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An Experience of Electronic Health Records Implementation in a Mexican Region

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Employing software engineering to build an integrated, standardized, and scalable solution is closely associated with the healthcare domain. Furthermore, new diagnostic techniques have been developed to obtain better results in less time, saving costs, and bringing services closer to the most unprotected areas. This paper presents the integration of a top-notch component, such as hardware, software, telecommunications, and medical equipment, to produce a complete system of Electronic Health Record (EHR). The EHR implementation aims to contribute to the expansion of the health services offer concerning people who live in locations where typically have difficult access to medical care. The methodology throughout the work is a Strategic Planning to set priorities, focus energy and resources, strengthen operations, ensure that directors, managers, employees, and other stakeholders are working toward common goals, establish agreement around intended outcomes/results. A medical and technical team is incorporated to complete the tasks of process and requirements analysis, software coding and design, technical support, training, and coaching for EHR system users throughout the implementation process. The adoption of those tools reflect notably some expected results and benefits on patient care. The EHR implementation ensures that information collection does not duplicate already existing information or duplicate effort and maximize the practical use of the data collected. Moreover, the EHR reduces mistakes in hospital readmissions, improves paperwork, promotes the progress of the state’s health care system providing emergency, specialty, and primary health care in a rural area of Campeche. The EHR implementation is critical to support decision making and to promote public health. The total number of consults increased markedly from 2012 (14021) to 2019 (34751). The most commonly treated diseases in this region of Mexico are hypertension (17632) and diabetes (13156). The best results are obtained in the Nutrition (20,61%) and clinical psychology services (16,67%), and the worst levels are registered in pediatric and surgical oncology services where only 1,59% and 1,97% of the patients are admitted in less than 30 min, respectively.

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References

  1. Gobierno de México, “Nom-24-SSA3-2012,” 2012.

  2. Clegg, A., et al. (May 2016). Development and validation of an electronic frailty index using routine primary care electronic health record data. Age and ageing, 45(3), 353–360. https://doi.org/10.1093/ageing/afw039.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Godfrey, E. M., West, I. I., Holmes, J., Keppel, G. A., & Baldwin, L.-M. (Dec. 2018). Use of an electronic health record data sharing system for identifying current contraceptive use within the WWAMI region practice and research network. Contraception, 98(6), 476–481. https://doi.org/10.1016/j.contraception.2018.06.005.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mathioudakis, A., Rousalova, I., Gagnat, A. A., Saad, N., & Hardavella, G. (2016). How to keep good clinical records. Breathe, 12(4), 371–375. https://doi.org/10.1183/20734735.018016.

    Article  Google Scholar 

  5. A. Gilbert et al., “Utilizing visualization to qualitatively evaluate electronic health record-derived database limitations.,” JCO, vol. 37, no. 27_suppl, pp. 317–317, Sep. 2019, doi: https://doi.org/10.1200/JCO.2019.37.27_suppl.317.

    Article  Google Scholar 

  6. M. G. Kahn et al., “A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data,” eGEMs, vol. 4, no. 1, p. 18, Sep. 2016, doi: https://doi.org/10.13063/2327-9214.1244.

    Article  Google Scholar 

  7. Rudorfer, M. V. (May 2017). Psychopharmacology in the age of ‘big data’: The promises and limitations of electronic prescription records. CNS drugs, 31(5), 417–419. https://doi.org/10.1007/s40263-017-0419-y.

    Article  PubMed  PubMed Central  Google Scholar 

  8. A. W. Jaspers, J. L. Cox, and H. M. Krumholz, “Copy Fees and Limitation of Patients’ Access to Their Own Medical Records,” JAMA Intern Med, vol. 177, no. 4, p. 457, Apr. 2017, doi: https://doi.org/10.1001/jamainternmed.2016.8560.

    Article  Google Scholar 

  9. C. S. Kruse, C. Kristof, B. Jones, E. Mitchell, and A. Martinez, “Barriers to Electronic Health Record Adoption: a Systematic Literature Review,” J Med Syst, vol. 40, no. 12, p. 252, Dec. 2016, doi: https://doi.org/10.1007/s10916-016-0628-9.

