Lessons Learned from Adapting a Generic Narrative Diabetic-Foot Guideline to an Institutional Decision-Support System

Clinical guidelines usually need to be adapted to fit local practice before they can be actually used by clinicians. Reasons for adaptation include variations of institution setting such as type of practice and location, availability of resources, differences in patient populations, local policies, and practice patterns. When a guideline is implemented for clinical decision support and integrated with an institution's clinical information system, the data model of the local electronic medical record (EMR) and the data actually collected and stored in it also influence the guideline's adaptation. The purpose of this work is: (1) to characterize a tool-supported process for guideline encoding that addresses local adaptation and EMR integration, and (2) to identify the types of changes in guideline encoding during the local adaptation process.

[1]  R. Shiffman.  Towards effective implementation of a pediatric asthma guideline: integration of decision support and clinical workflow support. , 1994, Proceedings. Symposium on Computer Applications in Medical Care.

[2]  M. Field,et al.  Clinical practice guidelines : directions for a new program , 1990 .

[3]  Henrik Eriksson,et al.  The evolution of Protégé: an environment for knowledge-based systems development , 2003, Int. J. Hum. Comput. Stud..

[4]  Omolola Ogunyemi,et al.  Toward a Representation Format for Sharable Clinical Guidelines , 2001, J. Biomed. Informatics.

[5]  Omolola Ogunyemi,et al.  GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines , 2004, J. Biomed. Informatics.

[6]  Alicia O. Scott-Wright,et al.  A Methodology for Modular Representation of Guidelines , 2004, MedInfo.

[7]  D. Goldmann,et al.  Adaptation of the Centers for Disease Control guidelines for the prevention of nosocomial infection in a pediatric intensive care unit in Jakarta, Indonesia. , 1991, The American journal of medicine.

[8]  A. Liberati,et al.  Essential requirements for practice guidelines at national and local levels , 2004, Neurological Sciences.

[9]  Robert A. Greenes,et al.  Support for guideline development through error classification and constraint checking , 2002, AMIA.

[10]  Alessio Bottrighi,et al.  A Context-Adaptable Approach to Clinical Guidelines , 2004, MedInfo.

[11]  D. Fridsma,et al.  Making generic guidelines site-specific. , 1996, Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium.

[12]  John Fox,et al.  Comparing computer-interpretable guideline models: a case-study approach. , 2003, Journal of the American Medical Informatics Association : JAMIA.

[13]  Omolola Ogunyemi,et al.  Design and implementation of the GLIF3 guideline execution engine , 2004, J. Biomed. Informatics.

[14]  Annemarie A. Edwards,et al.  Diabetic foot disorders: A clinical practice guideline , 2000 .

[15]  Mor Peleg,et al.  Approaches for Guideline Versioning Using GLIF , 2003, AMIA.

[16]  J. Marc Overhage,et al.  Case Report: Computerizing Guidelines to Improve Care and Patient Outcomes: The Example of Heart Failure , 1995, J. Am. Medical Informatics Assoc..

[17]  William DuMouchel,et al.  A meta-analysis of 16 randomized controlled trials to evaluate computer-based clinical reminder systems for preventive care in the ambulatory setting. , 1996, Journal of the American Medical Informatics Association : JAMIA.