Clinicians’ perceptions about use of computerized protocols: A multicenter study

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Abstract

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.

Introduction

Computerized guidelines providing decision support are increasingly being used in various domains of routine clinical practice. These tools decrease practice variation between clinicians [1], standardize patient care [2], and improve patient outcomes [2], [3]. Despite their proven value, clinicians’ adoption of guidelines has had limited success [4], [5], [6], [7], [8]. Phillips et al. described the failure to implement valid evidence-based guidelines as ‘clinical inertia’ [9]. Grol noted that barriers to adoption include failure to imbed the guideline into workflow, the ease of data entry, the degree of involvement regarding protocol design and the support of administration [10].

Computerized guidelines vary significantly in terms of how dynamic they are, the degree of specificity of their recommendations and the level of integration into workflow [1]. On one end of the spectrum exist non-explicit computerized guidelines that consist of a set of static recommendations [11] or pop-up reminders regarding a recommended care process [12]. On the other end are computerized guidelines that function as a set of standardized orders, with detailed, explicit instructions based on dynamic patient-specific parameters, available at the point-of-care in complex clinical scenarios [13], [14]. The focus of this paper is on the latter type of computerized guidelines which we call ‘explicit computerized protocols’ [15].

Explicit computerized protocols have been used in the intensive care unit (ICU) at Latter Day Saints (LDS) Hospital, in Salt Lake City, Utah, since 1985 [16]. Once a patient is ordered to be on a particular protocol by a physician, the nurses, respiratory therapists and other providers assigned to care for the patient follow a set of standardized orders. Detailed recommendations are received at the patient's bedside and changes in the patient's treatment plan are made by nurses and respiratory therapists based on the results of pre-established algorithms. The protocols were developed and monitored by an interdisciplinary team of ICU clinicians and based on the latest scientific evidence. The logical reasoning behind the instructions can be viewed directly and clinicians have the ability to over-ride the protocol instructions [1], [17].

The attitudes of physicians and the barriers to the use of guidelines in general have been studied previously [5], [18], [19]. Studies examining provider perceptions of non-explicit computerized guidelines have noted concerns with “black box” instructions, the accuracy of instructions, the lack of flexibility to adapt to varied situations and a reduced role for clinicians in medical practice [5]. Explicit computerized protocols are specifically developed to provide more standardized decision-making for patient care; these protocols thus provide even less flexibility for clinician judgment than non-explicit computerized guidelines. The socio-behavioral impact of explicit computerized protocols on clinicians may differ from that of non-explicit computerized guidelines. Previous studies have expressed the need for research on factors affecting adoption that are specific to the technology under consideration, so as to improve their predictive ability [20], [21]. Thus, there is a need to identify the specific cognitive and attitudinal factors associated with clinicians’ adoption of explicit computerized protocols.

The goal of this paper is to report the development and validation of an assessment instrument for assessing clinicians’ perceptions about use of explicit computerized protocols. An understanding of clinicians’ perceptions will help determine barriers to adoption and enable protocol developers, clinical administrators and health service researchers design interventions to better meet the needs of end-users [6].

Section snippets

Methods

The study was conducted in two stages. The first stage consisted of a qualitative assessment and analysis based on interviews of experienced clinicians giving rise to a cognitive model evaluating the factors that motivate clinicians to use explicit computerized protocols. Development and validation of the instrument constituted the second stage.

Results

The instrument had a response rate of 84.2%. The final sample consisted of 240 clinicians, including 53 physicians, 132 nurses and 55 respiratory therapists. The demographic characteristics of the participants are reported in Table 2.

Following factor analysis the scree plot indicated that nine factors had eigen values greater than 1.0. These nine factors explained 65.8% of the total variance after varimax rotation. Inspection of the factor structure revealed that except for the construct of

Discussion

The purpose of this study was to develop and validate an instrument for assessing clinicians’ perceptions about use of explicit computerized protocols. The results of this work provide a theoretical framework for assessing clinicians’ perceptions about adoption of explicit computerized protocols.

Many of the results found in this study were similar to previous work in the area of technology adoption. In our study, we found that the strongest predictor of clinicians’ intention to use computerized

Limitations

Certain limitations of this study could have potentially biased our findings. Some items were eliminated following factor analysis, owing to various reasons. The items “trust among clinicians” (Item 33) and “ability to customize the protocols” (Item 13) did not load onto any factor. We think that both these items might depend heavily on clinicians’ hands-on experience with use of computerized protocols. Clinicians, who were experienced in using these protocols in routine practice, clearly

Conclusions

The analysis of clinicians’ perceptions about use of computerized protocols in clinical practice has provided useful insights into those factors that may influence intention to adopt computerized protocols. The key to enhancing clinicians’ behavioral intention to adopt computerized protocols will be to recognize the implications of these factors and tailor the design and implementation so as to meet the needs of end-users. Associated with the need to understand these factors is a deeper need

Acknowledgements

The authors wish to thank Sherry P. Tesseyman for her help during the conceptualization of the qualitative model. The authors gratefully acknowledge Dr. Jonathan R. Nebeker for his helpful suggestions in revising this manuscript. This project was supported in part by NIH/NHLBI ARDS Network No 1-HR-46062. The qualitative model was presented at the national meeting of the American Medical Informatics Association, in 2003, at Washington, DC.

Summary points

What was known before the study?

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