Journal of the Thai Medical Informatics Association

Integrating artificial intelligence-driven wound detection into simulation-based forensic medicine training

Boonsak Hanterdsith — 2026-06-16

Forensic wound detection and classification is a fundamental skill in forensic medicine education, enabling medical students to interpret injury mechanisms and provide medico-legal opinions. Traditional teaching is often limited by insufficient exposure to real forensic cases and lack of interactive, feedback-driven learning opportunities. This study aimed to develop, validate, and implement a real-time, Artificial Intelligence (AI)-assisted wound detection and classification system using You Only Look Once version 8 nano (YOLOv8n) and deploy it as a web-based application to enhance simulation-based forensic teaching for medical students. A custom dataset comprising 595 images of eight wound types (gsw_entrance, gsw_exit, wound_burn, wound_hanging, wound_hesitation, wound_laceration, wound_open_fracture, wound_strangulation) was collected from physical wound simulations and augmented using Roboflow. Images were annotated in YOLOv8 format and split into training (70%), validation (20%), and test (10%) sets. The YOLOv8n model was trained for 100 epochs on Google Colab with an NVIDIA T4 Graphics Processing Unit (GPU). Model performance was evaluated using mean average precision at IoU 0.50 (mAP@50), mAP@50–95, precision, recall, F1-score, and confusion matrix analysis. The best-performing model (best.pt, 6 MB) was deployed via a Streamlit-based web application, enabling image upload and real-time camera detection. The YOLOv8n model achieved high detection performance, with overall mAP@50 = 0.99, mAP@50–95 = 0.64, precision = 0.98, and recall = 0.99 on the test set (n = 24 images, 27 instances). Per-class F1-scores exceeded 0.99 for most wound types, with highest performance observed for gunshot exit and hanging (1.00). Real-time application testing demonstrated smooth inference on CPU-only devices, enabling immediate visual feedback during simulation sessions. This study demonstrates the feasibility and educational value of integrating Artificial Intelligence-based wound detection and classification into simulation-based teaching. The deployed web application supports cross-platform access without installation requirements, making it a practical and scalable tool for medical education. Future work will focus on expanding the dataset, improving generalizability across wound severities, and conducting formal evaluations of student learning outcomes to quantify the pedagogical impact of this approach.

References

V.A. Cărcăle, “The future of artificial intelligence applications in forensics,” rais.education, 2025. [Online]. Available: https://rais.education/wp-content/ uploads/2025/05/0523.pdf

Prithwish and R. Pinki, “A lightweight end-to-end system for wound tissue analysis,” Sci. Direct, 2025. [Online]. Available: https://www.sciencedirect.com/ science/article/abs/pii/S1746809425002459

Hajare and M. Thalor, “AI-based crime scene simulation through 3D image processing and semantic segmentation,” IJSRET, vol. 11, no. 2, pp. 1576–1580, Mar.–Apr. 2025. [Online]. Available: https://ijsret.com/ wp-content/uploads/2025/03/IJSRET_V11_issue2_552.pdf

Jocher et al., “Explore Ultralytics YOLOv8,” 2025. [Online]. Available: https://docs.ultralytics.com/ models/yolov8/

Vazquez, J. A. Nuñez, X. Fu, P. Gu, and B. Fu, “Exploring transfer learning for deep learning polyp detection in colonoscopy images using YOLOv8,” arXiv preprint arXiv:2502.00133, Jan. 2023. [Online]. Available: https://arxiv.org/html/2502.00133v1

He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–778. [Online]. Available: https://arxiv. org/abs/1512.03385

Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2015, pp. 1–9. [Online]. Available: https:// arxiv.org/abs/1409.4842

Ma, X. Zhang, H.-T. Zheng, and J. Sun, “ShuffleNet V2: Practical guidelines for efficient CNN architecture design,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2018, pp. 116–131. [Online]. Available: https://arxiv.org/abs/1807.11164New

Albuquerque, D. M. de Oliveira, A. Oliveira, and G. Rodrigues, “Deep learning-based object detection algorithms in medical imaging: A review,” Heliyon, vol. 10, no. 9, e27379, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S240584402417168X

Smith, “Simulation-based education in the artificial intelligence era,” Cureus, vol. 15, 2023. [Online]. Available: https://www.cureus.com/articles/161951 -simulation-based-education-in-the-artificial-intelli gence-era

Topol, “Applications of artificial intelligence in medical education: a systematic review,” Cureus, 2025. [Online]. Available: https://www.cureus.com/ articles/334959-applications-of-artificial-intelli gence-in-medical-education-a-systematic-review

Hayden, B. Travis, and S. Benjamin K., “Ethics of artificial intelligence-assisted image interpretation in dermatology and radiology,” NCBI, 2025. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/ PMC11889981/

Vidit, M. Engilberge, and M. Salzmann, “A single domain generalization approach for object detection,” in Proc. CVPR, 2023. [Online]. Available: https://openaccess.thecvf.com/content/CVPR2023/papers/Vidit_CLIP_the_Gap_A_Single_Domain_Generali zation_Approach_for_Object_CVPR_2023_paper.pdf

D. A. Budiman et al., “Effectiveness of AI-driven assessments in enhancing learning outcomes,” Int. J. Inform. Educ. Technol., vol. 15, no. 7, 2025. [Online]. Available: https://www.ijiet.org/vol15/ IJIET-V15N7-2342.pdf

Lattas, C. Davis, C. Creamer, and G. Jefferies, “The use of simulation in forensic social work education: a scoping review,” J. Soc. Work Educ., 2024. [Online]. Available: https://journals.sagepub. com/doi/abs/10.1177/14999013241301097

Hu, Z. Li, J. Yu, X. Wan, H. Tan, and Z. Lin, “Efficient lightweight YOLO: improving small object detection for robustness under real-world conditions,” Cureus, 2023. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10385816/

Improving patient capacity for gynecological brachytherapy through an optimization model for Iridium-192 source decay: A case study at Udon Thani Cancer Hospital

Panlop Malasri — 2026-06-16

The radioactive decay of the Iridiu¬m-192 (Ir-192) source used in brachytherapy for gynecological cancer presents a significant scheduling challenge at Udon Thani Cancer Hospital. As the source decays over its 73.83-day half-life, treatment dwell times increase, creating a bottleneck that reduces the number of patients that can be treated daily. This research introduces an optimization model to counteract this effect by defining the optimal daily patient schedule. By analyzing the variables of source age and activity of source, the model prescribes a dynamic capacity: 9 patients per day for the initial 10 days post-replacement, reducing to 8 patients for days 11-82, and finally 7 patients from day 83 onward. The radioactivity readings revealed that when the source had levels of 9.1 and 4.59 curies (Ci), the encouragement capacity decreased by one patient. This enables Udon Thani Cancer Hospital to effectively manage resources and maximize service delivery throughout the entire lifecycle of the radiation source.

References

Bhatla N, Aoki D, Sharma DN, Sankaranarayanan R. Cancer of the cervix uteri. Int J Gynecol Obstet. 2018;143:22–36.

Oaknin A., T. J. Bosse, C. L. Creutzberg et al, Endometrial cancer, ESMO Guidelines Committee, Ann Oncol. 2022;33(9):860-877

Pötter R, Haie-Meder C, Van Limbergen E, et al. Recommendations from Gynaecological (GYN) GEC ESTRO Working Group (I): concepts and terms in 3D image-based treatment planning in cervix cancer brachytherapy-3D dose volume parameters and aspects of 3D image-based anatomy, radiation physics, radiobiology. Radiother Oncol. 2005;74(3): 251-60.

Nag S, Erickson B, Thomadsen B, Orton C, Demanes JD, Petereit D. The American Brachytherapy Society recommendations for high-dose-rate brachytherapy for carcinoma of the endometrium. Int J Radiat Oncol Biol Phys. 2000;48(3):779-90.

Libby B, Eifel P, Tanderup K, et al. Iridium-192. In: Thomadsen B, Rivard M, Butler W, eds. Brachytherapy Physics. 3rd ed. Madison, WI: Medical Physics Publishing; 2012: 245-268.

Rao CA, Singh Y, Kumar A, et al. Effect of Iridium-192 (Ir 192) HDR brachytherapy source decay during the treatment time in gynecological intracavitary brachytherapy treatment. J Chalmeda Anand Rao Inst Med Sci. 2019;18(2):1-7.

Lancellotta V, Kovács G, Tagliaferri L, et al. A cost-comparison analysis of adjuvant vaginal brachytherapy with electronic brachytherapy versus high-dose-rate 192Ir for endometrial cancer. J Contemp Brachytherapy. 2024;16(1):45-52.

Strohmaier S, Zwierzchowski G. Co-60 versus Ir-192 in HDR brachytherapy: Scientific and technological comparison. Rev Fis Med. 2012;13(2):125-30.

Green LV. Using operations research to reduce delays for healthcare. Tutorials n Operations Research. 2005;15:1-25.

ภาควิชาคณิตศาสตร์ คณะวิทยาศาสตร์ มหาวิทยาลัย มหิดล, "การวิจัยดำเนินงาน (Operations Research), "เข้าถึงได้จาก: https://mathematics.sc.mahidol.ac.th/th/ research-th/operations-research/.

