Clinical prediction methods as a learning tool in medical education

Resumo

The relevance of the study is determined by the need to develop the ability of medical students to make clinical prediction as a key component of professional thinking. The aim is to develop and evaluate the effectiveness of a training module on methods for predicting treatment outcomes in the training of future doctors. Research methods: modified Objective Structured Clinical Examination (OSCE), Clinical Reasoning Test, analytical test Medical Logic, self-assessment questionnaire, semi-structured group interview. As a result, the experimental group (EG) students (n=50) demonstrated significantly higher indicators on all criteria compared to the control group (CG) (n=50): Clinical Reasoning Test – 7.8 versus 5.1 points; OSCE – 8.3 versus 5.5; analytical thinking – 26.1 versus 19.2 points; improved self-esteem – up to +2.2 points. The training module turned out to be effective for the development of clinical reasoning, decision logic, and professional confidence. The academic novelty of the study is the first-ever approved integration of clinical prediction scales (CHA₂DS₂-VASc, SHFM, APACHE II, etc.) as a didactic tool in the Clinical Reasoning course. The prospects for future research is scaling up the module for interdisciplinary training, as well as in studying its impact on real clinical decisions of interns.

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Biografia do Autor

Tetiana Sviatenko, Dnipro State Medical University

PhD in Medicine from Dnipro State Medical University. Head of the Department of Skin and Venereal Diseases. Dnipro, Ukraine. E-mail: sviatenko.t@gmail.com

Inna Gogunska, National Academy of Medical Sciences of Ukraine

Doctor of medical sciences from State Institution “Institute of Otolaryngology Named after prof. O.S. Kolomiychenko of the National Academy of Medical Sciences of Ukraine”. Scientific Researcher. Kyiv, Ukraine. E-mail: innagogunska@gmail.com

Kostyantyn Prockopets, Taras Shevchenko National University of Kyiv

PhD in Medical Sciences from Taras Shevchenko National University of Kyiv. Associate Professor of the Department of Surgery ESC “Institute of Biology and Medicine”. Kyiv, Ukraine. E-mail: prockopets154@gmail.com

Olga Moroziuk, Odesa National Medical University

Graduated in medicine from the Odesa National Medical University. Assistant, Department of Occupational Pathology Clinical Laboratory and Functional Diagnostics. Odesa, Ukraine. E-mail: olgafast43@gmail.com

Natalia Dub, Andrey Krupynskyі Lviv Medical Academy

PhD in Public Administration from Andrey Krupynskyі Lviv Medical Academy. Associate Professor, Dean of the Faculty. Lviv, Ukraine. E-mail: nataliiadub@gmail.com

Referências

ALRASHED, Fahad; AHMAD, Tauseef; ALMURDI, Muneera; ALDERAA, Asma; ALHAMMAD, Saad; SERAJUDDIN, Mohammad; ALSUBIHEEN, Abdulrahman. Incorporating technology adoption in medical education: A qualitative study of medical students’ perspectives. Advances in Medical Education and Practice, v. 15, p. 615–625, 2024. https://doi.org/10.2147/AMEP.S464555

ANG, Wei; CHOI, Kai; LAU, Ying; SHAH, Lubna; KOH, Jun; TOH, Zheng; SIAH, Chiew; LIAW, Sok; LAU, Siew. Evaluation of a psychological readiness program and final clinical practicum among final year nursing students: A mixed methods study. Nurse Education Today, v. 141, 106317, 2024. https://doi.org/10.1016/j.nedt.2024.106317

ARSHI, Banafsheh; WYNANTS, Laure; RIJNHART, Eline; REEVE, Kelly; COWLEY, Laura E.; SMITS, Luc J. What proportion of clinical prediction models make it to clinical practice? Protocol for a two-track follow-up study of prediction model development publications. BMJ Open, v. 13, n. 5, e073174, 2023. https://doi.org/10.1136/bmjopen-2023-073174

BINUYA, Mae; ENGELHARDT, Ellen; SCHATS, Winnie; SCHMIDT, Marjanka; STEYERBERG, Ewout. Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review. BMC Medical Research Methodology, v. 22, Article 316, 2022. https:// 10.1186/s12874-022-01801-8.

