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Vol. 8, No. 2, May 2023

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Leveraging Text-Mining Techniques On Electronic Medical Records to Analyze National Drug-insured Medication Use

https://doi.org/10.22219/kinetik.v8i2.1695
Adhi Dharma Wibawa
Institut Teknologi Sepuluh Nopember
Prio Adi Ramadhani
Institut Teknologi Sepuluh Nopember, National Research and Innovation Agency
Ghulam Asrofi Buntoro
Institut Teknologi Sepuluh Nopember
Ridho Rahman Hariadi
Institut Teknologi Sepuluh Nopember
Putri Alief Siswanto
Institut Teknologi Sepuluh Nopember
Shoffi Izza Sabilla
Institut Teknologi Sepuluh Nopember

Corresponding Author(s) : Adhi Dharma Wibawa

adhiosa@te.its.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 8, No. 2, May 2023

Abstract

Processing electronic medical record (EMR) data has become a common practice among scientists for extracting valuable insights and studying diseases. Given the large volumes of text data in EMRs, efficient computerized text-mining techniques are necessary. As academics, we recognize that drug-used analysis from EMR data in Indonesia is currently limited. This study focuses on obtaining meaningful insights from EMR data to make positive recommendations for hospitals. The proposed method uses pattern-based Regular Expressions (regex) to extract drug names and a Levenshtein distance algorithm to check their compatibility. We developed the pattern based on analyzing Indonesia EMR data. The extracted drug names were compared to a list of selected drugs (National Drug-Insured/Fornas) that are required and must be provided at healthcare facilities in Indonesia. The Levenshtein distance threshold was set to two to decide whether the extracted drug names belonged to nationally drug-insured or not. Only about 11.09 – 16.11% of medications given by doctors are listed in the Fornas drug list. Between 2019 and 2021, there was an inaccuracy in the writing of prescriptions for Fornas drugs, with as many as 57.53% to 63.21% of drug names being written incorrectly. The results of this study indicate that the Levenshtein distance algorithm has promising potential for implementation in the Ministry of Health of Indonesia, with a precision rate of 97.07%.

