KaydedilenlerDerginizi ekleyin

Dergiler / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 9 Issue: 2 Special Issue

Development of a Financial Forecasting System for the Healthcare Sector

Kevser Şahinbaş(BEYKENT ÜNİVERSİTESİ)0000-0002-8076-3678
Abdurrahman Keskin(Pusula Healthcare Solutions)0000-0002-4986-6425
Özlem Akçakoca(Pusula Healthcare Solutions)0009-0006-3994-3227
Sayfa
—
Yayın Tarihi
2026-06-17
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The models developed as a result of the study are evaluated as contributing to the financial planning processes of healthcare organizations and supporting strategic decision-making mechanisms.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Healthcare expenditures constitute a significant portion of national budgets in many countries, and striking a balance between cost-effectiveness and service quality is becoming increasingly important.
📊
Temel Bulgular & Veri:In this context, financial analyses play a critical role in strategic decision-making processes for healthcare organizations.
💡
Sonuç & Katkı:The models developed as a result of the study are evaluated as contributing to the financial planning processes of healthcare organizations and supporting strategic decision-making mechanisms.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Financial planning in healthcare · Time series forecasting · Hospital revenue prediction

Abstract

Healthcare expenditures constitute a significant portion of national budgets in many countries, and striking a balance between cost-effectiveness and service quality is becoming increasingly important. In this context, financial analyses play a critical role in strategic decision-making processes for healthcare organizations. In particular, forecasting hospital turnover guides in many areas, such as budgeting, resource management, and personnel planning. This study aims to develop revenue forecasting models using the hospital’s historical turnover data to gain a better understanding of its financial status and to provide a more solid foundation for future financial decision-making in the healthcare sector. The present work reports a comparative time-series analysis of deep learning techniques (Recurrent Neural Networks with the Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) cells, and Neural Basis Expansion Analysis Time Series (N-Beats)) and statistical techniques (ARIMA and SARIMA) and the Naïve algorithm to forecast revenue using the hospital’s historical turnover data. The findings reveal that the SARIMA model provides high accuracy by successfully analyzing seasonal and trend components. In contrast, the N-BEATS model exhibits the best overall performance with the lowest error rate, highlighting its strong ability to capture complex temporal dependencies and outperforming both traditional recurrent architectures and conventional statistical methods. The models developed as a result of the study are evaluated as contributing to the financial planning processes of healthcare organizations and supporting strategic decision-making mechanisms.

🔖

Bu Makaleye Atıf Yap

Şahinbaş, K., Keskin, A., Akçakoca, Ö. (2026). Development of a Financial Forecasting System for the Healthcare Sector. Sakarya University Journal of Computer and Information Sciences, Volume: 9 Issue: 2 Special Issue. https://doi.org/10.35377/saucis...1740703

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (43)