  10. E. R. Melnick et al., “The Association Between Perceived Electronic Health Record Usability and Professional Burnout Among US Physicians,” Mayo Clinic Proceedings, p. S0025619619308365, Nov. 2019, doi: https://doi.org/10.1016/j.mayocp.2019.09.024.

    Article  Google Scholar 

  11. J. L. Howe, K. T. Adams, A. Z. Hettinger, and R. M. Ratwani, “Electronic Health Record Usability Issues and Potential Contribution to Patient Harm,” JAMA, vol. 319, no. 12, p. 1276, Mar. 2018, doi: https://doi.org/10.1001/jama.2018.1171.

    Article  Google Scholar 

  12. E. L. Palmer et al., “Assessing data availability and quality within an electronic health record system through external validation against an external clinical data source,” BMC Med Inform Decis Mak, vol. 19, no. 1, p. 143, Dec. 2019, doi: https://doi.org/10.1186/s12911-019-0864-2.

  13. Adler-Milstein, J., & Jha, A. K. (Aug. 2017). HITECH act drove large gains in hospital electronic health record adoption. Health affairs, 36(8), 1416–1422. https://doi.org/10.1377/hlthaff.2016.1651.

    Article  PubMed  Google Scholar 

  14. Gobierno de México, “Inicio - Secretaría de Planeación.” [Online]. Available: http://www.seplan.campeche.gob.mx/index.php. [Accessed: 03-Feb-2020].

  15. Gobierno de México, “Programa Sectorial de Salud.”

  16. Gobierno de México. (2013). Estrategia digital Nacional.

    Google Scholar 

  17. Carey, D. J., et al. (Sep. 2016). The Geisinger MyCode community health initiative: An electronic health record–linked biobank for precision medicine research. Genetics in medicine, 18(9), 906–913. https://doi.org/10.1038/gim.2015.187.

    Article  PubMed  PubMed Central  Google Scholar 

  18. “Omic and Electronic Health Record Big Data Analytics for Precision Medicine,” IEEE Trans. Biomed. Eng., vol. 64, no. 2, pp. 263–273, Feb. 2017, doi: https://doi.org/10.1109/TBME.2016.2573285.

  19. C. De Diputados, D. H. Congreso De, L. A. Unión, and N. Ley, “LEY GENERAL DE SALUD.”

  20. Barry, C., et al. (Jan. 2020). Optimization of the order menu in the electronic health record facilitates test patterns consistent with recommendations in the choosing wisely initiative. American journal of clinical pathology, 153(1), 94–98. https://doi.org/10.1093/ajcp/aqz134.

    Article  PubMed  Google Scholar 

  21. D. Agniel, I. S. Kohane, and G. M. Weber, “Biases in electronic health record data due to processes within the healthcare system: retrospective observational study,” BMJ, p. k1479, Apr. 2018, doi: https://doi.org/10.1136/bmj.k1479.

  22. N. L. Downing, D. W. Bates, and C. A. Longhurst, “Physician Burnout in the Electronic Health Record Era: Are We Ignoring the Real Cause?,” Ann Intern Med, vol. 169, no. 1, p. 50, Jul. 2018, doi: https://doi.org/10.7326/M18-0139.

    Article  Google Scholar 

  23. Beaulieu-Jones, B. K., & Greene, C. S. (Dec. 2016). Semi-supervised learning of the electronic health record for phenotype stratification. Journal of biomedical informatics, 64, 168–178. https://doi.org/10.1016/j.jbi.2016.10.007.

    Article  PubMed  Google Scholar 

  24. American College of Physicians Ethics, Professionalism and Human Rights Committee, Sulmasy, L. S., López, A. M., & Horwitch, C. A. (Aug. 2017). Ethical implications of the electronic health record: In the Service of the Patient. Journal of general internal medicine, 32(8), 935–939. https://doi.org/10.1007/s11606-017-4030-1.

