EHR-Independent Predictive Decision Support Architecture Based on OMOP

Abstract

Background The increasing availability of molecular and clinical data of cancer patients combined with novel machine learning techniques has the potential to enhance clinical decision support, example, for assessing a patient's relapse risk. While these prediction models often produce promising results, a deployment in clinical settings is rarely pursued.

Objectives In this study, we demonstrate how prediction tools can be integrated generically into a clinical setting and provide an exemplary use case for predicting relapse risk in melanoma patients.

Methods To make the decision support architecture independent of the electronic health record (EHR) and transferable to different hospital environments, it was based on the widely used Observational Medical Outcomes Partnership (OMOP) common data model (CDM) rather than on a proprietary EHR data structure. The usability of our exemplary implementation was evaluated by means of conducting user interviews including the thinking-aloud protocol and the system usability scale (SUS) questionnaire.

Results An extract-transform-load process was developed to extract relevant clinical and molecular data from their original sources and map them to OMOP. Further, the OMOP WebAPI was adapted to retrieve all data for a single patient and transfer them into the decision support Web application for enabling physicians to easily consult the prediction service including monitoring of transferred data. The evaluation of the application resulted in a SUS score of 86.7.

Conclusion This work proposes an EHR-independent means of integrating prediction models for deployment in clinical settings, utilizing the OMOP CDM. The usability evaluation revealed that the application is generally suitable for routine use while also illustrating small aspects for improvement.

Keywords

decision support techniques - OMOP - data integration - prediction model

Note

The present work was performed in (partial) fulfillment of the requirements for obtaining the degree “Dr. rer. biol. hum.” from the Friedrich-Alexander-Universität Erlangen-Nürnberg (P.U.).

Protection of Human and Animal Subjects

Ethical approval was not required.

• References

• 1 Luo W, Phung D, Tran T. , et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 2016; 18 (12) e323
• 2 Ayaru L, Ypsilantis PP, Nanapragasam A. , et al. Prediction of outcome in acute lower gastrointestinal bleeding using gradient boosting. PLoS One 2015; 10 (07) e0132485
• 3 Ogutu JO, Schulz-Streeck T, Piepho HP. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc 2012; 6 (Suppl. 02) S10
• 4 Khalilia M, Choi M, Henderson A, Iyengar S, Braunstein M, Sun J. Clinical predictive modeling development and deployment through FHIR web services. AMIA Annu Symp Proc 2015; 2015: 717-726
• 5 Lee JH, Dindorf J, Eberhardt M. , et al. Innate extracellular vesicles from melanoma patients suppress β-catenin in tumor cells by miRNA-34a. Life Sci Alliance 2019; 2 (02) e201800205
• 6 Hripcsak G, Duke JD, Shah NH. , et al. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform 2015; 216: 574-578
• 7 Povey S, Lovering R, Bruford E, Wright M, Lush M, Wain H. The HUGO Gene Nomenclature Committee (HGNC). Hum Genet 2001; 109 (06) 678-680
• 8 Maier C, Lang L, Storf H. , et al. Towards implementation of OMOP in a German University Hospital Consortium. Appl Clin Inform 2018; 9 (01) 54-61
• 9 McCormack JL, Ash JS. Clinician perspectives on the quality of patient data used for clinical decision support: a qualitative study. In, AMIA Annual Symposium Proceedings: American Medical Informatics Association; 2012: 1302
• 10 Brooke J. SUS-A quick and dirty usability scale. Usabil Eval Ind 1996; 189: 4-7
• 11 Unberath P. 2019 . Available at: https://github.com/Unberath/WebAPI/tree/v2.4.0-custom . Accessed April 16, 2020
• 12 Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform 2008; 77 (02) 81-97
• 13 Yoon D, Ahn EK, Park MY. , et al. Conversion and data quality assessment of electronic health record data at a Korean tertiary teaching hospital to a common data model for distributed network research. Healthc Inform Res 2016; 22 (01) 54-58
• 14 Zhou X, Murugesan S, Bhullar H. , et al. An evaluation of the THIN database in the OMOP Common Data Model for active drug safety surveillance. Drug Saf 2013; 36 (02) 119-134
• 15 Lamer A, Depas N, Doutreligne M. , et al. Transforming French electronic health records into the Observational Medical Outcome Partnership's common data model: a feasibility study. Appl Clin Inform 2020; 11 (01) 13-22
• 16 Lynch KE, Deppen SA, DuVall SL. , et al. Incrementally transforming electronic medical records into the Observational Medical Outcomes Partnership common data model: a multidimensional quality assurance approach. Appl Clin Inform 2019; 10 (05) 794-803
• 17 FitzHenry F, Resnic FS, Robbins SL. , et al. Creating a common data model for comparative effectiveness with the observational medical outcomes partnership. Appl Clin Inform 2015; 6 (03) 536-547
• 18 Murphy SN, Weber G, Mendis M. , et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc 2010; 17 (02) 124-130
• 19 Bender D, Sartipi K. HL7 FHIR: an agile and RESTful approach to healthcare information exchange. In, Proceedings of the 26th IEEE international symposium on computer-based medical systems: IEEE; 2013: 326-331
• 20 Shin SJ, You SC, Park YR. , et al. Genomic common data model for seamless interoperation of biomedical data in clinical practice: retrospective study. J Med Internet Res 2019; 21 (03) e13249
• 21 Virzi RA. Refining the test phase of usability evaluation: how many subjects is enough?. Hum Factors 1992; 34: 457-468
• 22 Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud 2009; 4: 114-123
• 23 O'Sullivan D, Fraccaro P, Carson E, Weller P. Decision time for clinical decision support systems. Clin Med (Lond) 2014; 14 (04) 338-341
• 24 Bates DW, Kuperman GJ, Wang S. , et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530