Baseline Projection of Outpatient Visits Using Simple Linear Regression: A Case Study at RSU Ananda Purwokerto
DOI:
https://doi.org/10.54065/likelihood.1660Keywords:
Outpatient Visits, Simple Linear Regression, Hospital Demand Forecasting, MAPE, RapidminerAbstract
Anticipating outpatient demand can support hospital planning, although forecasts based on short and fluctuating annual series require cautious interpretation. This study aimed to develop a transparent baseline projection of outpatient visits at RSU Ananda Purwokerto using simple linear regression. The analysis used eight annual aggregate observations from 2018–2025; the 2025 value represented visits from January through October, whereas the preceding observations covered complete calendar years. Calendar year was used as the predictor and outpatient visits as the outcome. Manual calculations were compared with the Linear Regression output in RapidMiner, and in-sample fit was evaluated using the Mean Absolute Percentage Error (MAPE). The fitted model produced a slope of -2,103.57 visits per year and projected 62,590 visits in 2026, declining to 54,176 in 2030. However, the slope was not statistically significant (p = .369), and the model explained only 13.6% of the observed variation (R² = .136). The in-sample MAPE was 15.66%, which met the study's descriptive “good” category but did not constitute independent validation of future forecast accuracy. Therefore, the projections should be interpreted as preliminary baseline scenarios rather than certain future totals. The model may support initial operational discussion, but decisions should also consider current hospital information and should be updated using complete, preferably monthly, observations.
References
Areru, H. A., Dangisso, M. H., & Lindtjørn, B. (2021). Low and unequal use of outpatient health services in public primary health care facilities in southern Ethiopia: A facility-based cross-sectional study. BMC Health Services Research, 21, Article 776. https://doi.org/10.1186/s12913-021-06846-x
Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8(1), 69–80. https://doi.org/10.1016/0169-2070(92)90008-W
Baihaqi, W. M., Dianingrum, M., & Ramadhan, K. A. N. (2019). Regresi linier sederhana untuk memprediksi kunjungan pasien di rumah sakit berdasarkan jenis layanan dan umur pasien. Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi, 5(2), 55–62. https://doi.org/10.24014/coreit.v5i2.7067
Bai, L., Ding, G., Gu, S., Bi, P., Su, B., Qin, D., Xu, G., & Liu, Q. (2023). Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model. Scientific Reports, 13, Article 2812. https://doi.org/10.1038/s41598-023-29897-y
Barsasella, D., & Faturohmi, Z. A. (2025). Review of inpatient case-mix workforce needs with health workload analysis (ABK-KES) in the case-mix unit of Permata Bunda General Hospital, Tasikmalaya. Jurnal Persada Husada Indonesia, 12(2), 18–28. https://doi.org/10.56014/jphi.v12i2.428
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318. https://doi.org/10.1001/jama.2017.18391
Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70–83. https://doi.org/10.1016/j.csda.2017.11.003
Fernandes Prabhu, D., Gurupur, V., Stone, A., & Trader, E. (2025). Integrating artificial intelligence, electronic health records, and wearables for predictive, patient-centered decision support in healthcare. Healthcare, 13(21), Article 2753. https://doi.org/10.3390/healthcare13212753
Gneiting, T., & Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and Its Application, 1, 125–151. https://doi.org/10.1146/annurev-statistics-062713-085831
Haryanto, H., Winarto, H., & Juliane, C. (2024). Application of data mining techniques in healthcare: Identifying inter-disease relationships through association rule mining. Journal of World Science, 3(5), 520–529. https://doi.org/10.58344/jws.v3i5.597
Huang, Y., Xu, C., Ji, M., Xiang, W., & He, D. (2020). Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method. BMC Medical Informatics and Decision Making, 20, Article 237. https://doi.org/10.1186/s12911-020-01256-1
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