Keywords

Text-Mining Electronic Medical Records National Drug-Insured Regular Expression Levenshtein Distance
Wibawa, A. D., Ramadhani, P. A., Buntoro, G. A., Hariadi, R. R., Siswanto, P. A., & Sabilla, S. I. (2023). Leveraging Text-Mining Techniques On Electronic Medical Records to Analyze National Drug-insured Medication Use. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 8(2). https://doi.org/10.22219/kinetik.v8i2.1695
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References
  1. S. Honavar, “Electronic medical records – The good, the bad and the ugly,” Indian Journal of Ophthalmology, Vol. 68, No. 3, P. 417, 2020. https://doi.org/10.4103/ijo.IJO_278_20
  2. A. de Benedictis, E. Lettieri, L. Gastaldi, C. Masella, A. Urgu, and D. Tartaglini, “Electronic Medical Records implementation in hospital: An empirical investigation of individual and organizational determinants,” PLOS ONE, Vol. 15, No. 6, P. e0234108, 2020. https://doi.org/10.1371/journal.pone.0234108
  3. K. Adane, M. Gizachew, and S. Kendie, “The role of medical data in efficient patient care delivery: a review,” Risk Management and Healthcare Policy, Vol. Volume 12, Pp. 67–73, 2019. https://doi.org/10.2147/RMHP.S179259
  4. W. Sun, Z. Cai, Y. Li, F. Liu, S. Fang, and G. Wang, “Data processing and text mining technologies on electronic medical records: A review,” Journal of Healthcare Engineering, Vol. 2018. Hindawi Limited, 2018. https://doi.org/10.1155/2018/4302425
  5. F. Ridzuan and W. M. N. Wan Zainon, “A Review on Data Cleansing Methods for Big Data,” Procedia Computer Science, Vol. 161, Pp. 731–738, 2019. https://doi.org/10.1016/j.procs.2019.11.177
  6. S. Kusumadewi, C. I. Ratnasari, and L. Rosita, “Natural language parsing of patient complaints in Indonesian language,” in 2015 International Conference on Science and Technology (TICST), Nov. 2015, Pp. 292–297. https://doi.org/10.1109/TICST.2015.7369373
  7. A. H. Sangaji, Y. Pamungkas, S. M. S. Nugroho, and A. D. Wibawa, “Rule-based Disease Classification using Text Mining on Symptoms Extraction from Electronic Medical Records in Indonesian,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2022. https://doi.org/10.22219/kinetik.v7i1.1377
  8. M. Jamaluddin and A. D. Wibawa, “Patient Diagnosis Classification based on Electronic Medical Record using Text Mining and Support Vector Machine,” in 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2021, Pp. 243–248. https://doi.org/10.1109/iSemantic52711.2021.9573178
  9. Y. Maryati and A. Nurwahyuni, “Evaluasi Penggunaan Electronic Medical Record Rawat Jalan di Rumah Sakit Husada dengan Technology Acceptance Model,” Jurnal Manajemen Informasi Kesehatan Indonesia, Vol. 9, No. 2, Pp. 2337–585, 2021. https://doi.org/10.33560/jmiki.v9i2.374
  10. O. Metsker, E. Bolgova, A. Yakovlev, A. Funkner, and S. Kovalchuk, “Pattern-based Mining in Electronic Health Records for Complex Clinical Process Analysis,” Procedia Computer Science, Vol. 119, Pp. 197–206, 2017. https://doi.org/10.1016/j.procs.2017.11.177
  11. M. Jamaluddin and A. D. Wibawa, “Patient Diagnosis Classification based on Electronic Medical Record using Text Mining and Support Vector Machine,” in 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), 2021, Pp. 243–248. https://doi.org/10.1109/iSemantic52711.2021.9573178
  12. A. H. Sangaji, Y. Pamungkas, S. M. S. Nugroho, and A. D. Wibawa, “Rule-based Disease Classification using Text Mining on Symptoms Extraction from Electronic Medical Records in Indonesian,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2022. https://doi.org/10.22219/kinetik.v7i1.1377
  13. S. Winda, “National Formulary (FORNAS) and e-Catalogue of Medicines as Efforts to Prevent Corruption in Drug Administration of National Health Insurance (JKN),” Jurnal Integritas, Vol. 4, No. 2, Pp. 177–206, 2018. https://doi.org/10.32697/integritas.v4i2.328
  14. J. Beernaerts, E. Debever, M. Lenoir, B. de Baets, and N. Van de Weghe, “A method based on the Levenshtein distance metric for the comparison of multiple movement patterns described by matrix sequences of different length,” Expert Systems with Applications, Vol. 115, Pp. 373–385, 2019. https://doi.org/10.1016/j.eswa.2018.07.076
  15. M. Kashina, I. D. Lenivtceva, and G. D. Kopanitsa, “Preprocessing of unstructured medical data: the impact of each preprocessing stage on classification,” Procedia Computer Science, Vol. 178, Pp. 284–290, 2020. https://doi.org/10.1016/j.procs.2020.11.030
  16. A. Blanco, S. Remmer, A. Pérez, H. Dalianis, and A. Casillas, “Implementation of specialised attention mechanisms: ICD-10 classification of Gastrointestinal discharge summaries in English, Spanish and Swedish,” Journal of Biomedical Informatics, Vol. 130, P. 104050, 2022. https://doi.org/10.1016/j.jbi.2022.104050
