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DOI: 10.15265/IY-2015-036
Computerized Clinical Decision Support: Contributions from 2014
Correspondence to:
Publication History
13 August 2015
Publication Date:
10 March 2018 (online)
Summary
Objective: To summarize recent research and propose a selection of best papers published in 2014 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook.
Method: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry systems in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers.
Results: Among the 1,254 returned papers published in 2014, the full review process selected four best papers. The first one is an experimental contribution to a better understanding of unintended uses of CDSSs. The second paper describes the effective use of previously collected data to tailor and adapt a CDSS. The third paper presents an innovative application that uses pharmacogenomic information to support personalized medicine. The fourth paper reports on the long-term effect of the routine use of a CDSS for antibiotic therapy.
Conclusions: As health information technologies spread more and more meaningfully, CDSSs are improving to answer users’ needs more accurately. The exploitation of previously collected data and the use of genomic data for decision support has started to materialize. However, more work is still needed to address issues related to the correct usage of such technologies, and to assess their effective impact in the long term.
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Keywords
Medical informatics - International Medical Informatics Association - Yearbook - Clinical Decision Support Systems
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References
- 1 Sacchi L, Lanzola G, Viani N, Quaglini S. Personalization and patient involvement in decision support systems: current trends. Yearb Med Inform 2015; 10: 106-18.
- 2 Koutkias V, Thiessard F. Big data - smart health strategies. Findings from the yearbook 2014 special theme. Yearb Med Inform 2014 Aug 15 9 (Suppl. 01) 48-51.
- 3 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015; 54 (Suppl. 02) 135-44.
- 4 Goddard K, Roudsari A, Wyatt JC. Automation bias: empirical results assessing influencing factors. Int J Med Inform 2014; May 83 (Suppl. 05) 368-75.
- 5 Nwulu U, Brooks H, Richardson S, McFarland L, Coleman JJ. Electronic risk assessment for venous thromboembolism: investigating physicians’ rationale for bypassing clinical decision support recommendations. BMJ Open 2014 Sep 26 4 (Suppl. 09) e005647.
- 6 McCoy AB, Thomas EJ, Krousel-Wood M, Sittig DF. Clinical decision support alert appropriateness: a review and proposal for improvement. Ochsner J 2014; Summer 14 (Suppl. 02) 195-202.
- 7 Gupta A, Raja AS, Khorasani R. Examining clinical decision support integrity: is clinician self-reported data entry accurate?. J Am Med Inform Assoc 2014; Jan-Feb 21 (Suppl. 01) 23-6.
- 8 Bouaud J, Blaszka-Jaulerry B, Zelek L, Spano JP, Lefranc JP, Cojean-Zelek I. et al. Health information technology: use it well, or don’t! Findings from the use of a decision support system for breast cancer management. AMIA Annu Symp Proc 2014 Nov 14 2014: 315-24.
- 9 Lee J, Han H, Ock M, Lee SI, Lee S, Jo MW. Impact of a clinical decision support system for high-alert medications on the prevention of prescription errors. Int J Med Inform 2014; Dec 83 (Suppl. 12) 929-40.
- 10 Carnevale TJ, Meng D, Wang JJ, Littlewood M. Impact of an emergency medicine decision support and risk education system on computed tomography and magnetic resonance imaging use. J Emerg Med 2015; Jan 48 (Suppl. 01) 53-7.
- 11 Klann JG, Szolovits P, Downs SM, Schadow G. Decision support from local data: creating adaptive order menus from past clinician behavior. J Biomed Inform 2014; Apr 48: 84-93.
- 12 Rodriguez-Maresca M, Sorlozano A, Grau M, Rodriguez-Castaño R, Ruiz-Valverde A, Gutierrez-Fernande J. Implementation of a computerized decision support system to improve the appropriateness of antibiotic therapy using local microbiologic data. Biomed Res Int 2014; 395434.
- 13 Miñarro-Giménez JA, Blagec K, Boyce RD, Adlassnig KP, Samwald M. An ontology-based, mobile-optimized system for pharmacogenomic decision support at the point-of-care. PLoS One 2014 May 2 9 (Suppl. 05) e93769.
- 14 Bell GC, Crews KR, Wilkinson MR, Haidar CE, Hicks JK, Baker DK. et al. Development and use of active clinical decision support for preemptive pharmacogenomics. J Am Med Inform Assoc 2014; Feb 21 e1 e93-9.
- 15 Nachtigall I, Tafelski S, Deja M, Halle E, Grebe MC, Tamarkin A. et al. Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective ‘before/after’ cohort study. BMJ Open 2014 Dec 22 4 (Suppl. 12) e005370.
