Clinical Decision Support Capabilities of Commercially-available Clinical Information Systems
- Adam Wright, PhDa,b,c,
- Dean F Sittig, PhDd,
- Joan S Ash, PhDe,
- Sapna Sharma, MBIe,
- Justine E Panga,b,
- Blackford Middleton, MD, MPH, MSca,b,c
- aPartners HealthCare, Boston, MA
- bBrigham and Women's Hospital, Boston, MA
- cHarvard Medical School, Boston, MA
- dUT–Memorial Hermann Center for Healthcare Quality and Safety, University of Texas School of Health Information Sciences at Houston, Houston, TX
- eOregon Health & Science University, Portland, OR
- Correspondence: Adam Wright, PhD, Partners HealthCare System, 93 Worcester St, Wellesley, MA 02481 (Email: ).
- Received 17 December 2008
- Accepted 28 May 2009
Background The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems.
Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems.
Methods The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features.
Results Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert.
Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern.
Conclusions These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.
The authors are grateful to James Carpenter, Brian Churchill, Sarah Corley, Melissa Honour, Micheal Krall, James McCormack, Dolores Pratt, Sandi Rosenfeld, Eric Rose, and Nicole Vassar, who provided the information on system capabilities used in this work. Without their willingness to be interviewed, to conduct demonstrations and to provide us with access to their information systems, the authors could not have completed the study.
This study was funded, in part, by AHRQ contract HHHSA29020080010 and NLM Research Grant R56-LM006942-07A1.
The funding agencies had no role in the design of the study, analysis of the data, interpretation of the results, or the decision to publish.