Connecting the dots: rule-based decision support systems in the modern EMR era

被引:0
|
作者
Vitaly Herasevich
Daryl J. Kor
Arun Subramanian
Brian W. Pickering
机构
[1] Mayo Clinic,Division of Critical Care Medicine, Department of Anesthesiology
[2] Mayo Clinic,Multidisciplinary Epidemiology and Translational Research in Intensive Care (METRIC)
来源
Journal of Clinical Monitoring and Computing | 2013年 / 27卷
关键词
Alert; Decision support systems; Sniffers; Monitor; EMR; False-alert; ICU;
D O I
暂无
中图分类号
学科分类号
摘要
The intensive care unit (ICU) environment is rich in both medical device and electronic medical record (EMR) data. The ICU patient population is particularly vulnerable to medical error or delayed medical intervention both of which are associated with excess morbidity, mortality and cost. The development and deployment of smart alarms, computerized decision support systems (DSS) and “sniffers” within ICU clinical information systems has the potential to improve the safety and outcomes of critically ill hospitalized patients. However, the current generations of alerts, run largely through bedside monitors, are far from ideal and rarely support the clinician in the early recognition of complex physiologic syndromes or deviations from expected care pathways. False alerts and alert fatigue remain prevalent. In the coming era of widespread EMR implementation novel medical informatics methods may be adaptable to the development of next generation, rule-based DSS.
引用
收藏
页码:443 / 448
页数:5
相关论文
共 50 条
  • [1] Connecting the dots: rule-based decision support systems in the modern EMR era
    Herasevich, Vitaly
    Kor, Daryl J.
    Subramanian, Arun
    Pickering, Brian W.
    JOURNAL OF CLINICAL MONITORING AND COMPUTING, 2013, 27 (04) : 443 - 448
  • [2] Uncertainty Management for Rule-based Decision Support Systems
    Mahesar, Quratul-Ain
    Dimitrova, Vania G.
    Magee, Derek R.
    Cohn, Anthony G.
    2017 IEEE 29TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2017), 2017, : 884 - 891
  • [3] Rule-based extensions of fuzzy cognitive maps for decision support systems
    Jasinevicius, Raimundas
    Petrauskas, Vytautas
    INFORMATION TECHNOLOGIES' 2008, PROCEEDINGS, 2008, : 72 - 77
  • [4] A scoping review of rule-based clinical decision support malfunctions
    Thayer, Jeritt G.
    Franklin, Amy
    Miller, Jeffrey M.
    Grundmeier, Robert W.
    Rogith, Deevakar
    Wright, Adam
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2024, 31 (10) : 2405 - 2413
  • [5] A rule-based decision support system for evaluating and selecting IS projects
    Deng, Hepu
    Wibowo, Santoso
    IMECS 2008: INTERNATIONAL MULTICONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND II, 2008, : 1962 - 1968
  • [6] A rule-based decision support system for aiding iron deficiency management
    Celik Ertugrul, Duygu
    Toygar, Onsen
    Foroutan, Neda
    HEALTH INFORMATICS JOURNAL, 2021, 27 (04)
  • [7] A belief rule-based decision support system for clinical risk assessment of cardiac chest pain
    Kong, Guilan
    Xu, Dong-Ling
    Body, Richard
    Yang, Jian-Bo
    Mackway-Jones, Kevin
    Carley, Simon
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2012, 219 (03) : 564 - 573
  • [8] Modelling a decision-support system for oncology using rule-based and case-based reasoning methodologies
    Rossille, D
    Laurent, JF
    Burgun, A
    INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2005, 74 (2-4) : 299 - 306
  • [9] A STATISTICALLY RULE-BASED DECISION-SUPPORT SYSTEM FOR THE MANAGEMENT OF PATIENTS WITH SUSPECTED LIVER-DISEASE
    KRUSINSKA, E
    BABIC, A
    MATHIESEN, U
    CHOWDHURY, S
    WIGERTZ, O
    BODEMAR, G
    FRANZEN, L
    MEDICAL INFORMATICS, 1993, 18 (02): : 113 - 130
  • [10] Intelligent Decision Support Systems Development Based on Modern Modeling Methods
    Serova, Elena
    PROCEEDINGS OF THE 6TH EUROPEAN CONFERENCE ON INFORMATION MANAGEMENT AND EVALUATION, 2012, : 282 - 290