A Novel Surveillance Evaluation Approach Using Clinical Text Extraction from the Hospital Information System: Case Study of Somdejpraboromrachineenart Natawee Hospital, Songkhla Province, Southern Thailand for Influenza in 2024
DOI:
https://doi.org/10.59096/osir.v18i2.274302Keywords:
influenza, surveillance system, hospital information system, electronic medical record, clinical text extraction, report 506Abstract
In 2024, Na Thawi District, Songkhla Province, reported the highest influenza cases in Southern Thailand. This cross-sectional study evaluated the influenza surveillance system (R506) at Somdejpraboromrachineenart Natawee Hospital in 2024 using both quantitative and qualitative methods. Stakeholders involved in the epidemiological surveillance system were interviewed to describe the system qualitatively. Quantitatively, 8,758 medical records from the hospital information system (HIS) and 358 R506 reports were reviewed to assess sensitivity, positive predictive value (PPV), completeness, accuracy, and timeliness. The female-to-male ratios were 1.17:1 in HIS meeting the R506 definition and 1.08:1 in R506. Most cases were in the 25–60-year age group in HIS and 5–9-year group in R506. The lowest proportions were among those aged 60 years or more. Cases peaked in July; HIS showed a gradual rise from May, while R506 surged from June. Most cases occurred in Na Thawi and Chana districts. Subdistrict-level data showed consistent hotspots in Na Thawi, Sathon, and surrounding areas. The overall incidence in the area was higher than in the reporting system. The sensitivity was 8.52% and the PPV was 84.92% with R506 showing 100% completeness and accuracy, except for onset date (21.79%). Timeliness was high: 98.88% within 3 days and 99.72% within 7 days. From the qualitative study, the stakeholders accepted the surveillance system, describing it as simple, flexible, stable, and useful for planning and resource allocation. Clinical text extraction enabled full review without the need for sampling.
References
World Health Organization. Influenza (seasonal) [Internet]. Geneva: World Health Organization; 2023 [cited 2025 Mar 8]. <https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal)>
Thompson WW, Weintraub E, Dhankhar P, Cheng PY, Brammer L, Meltzer MI, et al. Estimates of US influenza-associated deaths made using four different methods. Influenza Other Respir Viruses. 2009 Jan;3(1):37–49. doi:10.1111/j.1750-2659.2009.00073.x.
Department of Disease Control, Ministry of Public Health. Influenza [Internet]. Nonthaburi: Department of Disease Control; [cited 2024 Dec 3]. <https://ddc.moph.go.th/disease_detail.php?d=13>. Thai.
Centers for Disease Control and Prevention (US). How flu spreads [Internet]. Atlanta: Centers for Disease Control and Prevention; 2021 [cited 2024 Dec 3]. <https://www.cdc.gov/flu/about/disease/spread.htm>
Department of Disease Control, Ministry of Public Health. Influenza situation [Internet]. Nonthaburi: Department of Disease Control; [cited 2024 Dec 16]. <https://ddc.moph.go.th/doe/pagecontent.php?page=607&dept=doe>. Thai.
Department of Disease Control, Ministry of Public Health. Influenza situation report [Internet]. Nonthaburi: Department of Disease Control; [cited 2025 Jan 3] <https://dvis3.ddc.moph.go.th/t/DDC_CENTER_DOE/views/DDS2/sheet33?:embed=y&:isGuestRedirectFromVizportal=y>. Thai.
Na Thawi District Public Health Office. Influenza situation 2024: Program R506. Songkhla: Na Thawi District Public Health Office; 2024.
Weeks HL, Beck C, McNeer E, Williams ML, Bejan CA, Denny JC, et al. medExtractR: a targeted, customizable approach to medication extraction from electronic health records. J Am Med Inform Assoc. 2020 Mar 8;27(3):407–18. doi:10.1093/jamia/ocz207.
Rotejanaprasert C, Armatrmontree P, Chienwichai P, Maude RJ. Perspectives and challenges in developing and implementing integrated dengue surveillance tools and technology in Thailand: a qualitative study. PLoS Negl Trop Dis. 2024 Aug 14;18(8):e0012387. doi:10.1371/journal.pntd.0012387.
Neira RAQ, de Vries GJ, Caffarel J, Stretton E. Extraction of Data from a Hospital Information System to Perform Process Mining. Stud Health Technol Inform. 2017;245:554–8.
Ong TC, Kahn MG, Kwan BM, Brandt P, Folck B, Marquard J. Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading. BMC Med Inform Decis Mak. 2017;17:134. doi:10.1186/s12911-017-0532-3.
Wongsanuphat S, Sangwanloy S, Sopha P, Buangsuang W, Denduang S, Thongplean A, et al. Enhancing coronavirus disease (COVID-19) surveillance system through information technology, Thailand, 2020. OSIR. 2020 Sep;13(3):90–100. doi: 10.59096/osir.v13i3.262804.