Hillier FS, Lieberman GJ. Introduction to Operations Research. McGraw-Hill Education; 2015.

Gupta D, Denton B. Appointment scheduling in health care: Challenges and opportunities. IIE Transactions. 2008;40(9):800-19.

Microsoft Corporation. (2021). Microsoft Excel 2021.

PremiumSoft CyberTech Ltd. (2025). Navicat Premium [Computer software]. Retrieved from https:// navicat.com

Chen Z, King W, Pearcey R, Kerba M, Mackillop WJ. The relationship between waiting time for radiotherapy and clinical outcomes: a systematic review of the literature. Radiother Oncol. 2008;87 (1):3-16.

Fountzilas E, et al. Workflow analysis and optimization in brachytherapy: a motion and time study. Journal of Applied Clinical Medical Physics. 2019;20(9):105-112.

Sturdza A, Pötter R, Fokter Dovnik N, et al. Image guided brachytherapy in locally advanced cervical cancer: an update. Brachytherapy. 2016;15(5): 625-35.

Madan R, Pathy S, Subramani V, et al. Comparative evaluation of 2-dimensional radiography and 3-dimensional computed tomography based dosimetry of high-dose-rate intracavitary brachytherapy in cervical cancer. Asian Pac J Cancer Prev. 2014;15(11):4717-21.

Stefan S., Comparison of 60Co and 192Ir sources in HDR brachytherapy, J Contemp Brachytherapy. 2011 Dec 30;3(4):199–208.

Park DW, Kim YS, Park SH, et al. A comparison of dose distributions of HDR intracavitary brachy therapy using different sources and treatment planning systems. Appl Radiat Isot. 2009;67: 67:1426–1431. doi: 10.1016/j.apradiso.2009.02.066.

Simulation model using computerized physician order entry in radiology request order for paperless radiology information system in Nopparat Rajathanee Hospital

Pinya Chanjaruvong — 2026-06-16

The Radiology Department at Nopparat Rajathanee Hospital has successfully implemented electronic systems for many core functions of its Radiology Information System (RIS). However, the radiology request order system still uses paper forms, leading to issues such as data inaccuracy, incomplete clinical information, and prolonged Turnaround Times (TAT) for scheduling appointments. Analyzing the current process of 926 patients scheduling ultrasonography (USG) appointments at the department found that the average service time per person was 3.83 hours, significantly impacting service quality and patient satisfaction. Therefore, the concept of implementing an electronic radiology request order system using Computerized Physician Order Entry (CPOE) was proposed. This study aimed to create and evaluate a Simulation Model comparing the total average service time of the current paper-based system with a redesigned electronic system using CPOE. The results showed that the redesigned system with CPOE could significantly reduce the patient's total average service time by 35.5%, decreasing it from 128.68 minutes to 82.9934 minutes. Furthermore, a simulation experiment to increase the number of service units at the OPD Appointment counter from 3 to 5 units in the redesigned system showed a potential to reduce the total average service time by approximately 50% compared to the current paper-based system. Thus, the implementation of CPOE for radiology request orders can significantly improve service quality and reduce patient waiting times, serving as a crucial step towards achieving a sustainable Paperless Radiology Information System (RIS).

References

Georgiou, M. Prgomet, A. Markewycz, E. Adams, and J. I. Westbrook, “The impact of computerized provider order entry systems on medical-imaging services: a systematic review,” Journal of the American Medical Informatics Association, vol. 18, no. 3, pp. 335–340, Mar. 2011, doi: https://doi.org/10.1136/amiajnl-2010-000043

European Society of Radiology, “‘Role of Radiology in a Multidisciplinary Approach to Patient care’: Summary of the ESR International Forum 2022,” Insights into Imaging, vol. 14, no. 1, Feb. 2023, doi: https://doi.org/10.1186/s13244-023-01377-x.

SALAM, and S.-U.-D. SAIF, “RADIOLOGY REQUEST FORM”;, The Professional Medical Journal, vol. 20, no. 02, pp. 308–312, Feb. 2013, doi: https://doi.org/10.29309/tpmj/2013.20.02.635

Wolters Kluwer, “Nursing Documentation: How to Avoid the Most Common Medical Documentation Errors,” www.wolterskluwer.com, Feb. 23, 2018, Available: https://www.wolterskluwer.com/en/ expert-insights/nursing-documentation-how-to-avoid -the-most-common-medical-documentation-errors

McIntyre and C. K. Chow, “Waiting Time as an Indicator for Health Services under Strain: a Narrative Review,” INQUIRY: the Journal of Health Care Organization, Provision, and Financing, vol. 57, no. 1, Jan. 2020, doi: https://doi.org/10.1177/00469580 20910305

AHRA, “Evaluating Turnaround Time for Medical Imaging Studies: Why Defining Key Performance Indicators Matters,” LINK, Jul. 09, 2025, Available: https://link.ahra.org/2025/07/09/evaluating-turn around-time-for-medical-imaging-studies-why-defin ing-key-performance-indicators-matters/

Bleustein, D. B. Rothschild, A. Valen, E. Valatis, L. Schweitzer, and R. Jones, “Wait times, patient satisfaction scores, and the perception of care,” The American Journal of Managed Care, vol. 20, no. 5, pp. 393–400, May 2014, Available: https://pubmed.ncbi.nlm.nih.gov/25181568/

Dixon and A. Zafar, “Inpatient Computerized Provider Order Entry (CPOE) | AHRQ Digital Health care Research: Informing Improvement in Care Quality, Safety, and Efficiency,” digital.ahrq.gov, Jan. 2009, Available: https://digital.ahrq.gov/ahrq-fund ed-projects/emerging-lessons/computerized-provid er-order-entry-inpatient/inpatient-computerized-provid er-order-entry-cpoe

M. Steele and M. DeBrow, “Efficiency Gains with Computerized Provider Order Entry,” PubMed, 2008, Available: https://www.ncbi.nlm.nih.gov/books/NBK43766/

K. Alkasab, J. R. Alkasab, and H. H. Abujudeh, “Effects of a Computerized Provider Order Entry System on Clinical Histories Provided in Emergency Department Radiology Requisitions,” Journal of the American College of Radiology, vol. 6, no. 3, pp. 194–200, Mar. 2009, doi: https://doi.org/10.1016/j.jacr.2008.11.013

Troude et al., “Improvement of radiology requisition,” Diagnostic and Interventional Imaging, vol. 95, no. 1, pp. 69–75, Jan. 2014, doi: https://doi.org/10.1016/j.diii.2013.07.002

Vecellio and A. Georgiou, “Integrating the Radiology Information System with Computerised Provider Order Entry: The Impact on Repeat Medical Imaging Investigations,” Studies in health technology and informatics, vol. 227, pp. 126–31, 2016, Available: https://pubmed.ncbi.nlm.nih.gov/27440300/

G. Poon, D. Blumenthal, T. Jaggi, M. M. Honour, D. W. Bates, and R. Kaushal, “Overcoming the Barriers to the Implementing Computerized Physician Order Entry Systems in US Hospitals: Perspectives from Senior Management,” AMIA Annual Symposium Proceedings, vol. 2003, p. 975, 2024, Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC1480209/

Hunpradit, “ประสิทธิภาพของกระบวนการสั่งใช้ยาผ่าน ระบบคอมพิวเตอร์ (Computerized Physician Order Entry) ในระบบบริการเภสัชกรรมผู้ป่วยนอก โรงพยาบาล สมุทรสาคร,” Hua Hin Medical Journal, vol. 4, no. 2, pp. 30–41, 2024, Accessed: Sep. 26, 2025. [Online]. Available: https://he01.tci-thaijo.org/index. php/hhsk/article/view/268195

Tharakulphan, “Reducing the patient waiting time for radiological examination appointments at Khon Kaen Hospital using the radiological information program system and the process redesign concept”, J Thai Med Inform Assoc, vol. 10, no. 1, pp. 59–64, Jun. 2024.

Ng, G. Vail, S. Thomas, and N. Schmidt, “Applying the Lean principles of the Toyota Production System to reduce wait times in the emergency department,” CJEM, vol. 12, no. 01, pp. 50–57, Jan. 2010, doi: https://doi.org/10.1017/s1481803500012021

M. Breen, R. Trepp, and N. Gavin, “Lean Process Improvement in the Emergency Department,” Emergency Medicine Clinics of North America, vol. 38, no. 3, pp. 633–646, Aug. 2020, doi: https://doi.org/10.1016/j.emc.2020.05.001

Chanpanitkitchot, “Process redesign and simulation model for reducing waiting time at Gynecologic Outpatient Department Rajavithi Hospital”, J Thai Med Inform Assoc, vol. 10, no. 2, pp. 19–28, Nov. 2024.

