From Repeated Entry to Interoperability: A Synthetic Feasibility Study of Process Mining and FHIR R4 Redesign

Muhammad Sobri Maulana; Dwitia Pratiwi; Arditya Prayogi

doi:10.59061/repit.v4i3.1549

Authors

Muhammad Sobri Maulana RSAU Prof. dr. Abdulrachman Saleh Jakarta
Dwitia Pratiwi RSAU Prof. dr. Abdulrachman Saleh Jakarta
Arditya Prayogi Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

DOI:

https://doi.org/10.59061/repit.v4i3.1549

Keywords:

Electronic Health Record, FHIR R4, Process Mining, SATUSEHAT, Workflow Redesign

Abstract

Fragmented hospital applications may require the same demographic, clinical, and administrative information to be entered repeatedly across roles and systems. This synthetic feasibility study develops an auditable framework that links field-level occurrence classification and process-variant analysis to a provisional Clinical Data Redundancy Index (CDRI) and a Fast Healthcare Interoperability Resources (FHIR) R4 target architecture. A fixed-seed simulation generated 480 synthetic journeys across outpatient, emergency, medical check-up, and inpatient pathways, yielding 6,634 events and 96 complete-sequence variants. Repeated occurrences were classified as passive display, clinical confirmation, correction, or full re-entry; only correction and full re-entry entered the provisional CDRI calculation. Under the prespecified base scenario of 88% automation coverage and a 0.87 residual waiting-time factor, median manual-entry time decreased from 10.72 to 7.13 minutes (33.44%), while median journey time decreased from 250.73 to 233.45 minutes (6.89%). Simulated discrepancy opportunities decreased from 210 to 48 (77.14%). These values demonstrate the behavior of the proposed evaluation chain under stated assumptions; they are not estimates of hospital performance or clinical effectiveness. The methodological contribution is the integration of field lineage, process mining, provisional redundancy measurement, FHIR redesign, paired scenario analysis, and an explicit empirical-validation pathway. External calibration and prospective hospital validation are required before practical adoption.

References

Arndt, B. G., Beasley, J. W., Watkinson, M. D., Temte, J. L., Tuan, W.-J., Sinsky, C. A., & Gilchrist, V. J. (2017). Tethered to the EHR: Primary care physician workload assessment using EHR event log data and time-motion observations. Annals of Family Medicine, 15(5), 419–426. https://doi.org/10.1370/afm.2121

Ayaz, M., Pasha, M. F., Alzahrani, M. Y., Budiarto, R., & Stiawan, D. (2021). The Fast Health Interoperability Resources (FHIR) standard: Systematic literature review of implementations, applications, challenges and opportunities. JMIR Medical Informatics, 9(7), e21929. https://doi.org/10.2196/21929

Bender, D., & Sartipi, K. (2013). HL7 FHIR: An agile and RESTful approach to healthcare information exchange. In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems (pp. 326–331). https://doi.org/10.1109/CBMS.2013.6627810

Berti, A., van Zelst, S., & Schuster, D. (2023). PM4Py: A process mining library for Python. Software Impacts, 17, 100556. https://doi.org/10.1016/j.simpa.2023.100556

Carayon, P., Schoofs Hundt, A., Karsh, B.-T., Gurses, A. P., Alvarado, C. J., Smith, M., & Brennan, P. F. (2006). Work system design for patient safety: The SEIPS model. Quality and Safety in Health Care, 15(Suppl. 1), i50–i58. https://doi.org/10.1136/qshc.2005.015842

Cresswell, K. M., Bates, D. W., & Sheikh, A. (2013). Ten key considerations for the successful implementation and adoption of large-scale health information technology. Journal of the American Medical Informatics Association, 20(e1), e9–e13. https://doi.org/10.1136/amiajnl-2013-001684

D'Amore, J. D., Mandel, J. C., Kreda, D. A., Swain, J. M., Kohane, I. S., & Mandl, K. D. (2014). Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. Journal of the American Medical Informatics Association, 21(6), 1060–1068. https://doi.org/10.1136/amiajnl-2014-002883

Evans, R. S. (2016). Electronic health records: Then, now, and in the future. Yearbook of Medical Informatics, (Suppl. 1), S48–S61. https://doi.org/10.15265/IYS-2016-s006

Health Level Seven International. (2019). FHIR Release 4 (R4) specification. https://hl7.org/fhir/R4/

