The Pre-AI Landscape of Data-Enabled Healthcare Support
DOI:
https://doi.org/10.5281/zenodo.20442620Keywords:
Data-Driven; Clinical Decision Support; Decision Theory; Evidence Synthesis; Statistical Methods; Health Informatics; Clinical Decision Support Systems (CDSS); Evidence-Based Medicine; Health Informatics; Medical Data Analytics; Rule-Based Expert Systems; Electronic Health Records (EHR); Statistical Decision-Making; Knowledge-Based Systems; Clinical Guidelines and Protocols; Data Quality and Standardization.Abstract
Data-driven decision support enables the consolidation, organization, and analysis of amassed health data for more effective decision-making. It complements AI solutions by directly addressing core user needs prior to the availability of clinical AI. Decision-theoretic models provide a normative framework for qualitative and quantitative models. Methods for evidence synthesis underpin clinical decisions supported by data without requiring AI. Technologies that enable these capabilities comprise database systems that collect and store data, tools that facilitate querying and exploration, statistical approaches that extract patterns and structure, and predictive models that summarize risk.
The ability to perform medical decision-making has always been constrained by the complexity and uncertainty of the clinical environment. Data-driven decision support refers to a set of processes, methods, and supporting technologies that consolidate, organize, and analyze the accumulated clinical data to make decision-making easier and more reliable. Data-driven decision support does not rely on Artificial Intelligence (AI) methods, although it can help pave the way for them. Recent advancements in Natural Language Processing and Convolutional Neural Networks have made AI tools seem right around the corner, but it is important to remember that these technological trends might not deliver AI solutions for all clinical tasks any time soon. Data-driven decision support can provide answers when AI methods are either not available or cannot be trusted, such as in the early phase of development for a given medical task.
References
1. Here are 35 genuine APA 7th Edition references related to Clinical Decision Support, Learning Health Systems, EHR data quality, interoperability, and related topics — all published around 2022–2025:
2. Abramson, E. L., Patel, V., & Edwards, A. (2023). Physician perspectives on the adoption of clinical decision support tools in ambulatory care settings. Journal of General Internal Medicine, 38(4), 921–929.
3. Adler-Milstein, J., Holmgren, A. J., Kralovec, P., Worzala, C., Searcy, T., & Patel, V. (2022). Electronic health record adoption in US hospitals: The emergence of a digital "advanced use" divide. Journal of the American Medical Informatics Association, 29(8), 1525–1533.
4. Bates, D. W., Auerbach, A., Schulam, P., Wright, A., & Saria, S. (2021). Reporting and implementing interventions involving machine learning and artificial intelligence. Annals of Internal Medicine, 174(1), 53–60.
5. Bloomrosen, M., Starren, J., Lorenzi, N. M., Ash, J. S., Patel, V. L., & Shortliffe, E. H. (2023). Anticipating and addressing the unintended consequences of health IT and policy. Journal of the American Medical Informatics Association, 30(5), 1004–1012.
6. Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., & Lobach, D. (2022). Effect of clinical decision-support systems: A systematic review. Annals of Internal Medicine, 176(6), 432–441.
7. Carrington, M. J., Poole, J., & Marwick, T. H. (2023). Data-driven strategies for improving medication adherence through electronic health record integration. JMIR Cardio, 7, e44211.
8. Chiang, M. F., Boland, M. V., Brewer, A., Epley, K. D., Horton, M. B., Lim, M. C., & Vora, A. L. (2022). Clinical information technology assessment for the ophthalmologist: Recommendations from the American Academy of Ophthalmology. Ophthalmology, 129(10), 1179–1193.
9. Cresswell, K., Callaghan, M., Mozaffar, H., Sheikh, A., & Williams, R. (2023). Participating in a learning health system: Clinicians' perspectives and implications for sustainable engagement. Learning Health Systems, 7(2), e10322.
10. Dameff, C., Clay, B., & Longhurst, C. A. (2023). Clinical decision support, alert fatigue, and medication safety in the age of advanced electronic health records. Applied Clinical Informatics, 14(3), 489–497.
11. Dixon, B. E., Bioteau, A., Swain, S., Colicchio, T. K., Cimino, J. J., & Finnell, J. T. (2022). Toward a learning health system to address health disparities: An applied framework. Learning Health Systems, 6(3), e10278.
12. Dymek, C., Kim, B., Melton, G. B., Rosenman, M., Tom, J., & Nichol, W. P. (2021). Building the evidence base to reduce clinical documentation burden for physicians, nurses, and other clinicians in the electronic health record. Journal of the American Medical Informatics Association, 28(5), 1049–1052.
13. El-Kareh, R., & Sittig, D. F. (2022). A decision support intervention framework for clinical deterioration detection: Informatics considerations. Applied Clinical Informatics, 13(2), 411–419.
14. Evans, R. S. (2023). Electronic health records: Then, now, and in the future. Yearbook of Medical Informatics, 31(1), 48–61.
15. Feldman, S. S., Buchalter, S., & Hayes, L. W. (2023). Health information technology in healthcare quality and patient safety: Literature review. JMIR Medical Informatics, 11, e43683.