Chanaprakhon, “Simulation model to reduce waiting times of hypertensive patients at Wang Nam Khiao Hospital”, J Thai Med Inform Assoc, vol. 10, no. 2, pp. 8–18, Nov. 2024

Development of a clinical decision support system for lower-limb prosthetic prescription

Chanapong Lertpanyawattanakul — 2026-06-16

Lower-limb prosthetic prescription is a complex process, and in Thailand, practice remains inconsistent because of the limited availability of ISPO Category I professionals and the absence of localized guidance. To address this gap, a clinical decision support system (CDSS) was developed to help standardize decision-making and improve the quality of amputee care. This study set out to evaluate whether the CDSS could enhance prescription accuracy, reduce decision time, and provide acceptable usability to its users. A prospective, simulation-based crossover trial was conducted, in which participants completed two standardized case vignettes—once with and once without CDSS support—in randomized order. Completion time was automatically recorded, and outcomes included component-level accuracy, decision time, and usability as measured by the System Usability Scale (SUS). Thirty-seven professionals participated, comprising 12 physiatrists, 14 ISPO Category I prosthetists/orthotists, and 11 ISPO Category III technicians, with a mean age of 39.5 years and nearly 60% having more than five years of professional experience. Overall, the use of CDSS led to improved accuracy, with median scores rising from 40% to 60%, and reduced decision time, which fell from 144 to 120 seconds, although these changes did not reach statistical significance. Subgroup analysis revealed that physiatrists showed greater gains in accuracy (40% to 70%) but required more time when using the system. By contrast, ISPO Category III technicians achieved stable accuracy while significantly reducing their decision time (133 to 99 seconds). ISPO Category I prosthetists/orthotists showed no meaningful differences across outcomes. The overall mean SUS score was 63.5, suggesting marginal usability, though less-experienced users rated the system more positively, with mean scores approaching 72. In this simulation, the CDSS did not yield statistically significant gains in accuracy or decision time. Nonetheless, with usability improvements, targeted training, and local adaptation, its decision-support value may increase. Adequately powered, real-world studies are needed to confirm clinical impact.

References

T. Sutton, D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, and K. I. Kroeker, “An overview of clinical decision support systems: benefits, risks, and strategies for success,” Npj Digit. Med., vol. 3, no. 1, p. 17, Feb. 2020, doi:10.1038/s41746-020-0221-y

Chen et al., “Harnessing the power of clinical decision support systems: challenges and opportunities,” Open Heart, vol. 10, no. 2, p. e002432, Nov. 2023, doi:10.1136/openhrt-2023-002432

H. Schwartz and A. G. Georgiadis, “Evidence Based Gait Analysis Interpretation Tools (EB-GAIT) treatment recommendation and outcome prediction models to support decision-making based on clinical gait analysis data,” PLOS ONE, vol. 20, no. 7, p. e0328036, July 2025, doi:10.1371/journal.pone.0328036

F. Sandal et al., “A digital decision support system (selfBACK) for improved self-management of low back pain: a pilot study with 6-week follow-up,” Pilot Feasibility Stud., vol. 6, no. 1, p. 72, May 2020, doi:10.1186/s40814-020-00604-2

Goud et al., “Effect of guideline based computerised decision support on decision making of multidisciplinary teams: cluster randomised trial in cardiac rehabilitation,” BMJ, vol. 338, p. b1440, Apr. 2009, doi:10.1136/bmj.b1440

A. Nikolaev and A. A. Nikolaev, “Perspectives of Decision Support System TeleRehab in the Management of Post-Stroke Telerehabilitation,” Life (Basel), vol. 14, no. 9, p. 1059, Aug. 2024, doi:10.3390/life14091059

Donaghy, S. Morgan, G. Kaufman, and D. Morgenroth, “Team Approach to Prosthetic Prescription Decision-Making,” Curr. Phys. Med. Rehabil. Rep., vol. 8, Dec. 2020, doi:10.1007/s40141-020-00289-x

Resnik and M. Borgia, “Predicting prosthetic prescription after major lower-limb amputation,” J. Rehabil. Res. Dev., vol. 52, no. 6, pp. 641–652, 2015, doi:10.1682/JRRD.2014.09.0216

P. T. Nation, “New limbs that save lives,” nationthailand. Accessed: Sept. 22, 2025. [Online]. Available: https://www.nationthailand.com/life/30348584

Marino et al., “Access to prosthetic devices in developing countries: Pathways and challenges,” in 2015 IEEE Global Humanitarian Technology Conference (GHTC), Oct. 2015, pp. 45–51. doi:10.1109/GHTC.2015.7343953

International Society for Prosthetics and Orthotics (ISPO), ISPO Education Standards for Prosthetic/ Orthotic Occupations. Brussels, Belgium: International Society for Prosthetics and Orthotics, 2018. [Online]. Available: https://www.ispoint.org

Fard et al., “Amputation and prosthetics of the lower extremity: The 2020 Dutch evidence-based multidisciplinary guideline,” Prosthet. Orthot. Int., vol. 47, no. 1, pp. 69–80, Feb. 2023, doi:10.1097/ PXR.0000000000000170

I. K. MD, M. S. P. MD, B. K. P. MD, and P. M. S. CPO Med, Atlas of Amputations & Limb Deficiencies, 4th edition. Lippincott Williams & Wilkins, 2018

M. Posada-Borrero, D. F. Patiño-Lugo, J. A. Plata-Contreras, J. C. Velasquez-Correa, and L. H. Lugo-Agudelo, “Development of a Clinical Practice Guideline for Lower Limb Amputees. A Knowledge Translation Process in a Middle Income Country,” Front. Rehabil. Sci., vol. 3, p. 873436, 2022, doi:10.3389/fresc.2022.873436

Geertzen et al., “Dutch evidence-based guidelines for amputation and prosthetics of the lower extremity: Rehabilitation process and prosthetics. Part 2,” Prosthet. Orthot. Int., vol. 39, no. 5, pp. 361–371, Oct. 2015, doi:10.1177/0309364614542725

Kılınç Kamacı and K. Aydemir, “Lower limb prosthetic prescription,” Turk. J. Phys. Med. Rehabil., vol. 69, no. 4, pp. 391–399, May 2023, doi:10.5606/tftrd.2023.12988

K. Chui, M. Jorge, S.-C. Yen, and M. M. Lusardi, Orthotics and prosthetics in rehabilitation, Fourth edition. St. Louis, Missouri: Elsevier, 2020.

Hofstad, H. Linde, J. Limbeek, and K. Postema, “Prescription of prosthetic ankle-foot mechanisms after lower limb amputation,” Cochrane Database Syst. Rev., vol. 2004, no. 1, p. CD003978, 2004, doi:10.1002/14651858.CD003978.pub2

M. Stevens, J. Rheinstein, and S. R. Wurdeman, “Prosthetic Foot Selection for Individuals with Lower-Limb Amputation: A Clinical Practice Guideline,” J. Prosthet. Orthot. JPO, vol. 30, no. 4, pp. 175–180, Oct. 2018, doi:10.1097/JPO.000000000 0000181

M. Stevens and S. R. Wurdeman, “Prosthetic Knee Selection for Individuals with Unilateral Transfemoral Amputation: A Clinical Practice Guideline,” J. Prosthet. Orthot. JPO, vol. 31, no. 1, pp. 2–8, Jan. 2019, doi:10.1097/JPO.0000000 000000214

O’Brien, P. M. Stevens, R. Miro, and M. J. Highsmith, “Transfemoral interface considerations: A clinical consensus practice guideline,” Prosthet. Orthot. Int., vol. 47, no. 1, pp. 54–59, Feb. 2023, doi:10.1097/PXR.0000000000000182

Gholizadeh, N. A. Abu Osman, A. Eshraghi, and S. Ali, “Transfemoral prosthesis suspension systems: a systematic review of the literature,” Am. J. Phys. Med. Rehabil., vol. 93, no. 9, pp. 809–823, Sept. 2014, doi:10.1097/PHM.0000000000000094

Brodie, L. Murray, and A. McGarry, “Transfemoral Prosthetic Socket Designs: A Review of the Literature,” JPO J. Prosthet. Orthot., vol. 34, no. 2, p. e73, Apr. 2022, doi:10.1097/JPO.0000000000000395

Stevens, R. DePalma, and S. Wurdeman, “Transtibial Socket Design, Interface, and Suspension: A Clinical Practice Guideline,” J. Prosthet. Orthot., vol. 31, p. 1, Nov. 2018, doi:10.1097/JPO.0000000000000219

Bangor, Aaron, P. Kortum, P. T., Miller, and J. T., “The System Usability Scale (SUS): an Empirical evaluation,” Int. J. Hum.-Comput. Interact., vol. 24, p. 574, Aug. 2008, doi:10.1080/10447310802205776

Nittayasupaporn, F. Tohsen, T. Poungpum, and L. Apipunyasopon, “Development and assessment of CUFastTech mobile application for plain radiograph,” Thai J. Radiol. Technol., vol. 45, no. 1, pp. 1–7, 2020, Accessed: Sept. 21, 2025. [Online]. Available :https://he02.tcithaijo.org/index.php/tjrt/article/view/246635

Donaghy, S. Morgan, G. Kaufman, and D. Morgenroth, “Team Approach to Prosthetic Prescription Decision-Making,” Curr. Phys. Med. Rehabil. Rep., vol. 8, Dec. 2020, doi: 10.1007/s40141-020-00289-x

Jansen-Kosterink, L. van Velsen, and M. Cabrita, “Clinician acceptance of complex clinical decision support systems for treatment allocation of patients with chronic low back pain,” BMC Med. Inform. Decis. Mak., vol. 21, no. 1, p. 137, Apr. 2021, doi: 10.1186/s12911-021-01502-0