- 16 McCullough JM, Zimmerman FJ, Rodriguez HP. Impact of clinical decision support on receipt of antibiotic prescriptions for acute bronchitis and upper respiratory tract infection. J Am Med Inform Assoc 2014; Nov-Dec 21 (Suppl. 06) 1091-7.
- 17 Bellos CC, Papadopoulos A, Rosso R, Fotiadis DI. Identification of COPD patients’ health status using an intelligent system in the CHRONIOUS wearable platform. IEEE J Biomed Health Inform 2014; May 18 (Suppl. 03) 731-8.
- 18 Eccher C, Seyfang A, Ferro A. Implementation and evaluation of an Asbru-based decision support system for adjuvant treatment in breast cancer. Comput Methods Programs Biomed 2014; Nov 117 (Suppl. 02) 308-21.
- 19 Brodin NP, Maraldo MV, Aznar MC, Vogelius IR, Petersen PM, Bentzen SM. et al. Interactive decision-support tool for risk-based radiation therapy plan comparison for Hodgkin lymphoma. Int J Radiat Oncol Biol Phys 2014 Feb 1 88 (Suppl. 02) 433-45.
- 20 Simpao AF, Ahumada LM, Desai BR, Bonafide CP, Gálvez JA, Rehman MA. et al. Optimization of drug-drug interaction alert rules in a pediatric hospital’s electronic health record system using a visual analytics dashboard. J Am Med Inform Assoc 2015; Mar 22 (Suppl. 02) 361-9.
- 21 Anani N, Chen R, Prazeres Moreira T, Koch S. Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language.
- 22 Gultepe E, Green JP, Nguyen H, Adams J, Albert-son T, Tagkopoulos I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Inform Assoc 2014; MarApr 21 (Suppl. 02) 315-25.
- 23 Mani S, Ozdas A, Aliferis C, Varol HA, Chen Q, Carnevale R. et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc 2014; Mar-Apr 21 (Suppl. 02) 326-36.
- 24 Amland RC, Hahn-Cover KE. Clinical decision support for early recognition of sepsis. Am J Med Qual 2014 Nov 10.
- 25 Jalali A, Buckley EM, Lynch JM, Schwab PJ, Licht DJ, Nataraj C. Prediction of periventricular leukomalacia occurrence in neonates after heart surgery. IEEE J Biomed Health Inform 2014; Jul 18 (Suppl. 04) 1453-60.
- 26 Bountris P, Haritou M, Pouliakis A, Margari N, Kyrgiou M, Spathis A. et al. An intelligent clinical decision support system for patient-specific predictions to improve cervical intraepithelial neoplasia detection. Biomed Res Int 2014; 2014: 341483.
- 27 El-Fakdi A, Gamero F, Meléndez J, Auffret V, Haigron P. eXiTCDSS: a framework for a work-flow-based CBR for interventional clinical decision support systems and its application to TAVI. Expert Syst Appl 2014; 41 (Suppl. 02) 284-94.
Correspondence to:
-
References
- 1 Sacchi L, Lanzola G, Viani N, Quaglini S. Personalization and patient involvement in decision support systems: current trends. Yearb Med Inform 2015; 10: 106-18.
- 2 Koutkias V, Thiessard F. Big data - smart health strategies. Findings from the yearbook 2014 special theme. Yearb Med Inform 2014 Aug 15 9 (Suppl. 01) 48-51.
- 3 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015; 54 (Suppl. 02) 135-44.
- 4 Goddard K, Roudsari A, Wyatt JC. Automation bias: empirical results assessing influencing factors. Int J Med Inform 2014; May 83 (Suppl. 05) 368-75.
- 5 Nwulu U, Brooks H, Richardson S, McFarland L, Coleman JJ. Electronic risk assessment for venous thromboembolism: investigating physicians’ rationale for bypassing clinical decision support recommendations. BMJ Open 2014 Sep 26 4 (Suppl. 09) e005647.
- 6 McCoy AB, Thomas EJ, Krousel-Wood M, Sittig DF. Clinical decision support alert appropriateness: a review and proposal for improvement. Ochsner J 2014; Summer 14 (Suppl. 02) 195-202.
- 7 Gupta A, Raja AS, Khorasani R. Examining clinical decision support integrity: is clinician self-reported data entry accurate?. J Am Med Inform Assoc 2014; Jan-Feb 21 (Suppl. 01) 23-6.
- 8 Bouaud J, Blaszka-Jaulerry B, Zelek L, Spano JP, Lefranc JP, Cojean-Zelek I. et al. Health information technology: use it well, or don’t! Findings from the use of a decision support system for breast cancer management. AMIA Annu Symp Proc 2014 Nov 14 2014: 315-24.