  1. M. Ordu, E. Demir, C. Tofallis, and M. M. Gunal, “A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation,” Healthcare Analytics, vol. 4, no. 1, Art. no. 100248, Dec. 2023, doi: 10.1016/j.health.2023.100248.
  2. B. Aboubacar and D. Xu, “The impact of health expenditure on the economic growth in Sub-Saharan Africa,” Theor. Econ. Lett., vol. 7, no. 3, pp. 615–622, Apr. 2017.
  3. M. Can, “Sağlık ekonomisi,” in Sağlık Ekonomisi, E. B. Biçer, Ed. Ankara, Türkiye: Nobel, 2020, pp. 339–360.
  4. M. Doğan, İşletme Ekonomisi ve Yönetimi. İstanbul, Türkiye: Nobel Akademik Yayıncılık, 2014.
  5. S. Büker, R. Aşıkoğlu, and G. G. Sevil, Finansal yönetim. Ankara, Türkiye: Sözkesen Matbaacılık, 2011.
  6. F. Dayı, Sağlık işletmelerinde uygulamalı finansal analiz. Bursa, Türkiye: Ekin Basın Yayın, 2013.
  7. C. Sonğur, A. Kar, M. Top, A. Gazi, and A. Babacan, “Türkiye Kamu Hastane Birlikleri hastanelerinin finansal performanslarının değerlendirilmesi: Finansal tablo analizleri,” Noştay Dergisi, no. 100, pp. 1–26, 2016.
  8. E. H. Ölmez, “Ankara ilinde faaliyet gösteren özel hastanelerin finansal performanslarını değerlendirmede kullandıkları ölçütlerin belirlenmesi,” Balkan Sos. Bilimler Derg., vol. 4, no. 7, pp. 1–20, 2015.
  9. S. Kahraman, “Konaklama işletmelerinin yönetim muhasebesi ve finansal analiz kapsamında değerlendirilmesi,” Yüksek lisans tezi, Sosyal Bilimler Enstitüsü, İnönü Üniversitesi, Malatya, Türkiye, 2012.
  10. A. L. Schwartz, B. E. Landon, A. G. Elshaug, M. E. Chernew, and J. M. McWilliams, “Measuring low-value care in Medicare,” JAMA Intern. Med., vol. 174, no. 7, pp. 1067–1076, Jul. 2014.
  11. K. R. Yabroff, T. Gansler, R. C. Wender, K. J. Cullen, and O. W. Brawley, “Minimizing the burden of cancer in the United States: Goals for a high-performing health care system,” CA Cancer J. Clin., vol. 69, no. 3, pp. 166–183, May/Jun. 2019.
  12. S. Schneeweiss, “Learning from big health care data,” N. Engl. J. Med., vol. 370, no. 23, pp. 2161–2163, Jun. 2014. doi: 10.1056/NEJMp1401111
  13. H. Atasoy, B. N. Greenwood, and J. S. McCullough, “The digitization of patient care: A review of the effects of electronic health records on health care quality and utilization,” Annu. Rev. Public Health, vol. 40, pp. 487–500, Apr. 2019.
  14. M. Ordu, E. Demir, and C. Tofallis, “A comprehensive modelling framework to forecast the demand for all hospital services,” Int. J. Health Plann. Manage., vol. 34, no. 2, pp. e1257–e1271, Apr./Jun. 2019.
  15. A. Kaya, C. Demirbaş, and S. Dağ, “Gündoğan (Ardeşen-Rize) köyü yerleşim alanındaki yamaç duraysızlığının jeoteknik açıdan incelenmesi,” Doğal Afetler Çevre Derg., vol. 4, no. 2, pp. 221–235, 2018.
  16. İ. H. Altaş and R. Çakmak, “A fuzzy decision maker to determine optimal starting time of shiftable loads in the smart grids,” Int. J. Reason.-Based Intell. Syst., vol. 12, no. 3, pp. 210–216, 2020.
  17. N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” Amer. Statist., vol. 46, no. 3, pp. 175–185, 1992.
  18. D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression, 3rd ed. Hoboken, NJ, USA: Wiley, 2013.
  19. E. Gavcar and H. M. Metin, “Hisse senedi değerlerinin makine öğrenimi (derin öğrenme) ile tahmini,” İzmir Katip Çelebi Üniv. Sos. Bilimler Derg., vol. 10, no. 2, pp. 1–11, 2021.
  20. M. A. Morid, O. R. L. Sheng, and J. Dunbar, “Time series prediction using deep learning methods in healthcare,” ACM Trans. Manage. Inf. Syst., vol. 14, no. 1, Art. no. 2, pp. 1–29, Jan. 2023.
  21. Y. Deng, H. Fan, and S. Wu, “A hybrid ARIMA-LSTM model optimized by BP in the forecast of outpatient visits,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 5, pp. 5517–5527, May 2023, doi: 10.1007/s12652-020-02602-x.
  22. T. Latruwe, M. Van der Wee, P. Vanleenhove, J. Devriese, S. Verbrugge, and D. Colle, “A long-term forecasting and simulation model for strategic planning of hospital bed capacity,” Oper. Res. Health Care, vol. 36, Art. no. 100375, Mar. 2023.
  23. A. R. Rao, R. Jain, M. Singh, and R. Garg, “Predictive interpretable analytics models for forecasting healthcare costs using open healthcare data,” Healthcare Analytics, vol. 6, Art. no. 100351, Dec. 2024.