Vasey et al., “Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems: A Systematic Review,” JAMA Netw. Open, vol. 4, no. 3, p. e211276, Mar. 2021, doi:10.1001/jamanetworkopen.2021.1276

Automated information extraction from echocardiography reports for the development of a congenital heart disease database

Paradorn Chan-On — 2026-06-16

Congenital Heart Disease (CHD) is a common condition in children in Thailand. Clinical data are often kept in different systems without standardization, and manual registry creation requires much time and staff effort. In hospitals with heavy workloads, this becomes a challenge. The aim of this study was to develop and evaluate an automated system to create a CHD registry from echocardiography reports. An automated data extraction workflow was developed using N8N as the primary integration platform. The system processes image-based echocardiography reports, beginning with Optical Character Recognition (OCR) to convert images to text. Subsequently, an agentic AI, powered by the ChatGPT-4o model, extracts key clinical information, transforming unstructured text into structured JSON data according to a specifically designed instruction set. This structured data is then automatically populated into a Google Sheet, which functions as the patient registry database. Accuracy was evaluated by comparing the system's output against data manually verified by a pediatric cardiologist. Efficiency was assessed by comparing the automated processing time with manual data entry time. A total of 301 echocardiography reports were processed. The automated system identified the main diagnosis with 86.7% accuracy and additional diagnoses with 88.0% accuracy. The average processing time was 12.3 seconds per report, compared with 84 seconds for manual entry, saving 87.7 seconds per report (p<0.001). The system exhibited a major error rate (process failure) of only 0.997% and a minor error rate (data extraction with issues) of 9.9%. The developed system proves to be an accurate, efficient, and reliable method for automatically creating a specialized CHD patient registry from existing clinical reports. This approach presents a viable solution to overcome resource limitations and enhance clinical data management within the context of the Thai public health system.

References

I. E. Hoffman and S. Kaplan, “The incidence of congenital heart disease,” J Am Coll Cardiol, vol. 39, no. 12, pp. 1890-1900, Jun. 2002.

ควรหาเวช ณ.,”โรคหัวใจแต่ก􀂷ำเนิดในทารกแรกเกิดและ เด็กเล็กที่พบบ่อยในทางเวชปฏิบัติ”,Thai J Pediatr, ปี 64, ฉบับที่ 2, น. 1-14, มิ.ย. 2025.

Nicholson, G. Strange, J. Ayer, M. Cheung, L. Grigg, R. Justo, et al., “A national Australian congenital heart disease registry; methods and initial results,” International Journal of Cardiology Congenital Heart Disease, vol. 17, p. 100538, Sep. 2024.

Watelle, L. O. Roy, J. Lauzon-Schnitka, G. Newell, A. Dumas, A. Nadeau, et al., “The Quebec congenital heart disease registry: A model of prospective databank to facilitate research in congenital cardiology,” CJC Pediatric and Congenital Heart Disease, vol. 3, no. 2, pp. 57-66, Apr. 2024.

M. Silva, I. M. Kuipers, F. Van Den Heuvel, R. Mendes, R. M. F. Berger, I. M. Van Beynum, et al., “KinCor, a national registry for paediatric patients with congenital and other types of heart disease in the Netherlands: Aims, design and interim results,” Neth Heart J, vol. 24, no. 11, pp. 628-639, Nov. 2016.

CipherNutz, “N8N Healthcare Automation: How It Works to Improve Workflows,” CipherNutz, 7 Aug. 2025. [Online]. Available: https://ciphernutz.com/ blog/n8n-healthcare-automation. [Accessed: 28-Sep-2025

Szekér, G. Fogarassy, and Á. Vathy-Fogarassy, “A general text mining method to extract echocardiography measurement results from echocardiography documents,” Artificial Intelligence in Medicine, vol. 143, p. 102584, Sept. 2023, doi: 10.1016/j.artmed.2023.102584.

Dong, N. Sunderland, A. Nightingale, D. P. Fudulu, J. Chan, B. Zhai, A. Freitas, M. Caputo, A. Dimagli, S. Mires, M. Wyatt, U. Benedetto, and G. D. Angelini, “Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database,” Bioengineering (Basel), vol. 10, no. 11, p. 1307, Nov. 2023, doi: 10.3390/bioengineering10111307.z

Sun, Z. Cai, Y. Li, F. Liu, S. Fang, and G. Wang, “Data Processing and Text Mining Technologies on Electronic Medical Records: A Review,” J Healthc Eng, vol. 2018, p. 4302425, 2018, doi: 10.1155 /2018/4302425.

L. Barra et al., “From prompt to platform: an agentic AI workflow for healthcare simulation scenario design,” Adv Simul, vol. 10, no. 1, p. 29, May 2025, doi: 10.1186/s41077-025-00357-z.

C.-J. Chao et al., “Evaluating large language models in echocardiography reporting: opportunities and challenges,” Eur Heart J Digit Health, vol. 6, no. 3, pp. 326–339, May 2025, doi: 10.1093/ ehjdh/ztae086

Fernandez et al., “Interoperability in universal healthcare systems: insights from Brazil’s experience integrating primary and hospital health care data,” Front. Digit. Health, vol. 7, Aug. 2025, doi: 10.3389/ fdgth.2025.1622302

Batra, N. Phalnikar, D. Kurmi, J. Tembhurne, P. Sahare, and T. Diwan, “OCR-MRD: performance analysis of different optical character recognition engines for medical report digitization,” Int. j. inf. tecnol., vol. 16, no. 1, pp. 447–455, Jan. 2024, doi: 10.1007/s41870-023-01610-2

Gifu, “AI-backed OCR in Healthcare,” Procedia Computer Science, vol. 207, pp. 1134–1143, Jan. 2022, doi: 10.1016/j.procs.2022.09.169.

“JSON-Based Patient Data Architecture: A Novel Approach to Healthcare Information Storage in Salesforce CRM | Request PDF,” ResearchGate. Accessed: Sept. 28, 2025. [Online]. Available : https://www.researchgate.net/publication/389204038_JSONBased_Patient_Data_Architecture_A_Novel_Approach_to_Healthcare_Information_Storage_in_Salesforce_CRM

Patil, T. F. Heston, and V. Bhuse, “Prompt Engineering in Healthcare,” Electronics, vol. 13, no. 15, p. 2961, Jan. 2024, doi: 10.3390/electronics13152961

K. Garg, V. L. Urs, A. A. Agarwal, S. K. Chaudhary, V. Paliwal, and S. K. Kar, “Exploring the role of ChatGPT in patient care (diagnosis and treatment) and medical research: A systematic review,” Health Promot Perspect, vol. 13, no. 3, pp. 183–191, Sept. 2023, doi: 10.34172/hpp.2023.22

Zayas-Cabán, S. N. Haque, and N. Kemper, “Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries,” Applied Clinical Informatics, vol. 12, pp. 686–697, July 2021, doi: 10.1055/s-0041-1731744

K. Baurasien et al., “Medical Errors and Patient Safety: Strategies for Reducing Errors Using Artificial Intelligence,” IJHS, vol. 7, no. S1, pp. 3471–3487, 2023, doi: 10.53730/ijhs.v7nS1.15143

“The Significance of Data Governance in Healthcare - A Case Study in a Tertiary Care Hospital,” Proceedings of the International Conference on Health Informatics, 2014, doi: 10.5220/0004738101780187

Javaid, A. Haleem, and R. P. Singh, “ChatGPT for healthcare services: An emerging stage for an innovative perspective,” 2023.

Predicting anatomic pathology turnaround time using machine learning models

Waanpa Kinnares — 2026-06-16

Turnaround Time (TAT) is a critical quality indicator in anatomic pathology laboratories, yet its management remains a challenge, particularly in resource-limited public hospitals in Thailand. While recent work shows the potential of machine learning (ML) for operational intelligence, studies focusing on TAT prediction in this specific context are scarce. This study aimed to develop and evaluate ML models for predicting the TAT of anatomic pathology specimens at Sakon Nakhon Hospital and to identify the key factors influencing TAT. A retrospective dataset of 37,515 cleaned pathology records was utilized. Feature engineering was performed to create variables reflecting workload (e.g., daily case count) and temporality (e.g., week of the year). Several models, including Random Forest and LightGBM, were developed and compared, with Mean Absolute Error (MAE) as the primary performance metric. The optimized Random Forest model demonstrated the highest performance, achieving an MAE of 1.85 days and an R-squared (R²) of 0.91 on the test set. Feature importance analysis revealed that the assigned pathologist, week of the year, and daily case count were the most significant predictors, highlighting the impact of personnel, temporal, and workload factors. Machine learning models, particularly Random Forest, show high potential for accurately predicting TAT. The findings can be leveraged to develop a decision support tool for proactive laboratory management, aiming to enhance operational efficiency and improve the quality of patient care in resource-constrained healthcare environments.

References

G. Hanna et al., “Recommendations for performance evaluation of machine learning in pathology: A concept paper from the college of american pathologists,” Arch Pathol Lab Med, vol. 148, no. 10, pp. e335–e361, Oct. 2024.

Frewing, A. B. Gibson, R. Robertson, P. M. Urie, and D. Della Corte, “A systematic review of machine learning for prostate cancer detection in pathology,” Arch Pathol Lab Med, vol. 148, no. 5, pp. 603–612, May 2024.

