Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems
- Adam Wright1,2,3,
- Dean F Sittig4,
- Joan S Ash5,
- Joshua Feblowitz1,2,
- Seth Meltzer2,
- Carmit McMullen6,
- Ken Guappone7,
- Jim Carpenter7,
- Joshua Richardson8,
- Linas Simonaitis9,
- R Scott Evans10,
- W Paul Nichol11,
- Blackford Middleton1,2,3
- 1Division of General Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
- 2Partners HealthCare, Boston, Massachusetts, USA
- 3Harvard Medical School, Boston, Massachusetts, USA
- 4UT – Memorial Hermann Center for Healthcare Quality and Safety, University of Texas School of Biomedical Informatics, Houston, Texas, USA
- 5Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
- 6Kaiser Permanente Center for Health Research, Portland, Oregon, USA
- 7Regional Information Services, Providence Portland Medical Center, Portland, Oregon, USA
- 8Center for Healthcare Informatics and Policy, Weill Cornell Medical College, New York City, USA
- 9Department of Medical Informatics, Regenstrief Institute, Inc, Indiana University School of Medicine, Indianapolis, Indiana, USA
- 10Department of Medical Informatics, Intermountain Health Care, Salt Lake City, Utah, USA
- 11Patient Care Services, Veterans Health Administration, Seattle, Washington, USA
- Correspondence to Adam Wright, Brigham and Women's Hospital, 1620 Tremont St, Boston, MA 02115, USA;
- Received 12 January 2011
- Accepted 9 February 2011
- Published Online First 17 March 2011
Background Clinical decision support (CDS) is a valuable tool for improving healthcare quality and lowering costs. However, there is no comprehensive taxonomy of types of CDS and there has been limited research on the availability of various CDS tools across current electronic health record (EHR) systems.
Objective To develop and validate a taxonomy of front-end CDS tools and to assess support for these tools in major commercial and internally developed EHRs.
Study design and methods We used a modified Delphi approach with a panel of 11 decision support experts to develop a taxonomy of 53 front-end CDS tools. Based on this taxonomy, a survey on CDS tools was sent to a purposive sample of commercial EHR vendors (n=9) and leading healthcare institutions with internally developed state-of-the-art EHRs (n=4).
Results Responses were received from all healthcare institutions and 7 of 9 EHR vendors (response rate: 85%). All 53 types of CDS tools identified in the taxonomy were found in at least one surveyed EHR system, but only 8 functions were present in all EHRs. Medication dosing support and order facilitators were the most commonly available classes of decision support, while expert systems (eg, diagnostic decision support, ventilator management suggestions) were the least common.
Conclusion We developed and validated a comprehensive taxonomy of front-end CDS tools. A subsequent survey of commercial EHR vendors and leading healthcare institutions revealed a small core set of common CDS tools, but identified significant variability in the remainder of clinical decision support content.
- Developing/using computerized provider order entry
- knowledge representations
- classical experimental and quasi-experimental study methods (lab and field)
- designing usable (responsive) resources and systems
- statistical analysis of large datasets
- text and data mining methods
- automated learning
- human-computer interaction and human-centered computing
- qualitative/ethnographic field study
- clinical decision support
- manning maddux
- decision support
- biomedical informatics
- developing and refining EHR data standards (including image standards)
- controlled terminologies and vocabularies
- measuring/improving patient safety and reducing medical errors
- machine learning
- electronic health records
- meaningful use
This paper is dedicated to the memory of POET team members Cody Curtis, MBA, and Richard Dykstra, MD, MS.
Funding This project was supported by NLM Grant R56-LM006942.
Competing interests None.
Provenance and peer review Not commissioned; externally peer reviewed.