Grand challenges in clinical decision support

  • Authors:

  • Dean F. Sittig;Adam Wright;Jerome A. Osheroff;Blackford Middleton;Jonathan M. Teich;Joan S. Ash;Emily Campbell;David W. Bates

  • Affiliations:

  • Department of Medical Informatics, Northwest Permanente, PC, Portland, OR, USA and Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, USA;Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, USA and Department of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Har ...;Thomson Healthcare, Denver, CO, USA and University of Pennsylvania Health System, Philadelphia, PA, USA;Clinical Informatics Research and Development, Partners HealthCare System, Boston, MA, USA;Elsevier Health Sciences, Philadelphia, PA, USA and Department of Medicine (Emergency Medicine), Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA;Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, USA;Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, USA;Department of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2008

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Abstract

There is a pressing need for high-quality, effective means of designing, developing, presenting, implementing, evaluating, and maintaining all types of clinical decision support capabilities for clinicians, patients and consumers. Using an iterative, consensus-building process we identified a rank-ordered list of the top 10 grand challenges in clinical decision support. This list was created to educate and inspire researchers, developers, funders, and policy-makers. The list of challenges in order of importance that they be solved if patients and organizations are to begin realizing the fullest benefits possible of these systems consists of: improve the human-computer interface; disseminate best practices in CDS design, development, and implementation; summarize patient-level information; prioritize and filter recommendations to the user; create an architecture for sharing executable CDS modules and services; combine recommendations for patients with co-morbidities; prioritize CDS content development and implementation; create internet-accessible clinical decision support repositories; use freetext information to drive clinical decision support; mine large clinical databases to create new CDS. Identification of solutions to these challenges is critical if clinical decision support is to achieve its potential and improve the quality, safety and efficiency of healthcare.