Breil, F. Fritz, V. Thiemann, and M. Dugas, “Mapping turnaround times (TAT) to a generic timeline: A systematic review of TAT definitions in clinical domains,” BMC Med Inform Decis Mak, vol. 11, no. 1, p. 34, May 2011.

E. Volmar, M. O. Idowu, R. J. Souers, D. S. Karcher, and R. E. Nakhleh, “Turnaround time for large or complex specimens in surgical pathology: A college of american pathologists Q-probes study of 56 institutions,” Arch Pathol Lab Med, vol. 139, no. 2, pp. 171–177, Feb. 2015.

S. B. D. D. C. Martins, R. A. C. Leite, and G. C. D. S. Lamb, “Anxiety and depression in the preoperative period of cardiac surgery,” Revista Brasileira de Cirurgia Cardiovascular, vol. 31, pp. 243–251, Jun. 2016.

O. S. Júnior, R. de C. S. A. M. de Deus, and P. S. F. de Sousa, “A systematic review of the literature on the use of lean healthcare in the radiology sector,” Journal of Health Management, vol. 26, no. 2, pp. 297–313, Apr. 2024.

พ. วรรณไกรโรจน์, “ภาพรวมของมาตรฐานห้องปฏิบัติ การพยาธิวิทยากายวิภาค,” ราชวิทยาลัยพยาธิแพทย์แห่ง ประเทศไทย, 2560.

S. Ahluwalia et al., “The need for computational pathology in resource-limited settings,” JCO Global Oncology, no. 8, p. e2200044, Aug. 2022.

Acs, “Telepathology for resource-limited settings,” J. Am. Soc. Cytopathol., vol. 11, no. 5, pp. 291–304, Sep. 2022.

S. McClintock, “Informatics and digital pathology,” Pathobiology, vol. 83, no. 2–3, pp. 73–82, 2016.

E. D’Alfonso and A. A. T. M. Reinders, “Artificial intelligence for digital pathology: a review of the literature and overview of a single-institution experience,” Seminars in Diagnostic Pathology, vol. 40, no. 7, pp. 524–532, Nov. 2023.

H. Harrison et al., “Introduction to artificial intelligence and machine learning for pathology,” Arch Pathol Lab Med, vol. 145, no. 10, pp. 1228–1254, Oct. 2021.

Z. Alom et al., “A state-of-the-art survey on deep learning theory and architectures,” Electronics, vol. 8, no. 3, p. 292, Mar. 2019.

van der Byl et al., “Artificial intelligence in diagnostic pathology: a review of the literature and overview of a single-institution experience,” Seminars in Diagnostic Pathology, vol. 40, no. 7, pp. 524–532, Nov. 2023.

B. Nassif, O. Al-Dahidi, S. Al-Ali, and M. A. Talib, “The battle of machine learning and deep learning in healthcare: A survey,” Journal of Big Data, vol. 11, no. 1, p. 95, Dec. 2024.

M. Abdul Latiff et al., “Voice pathology detection using machine learning algorithms based on different voice databases,” Results in Engineering, vol. 25, p. 103937, Mar. 2025.

M. T. Gebru, J. A. M. da Silva, A. M. da S. A. Filho, J. M. da S. A. Junior, and S. R. L. da Silva, “Turnaround time analysis using data mining from a laboratory information system,” The Journal of Health Care Organization, Provision, and Financing, vol. 62, Jan. 2025.

Karaca, “A comprehensive review of the evolution of deep learning: a recent survey,” Multimedia Tools and Applications, vol. 83, no. 17, pp. 50921–50974, Jun. 2024.

Reddy et al., “Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models,” Comput Methods Programs Biomed Updat, vol. 6, p. 100160, 2024.

van der Byl, D. van der Byl, P. Cooper, A. van der Merwe, J. Schneider, and J. M. Warwick, “The operationalisation of artificial intelligence in anatomical pathology,” J Clin Pathol, vol. 76, no. 4, pp. 225–231, Apr. 2023

Analysis of factors associated with inpatient reimbursement outcomes under the universal coverage scheme in Thailand

Veeradate Chalermpolprapa — 2026-06-16

Hospitals under Thailand’s Universal Coverage Scheme (UC) face financial challenges due to discrepancies between Diagnosis-Related Group (DRG) reimbursements and actual treatment costs. This study aimed to identify factors associated with hospital financial outcomes and evaluate the performance of Machine Learning (ML) models in predicting profit or loss for Nakhon Pathom Hospital. A retrospective analysis of 67,115 inpatient records (fiscal years 2023–2024) compared Random Forest and Extreme Gradient Boosting (XGBoost) algorithms. The Random Forest model demonstrated the best performance (Accuracy 84.5%, Receiver Operating Characteristic Area Under Curve (ROC AUC) 0.788). Length of Stay (LOS) was the most influential factor, followed by drug and non-drug costs. The model demonstrated strong capability in identifying loss cases. (True Negative Rate 95.8%). These findings indicate that Machine Learning, particularly the Random Forest algorithm, can serve as an effective decision-support tool for hospital financial management, enabling proactive risk mitigation and improved resource utilization within Thailand’s national reimbursement system.

References

Khiaocharoen et al., "Cost by Diagnosis Related Group: Outputs from the First Phase Cost per Disease Project," HISPA Compendium, vol. 1, no. 8, pp. 113–129, 2023. doi: 10.14456/hispa.2023.8

งานประกันสุขภาพ โรงพยาบาลโชคชัย, “ศูนย์จัดเก็บราย ได้คุณภาพ,” โรงพยาบาลโชคชัย, นครราชสีมา, ประเทศไทย, 2565. [ออนไลน์]. เข้าถึงได้จาก: https://www.uckkpho.com/wpcontent/uploads/2022/05/07.ศูนย์จัดเก็บรายได้ มีคุณภาพ.pdf [เข้าถึงเมื่อ: 28 ก.ย. 2568].

Kulkarni, S. S. Ambekar, and M. Hudnurkar, "Predicting the inpatient hospital cost using a machine learning approach," International Journal of Innovation Science, vol. 13, no. 1, pp. 87–104, 2021, doi: 10.1108/IIS-09-2020-0175

Thongpeth, A. Lim, A. Wongpairin, T. Thongpeth, and S. Chaimontree, "Comparison of linear, penalized linear and machine learning models predicting hospital visit costs from chronic disease in Thailand," Informatics in Medicine Unlocked, vol. 26, Art. no. 100769, 2021, doi: 10.1016/j.imu.2021.100769

Siwapraprapakorn, "Data analytic and predictive model for analysis of causes for a loss from diagnosis related groups (DRGs) payment of pediatric inpatient at Sunpasitthiprasong hospital," Journal of the Thai Medical Informatics Association, vol. 1, pp. 12–20, 2021.

Langenberger, T. Schulte, and O. Groene, "The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data," PLoS ONE, vol. 18, no. 1, p. e0279540, Jan. 2023, doi: 10.1371/journal.pone.0279540

Stone, R. Zwiggelaar, P. Jones, and N. Mac Parthaláin, "A systematic review of the prediction of hospital length of stay: Towards a unified framework," PLOS Digital Health, vol. 1, no. 4, p. e0000017, Apr. 2022, doi: 10.1371/journal.pdig.0000017

Jain, M. Singh, A. R. Rao, and R. Garg, "Predicting hospital length of stay using machine learning on a large open health dataset," BMC Health Services Research, vol. 24, Art. no. 860, 2024, doi: 10.1186/s12913-024-11238-y

Deniz, A. Şengül, Y. Aydemir, J. Ç. Emre, and M. H. Özhan, "Clinical factors and comorbidities affecting the cost of hospital-treated COPD," International Journal of COPD, vol. 11, pp. 3023–3030, Dec. 2016, doi: 10.2147/COPD.S121637

Haidar, R. Vazquez, and G. Medic, "Impact of surgical complications on hospital costs and revenues: retrospective database study of Medicare claims," Journal of Comparative Effectiveness Research, Art. no. e230080, Jun. 2023, doi: 10.57264/ cer-2023-0080

M. Sowjanya and O. Mrudula, "Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms," Applied Nanoscience, vol. 13, pp. 1829–1840, 2023, doi: 10.1007/s13204-021-02063-4

Computer health index: A framework for Udonthani Hospital

Poompat Wattanavinit — 2026-06-16

The management and maintenance of computer systems in hospitals are critical factors affecting the continuity of hospital services. This study proposes a conceptual framework called the “Computer Health Index” (CHI) to support planning and forecasting for computer management within hospital information systems. The framework is based on machine learning and statistical data analysis of baseline data and computer repair records, including Principal Component Analysis (PCA), Regression, Logistic Regression, and Clustering, to construct an integrated index. The results show that CHI can effectively reflect the health and computing environment of hospitals. Statistical findings highlight the significant importance of prioritization in system usage. This study supports data collection by IT staff, reporting and data recording by users, and policy decision-making by administrators. It can be applied to planning, procurement, replacement, maintenance, and budgeting for computer systems in the future.

References

OECD, Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD Publishing, 2008.

Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483–1510, 2006.

