Globally, healthcare organizations fail to use evidence optimally. A large gulf remains between what we know and what we practice. In 1973, Wennberg and Gittelsohn published their landmark article demonstrating substantial variation among different healthcare service providers[1]. 40 years later, this situation had not been changed. One study in 2003 showed that only 54.9% US patients received recommended care[2]. Similar findings have been reported globally in both developed and developing settings, in care provided by all disciplines[3]. There still lacks insufficient tools and incentives to promote rapid adoption of best practice[4].

To address this problem, knowledge translation has been proposed and widely studied over the past decades. According to the Canadian Institutes of Health Research (CIHR), knowledge translation has been defined as “ a dynamic and iterative process that includes the synthesis, dissemination, exchange and ethically sound application of knowledge to provide more effective health services and products and strengthen the healthcare system” [5]. Knowledge translation has now become the biggest challenge faced by evidence based medicine [6]. Knowledge translation includes knowledge acquisition, knowledge dissemination, and knowledge application in the form of clinical decision support (CDS).

The Knowledge Translation Platform was built based on a comprehensive motivation framework [7] to support the entire knowledge translation life cycle, which includes not only acquisition and dissemination, but also “ethically sound application of knowledge” in healthcare system. This project is a pilot effort in building a systematic knowledge translation platform in China. The platform has been implemented in a 2,600-bed Chinese hospital (DaYi Hospital, ShanXi Province, China).


1. Wennberg, J.E. and A.M. Gittelsohn. Small area variations in health care delivery. 1973: American Association for the Advancement of Science.
2. McGlynn, E.A., et al., The quality of health care delivered to adults in the United States. New England Journal of Medicine, 2003. 348(26): p. 2635-2645.
3. Grol, R., Successes and failures in the implementation of evidence-based guidelines for clinical practice. Medical care, 2001. 39(8): p. II-46-II-54.
4. Institute of Medicine . Committee on Quality of Health Care in America, Crossing the quality chasm: A new health system for the 21st century. 2001: National Academies Press.
5. Tetroe, J., Knowledge translation at the Canadian Institutes of Health Research: A primer. Focus Technical Brief, 2007. 18: p. 1-8.
6. Guyatt, G., D. Cook, and B. Haynes, Evidence based medicine has come a long way. British Medical Journal, 2004. 329(7473): p. 990.
7. Zhang Y, Li H, Duan H, and Y. Zhao, Mobilizing Clinical Decision Support to Facilitate Knowledge Translation: A Case Study in China[J]. Computers in Biology and Medicine, 2015,60. Download PDF


Zhang Y, Li H, Duan H. A Motivation Framework for Knowledge Translation in China. AMIA Symposium 2014, 2014. Download PDF


Based on the Knowledge Translation Platform, typical CDS applications have been developed and integrated into the CPOE (Computerized Physician Order Entry) system.

CDS applications in CPOE. (A) Diagnostic CDS; (B) Treatment Recommendation; (C). Notification area for drug use check; (D) Infobutton for drug information; (E) Retrieved drug information by Infobutton

(A) is a problem list in CPOE. It shows all detected clinical problems by diagnostic rules. For each problem, the diagnostic rule and related facts can be reviewed by physician.
(B) is a list of clinical pathways and standard order set recommended for specific clinical problem.
(C) shows drug use alert message. When double clicking a message, CPOE will navigate to corresponding medical order or prescription.

ShanXi Dayi Hospital is a 2,600-bed, also the biggest hospital in ShanXi Province, China.

The Knowledge Translation Platform was initiated by Dr. Haomin Li, PhD and Yinsheng Zhang, PhD

Supported by the China’s National High-Tech R&D Program (2012AA02A601) and the National Natural Science Foundation of China (30900329).