    Article  Google Scholar 

  25. Tai-Seale, M., et al. (Apr. 2017). Electronic health record logs indicate that physicians Split time evenly between seeing patients and desktop medicine. Health affairs, 36(4), 655–662. https://doi.org/10.1377/hlthaff.2016.0811.

    Article  PubMed  Google Scholar 

  26. S. L. Santoro, T. Bartman, C. L. Cua, S. Lemle, and B. G. Skotko, “Use of Electronic Health Record Integration for Down Syndrome Guidelines,” Pediatrics, vol. 142, no. 3, p. e20174119, Sep. 2018, doi: https://doi.org/10.1542/peds.2017-4119.

    Article  Google Scholar 

  27. Klubert, D. M., & Black, C. (Jul. 2018). Letter to the editor: Secondary use of electronic health record data for clinical workflow analysis. Journal of the American medical informatics association, 25(7), 919–919. https://doi.org/10.1093/jamia/ocy029.

    Article  PubMed  Google Scholar 

  28. de México, G. (2013). Plan Nacional de Desarrollo 2013-2018. Mexico.

  29. S. Attipoe, Y. Huang, S. Schweikhart, S. Rust, J. Hoffman, and S. Lin, “Factors Associated With Electronic Health Record Usage Among Primary Care Physicians After Hours: Retrospective Cohort Study,” JMIR Hum Factors, vol. 6, no. 3, p. e13779, Sep. 2019, doi: https://doi.org/10.2196/13779.

    Article  Google Scholar 

  30. J. Tavares and T. Oliveira, “Electronic Health Record Patient Portal Adoption by Health Care Consumers: An Acceptance Model and Survey,” J Med Internet Res, vol. 18, no. 3, p. e49, Mar. 2016, doi: https://doi.org/10.2196/jmir.5069.

    Article  Google Scholar 

  31. Hedderson, M. M., et al. (Jul. 2018). A tailored letter based on electronic health record data improves gestational weight gain among women with gestational diabetes mellitus: The gestational diabetes’ effects on moms (GEM) cluster-randomized controlled trial. Dia care, 41(7), 1370–1377. https://doi.org/10.2337/dc17-1133.

    Article  Google Scholar 

  32. M. Sue Kirkman et al., “211-LB: Developing ‘Gold Standard’ Diagnoses for Type 1 and Type 2 Diabetes in Adults from Electronic Health Record Data,” Diabetes, vol. 68, no. Supplement 1, pp. 211-LB, Jun. 2019, doi: https://doi.org/10.2337/db19-211-LB.

    Article  Google Scholar 

  33. G. A. Castillo-Sánchez, A. Berbey, I. de la Torre-Díez, and M. López-Coronado, “Information and Communications Technologies Health Projects in Panama: A Systematic Review and their Relation with Public Policies,” Journal of Medical Systems, vol. 41, no. 7, p. 110, Jul. 2017, doi: https://doi.org/10.1007/s10916-017-0755-y.

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Acknowledgments

This research has been partially supported by European Commission and the Ministry of Industry, Energy and Tourism under the project AAL-20125036 named BWetake Care: ICTbased Solution for (Self-) Management of Daily Living.

Thanks to the research grants from Senacyt, Panama.

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Authors and Affiliations

  1. Head of the Software Department, in the Subdirectorate of Information Technology, of the Secretary of Health of the State of Campeche, Campeche, Mexico

    Belmar Mex Uc

  2. Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Valladolid, Spain, Paseo de Belén 15, 47011, Valladolid, Spain

    Gema Castillo-Sánchez & Isabel de la Torre-Díez

  3. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

    Gonçalo Marques

  4. Escuela Politécnica. Universidad Europea del Atlántico, Santander, Spain

    Jon Arambarri

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  1. Belmar Mex Uc
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  2. Gema Castillo-Sánchez
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Correspondence to Gema Castillo-Sánchez.

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Uc, B.M., Castillo-Sánchez, G., Marques, G. et al. An Experience of Electronic Health Records Implementation in a Mexican Region. J Med Syst 44, 106 (2020). https://doi.org/10.1007/s10916-020-01575-w

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