Jiang, S., Liu, Q., & Ding, B. (2023). A systematic review of the modelling of patient arrivals in emergency departments. Quantitative Imaging in Medicine and Surgery, 13(3), 1957–1971. https://doi.org/10.21037/qims-22-268
Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., & Snow, G. L. (2008). Forecasting daily patient volumes in the emergency department. Academic Emergency Medicine, 15(2), 159–170. https://doi.org/10.1111/j.1553-2712.2007.00032.x
Lathifah, U., & Dana, R. D. (2024). Implementasi metode linear regression untuk prediksi harga properti real estate menggunakan RapidMiner. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 1129–1137. https://doi.org/10.36040/jati.v8i1.8919
Luo, L., Luo, L., Zhang, X., & He, X. (2017). Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Services Research, 17, Article 469. https://doi.org/10.1186/s12913-017-2407-9
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). The M4 Competition: Results, findings, conclusion and way forward. International Journal of Forecasting, 34(4), 802–808. https://doi.org/10.1016/j.ijforecast.2018.06.001
Nugroho, B. I., Fatkhurrozak, Andriani, W., & Gunawan. (2024). Prediksi jumlah kunjungan pasien rawat jalan menggunakan metode regresi linier sederhana. PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer, 11(2), 284–293. https://doi.org/10.30656/prosisko.v11i2.8756
Ratumurun, S. J., & Manik, M. J. (2025). Analysis of factors influencing outpatient waiting time: A literature review. Jurnal Penelitian Keperawatan Kontemporer, 5(5), 77–90. https://doi.org/10.59894/jpkk.v5i5.1087
RSU Ananda Purwokerto. (2025). Data rekam medis kunjungan pasien rawat jalan tahun 2018–2025. Unit Rekam Medis RSU Ananda Purwokerto.
Sabran, S., Azizah, S. N., & Rachmawati, E. (2024). Prediksi kunjungan pasien rawat jalan dengan metode analisis trend linear di RSUD Haji Provinsi Jawa Timur tahun 2023–2025. Jurnal Manajemen dan Administrasi Rumah Sakit Indonesia (MARSI), 8(1), 26–32. https://doi.org/10.52643/marsi.v8i1.4046
Setiatin, S., & Syahidin, Y. (2021). Analisis lama waktu tunggu pelayanan pasien rawat jalan di bagian pendaftaran Rumah Sakit X Bandung. Akrab Juara: Jurnal Ilmu-Ilmu Sosial, 6(5), 148–158. https://www.akrabjuara.com/index.php/akrabjuara/article/view/1692
Shirvia, B., Riyandari, L., & Safitri, R. (2025). Prediksi kunjungan pasien rawat jalan dengan interpretasi analisis SWOT menggunakan algoritma regresi linear sederhana: Studi kasus RSU Bunda Purwokerto. Jurnal Ilmiah Komputasi, 24(2), 243–252. https://doi.org/10.32409/jikstik.24.2.3787
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., & Kattan, M. W. (2010). Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology, 21(1), 128–138. https://doi.org/10.1097/EDE.0b013e3181c30fb2
Subramanian, L. (2021). Effective demand forecasting in health supply chains: Emerging trend, enablers, and blockers. Logistics, 5(1), Article 12. https://doi.org/10.3390/logistics5010012
Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. https://doi.org/10.1080/00031305.2017.1380080
Wang, M., & Wang, M. (2025). Forecasting the demand for human resources in a hospital using the grey forecasting model. Management Science and Business Decisions, 5(1), 15–21. https://doi.org/10.52812/msbd.110
Wardani, Y., Sopiah, & Zagladi, A. N. (2024). Application of the workload indicators of staffing need method in achieving hospital service quality in developing countries: Systematic literature review. Jurnal Pendidikan Administrasi Perkantoran (JPAP), 12(3), 391–405. https://doi.org/10.26740/jpap.v12n3.p391-405
Wargon, M., Guidet, B., Hoang, T. D., & Hejblum, G. (2009). A systematic review of models for forecasting the number of emergency department visits. Emergency Medicine Journal, 26(6), 395–399. https://doi.org/10.1136/emj.2008.062380
Yu, L., Hang, G., Tang, L., Zhao, Y., & Lai, K. K. (2017). Forecasting patient visits to hospitals using a WD&ANN-based decomposition and ensemble model. Eurasia Journal of Mathematics, Science and Technology Education, 13(12), 7615–7627. https://doi.org/10.12973/ejmste/80308
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