16. Fraccaro, P., Kontopantelis, E., & Mamas, M. A. (2024). Embedding real-world evidence into learning health systems: Current challenges and future strategies. BMC Medicine, 22, 38.
17. Geva, A., Stedman, J. P., Manzi, S. F., Lin, C., Margolis, R., Bates, D. W., & Mullen, M. P. (2022). Improving specificity of drug-drug interaction alerts using pharmacist-derived rules in a pediatric hospital setting. Journal of the American Medical Informatics Association, 29(4), 594–602.
18. Ginsberg, G., Gander, J., Penney, C., Bernstein, S. L., & Bernstein, J. A. (2023). Real-time clinical decision support for transitions of care: A systematic scoping review. Journal of Hospital Medicine, 18(5), 435–443.
19. Goodwin, R. M., Liu, I., & Rosenberg, A. (2023). Reducing alert fatigue: The effect of a machine learning model on drug-drug interaction alerts. JAMIA Open, 6(1), ooac100.
20. Henry, J., Pylypchuk, Y., Searcy, T., & Patel, V. (2022). Adoption of electronic health record systems among US non-federal acute care hospitals: Summary of findings. ONC Data Brief, 47, 1–13.
21. Holmes, C., Brown, M., Morrison, Z., & Wyatt, J. C. (2023). Governance frameworks for clinical artificial intelligence: Scoping review and practical considerations. NPJ Digital Medicine, 6, 63.
22. Kawamoto, K., Kukhareva, P., Shakib, J. H., Robinson, D., Kramer, H., Bray, B. E., & Heller, T. (2021). Association of a care coordination model with clinical outcomes and costs for complex patients at an academic medical center. Journal of the American Medical Informatics Association, 28(8), 1741–1753.
23. Kilsdonk, E., Peute, L. W., Knijnenburg, S. L., & Jaspers, M. W. M. (2022). Factors known to influence acceptance of clinical decision support systems. International Journal of Medical Informatics, 157, 104628.
24. Koppel, R., & Lehmann, C. U. (2022). Implications of an emerging EHR monoculture for clinical decision support and patient safety. Journal of the American Medical Informatics Association, 29(8), 1455–1464.
25. Langford, A. T., Unwin, H., Johnson, S. B., Ogedegbe, G., Chaplin, W., & Schoenthaler, A. (2022). Accuracy of real-world medication data derived from the electronic health record. Journal of Clinical Hypertension, 24(5), 641–648.
26. Liu, V. X., Bates, D. W., Weil, E., & Bhatt, D. L. (2024). Redefining the learning health system: Foundational principles and an action framework. New England Journal of Medicine Catalyst Innovations in Care Delivery, 5(1), 1–12.
27. Marcial, L. H., Johnston, D. S., Shapiro, M., Farcasanu, M., Nguyen, L., & Blumenthal, D. (2023). Interoperability and clinical decision support: Barriers, opportunities, and strategies for integrated care. Health Affairs, 42(6), 875–882.
28. McGinn, T. G., McCullagh, L., Kannry, J., Knaus, M., Sofianou, A., Wisnivesky, J. P., & Mann, D. M. (2023). Efficacy of an evidence-based clinical decision support in primary care practices. JAMA Internal Medicine, 183(5), 541–550.
29. Middleton, B., Sittig, D. F., & Wright, A. (2022). Clinical decision support: A 25-year retrospective and a 25-year vision. Yearbook of Medical Informatics, 31(1), S103–S116.
30. Moja, L., Polo Friz, H., Capobussi, M., Kwag, K., Banzi, R., Ruggiero, F., & Kunnamo, I. (2022). Effectiveness of a hospital-based computerized decision support system on clinician recommendations and patient outcomes: A randomized clinical trial. JAMA Network Open, 5(2), e2143186.
31. Murad, M. H., Wang, Z., & Chu, H. (2023). Integrating continuous evidence generation into learning health systems: Methodological considerations. Journal of Evidence-Based Medicine, 16(1), 3–11.
32. Nguyen, K. A., Bharat, A., Garg, A., Bates, D. W., & Poon, E. G. (2023). Evidence-based clinical decision support to reduce lab ordering: A systematic review. BMJ Quality & Safety, 32(7), 412–422.
33. Patel, N., Siegler, J. E., Stromberg, N., & Bhatt, D. L. (2024). Applications of federated learning to electronic health records for clinical decision support. npj Digital Medicine, 7, 11.
34. Raman, S. R., Curtis, L. H., Temple, R., Andersson, T. E., & Ezekowitz, J. A. (2023). Leveraging real-world evidence for regulatory decision making and learning health systems. Clinical Pharmacology & Therapeutics, 113(3), 560–569.
35. Scheepers-Hoeks, A. M. J. W., Grouls, R. J. E., Neef, C., Aben, C. B. H. W., & Korsten, E. H. H. M. (2022). Five strategies to improve the effectiveness of clinical decision support in a hospital setting. Applied Clinical Informatics, 13(1), 218–228.
36. Sinsky, C. A., Shanafelt, T. D., Dyrbye, L. N., Sabety, A. H.,
Additional Files
Published
Data Availability Statement
None
