Retrospective Clinical Decision Support
Keywords:
Clinical Decision Support Systems, Historical Patient Data, Clinical Decision Analytics, Healthcare Data Science, Analytical Methods For CDS, Clinical Insight Extraction, CDS System Architecture, Clinical Validation Frameworks, Evaluation Of Decision Support, User Interaction Design, Prediction Presentation Control, Bias In Clinical Data, Safety Of Historical Data, Logical Limitations Of Retrospective Analytics, Clinician Decision Support, Supervised Learning In Healthcare, Clinical Data Modeling, Evidence-Based Clinical Practice, Human-Centered CDS Design, Probabilistic Clinical Reasoning.Abstract
Clinical Decision Support systems based on historical patient data represent a model for investigation of a category of Clinical Decision Support (CDS) applications that have recently gained traction — consumption of historical data to support clinical decision making. An overview of the category is provided and addressed based on five research questions: What are the properties of historical data that provide insights? How are these insights used in Analytical Methods for Clinical Decision Support based on Historical Data? How is the architecture of the Clinical Decision Support System represented? How is clinical validation and evaluation achieved? How is the presentation and user interaction with predictions controlled? Despite being a nascent area, numerous published works are available on the topic, and clinical validation efforts are in various stages of maturity.
Historical data have become increasingly more available, are generally regarded as safe, and, unlike predictive models constructed on Future Data, do not raise questions regarding biases introduced in the construction of supervised learning models. However, while safety and bias concerns are reduced, historical data are prone to their own logical shortcomings. Therefore, users must exercise caution when interpreting the insights derived from Historical Data Analytics. Properly defined and presented, such insights do not impose a decision making burden on clinicians but instead ease the decision making process. The existence of a predictive model that maps the Clinical Data relevant to a Clinical Decision to the Clinical Decision itself is often regarded as a requirement for CDS to be genuinely useful for everyday clinical practice. Such a statement suffers from circular reasoning. In summary, because no method of construction of predictive models can claim to be free from bias, the pannational nature of probability suggests that, provided sufficient Causally Uncorrelated Data points are available notwithstanding time, space and biological differences, the possibility of identifying and using a Supervised Learning Model that accurately predicts the Decision of a Clinician can not be excluded.
References
1. Ackermann, K., Baker, J., Green, M., Fullick, M., Varinli, H., Westbrook, J., & Li, L. (2022). Computerized clinical decision support systems for the early detection of sepsis among adult inpatients: Scoping review. Journal of Medical Internet Research, 24(2), e31083.
2. Afshar, M., Adelaine, S., Resnik, F., Mundt, M. P., Long, J., Leaf, M., Ampian, T., Wills, G. J., Schnapp, B., Chao, M., Brown, R., Joyce, C., Sharma, B., Dligach, D., Burnside, E. S., Mahoney, J., Churpek, M. M., Patterson, B. W., & Liao, F. (2023). Deployment of real-time natural language processing and deep learning clinical decision support in the electronic health record: Pipeline implementation for an opioid misuse screener in hospitalized adults. JMIR Medical Informatics, 11, e44977.
3. Amann, J., Vetter, D., Blomberg, S. N., Christensen, H. C., Coffee, M., Gerke, S., et al. (2022). To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems. PLOS Digital Health, 1(2), e0000016.
4. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
5. Durant, T. J. S., & Peaper, D. R. (2023). Retrospective evaluation of clinical decision support for within-laboratory optimization of SARS-CoV-2 NAAT workflow. Journal of Clinical Microbiology, 62(2), e00785-23.
6. El-Kareh, R., & Sittig, D. F. (2022). Enhancing diagnosis through technology: Decision support, artificial intelligence, and beyond. Critical Care Clinics, 38(1), 129–139.
7. Hak, F., Guimarães, T., & Santos, M. (2022). Towards effective clinical decision support systems: A systematic review. PLOS ONE, 17(8), e0272846.
8. Hasan, M. M., Young, G. J., Shi, J., Mohite, P., Young, L. D., Weiner, S. G., & Noor-E-Alam, M. (2021). A machine learning based two-stage clinical decision support system for predicting patients’ discontinuation from opioid use disorder treatment: Retrospective observational study. BMC Medical Informatics and Decision Making, 21, 331.
9. Bandi, V. D. V. K. (2023, November). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205.
10. Hong, N., Liu, C., Gao, J., Han, L., Chang, F., Gong, M., & Su, L. (2022). State of the art of machine learning–enabled clinical decision support in intensive care units: Literature review. JMIR Medical Informatics, 10(3), e28781.
11. Lapp, L., Egan, K., McCann, L., Mackenzie, M., Wales, A., & Maguire, R. (2022). Decision support tools in adult long-term care facilities: Scoping review. Journal of Medical Internet Research, 24(9), e39681.
