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      Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years

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          Abstract

          Objective

          To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years.

          Materials and methods

          Temporal 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.

          Results

          Four 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.

          Conclusions

          The 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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          Most cited references 30

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

          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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            Workflow mining: discovering process models from event logs

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              Evaluation of data quality in the cancer registry: principles and methods. Part I: comparability, validity and timeliness.

              The value of the modern cancer registry and its ability to carry out cancer control activities rely heavily on the underlying quality of its data and the quality control procedures in place. This two-part review provides an update of the practical aspects and techniques for addressing data quality at the cancer registry. This first installment of the review examines the factors influencing three of the four key aspects, namely, the comparability, validity and timeliness of cancer registry data. Comparability of cancer data may be established through a comprehensive review of the registration routines in place. Validity is examined via numerical indices of that permit comparisons with other registries, or, within a registry, over time, or with respect to specified subsets of cases. There are no international guidelines for timeliness at present, although specific standards for the abstraction and reporting of registry have been set out by certain organisations.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                7 August 2019
                2019
                : 14
                : 8
                Affiliations
                [1 ] Biomedical Data Science Lab, Instituto Universitario de Tecnologías de Información y Comunicaciones Avanzadas (ITACA), Univeritat Politécnica de València, València, Spain
                [2 ] Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de València, València, Spain
                [3 ] Unidad conjunta de investigación en reingeniería de procesos socio-sanitarios, Instituto de Investigación Sanitaria La Fe, Hospital Universitario La Fe, València, Spain
                [4 ] Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), València, Spain
                Medical University Graz, AUSTRIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-19-08763
                10.1371/journal.pone.0220369
                6685618
                31390350
                © 2019 Pérez-Benito et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 6, Tables: 3, Pages: 19
                Product
                Funding
                Funded by: Universitat Politècnica de València (ES)
                Award ID: ANÁLISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MÉDICOS
                Award Recipient :
                Funded by: Universitat Politècnica de València (ES)
                Award ID: ANÁLISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MÉDICOS
                Award Recipient :
                Funded by: Universitat Politècnica de València (ES)
                Award ID: ANALISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MÉDICOS
                Award Recipient :
                Funded by: Universitat Politècnica de València (ES)
                Award ID: ANALISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MÉDICOS
                Award Recipient :
                F.J.P.B, C.S., J.M.G.G. and J.A.C. were funded Universitat Politècnica de València, project “ANÁLISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MÉDICOS”. www.upv.es. J.M.G.G. is also partially supported by: Ministerio de Economía y Competitividad of Spain through MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); and European Commission projects H2020-SC1-2016-CNECT Project (No. 727560) and H2020-SC1-BHC-2018-2020 (No. 825750). The funders did not play any role in the study design, data collection and analysis, decision to publish, nor preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Health Care
                Health Care Facilities
                Hospitals
                Engineering and Technology
                Management Engineering
                Business Process Reengineering
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Multivariate Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Multivariate Analysis
                Computer and Information Sciences
                Data Management
                Medicine and Health Sciences
                Surgical and Invasive Medical Procedures
                Research and Analysis Methods
                Database and Informatics Methods
                Health Informatics
                Electronic Medical Records
                Research and Analysis Methods
                Database and Informatics Methods
                People and places
                Geographical locations
                Europe
                European Union
                Spain
                Custom metadata
                The data underlying the results presented in the study are owned by the Hospital La Fe. The data with research purposes could be accessed after an official request on the platform Alumbra at http://www.san.gva.es/web/dgfps/acceso-a-la-aplicacion. The authors did not have any special access privileges that others would not have.

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