Development and Implementation of Computerized Clinical Guidelines: Barriers and Solutions

Abstract

SummaryNext, the article discusses the need to incorporate the use of surveys, questionnaires, or rating instruments for the collection of end-user feedback during and after the implementation process. A description of the types of rating instruments that will facilitate the assessment of user satisfaction is provided. Initial results from physician feedback during the implementation of our prototype are discussed. Research indicates that computerized decision support systems (CDSSs) can improve clinical performance and patient outcomes, and yet CDSSs are not in widespread use. Physician guidelines, in general, face barriers in implementation. Guidelines in a computerized format can overcome some of the barriers to conventional text-form guidelines; however, computerized programs have novel aspects that have to be considered, aspects such as technical problems/support and user interface issues that can act as barriers. Though the literature points out that human, organizational, and technical issues can act as barriers in the implementation of CDSSs, studies clearly indicate that there are methods that can overcome these barriers and improve CDSS acceptance and use. These methods come from lessons learned from a variety of CDSS implementation ventures. Notably, most of the methods that improve acceptance and use of a CDSS require feedback and involvement of end-users. Measuring and addressing physician or user attitudes toward the computerized support system has been shown to be important in the successful implementation of a CDSS. This article discusses: 1) the barriers of implementation of guidelines in general and of CDSSs; 2) the importance of the physician’s role in development, implementation, and adherence; 3) methods that can improve CDSS acceptance and use; and 4) the types of tools needed to obtain end-user feedback.

Maximizing the Adequacy of Medication Treatment in Controlled Trials and Clinical Practice: STAR*D Measurement-Based Care

“…The success of even the most well-developed protocols has been very limited in real-world practice (Cabana et al, 1999(Cabana et al, , 2002Trivedi et al, 2002). Further, even effective protocols have not been sufficient to meet patient needs in routine clinical care owing to Axis I and III comorbidities.…”

“…Computerizing an algorithm with feedback will help the physician to assimilate the information correctly and respond effectively. Subsequent analyses will address the impact of varying degrees of algorithm fidelity on patientlevel outcomes (Trivedi et al, , 2002.…”

“…However, even with enhanced treatment involving depression care specialists or clinical coordinators, remission rates often remain low (15-30%; Rush et al, 2004b). Wellestablished findings indicate that specific recommendations to guide treatment decisions are most effective when they are individualized and are provided while the clinical decision is being made (Trivedi et al, , 2002. Treatment trials for the management of major chronic medical illnesses, such as diabetes mellitus, hypertension, and asthma utilize laboratory as well as symptom and function measures in research settings that are readily usable in clinical practice.…”

Success Factors and Barriers for Implementation of Advanced Clinical Decision Support Systems

“…(Wessels-Basten et al, 2007) This problem can be solved by involving a multidisciplinary expert team to review and optimize the clinical rules. (Dexter et al, 2001;Trivedi et al, 2002;Osheroff et al, 2005;Leslie and Denvir, 2007;Wessels-Basten et al, 2007) One should compose the expert team carefully, being a reflection of end users and recognised experts in the particular field, as this determines the strength of the clinical rule developed. In fact, this is generally alike to the development of a new guideline.…”

“…This is a challenging task, because evidence based recommendations are often non-specific, multi-interpretable and either too complex to recall or too general to apply specifically. (Trivedi et al, 2002;Denekamp, 2007;Kuperman et al, 2007;Sucher et al, 2008) For example, according to guidelines, leukocyte counts should be measured frequent during lithium therapy, but the exact frequency, cut-off values and relevance is lacking. (Wessels-Basten et al, 2007) This problem can be solved by involving a multidisciplinary expert team to review and optimize the clinical rules.…”