ArticlePloS one2019
Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Evaluation of Electronic Health Record-Based Suicide Risk Prediction Models on Contemporary Data.Applied clinical informatics · 2021Article
- Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients.IEEE access : practical innovations, open solutions · 2021Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. MATERIALS AND
methodsTemporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index.
resultsFour Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. DISCUSSION: TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities' relocation and increment of citizens (findings 1, 3-4), the impact of strategies (findings 2-3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse.
conclusionsThe effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.
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