จิราภรณ์ รัตนศรี, “การประยุกต์ใช้ระบบสารสนเทศทางการ แพทย์ในโรงพยาบาลระดับจังหวัด,” วารสารวิจัยระบบ สาธารณสุข, ปีที่ 15, ฉบับที่ 2, หน้า 101–112, 2565.

สมชาย บุญมี, “การจัดการความเสี่ยงระบบไอทีในโรงพยาบาล ชุมชน,” การประชุมวิชาการสารสนเทศสุขภาพแห่งชาติ, 2564.

World Health Organization, World Health Statistics 2023. Geneva: WHO, 2023.

T. Jolliffe and J. Cadima, “Principal component analysis: a review and recent developments,” Philosophical Transactions of the Royal Society A, vol. 374, no. 2065, 2016.

Kushniruk and V. Borycki, “Health information systems: challenges to evidence-based evaluation,” Healthcare Quarterly, vol. 9, no. 4, pp. 48–52, 2006.

De Marco, “IT asset management: a structured approach,” International Journal of IT/Business Alignment and Governance, vol. 1, no. 1, pp. 1–17, 2010.

C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis, 6th ed., Wiley, 2021.

MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1967.

Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881–892, 2002.

McCullagh, “Regression models for ordinal data,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 42, no. 2, pp. 109–142, 1980.

J. Horton, “Proportional odds models for ordinal response variables,” Encyclopedia of Biostatistics, Wiley, 2005.

Development of a machine learning model to predict acute kidney injury in patients receiving colistin : A case study at Kamphaeng Phet Hospital

Thirawut Morasuk — 2026-06-16

Colistin is an essential last-resort antibiotic for multidrug-resistant Gram-negative bacterial infections, but its use is constrained by a high risk of nephrotoxicity. Early identification of patients at risk of colistin-associated acute kidney injury (AKI) is crucial to improve outcomes and optimize clinical management. This study aimed to develop and evaluate machine learning (ML) models for predicting AKI in patients receiving intravenous colistin. A retrospective analytical study was conducted using electronic medical records from Kamphaeng Phet Hospital, Thailand, between 2013 and 2025. Adult patients who received intravenous colistin were included. AKI was defined according to KDIGO criteria. Thirty-two baseline variables were extracted. Three ML algorithms—Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB)—were trained and tested using an 80/20 split, with Synthetic Minority Oversampling Technique (SMOTE) applied to address class imbalance. Model performance was assessed by recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Of 3,176 patients, 1,132 (35.6%) developed AKI. Patients with AKI were older (70.4 vs. 62.2 years, p<0.001), had lower baseline eGFR, and more frequent exposure to concomitant nephrotoxic drugs. The LR model with SMOTE achieved the best predictive performance (recall 0.727, F1-score 0.657, AUC-ROC 0.802). The most influential predictors were concomitant use of ≥ 2 nephrotoxic drugs, chronic liver disease, loop diuretic use, prior AKI, and carbapenem exposure. The LR model with SMOTE demonstrated robust performance in predicting colistin -associated AKI. This model may serve as a practical screening tool to identify high-risk patients, guide nephroprotective strategies, and support clinical decision-making in antimicrobial stewardship.

References

ศูนย์เฝ้าระวังเชื้อดื้อยาต้านจุลชีพแห่งชาติ สถาบันวิจัย วิทยาศาสตร์สาธารณสุข กรมวิทยาศาสตร์การแพทย์ กระทรวงสาธารณสุข, รายงานประจำปี NARST ศูนย์เฝ้า ระวังเชื้อดื้อยาต้านจุลชีพแห่งชาติ ประจำปีงบประมาณ 2567. [Online]. Available: https://narst.dmsc.moph.go.th/static/NARST%20Annual%20Report%202567.pdf

Jirasakpisarn, "Prevalence of Antibiotic-Resistant Gram-Negative Bacteria and Susceptibility Patterns in Provincial Hospital, Thailand: A Decade Review," J Med Assoc Thai, vol. 108, no. 3, pp. 232-40, 2025, doi: 10.35755/jmedassocthai.2025.3.232-240-02250

World Health Organization, Health and Economic Impacts of Antimicrobial Resistance in the Western Pacific Region, 2020–2030, WHO Regional Office for the Western Pacific, 13 June 2023, ISBN: 978- 9290620112. [Online]. Available: https://www.who.int/southeastasia/publications/i/item/9789290620112

Macesic, A. C. Uhlemann, and A. Y. Peleg, "Multidrug-resistant Gram-negative bacterial infections," (in eng), Lancet, vol. 405, no. 10474, pp. 257-272, Jan 18 2025, doi: 10.1016/s0140-6736(24)02081-6

Li, E. Ebrahimi, M. Sholeh, R. Dousti, and E. Kouhsari, "A systematic review and meta-analysis: rising prevalence of colistin resistance in ICU-acquired Gram-negative bacteria," APMIS, vol. 133, no. 1, p. e13508, Jan 2025, doi: 10.1111/apm.13508

Wongsampan, A. Jittsue, N. Santiyanon, S. Yeepu, K. Ariyawithaya, and N. Kitjakarn, "Efficacy and Safety of the Treatment of Acinetobacter baumannii Infection with Colistin Injection," TJPP, vol. 10, no. 2, pp. 375-81, 2019.

Namwong, "Evaluation of Predictive Scoring System to Forecast the Acute Kidney Injury Associated with Intravenous Colistin in Neurosurgical Patients," Isan J Pharm Sci, vol. 21, no. 1, pp. 31-50, 2025, doi: 10.14456/ijps.2025.3

Seanglaw and T. Morasert, "Development of a Prediction Model for Acute Kidney Injury after Colistin Treatment for Multidrug-resistant Acinetobacter baumanii Ventilator-Associated Pneumonia: A Pilot Study," Journal of Health Science and Medical Research, vol. 41, no. 1, pp. 1-12, 04/19 2023, doi: 10.31584/jhsmr.2022891

Moghnieh et al., "The Prevalence and Risk Factors of Acute Kidney Injury during Colistin Therapy: A Retrospective Cohort Study from Lebanon," Antibiotics (Basel), vol. 12, no. 7, Jul 13 2023, doi: 10.3390/antibiotics12071183

Arrayasillapatorn, P. Promsen, K. Kritmetapak, S. Anunnatsiri, W. Chotmongkol, and S. Anutrakulchai, "Colistin-Induced Acute Kidney Injury and the Effect on Survival in Patients with Multidrug-Resistant Gram-Negative Infections: Significance of Drug Doses Adjusted to Ideal Body Weight," Int J Nephrol, vol. 2021, p. 7795096, 2021, doi: 10.1155/2021 /7795096.

B. Bufkin, Z. A. Karim, and J. Silva, "Review of the limitations of current biomarkers in acute kidney injury clinical practices," (in eng), SAGE Open Med, vol. 12, p. 20503121241228446, 2024, doi: 10.1177/20503121241228446

W. Chiu et al., "Machine learning algorithms to predict colistin-induced nephrotoxicity from electronic health records in patients with multidrug-resistant Gram-negative infection," Int J Antimicrob Agents, vol. 64, no. 1, p. 107175, Jul 2024, doi: 10.1016/jijantimicag.2024.107175

Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group, “KDIGO Clinical Practice Guideline for Acute Kidney Injury,” Kidney Int. Suppl., vol. 2, no. 1, pp. 1–138, 2012. [Online]. Available: https://kdigo.org/wp-content/uploads/2016/10/KDIGO-2012-AKI-Guideline-English.pdf

K. Shields et al., "Defining the incidence and risk factors of colistin-induced acute kidney injury by KDIGO criteria," PLoS One, vol. 12, no. 3, p. e0173286, 2017, doi: 10.1371/journal.pone.0173286

Deng et al., "Does Monitoring Total and Free Polymyxin B1 Plasma Concentrations Predict Polymyxin B-Induced Nephrotoxicity? A Retrospective Study in Critically Ill Patients," (in eng), Infect Dis Ther, vol. 11, no. 4, pp. 1591-1608, Aug 2022, doi: 10.1007/s40121-022-00655-3

Analysis of patient databases to identify principal diagnosis, comorbidities, and complications influencing inpatient costs beyond DRGs coverage

Somjing Patarowas — 2026-06-16

Ban Bueng Hospital is a 120-bed community hospital (actual service 170 beds) where most patients are covered by the Universal Coverage Scheme (UCS). Reimbursement from the UCS is therefore crucial for hospital sustainability. This study analyzed inpatient data from October 2023 to September 2024, including 8,350 admissions. Of these, 5,225 cases incurred actual treatment costs exceeding UCS reimbursement, with a total deficit of 39,706,507 THB. Data were extracted from the EMR system (43 files: PERSON, ADMISSION, DIAGNOSIS_IPD, CHARGE_IPD) and reimbursement Excel files, then analyzed using QlikView and Excel. Pareto analysis identified three major disease groups with costs exceeding DRGs: N39 (Other disorders of urinary system), J18 (Pneumonia, organism unspecified), and I63 (Cerebral infarction). The most common comorbidities and complications across these groups were E87 (Fluid, electrolyte, and acid-base disorders), I10 (Essential hypertension), and E11 (Type 2 diabetes mellitus). The findings highlight key disease groups with significant cost–reimbursement gaps and provide essential information for prioritizing clinical pathways and multidisciplinary care strategies to enhance hospital efficiency and effectiveness.