Wongsanuphat S, Sangwanloy S, Sopha P, Buangsuang W, Denduang S, Thongplean A, et al. Evaluation of enhancing the information system for COVID-19 surveillance in Thailand, 2020: A pre-post intervention comparison. OSIR. 2020 Sep;13(3):101–9. doi:10.59096/osir.v13i3.262806.
Wongsanuphat S, Malaikham J, Suriya S, Prommongkhol J, Khampha N, Khempetch T. Enhancing digital disease surveillance in Thailand using information technology, data engineering, data science, and artificial intelligence. OSIR. 2025 Mar;18(1):1–10. doi:10.59096/osir.v18i1.272056.
Watcharaporn N, Nutthien T, Dis Wises W, Kaewprasit P, Sirithavorn SA. Study of influenza surveillance system and policy proposals for developing the response to emerging respiratory infectious diseases in Bangkok area in 2016. Public Health Academic Journal. 2017;26:63–72.
Chobkatanyoo A, Sangsawang C, Thepparat T, Kaesaman S, Malae S. Influenza surveillance system evaluation in Narathiwat Province, Thailand, 2019. Weekly Epidemiological Surveillance Report [Internet]. 2021 [cited 2025 Mar 8];52:625–33. https://he05.tci-thaijo.org/index.php/WESR/article/view/1762
Thongkham J. Evaluation of the influenza surveillance system health region 8, fiscal year 2020. The Office of Disease Prevention and Control 8 Udon Thani Jounal [Internet]. 2023 [cited 2025 Mar 9];1(3):20–32. <https://he03.tci-thaijo.org/index.php/JODPC8/article/view/497/833>
Barrett N, Weber-Jahnke JH, Thai V. Engineering natural language processing solutions for structured information from clinical text: extracting sentinel events from palliative care consult letters. Stud Health Technol Inform. 2013;192:594–8. doi:10.3233/978-1-61499-289-9-594.
Mincheva Z, Vasilev N, Nikolov V, Antonov A. Extracting structured data from text in natural language. International Journal of Intelligent Information Systems. 2021;10(4):74–80. doi:10.11648/j.ijiis.20211004.16.
Montgomery MP, Morris SE, Rolfes MA, Kittikraisak W, Samuels AM, Biggerstaff M, et al. The role of asymptomatic infections in influenza transmission: what do we really know. Lancet Infect Dis. 2024 Jun;24(6):e394–404. doi:10.1016/S1473-3099(23)00619-9.
German RR, Lee LM, Horan JM, Milstein RL, Pertowski CA, Waller MN, et al. Updated guidelines for evaluating public health surveillance systems: recommendations from the Guidelines Working Group. MMWR Recomm Rep. 2001 Jul 27;50(RR-13):1–35.
Division of Epidemiology, Department of Disease Control. Case definitions and reporting guidelines for dangerous communicable diseases and diseases under surveillance in Thailand [Internet]. Nonthaburi: Department of Disease Control; 2020 [cited 2025 May 22]. 500 p. <https://klb.ddc.moph.go.th/dataentry/handbook/form/113>. Thai.
Miller TA, Bethard S, Dligach D, Lin C, Savova GK. Extracting time expressions from clinical text. Proceedings of the 2015 Workshop on Biomedical Natural Language Processing; 2015 Jul 30; Beijing, China. Kerrville (TX): Association for Computational Linguistics; 2015; 11 p. doi:10.18653/v1/w15-3809.
Lai KH, Topaz M, Goss FR, Zhou L. Automated misspelling detection and correction in clinical free-text records. J Biomed Inform. 2015 Jun:55:188–95. doi:10.1016/j.jbi.2015.04.008.
Citest Diagnostics Inc. Influenza A+B rapid test cassette [Internet]. Vancouver: Diagnostics Inc; [cited 2025 Mar 9] <https://www.citestdiagnostics.com/Home/ProductInfo/232>
Muller AM, Schmohl KA, Knoop K, Schug C, Urnauer S, Hagenhoff A, et al. Hypoxia-targeted 131I therapy of hepatocellular cancer after systemic mesenchymal stem cell-mediated sodium iodide symporter gene delivery. Oncotarget. 2016 Aug 23;7(34):54795-54810. doi:10.18632/oncotarget.10758.
Freitas FTM, Cabral APS, Barros ENC, Burigo MJO, Prochnow RD, Silva LA, et al. Pre-symptomatic transmission of pandemic influenza H1N1 2009: investigation of a family cluster, Brazil. Epidemiol Infect. 2013 Apr;141(4):763–6. doi:10.1017/S0950268812001501.
Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. J Biomed Inform. 2017 Sep;73:14–29. doi:10.1016/j.jbi.2017.07.012.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Outbreak, Surveillance, Investigation & Response (OSIR) Journal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