Kahn, M. G., Callahan, T. J., Barnard, J., Bauck, A. E., Brown, J., Davidson, B. N., Estiri, H., Goerg, C., Holve, E., Johnson, S. G., Liaw, S.-T., Hamilton-Lopez, M., Meeker, D., Ong, T. C., Ryan, P., Shang, N., Weiskopf, N. G., Weng, C., Zozus, M., & Schilling, L. (2016). A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. eGEMs, 4(1), 1244. https://doi.org/10.13063/2327-9214.1244

Koppel, R., Metlay, J. P., Cohen, A., Abaluck, B., Localio, A. R., Kimmel, S. E., & Strom, B. L. (2005). Role of computerized physician order entry systems in facilitating medication errors. JAMA, 293(10), 1197–1203. https://doi.org/10.1001/jama.293.10.1197

Lehne, M., Sass, J., Essenwanger, A., Schepers, J., & Thun, S. (2019). Why digital medicine depends on interoperability. npj Digital Medicine, 2, 79. https://doi.org/10.1038/s41746-019-0158-1

Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016). SMART on FHIR: A standards-based, interoperable apps platform for electronic health records. Journal of the American Medical Informatics Association, 23(5), 899–908. https://doi.org/10.1093/jamia/ocv189

Maulana, M. S., Pratiwi, D., & Prayogi, A. (2026). MolecuTrace: Development of a web-based molecular oncology dashboard for integrating sample metadata, SNV profiles, and cancer stage classification. Journal of Intelligent Systems and Information Technology, 3(2). https://doi.org/10.61971/jisit.v3i2.307

Maulana, M. S., Pratiwi, D., & Prayogi, A. (2026). Vaccination programs and infectious disease burden among military personnel: A systematic review with pathogen-specific quantitative synthesis. The ASEAN Journal of Military and Preventive Medicine, 3(2). https://doi.org/10.47353/ajmpm.v3i2.17

Ministry of Health of the Republic of Indonesia. (n.d.). SATUSEHAT FHIR documentation, interoperability resources, and Patient resource guidance. Retrieved August 3, 2026, from https://satusehat.kemkes.go.id/platform/docs/id/fhir/

Moy, A. J., Schwartz, J. M., Chen, R., Sadri, S., Lucas, E., Cato, K. D., & Collins Rossetti, S. (2021). Measurement of clinical documentation burden among physicians and nurses using electronic health records: A scoping review. Journal of the American Medical Informatics Association, 28(5), 998–1008. https://doi.org/10.1093/jamia/ocaa325

Munoz-Gama, J., Martin, N., Fernandez-Llatas, C., Johnson, O. A., Sepulveda, M., Helm, E., Galvez-Yanjari, V., Rojas, E., Martinez-Millana, A., Aloini, D., Amantea, I. A., Andrews, R., Arias, M., Beerepoot, I., Benevento, E., Burattin, A., Capon, H., Carmona, J., Comuzzi, M., … Dumas, M. (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics, 127, 103994. https://doi.org/10.1016/j.jbi.2022.103994

Rojas, E., Munoz-Gama, J., Sepulveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224–236. https://doi.org/10.1016/j.jbi.2016.04.007

Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., Westbrook, J., Tutty, M., & Blike, G. (2016). Allocation of physician time in ambulatory practice: A time and motion study in 4 specialties. Annals of Internal Medicine, 165(11), 753–760. https://doi.org/10.7326/M16-0961

Tai-Seale, M., Olson, C. W., Li, J., Chan, A. S., Morikawa, C., Durbin, M., Wang, W., & Luft, H. S. (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

van der Aalst, W. M. P. (2016). Process mining: Data science in action (2nd ed.). Springer. https://doi.org/10.1007/978-3-662-49851-4

van der Aalst, W. M. P., Adriansyah, A., de Medeiros, A. K. A., Arcieri, F., Baier, T., Blickle, T., Bose, J. C., van den Brand, P., Brandtjen, R., Buijs, J., Burattin, A., Carmona, J., Castellanos, M., Claes, J., Cook, J., Costantini, S., Curbera, F., Damiani, E., de Leoni, M., … Wynn, M. (2012). Process mining manifesto. In Business process management workshops: Lecture notes in business information processing (Vol. 99, pp. 169–194). Springer. https://doi.org/10.1007/978-3-642-28108-2_19

Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144–151. https://doi.org/10.1136/amiajnl-2011-000681

World Health Organization. (2021). Global strategy on digital health 2020–2025. WHO.