References

อ. วรรณศรี และ ส. ศรีธำรงสวัสดิ์, "การจ่ายค่าบริการตาม ระบบกลุ่มวินิจฉัยโรคร่วมของประเทศไทยและต่างประเทศ," Journal of Health Systems Research, vol. 8, no. 1,pp. 1-10, Jan.-Mar. 2014.

ส. ภักดีพันธ์, ภ. อนันตโชติ, ธ. เพ็งสุภาพ, และ ส. ตระ กูลกาญจน์, "สถานะทางการเงินและคุณภาพ การบริการ ของโรงพยาบาลภายใต้กลไกการจ่ายเงินแบบกลุ่ม วินิจฉัย โรคร่วม," วารสารไทยเภสัชศาสตร์และวิทยาการสุขภาพ, vol. 9, no. 4, pp. 213-221, 2014.

สำนักงานสารสนเทศบริการสุขภาพ สถาบันวิจัยระบบ สาธารณสุข, "ระบบ CSMBS" [Online]. Available: https:// www.chi.or.th/. [Accessed: 6-Jun-2020].

สำนักพัฒนากลุ่มโรคร่วมไทย, DRG คืออะไร [online]. Available: http://www.tcmc.or.th/.[Acessed: 20-Feb-2024] [5] ก. แสนเทียะ, อ. แจ้งคล้อย, การวิเคราะห์ต้นทุนต่อ สิทธิการักษาโรงพยาบาลชุมชนในจังหวัด นครราชสีมา. วารสารวิทยาการจัดการ 2559;3(1):84-108.

ก.หาญผดุงกิจ, ช. ภู่ประเสริฐ, “ค่าบริการและการจ่ายเงิน แบบกลุ่มวินิจฉัยโรคร่วมของผู้ป่วย หอผู้ป่วยเวชศาสตร์ฟื้นฟู โรงพยาบาลศิริราช,”เวชศาสตร์ฟื้นฟูสาร 2559; 26(3):111-118.

ณ. สินธุวนิช, การวิเคราะห์ฐานข้อมูล และการสร้างแบบ จำลองเพื่อหาปัจจัยที่พยากรณ์การขาดทุนจากการ เบิกจ่าย ตามกลุ่มวินิจฉัยโรคร่วมของผู้ป่วยในศูนย์การแพทย์กาญ จาภิเษก คณะแพทย์ศาสตร์ศิริราชพยาบาล มหาวิทยาลัย มหิดล. การประชุมระดับชาติด้านเวชสาร สนเทศครั้งที่ 8 และการประชุมวิชาการสมาคมเวชสาร สนเทศไทย ประจำปี พ.ศ. 2562; วันที่ 20-22 พฤศจิกายน 2562; โรงแรม ดิ อมเมอรัลด์; กรุงเทพมหานคร: บริษัท สามเจริญพาณิชย์ (กรุงเทพ) จำกัด; 2562:24-28.

Sitivutworapant, "Data analytsis to study the main diseases, comorbodotoes and complications that cost more than DRG of inpatients," Journal of the Thai Medical Informatics Association, vol. 11, no. 1, pp. 25-32, 2025.

Rattanavipapong, et al., "Retrospective secondary data analysis to identify high-cost users in inpatient department of hospitals in Thailand, a middle-income country with universal healthcare coverage," BMJ Open, vol. 11, no. 7, p. e047330, 2021.

Siwaprapakorn, "Data analytic and predictive model for analysis of causes for a loss from diag nosis related groups (DRGs) payment of pediatric inpatient at Sunpasitthiprasong hospital," Journal of the Thai Medical Informatics Association, vol. 7, no. 1, pp. 12-20, 2021.

Prediction of ischemic heart disease and stroke using machine learning

Narisara Doncharpai — 2026-06-16

Cardiovascular disease (CVD) is the leading cause of death worldwide and represents a group of disorders affecting the heart and blood vessels. A major underlying cause is Arteriosclerosis and degeneration of vessels, which lead to insufficient blood flow to the heart and brain. Risk factors include age, sex, blood pressure, and blood glucose level etc. The objective of this study was to develop machine learning models for predicting Ischemic heart disease and Stroke to investigate the influential risk factors. Patient data diagnosed with cardiovascular disease were collected from Fort Surasi Hospital, comprising 448 records. The dataset was divided into training and testing sets at an 80:20 ratio, and three models were compared: Logistic Regression, Random Forest, and XGBoost. The results revealed that the Random Forest Model achieved the highest predictive performance with an accuracy of 73.46%, indicating a moderate level of Predictive. The most influential factors contributing to Cardiovascular disease were age, Cholesterol, and Systolic Blood Pressure. Although the developed models were limited by data, which affected predictive accuracy, they can still be applied for initial risk assessment and as a guideline for planning and preventive. Furthermore, the models can be further refined to improve predictive performance for medical use in the future.

References

World Health Organization, “Cardiovascular diseases,” WHO, 2025. [Online]. Available: https://www.who.int/ health-topics/cardiovascular-diseases.

กรมควบคุมโรค, “กรมควบคุมโรค ร่วมรณรงค์วันหัวใจโลก 2566 เผยคนไทยเสียชีวิตโรคหัวใจและหลอดเลือดมากถึง 7 หมื่นราย,” ข่าวสาร กรมควบคุมโรค. [Online]. Available: https://ddc.moph.go.th/brc/news.php?news=37372&deptcode=brc

N.D. Wong et al., “Atherosclerotic cardiovascular disease risk assessment: An American Society for Preventive Cardiology clinical practice statement,” Am. J. Prev. Cardiol., vol. 10, p. 100335.

J. Bosomworth, “Practical use of the Framingham risk score in primary prevention: Canadian perspective,” Can. Fam. Physician, vol. 57, no. 4, pp. 417–423, Apr. 2011. K. Elissa, “Title of paper if known,” unpublished.

คณะแพทยศาสตร์ โรงพยาบาลรามาธิบดี มหาวิทยาลัย มหิดล, “Thai CV risk score,” เว็บไซต์คณะแพทยศาสตร์ โรงพยาบาลรามาธิบดี มหาวิทยาลัยมหิดล, [ออนไลน์]. เข้าถึงได้จาก: https://www.rama.mahidol.ac.th/cardio_vascular_risk/thai_cv_risk_score/

A. Jabbar, B. L. Deekshatulu, and P. Chandra, “Prediction of cardiovascular disease (CVD) using hybrid machine learning techniques,” in Proc. Int. Conf. Current Trends Towards Converging Technologies (ICCTCT), Coimbatore, India, Mar. 2018, pp. 1–7, doi: 10.1109/ICCTCT.2018.8550857

A. Quesada et al., “Machine learning to predict cardiovascular risk,” Int. J. Clin. Pract., vol. 73, no. 10, p. e13389, Oct. 2019, doi: 10.1111/ijcp.13389

Subramani et al., “Cardiovascular diseases prediction by machine learning incorporation with deep learning,” Front. Med., vol. 10, p. 1150933, Apr. 2023, doi: 10.3389/fmed.2023.1150933

Bagheri et al., “Multimodal Learning for Cardiovascular Risk Prediction using EHR Data,” arXiv preprint arXiv:2008.11979, 2020.

Motwani et al., “Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis,” Eur. Heart J., vol. 38, no. 7, pp. 500-507, Feb. 2017, doi: 10.1093/eurheartj/ehw188

Chomsri, S. Suratana, W. Henkaew, M. Matrakul, and A. Suntranon, “The association between cardiovasclar risk factors with levels of cardiovascular disease risk factors and the health literacy in olde r adult,” Royal Thai Army Nurses Journal, vol. 22, no. 3, pp. 387–395, Sep.–Dec. 2021.

Wachirapant, S. Chotnaphan, K. Jamroonsawat, P. Tanyasitthasuntorn, “Association of behavioral risks and cardiovascular disease in Thai population,” Journal of Bamrasnaradura Institute, vol. 17, no. 1, pp. 1–12, Jan.–Apr. 2023.

Information governance: A case study of Fort Surasi Hospital

Patipol Karnsomjai — 2026-06-16

Fort Surasi Hospital has implemented Information Governance (IG) as a means to organize its data assets, assess and manage information-related risks, and enhance data quality to ensure accuracy, completeness, and readiness for use. The initiative was conducted as a Proof of Concept (PoC) covering 27 hospital departments, following seven key steps: developing a data catalog, reviewing data processes, assessing and managing risks, improving data systems, promoting data utilization, classifying and retrieving data, and monitoring data quality. The findings revealed that the hospital successfully developed a data catalog comprising 410 items, with improvements made by reducing duplication and addressing incomplete records across departments. A risk management plan was also established, identifying high-risk areas such as data errors, data corruption/ duplication, and data breaches, with corresponding mitigation strategies introduced. Implementation of IG has provided the hospital with a more systematic database, clearer risk control measures, and higher-quality data ready for use. Nonetheless, further development is required to ensure sustainability, guided by the PDCA (Plan–Do–Check–Act) cycle, in order to achieve continuous improvement and long-term quality in hospital information management.

References

F. Smallwood, Information Governance: Concepts, Strategies, and Best Practices, 2nd ed. Hoboken, NJ: John Wiley & Sons, Inc., 2020.

