Contributions on Clinical Decision Support from the 2018 Literature

Summary

Objectives: To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook.

Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation.

Results: Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy.

Conclusions: Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.

Keywords

Medical informatics - International Medical Informatics Association - Yearbook - Decision Support Systems

• References

• 1 Montani S, Striani M. Artificial Intelligence in clinical decision support: a focused literature survey. Yearb Med Inform 2019; 120-7
• 2 Lamy JB, Seroussi B, Griffon N, Kerdelhue G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med. 2015; 54 (02) 135-44
• 3 Koutkias V, Bouaud J. Contributions from the 2016 Literature on Clinical Decision Support. Yearb Med Inform 2017; 26 (01) 133-8
• 4 Koutkias V, Bouaud J. Contributions from the 2017 Literature on Clinical Decision Support. Yearb Med Inform 2018; 27 (01) 122-8
• 5 Banerjee I, Gensheimer MF, Wood DJ, Henry S, Aggarwal S, Chang DT. , et al. Probabilistic prognostic estimates of survival in metastatic cancer patients (PPES-Met) utilizing free-text clinical narratives. Sci Rep. 2018; 8 (01) 10037
• 6 Ray S, McEvoy DS, Aaron S, Hickman TT, Wright A. Using statistical anomaly detection models to find clinical decision support malfunctions. J Am Med Inform Assoc. 2018; 25 (07) 862-71
• 7 Simon G, DiNardo CD, Takahashi K, Cascone T, Powers C, Stevens R. , et al. Applying Artificial Intelligence to address the knowledge gaps in cancer care. Oncologist. 2018 Nov 16. pii: theoncologist.2018-0257
• 8 Tsopra R, Lamy JB, Sedki K. Using preference learning for detecting inconsistencies in clinical practice guidelines: methods and application to antibiotherapy. Artif Intell Med. 2018; 89: 24-33
• 9 Kohn MS, Sun J, Knoop S, Shabo A, Carmeli B, Sow D. , et al. IBM’s Health Analytics and Clinical Decision Support. Yearb Med Inform. 2014; 9: 154-62
• 10 Peleg M, Michalowski W, Wilk S, Parimbelli E, Bonaccio S, O’sullivan D. , et al. Ideating mobile health behavioral support for compliance to therapy for patients with chronic disease: a case study ofatrial fibrillation management. J Med Syst. 2018; 42 (11) 234
• 11 Kamisalic A, Riano D, Welzer T. Formalization and acquisition of temporal knowledge for decision support in medical processes. Comput Methods Programs Biomed. 2018; 158: 207-28
• 12 Yan C, Lindgren H, Nieves JC. A dialogue-based approach for dealing with uncertain and conflicting information in medical diagnosis. Auton Agent Multi-Ag 2018; 32 (06) 861-85
• 13 Goodwin TR, Harabagiu SM. Knowledge representations and inference techniques for medical question answering. ACM Transactions on Intelligent Systems and Technology 2018; 9 (02) 14
• 14 Wulff A, Haarbrandt B, Tute E, Marschollek M, Beerbaum P, Jack T. An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using open EHR. Artif Intell Med. 2018; 89: 10-23
• 15 El-Sappagh S, Alonso JM, Ali F, Ali A, Jang JH, Kwak KS. An ontology-based interpretable fuzzy decision support system for diabetes diagnosis. IEEE Access 2018; 6: 37371-94
• 16 Gombolay M, Jessie Yang X, Hayes B, Seo N, Liu Z, Wadhwania S. , et al. Robotic assistance in the coordination of patient care. The International Journal of Robotics Research 2018; 37 (10) 1300-16
• 17 Chattopadhyay D, Verma N, Duke J, Bolchini D. Design and evaluation of trust-eliciting cues in drug-drug interaction alerts. Interacting with Computers 2018; 30 (02) 85-98
• 18 Kheterpal S, Shanks A, Tremper KK. Impact of a novel multiparameter decision support system on intraoperative processes of care and postoperative outcomes. Anesthesiology. 2018; 128 (02) 272-82
• 19 Wang JK, Hom J, Balasubramanian S, Schuler A, Shah NH, Goldstein MK. , et al. An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes. J Biomed Inform. 2018; 86: 109-19
• 20 Parshuram CS, Dryden-Palmer K, Farrell C, Gottesman R, Gray M, Hutchison JS. , et al. Effect of a pediatric early warning system on all-cause mortality in hospitalized pediatric patients: the EPOCH randomized clinical trial. JAMA 2018; 319 (10) 1002-12