  24. S. Ramalingam, P. Sivathapandi, and J. Jeyaraman, “Deep learning approaches for healthcare cost forecasting: Reducing uncertainty in financial planning for hospitals and providers,” Adv. Deep Learn. Techn., vol. 2, no. 1, pp. 69–110, Jan. 2022.
  25. O. Cece and M. Gençtürk, “Net profit margin forecasting with machine learning methods in hospital finance management,” J. Health Syst. Policies, vol. 5, no. 2, pp. 103–119, Dec. 2023, doi: 10.52675/jhesp.1335249.
  26. S. Kaushik et al., “AI in healthcare: Time-series forecasting using statistical, neural, and ensemble architectures,” Front. Big Data, vol. 3, Art. no. 4, Mar. 2020, doi: 10.3389/fdata.2020.00004.
  27. Y. Mahmoudian, A. Nemati, and A. S. Safaei, “A forecasting approach for hospital bed capacity planning using machine learning and deep learning with application to public hospitals,” Healthcare Analytics, vol. 4, Art. no. 100245, Dec. 2023, doi: 10.1016/j.health.2023.100245.
  28. X. Zhao, J. W. Lai, A. F. W. Ho, N. Liu, M. E. H. Ong, and K. H. Cheong, “Predicting hospital emergency department visits with deep learning approaches,” Biocybern. Biomed. Eng., vol. 42, no. 3, pp. 1051–1065, Jul. 2022.
  29. D.-H. Yang, K.-H. Zhu, and R.-N. Wang, “Forecasting healthcare service volumes with machine learning algorithms,” J. Forecast., vol. 43, no. 6, pp. 2358–2377, Sep. 2024.
  30. C.-F. Chen, W.-H. Ho, H.-Y. Chou, S.-M. Yang, I.-T. Chen, and H.-Y. Shi, “Long-term prediction of emergency department revenue and visitor volume using autoregressive integrated moving average model,” Comput. Math. Methods Med., vol. 2011, Art. no. 395690, 2011.
  31. R. J. Hyndman, A. B. Koehler, R. D. Snyder, and S. Grose, “A state space framework for automatic forecasting using exponential smoothing methods,” Int. J. Forecast., vol. 18, no. 3, pp. 439–454, Jul. 2002.
  32. D. Namakhwa, B. H. M. Tchereni, W. Masanjala, C. D. Namakhwa, S. L. Kuchande, and W. R. Mgomezulu, “Modelling and forecasting mobile money customer transaction volumes in rural and semi-urban Malawi: An autoregressive integrated moving average spatial decomposition,” Sci. Afr., vol. 26, Art. no. e02430, Oct. 2024, doi: 10.1016/j.sciaf.2024.e02430.
  33. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time series analysis: Forecasting and control, 4th ed. Hoboken, NJ, USA: Wiley, 2008.
  34. R. H. Shumway and D. S. Stoffer, Time series analysis and its applications: With R examples, 4th ed. New York, NY, USA: Springer, 2017.
  35. P. J. Brockwell and R. A. Davis, Introduction to time series and forecasting, 2nd ed. New York, NY, USA: Springer, 2002.
  36. K. E. ArunKumar, D. V. Kalaga, C. M. Sai Kumar, M. Kawaji, and T. M. Brenza, “Comparative analysis of gated recurrent units, long short-term memory cells, autoregressive integrated moving average, seasonal autoregressive integrated moving average for forecasting COVID-19 trends,” Alex. Eng. J., vol. 61, no. 10, pp. 7585–7603, Oct. 2022, doi: 10.1016/j.aej.2022.01.015.
  37. B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio, “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting,” in Proc. Int. Conf. Learn. Represent. (ICLR), Feb. 2020.
  38. B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio, “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting,” in Proc. Int. Conf. Learn. Represent. (ICLR), Feb. 2020, doi: 10.1016/j.neucom.2020.05.101.
  39. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, pp. 533–536, Oct. 1986.
  40. Y. Fu, F. Lou, F. Meng, Z. Tian, H. Zhang, and F. Jiang, “An intelligent network attack detection method based on RNN,” in Proc. IEEE 3rd Int. Conf. Data Sci. Cyberspace (DSC), Guangzhou, China, Jun. 2018, pp. 483–489, doi: 10.1109/DSC.2018.00078
  41. J. Wang, Y. Du, and J. Wang, “LSTM-based long-term energy consumption prediction with periodicity,” Energy, vol. 197, Art. no. 117197, Mar. 2020, doi: 10.1016/j.energy.2020.117197
  42. D. Zhang and M. R. Kabuka, “Combining weather condition data to predict traffic flow: A GRU-based deep learning approach,” IET Intell. Transp. Syst., vol. 12, no. 7, pp. 578–585, Sep. 2018, doi: 10.1049/iet-its.2017.0300
  43. C. J. Willmott, “Some comments on the evaluation of model performance,” Bull. Amer. Meteorol. Soc., vol. 63, no. 11, pp. 1309–1313, Nov. 1982.