คณะกรรมการศึกษากระบวนการอภิบาลและการเปิดเผย ข้อมูลดิจิทัล, อภิบาลข้อมูลภาครัฐ (Data Governance for Government), เวอร์ชัน 1.0, กรุงเทพฯ: สำนักงานพัฒนา รัฐบาลดิจิทัล (องค์การมหาชน), พ.ย. 2562.

ชุษณะ มะกรสาร และ วรรษา เปาอินทร์, แนวทางการพัฒนา คุณภาพระบบเทคโนโลยีสารสนเทศโรงพยาบาล (HAIT): ตามเกณฑ์ Thai Medical Informatics Association TMI Maturity Model. กรุงเทพฯ: สมาคมเวชสารสนเทศไทย, 2568.

Askham, “Squaring the circle: Using a data governance framework to support data quality,” Experian Information Solutions, Inc., 2016.

G. Heshajin, A. H. Shamsabadi, F. R. Aghdam, M. A. Hasankhani, and M. R. Rezapour, “A framework for health information governance: a scoping review,” Health Research Policy and Systems, vol. 22, no. 1, 2024. [Online]. Available: BioMed Central.

Powell, “Information Governance in an Academic Medical Center,” in Proc. Int. Conf. Information Quality (ICIQ), UA Little Rock, 2017. [Online]. Available: UA Little Rock Repository.

O. Boadu, M. A. Adzovie, and J. Y. Quansah, “Examine frameworks, policies and strategies for effective information governance in healthcare,” PLOS ONE, vol. 20, no. 3, 2025. [Online]. Available: PLOS.

Oktaviana, H. R. Prabowo, and N. S. Dewi, “Health care data governance assessment based on hospital management perspectives,” Health Technology and Informatics Journal, vol. 12, 2025. [Online]. Available: ScienceDirect.

M. Mohammed and H. Y. Salifu, “Health Data Governance Issues in Healthcare Facilities: Perspective of Hospital Management,” International Journal of Healthcare Management, 2022. [Online]. Available: ResearchGate.

American Health Information Management Association (AHIMA), Information Governance Toolkit 3.0, AHIMA, 2017. [Online]. Available: AHIMA.org.

Effectiveness of an integrated nutritional care program for malnourished patients: A retrospective quasi-experimental study at Chomthong Hospital

Ekkawit Iamthongin — 2026-06-16

Malnutrition affects approximately 20–50% of newly admitted inpatients but is often under-diagnosed or inadequately managed, leading to unfavorable clinical outcomes and higher care costs. Key barriers include complex care processes, limited personnel, suboptimal screening/assessment tools, and insufficient awareness. This study evaluated an integrated Nutrition Care program that embeds screening/assessment workflows into routine care, links data to the Hospital Information System (HIS), and uses data-analytics tools (Python/R, QlikView, and AI-assisted analytics). The objective of this study is to assess the impact of the Chomthong Nutrition Care program on clinical outcomes (in-hospital mortality, 30-day readmission, complications), operational outcomes (screening and assessment coverage), and financial indicators (adjusted relative weight [adjRw], unit cost) at Chomthong Hospital, Chiang Mai. We conducted a retrospective quasi-experimental analysis using a difference-in-differences (DiD) design. Adult inpatient episodes (age ≥18 years) were compared between the pre-implementation period (September 2024–February 2025; n=3,165) and the post-implementation period (March–August 2025; n=2,973). The intervention was defined as documented nutrition screening. Models adjusted for age, sex, payer type, ward, specialty, comorbidity, and calendar month. Sensitivity analyses included inverse probability of treatment weighting (IPTW) and post-period comparisons by NAF severity. The results showed that post-implementation, nutrition screening coverage increased to 92.3%. The adjusted complication rate decreased significantly (-18.6 percentage points; p=.001), while mortality and readmission showed no significant change. Length of stay increased (RR=1.51; p<.001), whereas adjRw (RR=0.64; p<.001) and cost (RR=0.90; p=.009) showed modest declines. Within the post period, patients classified as NAF C had higher readmission (+14.3 pp; p<.001) and longer LOS (RR=1.28; p=.004) compared with NAF A. In conclusion, implementation of the Chomthong Nutrition Care program improved access to nutrition services and significantly reduced complications, with no detectable effect on mortality or readmission. Financially, costs declined and adjRw decreased. Moderate and severe nutritional risk (NAF B/C) predicted adverse outcomes. Integrating nutrition screening with data-analytics tools is a feasible and valuable approach to enhance nutrition care quality in resource-limited hospitals.

References

Hudson, L. M. (2021). Disease-associated undernutrition in Southeast Asia: A retrospective study in Vietnamese hospitalized adults. Clinical Nutrition.

Yin, L., et al. (2024). Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC). Clinical Oncology Journal.

Kyle, U. G., et al. (2005). Prevalence of malnutrition at hospital admission: A body composition study. Clinical Nutrition, 24(6), 1027–1035.

Meijers, J. M., et al. (2010). Malnutrition in Dutch healthcare: Prevalence and quality indicators. Nutrition, 26(11–12), 1189–1196.

Crowe, T. C. (2024). Artificial Intelligence in Malnutrition: A Systematic Literature Review. Advances in Nutrition, 15(9), 100264.

American Society for Parenteral and Enteral Nutrition (ASPEN). (2012). Consensus statement: Characteristics recommended for the identification and documentation of adult malnutrition.

Gandy, J. (2019). Manual of Dietetic Practice (6th ed.). Wiley-Blackwell.

Compher, C. (2023). Barriers and enablers of malnutrition screening in hospitals: A U.S. perspective. Journal of Nutrition in Clinical Practice.

Janssen, S. M. W., Bouzembrak, Y., & Tekinerdogan, B. (2021). A fusion decision system to identify and grade malnutrition in cancer patients. Clinical Nutrition, 40(8), 4958–4970.

Liangyu, Y., et al. (2024). Machine learning-based risk stratification for malnutrition in hospital settings. Journal of Medical Systems.

Cederholm, T., et al. (2017). ESPEN guidelines on definitions and terminology of clinical nutrition. Clinical Nutrition, 36(1), 49–64.

Wang, M., et al. (2023). Clinical Decision Support Systems for Malnutrition Screening: A Systematic Review. BMC Medical Informatics and Decision Making.

Pongchaiyakul, C., et al. (2023). Development of a QlikView dashboard for nutrition workflow monitoring. Thai Journal of Clinical Nutrition.

Ruangrit, P., et al. (2024). Utilizing R and Python in hospital nutrition analytics: A case study. Proceedings of Thai Health Informatics Conference.

Saengthong, K., et al. (2024). Comparing manual and automated nutrition screening in regional Thai hospitals. Chiang Mai Medical Journal.

Correia, M. I., & Waitzberg, D. L. (2003). The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis. Clinical Nutrition, 22(3), 235–239.

Dent E, Hoogendijk EO, Visvanathan R, Wright ORL. Malnutrition Screening and Assessment in Hospitalised Older People: a Review. J Nutr Health Aging. 2019;23(5):431-441. doi: 10.1007/s12603-019-1176-z. PMID: 31021360; PMCID: PMC12280429.

Y.-C. Lin, R. Hagen, B. D. Powers, S. P. Dineen, J. Milano, E. Hume, O. Sprow, S. Diaz-Carraway, J. B. Permuth, J. Deneve, A. Alishahi Tabriz, and K. Turner, “Digital Health Intervention to Reduce Malnutrition Among Individuals With Gastrointestinal Cancer Receiving Cytoreductive Surgery Combined With Hyperthermic Intraperitoneal Chemotherapy: Feasibility, Acceptability, and Usability Trial,” JMIR Cancer, vol. 11, p. e67108, 2025.

H. van den Berg, G. G. J. Huisman-de Waal, H. Vermeulen, and M. A. E. de van der Schueren, “Effects of nursing nutrition interventions on outcomes in malnourished hospital inpatients and nursing home residents: A systematic review,” Int. J. Nurs. Stud., vol. 117, p. 103896, 2021.

Reber, K. Norman, O. Endrich, P. Schuetz, A. Frei, and Z. Stanga, “Economic Challenges in Nutritional Management,” J. Clin. Med., vol. 8, no. 7, p. 1005, Jul. 2019.

Huang, M. D. Banks, P. M. Sowa, and J. D. Bauer, “A Cost–Consequence Analysis of Nutritional Interventions Used in Hospital Settings for Older Adults with or at Risk of Malnutrition,” Healthcare, vol. 12, no. 10, Art. no. 1041, May 2024.

Khalatbari-Soltani and P. Marques-Vidal, “Impact of nutritional risk screening in hospitalized patients on management, outcome and costs: A retrospective study,” Clin. Nutr., vol. 35, no. 6, pp. 1340–1346, Dec. 2016.

Khalatbari-Soltani and P. Marques-Vidal, “The economic cost of hospital malnutrition in Europe; a narrative review,” Clin. Nutr. ESPEN, vol. 9, no. 3, pp. e139–e145, Jun. 2015.

A. Cano-Torres et al., “Impact of nutritional intervention on length of hospital stay and mortality among hospitalized patients with malnutrition: A clinical randomized controlled trial,” J. Am. Coll. Nutr., vol. 36, no. 3, pp. 160–165, Mar. 2017.