CHAVEZ-ECOS, Fabian A.; CHAVEZ-ECOS, Rodrigo; VERGARA SANCHEZ, Carlos; CHAVEZ-GUTARRA, Miguel A.; AGARWALA, Anandita; CAMACHO-CABALLERO, Kiara. Mobile health apps for cardiovascular risk assessment: A systematic review. Frontiers in Cardiovascular Medicine, v. 11, Article 1420274, 2024. https://doi.org/10.3389/fcvm.2024.1420274

CHEN, Lingxiao. Overview of clinical prediction models. Annals of Translational Medicine, v. 8, n. 4, 71, 2020. https://doi.org/10.21037/atm.2019.11.121

CHOWDHURY, Plaban; VAISH, Abhishek; VAISHYA, Raju. Medical education technology: Past, present and future. Journal of Clinical Orthopaedics and Trauma, v. 21, n. 4, 2024. https://doi.org/10.1177/09760016241256202

CLIFT, Ashley. How outcome prediction could aid clinical practice. British Journal of Hospital Medicine, v. 86, n. 1, 2025. https://doi.org/10.12968/hmed.2024.0781

DELAVARI, Somayeh; BARZKAR, Farzaneh; RIKERS, Remy; POURAHMADI, Mohammadreza; SOLTANI ARABSHAHI, Seyed; KESHTKAR, Abbasali; DARGAHI, Helen; YAGHMAEI, Minoo; MONAJEMI, Alireza. Teaching and learning clinical reasoning skill in undergraduate medical students: A scoping review. PLOS ONE, v. 19, n. 10, e0309606, 2024. https://doi.org/10.1371/journal.pone.0309606

GRAINGER, Rebecca; LIU, Qian; GLADMAN, Tehmina. Learning technology in health professions education: Realising an (un)imagined future. Medical Education, v. 57, n. 12, p. 1129–1137, 2023. https://doi.org/10.1111/medu.15185

HERASYMENKO, O.; POLESOVA, Tamila; GERASYMENKO, V.; KUKHAREVA, N. Distance education in the professional training of future doctors: Pro et contra. TRAUMA, v. 22, n. 5, p. 38–40, 2022. https://doi.org/10.22141/1608-1706.5.22.2021.244466

HERMASARI, Bulan; NUGROHO, Dian; MAFTUHAH, Atik; PAMUNGKASARI, Eti; BUDIASTUTI, Veronika; LARAS, Adaninggar. Promoting medical student’s clinical reasoning during COVID-19 pandemic. Korean Journal of Medical Education, v. 35, n. 2, p. 187–198, 2023. https://doi.org/10.3946/kjme.2023.259

ISHIZUKA, Kosuke; SHIKINO, Kiyoshi; TAKADA, Naoko; SAKAI, Yokei; OTOTAKE, Yasushi; KOBAYASHI, Takashi; INOUE, Tetsuhiko; JIKUYA, Ryosuke; IWATA, Yuri; NISHIMURA, Kenichi; YOSHIMI, Ryusuke; OI, Yasufumi; WATANABE, Yuko; TOGASHI, Yu; OGAWA, Fumihiro; SANO, Daisuke; ASAMI, Takeshi; IMAI, Yuichi; TAKEUCHI, Ichiro; FUNAKOSHI, Kengo; OHTA, Mitsuyasu; INAMORI, Masahiko; KUSAKABE, Akihiko. Enhancing clinical reasoning skills in medical students through team-based learning: A mixed-methods study. BMC Medical Education, v. 25, Article 221, 2025. https://doi.org/10.1186/s12909-025-06784-w

KHALIFA, Mohamed; ALBADAWY, Mona. Artificial intelligence for clinical prediction: Exploring key domains and essential functions. Computer Methods and Programs in Biomedicine Update, v. 5, 100148, 2024. https://doi.org/10.1016/j.cmpbup.2024.100148