- 9 Lee J, Han H, Ock M, Lee SI, Lee S, Jo MW. Impact of a clinical decision support system for high-alert medications on the prevention of prescription errors. Int J Med Inform 2014; Dec 83 (Suppl. 12) 929-40.
- 10 Carnevale TJ, Meng D, Wang JJ, Littlewood M. Impact of an emergency medicine decision support and risk education system on computed tomography and magnetic resonance imaging use. J Emerg Med 2015; Jan 48 (Suppl. 01) 53-7.
- 11 Klann JG, Szolovits P, Downs SM, Schadow G. Decision support from local data: creating adaptive order menus from past clinician behavior. J Biomed Inform 2014; Apr 48: 84-93.
- 12 Rodriguez-Maresca M, Sorlozano A, Grau M, Rodriguez-Castaño R, Ruiz-Valverde A, Gutierrez-Fernande J. Implementation of a computerized decision support system to improve the appropriateness of antibiotic therapy using local microbiologic data. Biomed Res Int 2014; 395434.
- 13 Miñarro-Giménez JA, Blagec K, Boyce RD, Adlassnig KP, Samwald M. An ontology-based, mobile-optimized system for pharmacogenomic decision support at the point-of-care. PLoS One 2014 May 2 9 (Suppl. 05) e93769.
- 14 Bell GC, Crews KR, Wilkinson MR, Haidar CE, Hicks JK, Baker DK. et al. Development and use of active clinical decision support for preemptive pharmacogenomics. J Am Med Inform Assoc 2014; Feb 21 e1 e93-9.
- 15 Nachtigall I, Tafelski S, Deja M, Halle E, Grebe MC, Tamarkin A. et al. Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective ‘before/after’ cohort study. BMJ Open 2014 Dec 22 4 (Suppl. 12) e005370.
- 16 McCullough JM, Zimmerman FJ, Rodriguez HP. Impact of clinical decision support on receipt of antibiotic prescriptions for acute bronchitis and upper respiratory tract infection. J Am Med Inform Assoc 2014; Nov-Dec 21 (Suppl. 06) 1091-7.
- 17 Bellos CC, Papadopoulos A, Rosso R, Fotiadis DI. Identification of COPD patients’ health status using an intelligent system in the CHRONIOUS wearable platform. IEEE J Biomed Health Inform 2014; May 18 (Suppl. 03) 731-8.
- 18 Eccher C, Seyfang A, Ferro A. Implementation and evaluation of an Asbru-based decision support system for adjuvant treatment in breast cancer. Comput Methods Programs Biomed 2014; Nov 117 (Suppl. 02) 308-21.
- 19 Brodin NP, Maraldo MV, Aznar MC, Vogelius IR, Petersen PM, Bentzen SM. et al. Interactive decision-support tool for risk-based radiation therapy plan comparison for Hodgkin lymphoma. Int J Radiat Oncol Biol Phys 2014 Feb 1 88 (Suppl. 02) 433-45.
- 20 Simpao AF, Ahumada LM, Desai BR, Bonafide CP, Gálvez JA, Rehman MA. et al. Optimization of drug-drug interaction alert rules in a pediatric hospital’s electronic health record system using a visual analytics dashboard. J Am Med Inform Assoc 2015; Mar 22 (Suppl. 02) 361-9.
- 21 Anani N, Chen R, Prazeres Moreira T, Koch S. Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language.
- 22 Gultepe E, Green JP, Nguyen H, Adams J, Albert-son T, Tagkopoulos I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Inform Assoc 2014; MarApr 21 (Suppl. 02) 315-25.
- 23 Mani S, Ozdas A, Aliferis C, Varol HA, Chen Q, Carnevale R. et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc 2014; Mar-Apr 21 (Suppl. 02) 326-36.
- 24 Amland RC, Hahn-Cover KE. Clinical decision support for early recognition of sepsis. Am J Med Qual 2014 Nov 10.
- 25 Jalali A, Buckley EM, Lynch JM, Schwab PJ, Licht DJ, Nataraj C. Prediction of periventricular leukomalacia occurrence in neonates after heart surgery. IEEE J Biomed Health Inform 2014; Jul 18 (Suppl. 04) 1453-60.
- 26 Bountris P, Haritou M, Pouliakis A, Margari N, Kyrgiou M, Spathis A. et al. An intelligent clinical decision support system for patient-specific predictions to improve cervical intraepithelial neoplasia detection. Biomed Res Int 2014; 2014: 341483.
- 27 El-Fakdi A, Gamero F, Meléndez J, Auffret V, Haigron P. eXiTCDSS: a framework for a work-flow-based CBR for interventional clinical decision support systems and its application to TAVI. Expert Syst Appl 2014; 41 (Suppl. 02) 284-94.