背景：全民健康保險研究資料庫長期為臺灣醫療、公共衛生、臨床流行病學與健康政策研究之重要資料來源。2016年前後，健保資料庫近用與使用規範轉向較集中化與管制化之資料治理模式，可能提高資料申請、分析與結果輸出成本，進而影響相關研究之學術生產力與資料使用情形。

目的：本研究探討2016年健保資料庫使用限制對學術生產力之影響，並檢視政策效果是否可能因研究執行與出版流程而延後反映於已發表論文。

方法：本研究以Scopus收錄之健保資料庫相關論文為主分析樣本，並以衛生福利部中央健康保險署官方論文清單作為外部檢核資料。研究先進行描述性統計與書目計量分析，再以2016年為政策變化點，建構雙重差分法模型，比較健保資料庫相關論文與非健保資料庫控制組於政策前後的相對變化；另以中斷時間序列分析檢查健保資料庫論文本身是否出現水準或斜率變化。

結果：本研究共納入6,369篇Scopus健保資料庫相關論文。描述性統計顯示，2016年後論文數量未立即明顯下降，政策後數年仍維持高產出；雙重差分法結果亦未支持健保資料庫論文在論文數量、期刊層級、論文層級學術影響及合作結構上出現一致且穩健之負向相對變化。然而，中斷時間序列分析顯示，部分指標存在成長斜率轉弱、引用影響力下降與資料落後年數上升等趨勢訊號。

結論：本研究未發現2016年健保資料庫使用限制後，相關論文之數量、期刊層級、論文層級學術影響或合作結構出現可由已發表成果直接觀察到的立即性、全面性負向變化。惟政策影響可能先反映於資料使用遞延、研究成長動能趨緩與研究者組成變化，而非立即反映為發表篇數下降。

Background: Taiwan’s National Health Insurance Research Database (NHIRD) has long supported medical and health policy research. Around 2016, access became more centralized and regulated, potentially affecting academic productivity.

Purpose: This study examines whether the 2016 NHIRD access restrictions affected academic productivity and whether effects emerged after publication delays.

Methods: Scopus-indexed NHIRD-related publications were analyzed, with Taiwan’s National Health Insurance Administration list used for external validation. Descriptive and bibliometric analyses were followed by difference-in-differences models comparing NHIRD-related publications with a non-NHIRD control group, and interrupted time series analysis.

Results: A total of 6,369 NHIRD-related publications were included. Publication output did not decline markedly after 2016. Difference-in-differences estimates showed no consistent negative changes in publication output, journal-level performance, article-level scholarly impact, or collaboration structure. Interrupted time series results indicated weaker growth, lower citation impact, and longer data-use lags.

Conclusions: No immediate, comprehensive deterioration in NHIRD-related research was observed after the 2016 restrictions. Policy effects may instead emerge through delayed data use, weaker growth, and changes in researcher composition.

中央健康保險署（2021年4月20日）。健保大數據助攻學術研究。衛生福利部。https://mohw.gov.tw/cp-2704-59187-1.html
中央健康保險署（2025年8月12日）。健保署依憲法法庭判決意旨，即日起提供民眾退出資料目的外使用。衛生福利部。https://www.mohw.gov.tw/cp-7175-83466-1.html
中華民國總統府（2025年12月19日）。制定全民健康保險資料管理條例。https://www.president.gov.tw/Page/294/50094
立法院（2025年12月2日）。制定全民健康保險資料管理條例。https://ppg.ly.gov.tw/ppg/BillThirdReadingPassesClause/download/resources/ppgb32100/D_202512221630020.pdf
司法院憲法法庭（2022年8月12日）。111年憲判字第13號【健保資料庫案】判決。https://cons.judicial.gov.tw/docdata.aspx?fid=97&id=309956