LEITÃO, Andressa; ESTEVES, Roberto. Perception of medical students on the development of the clinical reasoning competence. Revista Brasileira de Educação Médica, v. 47, n. 1, 2023. https://doi.org/10.1590/1981-5271v47.1-20220127.ing

LI, Dengkai; WEI, Yanfang; ZHANG, Chunfang; YANG, Yun; WANG, Zhenqiang; LU, Yaru; LIU, Lei. Value of SOFA score, APACHE II score, and WBC count for mortality risk assessment in septic patients: A retrospective study. Medicine (Baltimore), v. 104, n. 20, e42464, 2025. https://doi.org/10.1097/MD.0000000000042464

LIU, Changbo; SUO, Shuzhen; LUO, Liya; CHEN, Xixian; LING, Chunxiang; CAO, Shixiong. SOFA score in relation to Sepsis: Clinical implications in diagnosis, treatment, and prognostic assessment. Computational and Mathematical Methods in Medicine, v. 2022, n. 11, p. 1–8, 2022. https://doi.org/10.1155/2022/7870434

LOCKE, Rachel; MASON, Alice; COLES, Colin; LUSZNAT, Rosie-Marie; MASDING, Mike. The development of clinical thinking in trainee physicians: The educator perspective. BMC Medical Education, v. 20, n. 1, 2020. https://doi.org/10.1186/s12909-020-02138-w

MEHTA, Pranav; PATIL, Shilpa. A comparative study to evaluate use of APACHE II and SOFA score in Sepsis patients in intensive care unit of a tertiary level hospital in Western Maharashtra. International Journal of Health Sciences, v. 6, n. S1, p. 4078-4089, 2022. https://doi.org/10.53730/ijhs.v6nS1.5747

MEIJERINK, Lotta; DUNIAS, Zoë; LEEUWENBERG, Artuur; DE HOND, Anne. H.; JENKINS, David; MARTIN, Glen; SPERRIN, Matthew; PEEK, Niels; SPIJKER, Rene; HOOFT, Lotty; MOONS, Karel; VAN SMEDEN, Maarten; SCHUIT, Ewoud. Updating methods for artificial intelligence–based clinical prediction models: a scoping review. Journal of Clinical Epidemiology, v. 178, 111636, 2025. https://doi.org/10.1016/j.jclinepi.2024.111636

MENG, Meiqi; CHEN, Sihan; YANG, Dan; ZHANG, Xiaoyan; YANG, Yajuan; WANG, Ziyan; ZHANG, Jingyuan; LI, Xuejing; HAO, Yufang. Measuring readiness for nurse-led shared decision making in clinical practice: Development and first testing of the RSDM-N scale. BMC Nursing, v. 24, Article 417, 2025. https://doi.org/10.1186/s12912-025-03070-4

MICHALIK, Beniamin; SĘK, Michał; SZYPUŁA, Aleksandra; HAJDUK-MAŚLAK, Katarzyna; SKÓRA, Adrianna; GALASIŃSKA, Iwona. New technological developments in medical education. Journal of Education, Health and Sport, v. 60, 204–220, 2024. https://doi.org/10.12775/JEHS.2024.60.014

MORIARTY, Andrew; CASTLETON, Joanne; MCMILLAN, Dean; RILEY, Richard D.; SNELL, Kym; ARCHER, Lucinda; PATON, Lewis; GILBODY, Simon; CHEW‐GRAHAM, Carolyn. The value of clinical prediction models in general practice: A qualitative study exploring the perspectives of people with lived experience of depression and general practitioners. Health Expectations, v. 27, n. 6, e70059, 2024. https://doi.org/10.1111/hex.70059

MORID, Mohammad A.; LIU SHENG, Olivia R.; DUNBAR, Joseph. Time series prediction using deep learning methods in healthcare. ACM Transactions on Management Information Systems, v. 14, n. 1, Article 2, 2023. https://doi.org/10.1145/3531326