  17. J. Santos-Pereira, L. Gruenwald, and J. Bernardino, “Top data mining tools for the healthcare industry,” Journal of King Saud University - Computer and Information Sciences, Vol. 34, No. 8, Pp. 4968–4982, 2022. https://doi.org/10.1016/j.jksuci.2021.06.002
  18. C. Chojenta, J. Byles, and B. K. Nair, “Rehabilitation and convalescent hospital stay in New South Wales: an analysis of 3,979 women aged 75+,” Australian and New Zealand Journal of Public Health, Vol. 42, No. 2, Pp. 195–199, 2018. https://doi.org/10.1111/1753-6405.12731
  19. I. U. of I. Pharmacy Study Program, “Availability of Medicine in the Era of National Health Insurance,” Yogyakarta, 2018.
  20. H. Humas, “BPJS Hears 2022 Nets of Feedback on JKN Management in the Future,” Indonesia Health Social Security Administering Agency Official Website, Jul. 24, 2022.
  21. O. Z, “273 Million Indonesian Population Updated Version of the Ministry of Home Affairs,” Ministry of Home Affairs Official Website, Feb. 24, 2022.
  22. L. J. Seppala et al., “Fall-Risk-Increasing Drugs: A Systematic Review and Meta-Analysis: II. Psychotropics,” Journal of the American Medical Directors Association, Vol. 19, No. 4, Pp. 371.e11-371.e17, 2018. https://doi.org/10.1016/j.jamda.2017.12.098
  23. S. Laberge and A. M. Crizzle, “A Literature Review of Psychotropic Medications and Alcohol as Risk Factors for Falls in Community Dwelling Older Adults,” Clinical Drug Investigation, Vol. 39, No. 2, Pp. 117–139, 2019. https://doi.org/10.1007/s40261-018-0721-6
  24. N. Ait-Daoud, A. S. Hamby, S. Sharma, and D. Blevins, “A Review of Alprazolam Use, Misuse, and Withdrawal,” Journal of Addiction Medicine, Vol. 12, No. 1, Pp. 4–10, 2018. https://doi.org/10.1097/ADM.0000000000000350
  25. BNN Public Relations, “What is Psychotropic and its Dangers?,” BNN Official Website, Jan. 02, 2019.
  26. M. J. Anwar, K. K. Pillai, R. Khanam, M. Akhtar, and D. Vohora, “Effect of alprazolam on anxiety and cardiomyopathy induced by doxorubicin in mice,” Fundamental & Clinical Pharmacology, Vol. 26, No. 3, Pp. 356–362, 2012. https://doi.org/10.1111/j.1472-8206.2011.00925.x
  27. S. Yilmaz, M. Pekdemir, Ü. Tural, and M. Uygun, “Comparison of alprazolam versus captopril in high blood pressure: A randomized controlled trial,” Blood Pressure, Vol. 20, No. 4, Pp. 239–243, 2011. https://doi.org/10.3109/08037051.2011.553934
  28. N. Ridarineni and D. Muhammad, “Daily Salt Consumption in Indonesia 15 Grams Higher,” Republika News - Leasure, Oct. 06, 2013.
  29. Y. Li et al., “Longitudinal Change of Perceived Salt Intake and Stroke Risk in a Chinese Population,” Stroke, Vol. 49, No. 6, Pp. 1332–1339, 2018. https://doi.org/10.1161/STROKEAHA.117.020277
  30. M. Hu et al., “High-salt diet downregulates TREM2 expression and blunts efferocytosis of macrophages after acute ischemic stroke,” Journal of Neuroinflammation, Vol. 18, No. 1, P. 90, 2021. https://doi.org/10.1186/s12974-021-02144-9
  31. S. K. Park et al., “The risk for incident ischemic heart disease according to estimated glomerular filtration rate in a korean population,” Journal of Atherosclerosis and Thrombosis, Vol. 27, No. 5, Pp. 461–470, 2020. https://doi.org/10.5551/jat.50757
  32. M. A. Khan et al., “Global Epidemiology of Ischemic Heart Disease: Results from the Global Burden of Disease Study,” Cureus, 2020. https://doi.org/10.7759/cureus.9349
  33. R. Gupta and D. A. Wood, “Primary prevention of ischaemic heart disease: populations, individuals, and health professionals.,” Lancet (London, England), Vol. 394, No. 10199, Pp. 685–696, 2019. https://doi.org/10.1016/S0140-6736(19)31893-8
  34. A. Grillo, L. Salvi, P. Coruzzi, P. Salvi, and G. Parati, “Sodium intake and hypertension,” Nutrients, Vol. 11, No. 9, 2019. https://doi.org/10.3390/nu11091970
  35. H. Litbangkes, “Adult smokers in Indonesia have increased in the last ten years,” Health Research and Development Agency, Ministry of Health of Indonesia Official Website.
  36. S. W. Oh and S. Y. Han, “Loop diuretics in clinical practice,” Electrolyte and Blood Pressure, Vol. 13, No. 1. Korean Society of Electrolyte and Blood Pressure Research, Pp. 17–21, Jun. 01, 2015. https://doi.org/10.5049/EBP.2015.13.1.17
  37. G. C. Roush, R. Kaur, and M. E. Ernst, “Diuretics: a review and update.,” Journal of cardiovascular pharmacology and therapeutics, Vol. 19, No. 1, Pp. 5–13, 2014. https://doi.org/10.1177/1074248413497257
  38. T. M. Khan, R. Patel, and A. H. Siddiqui, Furosemide. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing.
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