Exploring the Value of Electronic Health Records from Multiple Datasets
During the last decades, most European countries dedicated huge efforts in collecting and maintaining Electronic Health Records (EHR). With the continuous grow of these datasets, it became obvious that its secondary use for research may lead to new insights about diseases and treatments outcomes.
EHR databases can be used to speed up and reduce the cost of health research studies, which are essential for the advance and improvement of health services. However, many times, a single observational data source is not enough for a clinical study, thus data interoperability has a major impact on the exploration of value of EHRs. Despite the recognized benefit of data sharing, database owners remain reluctant in conceding access to the contents of their databases, mainly due to ownership, privacy and security issues.
In this paper, we exploit two major international initiatives, the European Medical Information Framework (EMIF) and the Observational Health Data Sciences and Informatics (OHDSI), to provide a methodology through which multiple longitudinal clinical repositories can be queried, without the data leaving its original repository.
KeywordsElectronic Health Records Observational studies Data interoperability Clinical research Secondary use
This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF grant n. 115372), and from the Integrated Programme of SR&TD ‘SOCA’ (CENTRO-01-0145-FEDER-000010), co-funded by Centro 2020 program, Portugal 2020, European Union, through the European Regional Development Fund.
- 1.Cheng, H.G., Phillips, M.R.: Secondary analysis of existing data: opportunities and implementation. Shanghai Arch. Psychiatry 26(6), 371 (2014)Google Scholar
- 3.Daniel, C., et al.: Cross border semantic interoperability for clinical research: the EHR4CR semantic resources and services. AMIA Summits Transl. Sci. Proc. 2016, 51 (2016)Google Scholar
- 5.Doolan, D.M., Winters, J., Nouredini, S.: Answering research questions using an existing data set. Med. Res. Arch. 5(9), 1–14 (2017)Google Scholar
- 6.Fajarda, O., Silva, L.B., Rijnbeek, P., Van Speybroeck, M., Oliveira, J.L.: A methodology to perform semi-automatic distributed EHR database queries. In: 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018), vol. 5, pp. 127–134 (2018)Google Scholar
- 8.Hripcsak, G., et al.: Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud. Health Technol. Inform. 216, 574 (2015) Google Scholar
- 9.Huser, V., Kahn, M.G., Brown, J.S., Gouripeddi, R.: Methods for examining data quality in healthcare integrated data repositories. In: Pacific Symposium on Biocomputing (PSB), pp. 628–633 (2017)Google Scholar
- 11.Lopes, P., Silva, L.B., Oliveira, J.L.: Challenges and opportunities for exploring patient-level data. BioMed Res. Int. 2015, 11 (2015)Google Scholar
- 14.Meystre, S., Lovis, C., Bürkle, T., Tognola, G., Budrionis, A., Lehmann, C., et al.: Clinical data reuse or secondary use: current status and potential future progress. IMIA Yearb. 26, 38–52 (2017)Google Scholar
- 17.Nass, S.J., Levit, L.A., Gostin, L.O., et al.: The value, importance, and oversight of health research. In: Beyond the HIPAA Privacy Rule: Enhancing Privacy Improving Health Through Research (2009)Google Scholar
- 19.Pakhomov, S., Weston, S.A., Jacobsen, S.J., Chute, C.G., Meverden, R., Roger, V.L., et al.: Electronic medical records for clinical research: application to the identification of heart failure. Am. J. Manag. Care 13(6 Part 1), 281–288 (2007)Google Scholar
- 21.Platt, R., Carnahan, R.: The us food and drug administration’s mini-sentinel program. Pharmacoepidemiol. Drug Saf. 21(S1), 1–303 (2012)Google Scholar
- 22.Pringle, S., Lippitt, A.: Interoperability of electronic health records and personal health records: key interoperability issues associated with information exchange. J. Healthc. Inf. Manag.: JHIM 23(3), 31–37 (2009)Google Scholar
- 23.Reisman, M.: EHRs: the challenge of making electronic data usable and interoperable. Pharm. Ther. 42(9), 572 (2017)Google Scholar
- 25.Si, Y., Weng, C.: An OMOP CDM-based relational database of clinical research eligibility criteria. Stud. Health Technol. Inform. 245, 950 (2017)Google Scholar
- 27.Trifan, A., Díaz, C., Oliveira, J., et al.: A methodology for fine-grained access control in exposing biomedical data. Stud. Health Technol. Inform. 247, 561–565 (2018)Google Scholar