PEBOLO, Pebalo F.; JACKLINE, Auikoru; OPWONYA, Maxwell; OTIM, Raymond; BONGOMIN, Felix. Medical education technology in resource-limited settings. In: Z. O. Amarin (Ed.), Advances in medical education and training. IntechOpen, 2024. https://doi.org/10.5772/intechopen.115049

RADZIIEVSKA, Iryna. The analysis of pedagogical technologies efficiency in the professional training of medical specialists. Science Rise: Pedagogical Education, v. 2, n. 47, p. 24–29, 2022. https://doi.org/10.15587/2519-4984.2022.254833

REINHOLD, Martina; BACON-BAGULEY, Theresa A. Integrating an evidence-based medicine curriculum into physician assistant education: Teaching skills for lifelong decision-making! Education in the Health Professions, v. 4, n. 1, p. 4–10, 2021. https://doi.org/10.4103/ehp.ehp_1_21

RILEY, Richard; COLLINS, Gary S. Stability of clinical prediction models developed using statistical or machine learning methods. Biometrical Journal, v. 65, n. 8, Article 2200302, 2023. https://doi.org/10.1002/bimj.20220030

RILEY, Richard; PATE, Alexander; DHIMAN, Paula; ARCHER, Lucinda; MARTIN, Glen; COLLINS, Gary. Clinical prediction models and the multiverse of madness. BMC Medicine, v. 21, Article 502, 2023. https://doi.org/10.1186/s12916-023-03212-y

SAYED, Mohamed; HEGAZI, Moustafa; ZUBAIRI, Nadeem; ALAHMADI, Turki; SAEEDI, Fajr. Undergraduate medical students’ perceptions and perspectives on their clinical reasoning learning experiences. Scientific Reports, v. 15, Article 6229, 2025. Retrieved from https://www.nature.com/articles/s41598-025-90656-2

TENNY, Steven; VARACALLO, Matthew A. Evidence-based medicine. In StatPearls. StatPearls Publishing, 2024. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK470182/

WAGNER, Gerit; RINGEVAL, Mickaël; RAYMOND, Louis; PARÉ, Guy. Digital health competences and AI beliefs as conditions for the practice of evidence-based medicine: A study of prospective physicians in Canada. Medical Education Online, v. 30, n. 1, 2459910, 2025. https://doi.org/10.1080/10872981.2025.2459910

WU, Ruo; JIANG, Haiyan; MAO, Guomin; REN, Yuting; WANG, Yue; YAN, Dajun; HUANG, Zhongwei; QI, Lei; PAN, Rongrong. Sepsis prognosis related scoring standards: A comprehensive review. Biotarget, v. 5, 2022. https://doi.org/10.21037/biotarget-21-5

XIE, Duowen; SHI, Xiaoyuan Research of the combination of APACHE II and SOFA score with CDSS score in the prognosis of Sepsis. Archives of Clinical and Experimental Medicine, v. 49, n. 6, 2022. Retrieved from https://archivespsy.com/menu-script/index.php/ACF/article/view/2138

YEFREMOVA, Oksana; HUMENIUK, Mariia; SALYZHYN, Tetiana; HUMENIUK, Vasyl; KORNIICHUK, Oleksandr. Professional training of future doctors using cloud technologies. Cadernos de Educação, Tecnologia e Sociedade, v. 17, n. se5, p. 60–72, 2024. https://doi.org/10.14571/brajets.v17.nse5.60-72

ZAINAL, Nur; ISLAM, Md Asiful; RASUDIN, Nur, MAMAT, Zakira; HANIS, Tengku; RODZLAN HASANI, Wan; MUSA, Kamarul I. Critical thinking and clinical decision making among registered nurses in clinical practice: A systematic review and meta-analysis. Nursing Reports, v. 15, n. 5, 175, 2025. https://doi.org/10.3390/nursrep15050175.

Publicado
2026-01-05
Como Citar
SVIATENKO, T.; GOGUNSKA, I.; PROCKOPETS, K.; MOROZIUK, O.; DUB, N. Clinical prediction methods as a learning tool in medical education. Perspectivas em Diálogo: Revista de Educação e Sociedade, v. 13, n. 34, p. 79-100, 5 jan. 2026.
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