Abramo, G., & D’Angelo, C. A. (2014). How do you define and measure research productivity? Scientometrics, 101(2), 1129–1144. https://doi.org/10.1007/s11192-014-1269-8
Adams, J. (2013). The fourth age of research. Nature, 497(7451), 557–560. https://doi.org/10.1038/497557a
Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics, 119(1), 249–275. https://doi.org/10.1162/003355304772839588
Bornmann, L. (2014). How are excellent (highly cited) papers defined in bibliometrics? A quantitative analysis of the literature. Research Evaluation, 23(2), 166–173. https://doi.org/10.1093/reseval/rvu002
Bornmann, L., & Mutz, R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology, 66(11), 2215–2222. https://doi.org/10.1002/asi.23329
Callaway, B., & Sant’Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230. https://doi.org/10.1016/j.jeconom.2020.12.001
Chen, Y.-C., Yeh, H.-Y., Wu, J.-C., Haschler, I., Chen, T.-J., & Wetter, T. (2011). Taiwan’s National Health Insurance Research Database: Administrative health care database as study object in bibliometrics. Scientometrics, 86(2), 365–380. https://doi.org/10.1007/s11192-010-0289-2
Danish Health Data Authority. (n.d.). The Secure Research Platform. Retrieved August 27, 2026, from https://english.sundhedsdatastyrelsen.dk/health-data-and-registers/research-services/the-secure-research-platform
Dimick, J. B., & Ryan, A. M. (2014). Methods for evaluating changes in health care policy: The difference-in-differences approach. JAMA, 312(22), 2401–2402. https://doi.org/10.1001/jama.2014.16153
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L., Wang, D., & Barabási, A.-L. (2018). Science of science. Science, 359(6379), eaao0185. https://doi.org/10.1126/science.aao0185
González-Pereira, B., Guerrero-Bote, V. P., & Moya-Anegón, F. (2010). A new approach to the metric of journals’ scientific prestige: The SJR indicator. Journal of Informetrics, 4(3), 379–391. https://doi.org/10.1016/j.joi.2010.03.002
Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520(7548), 429–431. https://doi.org/10.1038/520429a
Hsieh, C.-Y., Su, C.-C., Shao, S.-C., Sung, S.-F., Lin, S.-J., Yang, Y.-H. K., & Lai, E. C.-C. (2019). Taiwan’s National Health Insurance Research Database: Past and future. Clinical Epidemiology, 11, 349–358. https://doi.org/10.2147/CLEP.S196293
James, C., Colledge, L., Meester, W., Azoulay, N., & Plume, A. (2019). CiteScore metrics: Creating journal metrics from the Scopus citation index. Learned Publishing, 32(4), 367–374. https://doi.org/10.1002/leap.1246
Lin, L.-Y., Warren-Gash, C., Smeeth, L., & Chen, P.-C. (2018). Data resource profile: The National Health Insurance Research Database (NHIRD). Epidemiology and Health, 40, e2018062. https://doi.org/10.4178/epih.e2018062
Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: A tutorial. International Journal of Epidemiology, 46(1), 348–355. https://doi.org/10.1093/ije/dyw098
Moed, H. F. (2010). Measuring contextual citation impact of scientific journals. Journal of Informetrics, 4(3), 265–277. https://doi.org/10.1016/j.joi.2010.01.002
Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. https://doi.org/10.2307/1913610
Regulation (EU) 2025/327 of the European Parliament and of the Council of 11 February 2025 on the European Health Data Space and amending Directive 2011/24/EU and Regulation (EU) 2024/2847 (Text with EEA relevance), Official Journal of the European Union, L 2025/327 (2025). https://data.europa.eu/eli/reg/2025/327/oj
Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023). What’s trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218–2244. https://doi.org/10.1016/j.jeconom.2023.03.008
Thelwall, M., Kousha, K., Stuart, E., Makita, M., Abdoli, M., Wilson, P., & Levitt, J. (2023). In which fields are citations indicators of research quality? Journal of the Association for Information Science and Technology, 74(8), 941–953. https://doi.org/10.1002/asi.24767
Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299–309. https://doi.org/10.1046/j.1365-2710.2002.00430.x
Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365–391. https://doi.org/10.1016/j.joi.2016.02.007
Wang, T. H., Tsai, Y. T., & Lee, P. C. (2023). Health big data in Taiwan: A national health insurance research database. Journal of the Formosan Medical Association, 122(4), 296–298. https://doi.org/10.1016/j.jfma.2022.12.016
Wing, C., Simon, K., & Bello-Gomez, R. A. (2018). Designing difference in difference studies: Best practices for public health policy research. Annual Review of Public Health, 39, 453–469. https://doi.org/10.1146/annurev-publhealth-040617-013507
Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039. https://doi.org/10.1126/science.1136099
Wu, L., Wang, D., & Evans, J. A. (2019). Large teams develop and small teams disrupt science and technology. Nature, 566(7744), 378–382. https://doi.org/10.1038/s41586-019-0941-9