ArticleBMC medical informatics and decision making2016
Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data.
Article in BMC medical informatics and decision making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review of the use and challenges of electronic health records in physiotherapy practice.BMJ open · 2025Pooled it
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Where is the patient in the records? Evaluating physiotherapists' first visit in occupational health primary care pathway for low back pain.BMJ open quality · 2026Trial
- Automating the assessment of quality indicators using a clinical data warehouse: a pilot study on door-to-imaging time in stroke management.Scientific reports · 2026Observational
- Age- and sex-stratified prevalence of obstructive sleep apnea and stroke risk comorbidities: A large cross-sectional EHR study.PloS one · 2026Article
- A Snapshot of Hip and Knee Replacement Rehabilitation Care Across Canada: A Cross-Sectional Survey Using an Online Clinician Quality Indicator Questionnaire.Physiotherapy Canada. Physiotherapie Canada · 2025Article
- Building a Foundation for High-Quality Health Data: Multihospital Case Study in Belgium.JMIR medical informatics · 2024Article
- What, Where, and How to Collect Real-World Data and Generate Real-World Evidence to Support Drug Reimbursement Decision-Making in Asia: A reflection Into the Past and A Way Forward.International journal of health policy and management · 2023Article
- From Survey Results to a Decision-Making Matrix for Strategic Planning in Healthcare: The Case of Clinical Pathways.International journal of environmental research and public health · 2022Article
- Using a Learning Health System to Improve Physical Therapy Care for Patients With Intermittent Claudication: Lessons Learned From the ClaudicatioNet Quality System.Physical therapy · 2022Article
- Psychological assessments by manual physiotherapists in the Netherlands in patients with nonspecific low back pain.The Journal of manual & manipulative therapy · 2021Article
- Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health.Frontiers in digital health · 2021Article
- A Rule-Based Data Quality Assessment System for Electronic Health Record Data.Applied clinical informatics · 2020Article
- Extracting Structured Genotype Information from Free-Text HLA Reports Using a Rule-Based Approach.Journal of Korean medical science · 2020Article
- Characteristics and Health Care Utilization of Different Segments of a Multiethnic Asian Population in Singapore.JAMA network open · 2019Article
- Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.Yearbook of medical informatics · 2017Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the emergence of the electronic health records (EHRs) as a pervasive healthcare information technology, new opportunities and challenges for use of clinical data for quality measurements arise with respect to data quality, data availability and comparability. The objective of this study is to test whether data extracted from electronic health records (EHRs) was of comparable quality as survey data for the calculation of quality indicators.
methodsData from surveys describing patient cases and filled out by physiotherapists in 2009-2010 were used to calculate scores on eight quality indicators (QIs) to measure the quality of physiotherapy care. In 2011, data was extracted directly from EHRs. The data collection methods were evaluated for comparability. EHR data was compared to survey data on completeness and correctness.
resultsFive of the eight QIs could be extracted from the EHRs. Three were omitted from the indicator set, as they proved too difficult to be extracted from the EHRs. Another QI proved incomparable due to errors in the extraction software of some of the EHRs. Three out of four comparable QIs performed better (p < 0.001) in EHR data on completeness. EHR data also proved to be correct; the relative change in indicator scores between EHR and survey data were small (<5 %) in three out of four QIs.
conclusionData quality of EHRs was sufficient to be used for the calculation of QIs, although comparability to survey data was problematic. Standardization is needed, not only to be able to compare different data collection methods properly, but also to compare between practices with different EHRs. EHRs have the option to administrate narrative data, but natural language processing tools are needed to quantify these text boxes. Such development, can narrow the comparability gap between scoring QIs based on EHR data and based on survey data. EHRs have the potential to provide real time feedback to professionals and quality measurements for research, but more effort is needed to create unambiguous and uniform information and to unlock written text in a standardized manner.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.