred Reporting Items for Systematic Review and Meta-Analyses Protocols guidelines. Findings will be disseminated widely through peer-reviewed publication and conference presentations, and patient partners will be included in summarising the research findings into lay summaries and reports.PROSPERO registration numberCRD42017070092.

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