The evolution of eProtocols that enable reproducible clinical research and care methods.

Unnecessary variation in clinical care and clinical research reduces our ability to determine what healthcare interventions are effective. Reducing this unnecessary variation could lead to further healthcare quality improvement and more effective clinical research. We have developed and used electronic decision support tools (eProtocols) to reduce unnecessary variation. Our eProtocols have progressed from a locally developed mainframe computer application in one clinical site (LDS Hospital) to web-based applications available in multiple languages and used internationally. We use eProtocol-insulin as an example to illustrate this evolution. We initially developed eProtocol-insulin as a local quality improvement effort to manage stress hyperglycemia in the adult intensive care unit (ICU). We extended eProtocol-insulin use to translate our quality improvement results into usual clinical care at Intermountain Healthcare ICUs. We exported eProtocol-insulin to support research in other US and international institutions, and extended our work to the pediatric ICU. We iteratively refined eProtocol-insulin throughout these transitions, and incorporated new knowledge about managing stress hyperglycemia in the ICU. Based on our experience in the development and clinical use of eProtocols, we outline remaining challenges to eProtocol development, widespread distribution and use, and suggest a process for eProtocol development. Technical and regulatory issues, as well as standardization of protocol development, validation and maintenance, need to be addressed. Resolution of these issues should facilitate general use of eProtocols to improve patient care.

Evaluation and comparison of IV insulin-treatment protocols using data from critically ill patients in the ICU.

In critically ill patients control of blood sugar levels with IV insulin has been shown to improve clinical outcomes in the intensive care units. We have developed an analytical framework with which to evaluate and compare IV insulin-treatment models and protocols. Performance of the analytical framework is demonstrated using protocols published by others and new protocols under development by our group.

Using a diabetes data mart in individualizing diabetes management.

Constantly changing diabetes care standards makes it challenging to deliver care adapted to the unique condition of the individual patient. The availability of large amounts of data from patient's electronic medical records makes it possible to individualize diabetes management. Initial design of a "patient-specific" hybrid system (physiological-causal probabilistic) of adaptive diabetes models and insulin treatment algorithms will be presented. The system is uniquely derived and tested using a diabetes data mart of about 33,000 patients.

Clinicians' perceptions about use of computerized protocols: a multicenter study.

PURPOSE: Implementation of evidence-based techniques, such as explicit computerized protocols, has achieved limited success among clinicians. In this study, we describe the development and validation of an instrument for assessing clinicians' perceptions about use of explicit computerized protocols. METHODS: Qualitative assessment of semi-structured interviews with clinicians gave rise to a cognitive model evaluating the factors that motivate clinicians to use explicit computerized protocols. Using these constructs we developed a 35-item instrument which was administered to 240 clinicians (132 nurses, 53 physicians and 55 respiratory therapists), in three health-care institutions. RESULTS: Factor analysis identified nine factors that accounted for 66% of the total variance cumulatively. Factors identified were: Beliefs regarding Self-Efficacy, Environmental Support, Role Relevance, Work Importance, Beliefs regarding Control, Attitude towards Information Quality, Social Pressure, Culture, and Behavioral Intention. The strongest predictor was Beliefs regarding Self-Efficacy, which accounted for 26% of the total variance of intention to use explicit computerized protocols. Results supported the reliability and construct validity of the instrument. CONCLUSIONS: Clinicians' perceptions play a critical role in determining their intention to use explicit computerized protocols in routine clinical practice. Behavioral theories will help us understand factors predicting clinicians' intention to use explicit computerized protocols and recognize the implications of these factors in the design and implementation of these protocols.

Cognitive evaluation of the predictors of use of computerized protocols by clinicians.

We describe a cognitive approach to evaluating the factors that motivate clinicians to use computerized protocols. Using Value-Expectancy theories we developed an open-ended interview to assess clinicians' beliefs and experiences about the use of computerized protocols. Using a qualitative methodology, 3 reviewers independently identified key concepts raised by 13 interviewees. These concepts were aggregated and independently sorted into 39 categories. Then final categories were chosen by consensus. Analysis of the concepts showed consistency across clinician specialties of physicians, nurses and respiratory therapists. Inter-rater reliability calculated using Cohen's Kappa was 0.474. Identified constructs from Value-Expectancy and Intrinsic Motivation theories were: Work Importance, Perception of Situation, Role Relevance, Beliefs regarding Control, Beliefs regarding Normative Expectations, Beliefs regarding Self-Efficacy, Attitude, Habit, Environmental Support and Pre-Behavior. This model will form the basis for an instrument to assess the beliefs and expectations of clinical use regarding the use of computerized protocols.