Health data quality criteria: a quantitative analysis abstract

Authors

DOI:

https://doi.org/10.5433/1981-8920.2022v27n2p466

Keywords:

Data quality, Data quality criteria, Health data quality, Health

Abstract

Objective: This article proposes a reflection on how data quality criteria have been approached in works that discuss data from the health area, making it possible to recognize the panorama on these criteria and identify the gaps that require greater efforts.
Methodology: The methodological procedures consist of a systematic literature review that aimed to identify, analyze and quantify the criteria of quality of data that are addressed in health.
Results: As results, the quality criteria of mapped and categorized data are presented, identifying the accuracy, consistency and completeness as the most addressed criteria and currentness, temporality, confidentiality and plausibility, being the least mentioned.
Conclusions: It is concluded that there is both overload and the lack of use of certain criteria, therefore, the criteria that presented this lack generate the possibility of being better addressed and explored in future works and, also, the relevance of these criteria cannot be ruled out, considering that its application depends on specific scenarios and situations.

Author Biographies

Fabrício Amadeu Gualdani, Universidade Estadual Paulista Júlio de Mesquita Filho - UNESP

PhD candidate in the Graduate Program in Information Science at the Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Marília campus. 

Késsia Rita da Costa Marchi, Universidade Estadual Paulista Júlio de Mesquita Filho - UNESP

Master in the Graduate Program in Information Science from the Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Marília campus. Systems Analyst. 

Fábio Henrique Alves, Universidade Estadual Paulista Júlio de Mesquita Filho - UNESP

PhD candidate in the Graduate Program in Information Science at the Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Marília campus. Professor at the Federal Institute of Paraná (IFPR) - Paranavaí campus.

Leonardo Castro Botega, Universidade Estadual Paulista Júlio de Mesquita Filho - UNESP

PhD in Computer Science from the Universidade Federal de São Carlos (UFSCar). Professor of the Graduate Program in Information Science at the Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Marília campus. 

References

ALI, Farman et al. A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, v. 63, p. 208-222, 2020.

ALMUTIRY, Omar; WILLS, Gary; CROWDER, Richard. Towards a framework for data quality in electronic health records. IADIS International Conference, e-Society, Lisbon, Portugal. 2013.

BARON, Richard J. Quality improvement with an electronic health record: achievable, but not automatic. Annals of internal medicine, v. 147, n. 8, p. 549-552, 2007.

BOVEE, Matthew; SRIVASTAVA, Rajendra P.; MAK, Brenda. A conceptual framework and belief‐function approach to assessing overall information quality. International journal of intelligent systems, v. 18, n. 1, p. 51-74, 2003.

BOWMAN, Sue. Impact of electronic health record systems on information integrity: quality and safety implications. Perspectives in health information management, v. 10, n. Fall, 2013.

BROWN, Philip JB; WARMINGTON, Victoria. Data quality probes—exploiting and improving the quality of electronic patient record data and patient care. International journal of medical informatics, v. 68, n. 1-3, p. 91-98, 2002.

BYRD, James Brian et al. Data quality of an electronic health record tool to support VA cardiac catheterization laboratory quality improvement: the VA Clinical Assessment, Reporting, and Tracking System for Cath Labs (CART) program. American heart journal, v. 165, n. 3, p. 434-440, 2013.

CHAN, Kitty S.; FOWLES, Jinnet B.; WEINER, Jonathan P. Electronic health records and the reliability and validity of quality measures: a review of the literature. Medical Care Research and Review, v. 67, n. 5, p. 503-527, 2010.

COLQUHOUN, Douglas A. et al. Considerations for integration of perioperative electronic health records across institutions for research and quality improvement: the approach taken by the Multicenter Perioperative Outcomes Group. Anesthesia and analgesia, v. 130, n. 5, p. 1133, 2020.

CONEGLIAN, C. S.; DIEGER, R.; SEGUNDO, J. E. S.; CAPRETZ, M. A. M. O papel da web semântica nos processos da big data. Encontros Bibli: Revista Eletrônica de Biblioteconomia e Ciência da Informação, v. 23, n. 53, p. 137-146, 2018. Disponível em: http://www.brapci.inf.br/index.php/res/v/39601. Acesso em: 02 setembro 2020.

DIXON, Brian E.; MCGOWAN, Julie J.; GRANNIS, Shaun J. Electronic laboratory data quality and the value of a health information exchange to support public health reporting processes. In: AMIA annual symposium proceedings. American Medical Informatics Association, 2011. p. 322.

DOKTORCHIK, Chelsea et al. A qualitative evaluation of clinically coded data quality from health information manager perspectives. Health Information Management Journal, v. 49, n. 1, p. 19-27, 2020.

DUARTE, Julio et al. Data quality evaluation of electronic health records in the hospital admission process. In: 2010 IEEE/ACIS 9th International Conference on Computer and Information Science. IEEE, 2010. p. 201-206.

DZIADKOWIEC, Oliwier et al. Using a data quality framework to clean data extracted from the electronic health record: a case study. eGEMs, v. 4, n. 1, 2016.

FONTES-PEREIRA, A. Revisão Sistemática da Literatura: Como Escrever um Artigo Científico em 72 Horas. Rio de Janeiro: Edição do Kindle., 2017.

FÜRBER, Christian. Data Quality in the Semantic Web. In: Data Quality Management with Semantic Technologies. Springer Gabler, Wiesbaden, 2016.