12. Martinez-Garcia, A., Naranjo-Saucedo, A. B., Rivas, J. A., Romero Tabares, A., Marín Cassinello, A., Andrés-Martín, A., Sánchez Laguna, F. J., Villegas, R., Pérez León, F. D. P., Moreno Conde, J., & Parra Calderón, C. L. (2021). A clinical decision support system (KNOWBED) to integrate scientific knowledge at the bedside: Development and evaluation study. JMIR Medical Informatics, 9(3), e13182.
13. Nafees, A., Khan, M., Chow, R., Fazelzad, R., Hope, A., Liu, G., Letourneau, D., & Raman, S. (2023). Evaluation of clinical decision support systems in oncology: An updated systematic review. Critical Reviews in Oncology/Hematology, 192, 104143.
14. Davuluri, P. S. L. N. (2021). Event-Driven Compliance Systems: Modernizing Financial Crime Detection Without Machine Intelligence. Journal of International Crisis and Risk Communication Research, 339-354.
15. Nuutinen, M., & Leskelä, R.-L. (2023). Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support system. Health and Technology, 13, 557–570.
16. Olakotan, O. O., & Yusof, M. M. (2021). The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review. Health Informatics Journal, 27(2).
17. Panchavati, S., Lam, C., Zelin, N. S., Pellegrini, E., Barnes, G., Hoffman, J., Garikipati, A., Calvert, J., Mao, Q., & Das, R. (2021). Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification. Healthcare Technology Letters, 8(6), 139–147.
18. Mangalampalli, B. M. (2023). AI-Driven Anomaly Detection in Healthcare Claims Data: A Business Intelligence Perspective. Journal of Rare Cardiovascular Diseases.
19. Park, H., Chae, M. K., Jeong, W., Yu, J., Jung, W., Chang, H., & Cha, W. C. (2022). Appropriateness of alerts and physicians’ responses with a medication-related clinical decision support system: Retrospective observational study. JMIR Medical Informatics, 10(10), e40511.
20. Pereyra, L., Steinberg, L., Lasa, J., et al. (2023). Management of colorectal cancer screening backlog due to the COVID-19 pandemic: A retrospective analysis of the use of a colorectal cancer screening clinical-decision support tool in Argentina. Gastroenterología y Hepatología, 46, 1–8.
21. Ronan, C. E., Crable, E. L., Drainoni, M.-L., & Walkey, A. J. (2022). The impact of clinical decision support systems on provider behavior in the inpatient setting: A systematic review and meta-analysis. Journal of Hospital Medicine, 17(5), 368–383.
22. Schütze, D., Holtz, S., Neff, M. C., et al. (2023). Requirements analysis for an AI-based clinical decision support system for general practitioners: A user-centered design process. BMC Medical Informatics and Decision Making, 23, 144.
23. Kolla, S. H. (2023). Large Language Model-Driven Enterprise Service Intelligence for Digital Workflow Transformation. International Journal of Research and Applied Innovations, 6(1), 8380-8391.
24. Skalafouris, C., Blanc, A.-L., Grosgurin, O., Marti, C., Samer, C., Lovis, C., Bonnabry, P., & Guignard, B. (2023). Development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists. International Journal of Clinical Pharmacy, 45(2), 406–413.
25. Susanto, A. P., Lyell, D., Widyantoro, B., Berkovsky, S., & Magrabi, F. (2023). Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: A scoping review. Journal of the American Medical Informatics Association, 30(12), 2050–2063.
26. Taheri Moghadam, S., Sadoughi, F., Velayati, F., Ehsanzadeh, S. J., & Poursharif, S. (2021). The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: A systematic review and meta-analysis. BMC Medical Informatics and Decision Making, 21, 98.
27. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260.
28. Wiwatkunupakarn, N., Aramrat, C., Pliannuom, S., Buawangpong, N., Pinyopornpanish, K., Nantsupawat, N., Carson Mallinson, P. A., Kinra, S., & Angkurawaranon, C. (2023). The integration of clinical decision support systems into telemedicine for patients with multimorbidity in primary care settings: Scoping review. Journal of Medical Internet Research, 25, e45944.
29. Wysocki, O., Davies, J. K., Vigo, M., Armstrong, A. C., Landers, D., Lee, R., & Freitas, A. (2023). Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making. Artificial Intelligence, 316, 103839.
30. Xu, Q., Xie, W., Liao, B., Hu, C., Qin, L., Yang, Z., Xiong, H., Lyu, Y., Zhou, Y., & Luo, A. (2023). Interpretability of clinical decision support systems based on artificial intelligence from technological and medical perspective: A systematic review. Journal of Healthcare Engineering, 2023, 9919269.
31. Zare, S., Meidani, Z., Shirdeli, M., & Nabovati, E. (2021). Laboratory test ordering in inpatient hospitals: A systematic review on the effects and features of clinical decision support systems. BMC Medical Informatics and Decision Making, 21, 20.