HUANG, Shih-Cheng et al. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ digital medicine, v. 3, n. 1, p. 1-9, 2020.

HUANG, Shih-Cheng et al. Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection. Scientific reports, v. 10, n. 1, p. 1-9, 2020.

JARKE, Matthias et al. ConceptBase—a deductive object base for meta data management. Journal of Intelligent Information Systems, v. 4, n. 2, p. 167-192, 1995.

KAHN, Michael G. et al. A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research. Medical care, v. 50, 2012.

LEMMA, Seblewengel et al. Improving quality and use of routine health information system data in low-and middle-income countries: A scoping review. PloS one, v. 15, n. 10, p. e0239683, 2020.

LIAW S-T et al. Integrating electronic health record information to support integrated care: Practical application of ontologies to improve the accuracy of diabetes disease registers. J Biomed Inform, 2014.

LIU, Liping; CHI, Lauren. Evolutional Data Quality: A Theory-Specific View. In: ICIQ. 2002. p. 292-304.

NDIRA, S. P.; ROSENBERGER, K. D.; WETTER, T. Assessment of data quality of and staff satisfaction with an electronic health record system in a developing country (Uganda). Methods of information in medicine, v. 47, n. 06, p. 489-498, 2008.

MIETTINEN, Merja; KORHONEN, Maritta. Information quality in healthcare: coherence of data compared between organization's electronic patient records. In: 2008 21st IEEE International Symposium on Computer-Based Medical Systems. IEEE, 2008. p. 488-493.

MORAES, M. F. Requisitos de qualidade e segurança para prontuários do paciente. Informação em Pauta, v. 3, p. 141-160, 2018. Disponível em: http://www.brapci.inf.br/index.php/res/v/106556. Acesso em: 02 setembro 2020.

MORANDI, M. I. W. M.; CAMARGO, L. F. R. Revisão Sistemática de Literatura. In: Design Science Research: Método de pesquisa para avanço da ciência e tecnologia. Porto Alegre: Bookman, 2015. p. 181.

MOULTON, B. D.; CHACZKO, Z. C.; KARATOVIC, Mark. Data fusion and aggregation methods for pre-processing ambulatory monitoring and remote sensor data for upload to personal electronic health records. International Journal of Digital Content Technology a..., 2009.

ORFANIDIS, Leonidas; BAMIDIS, Panagiotis D.; EAGLESTONE, Barry. Data quality issues in electronic health records: an adaptation framework for the Greek health system. Health informatics journal, v. 10, n. 1, p. 23-36, 2004.

POMPILIO, Antonio Pompilio Junior; ERMETICE, Edson. Indicadores de uso do prontuário eletrônico do paciente. Journal of Health Informatics, v. 3, n. 1, 2011.

REDMAN, T. C. Data quality for the information age. Norwood, Mass.: Artech House. 1996.

SANT’ANA, Ricardo César Gonçalves. Ciclo de vida dos dados: uma perspectiva a partir da ciência da informação. Informação & Informação, [S.l.], v. 21, n. 2, p. 116–142, dez. 2016. ISSN 1981-8920. Disponível em: <http://www.uel.br/revistas/uel/index.php/informacao/article/view/27940>. Acesso em: 17 abr. 2021. doi:http://dx.doi.org/10.5433/1981-8920.2016v21n2p116.

SANT'ANA, R.C.G. Campo Informacional Resultante da Interação de Ciclos de Vida dos Dados. In: DIAS, G.; FREIRE, B. (org). Dados Científicos: perspectivas e desafios. Editora UFPB - João Pessoa. 2019 p.5-19

SCHOLTE, Marijn et al. Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data. BMC medical informatics and decision making, v. 16, n. 1, p. 141, 2016.

TANG, Paul C. et al. Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures. Journal of the American Medical Informatics Association, v. 14, n. 1, p. 10-15, 2007.

THIRU, Krish; HASSEY, Alan; SULLIVAN, Frank. Systematic review of scope and quality of electronic patient record data in primary care. Bmj, v. 326, n. 7398, p. 1070, 2003.

VIMALACHANDRAN, Pasupathy et al. Ensuring data integrity in electronic health records: a quality health care implication. In: 2016 International Conference on Orange Technologies (ICOT). IEEE, 2016. p. 20-27.

WAND, Yair; WANG, Richard Y. Anchoring data quality dimensions in ontological foundations. Communications of the ACM, v. 39, n. 11, p. 86-95, 1996.

WANG, R. Y.; STRONG, D. M. Beyond accuracy: what data quality means to data consumers. Journal of Management Information Systems, v. 12, n. 4, p. 5-33, 1996.

WEISKOPF, Nicole Gray; WENG, Chunhua. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, v. 20, n. 1, p. 144-151, 2013.

ZHENG, Yi; HU, Xiangpei. Healthcare predictive analytics for disease progression: a longitudinal data fusion approach. Journal of Intelligent Information Systems, v. 55, p. 351-369, 2020.

Published

2022-12-31

How to Cite

Gualdani, F. A., Marchi, K. R. da C., Alves, F. H., & Botega, L. C. (2022). Health data quality criteria: a quantitative analysis abstract . Informação & Informação, 27(2), 466–490. https://doi.org/10.5433/1981-8920.2022v27n2p466

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Artigos