Secondary Logo

Journal Logo

FEATURE ARTICLE

Clinical Decision Support Systems in Nursing

Synthesis of the Science for Evidence-Based Practice

ANDERSON, JANE A. MSN, RN, FNP, BC; WILLSON, PAMELA PhD, RN, FNP, BC

Author Information
CIN: Computers, Informatics, Nursing: May 2008 - Volume 26 - Issue 3 - p 151-158
doi: 10.1097/01.NCN.0000304783.72811.8e
  • Free

Abstract

Evidence-based practice is a systematic approach to healthcare delivery that requires the application of current research to ensure that patients receive the most consistent and best care possible.1 Clinical practice guidelines (CPG), based on the latest available research and expert consensus, are an important tool for implementing evidence-based practice and have been shown to reduce practice variability and improve patient outcomes.2 Despite the inherent benefits, clinicians' adherence to the recommendations contained in CPG remains low.3-5 The use of information technology to translate research findings into practice has been encouraged by policymakers.6

Clinical decision support systems (CDSSs) are computer software applications that match patient characteristics with a knowledge base to generate specific recommendations. When CDSSs apply evidence-based recommendations at the point of care, they are termed evidence-adaptive; moreover, these systems show promise as a means for bridging the gap between evidence and practice.7 The aim of this article is to present the state of nursing science regarding the development, use, and application of CDSSs for the implementation of evidence-based practice in nursing. Conclusions and recommendations for future research are discussed.

METHODS

We sought to answer three specific questions in this metasynthesis: (1) What progress has nursing science made regarding the development and use of CDSSs?; (2) What research methods and theoretical models are being applied by nurse researchers in this area?; and (3) Are there evidence-adaptive CDSSs designed specifically to aid nurses' decisions related to evidence-based practice?

Articles included in our synthesis were English only and randomized and nonrandomized clinical trials. We included articles that discussed CDSSs specific for nurses' decisions that also met the established a priori definitions for CDSSs and evidence-adaptive CDSSs as follows: (1)CDSSs are computer applications that match patient characteristics with a computer knowledge base and provide the clinician with patient-specific recommendations to assist clinical decision making and (2)evidence-adaptive CDSSs are computer applications that link to a clinical knowledge base that is derived from, and continually reflects, the most up-to-date evidence from the research literature and practice-based sources.7

An automated literature search was completed using the databases MEDLINE, CINAHL, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews. Both keyword and MeSH search terms were applied. The phrase nursing or clinical practice guidelines or evidence-based practice was entered individually with each of the following search terms: decision support systems, expert systems (ESs), computer decision aids, computer-assisted reminders, information systems, and computers. Additional search methods included PubMed-related articles, informatics conference proceedings, hand search of nursing informatics' journals, and the Internet search engine Google.

From a total of 183 citations, 57 articles were screened on the basis of the content of the abstract, and 23 were identified by both reviewers for inclusion after retrieval of the publication. Articles on CDSSs for use by both physicians and nurses were omitted, leaving a total of 17 research articles that investigated CDSSs specifically for facilitating nurses' clinical decision making (Figure 1).

FIGURE 1
FIGURE 1:
Inclusion and exclusion criteria for literature search on evidence adaptive CDSSs in nursing.

From the 17 research articles identified, six were specific for CDSSs to aid nurses' decisions in implementing evidence-based practice (Table 1).

Table 1
Table 1:
Clinical Decision Support Systems for Evidence-Based Nursing Care

PROGRESS ON THE DEVELOPMENT AND USE OF CLINICAL DECISION SUPPORT SYSTEMS IN NURSING

The first CDSS application for nursing was developed in the 1970s and known as the Creighton Online Multiple Modular Expert System or COMMES. It was designed to assist nurses in care planning activities and evaluated in multiple settings.8 Advances in technology have now made computerized decision support for care planning available to nurses on handheld computers.9 Multiple CDSS prototypes have been developed to assist with determining nursing diagnoses,10,11 but most of these systems were developed as tools for evaluating the decisions that nurses make, rather than as tools to help nurses make clinical decisions.12 More recently, systems have focused on assisting nurses in decision making based on specific patient care problems. Several CDSSs have been developed to aid nurses in decisions on the prevention and treatment of pressure ulcers.13,14 Nurse advisors use decision support software to advise callers on multiple acute and chronic conditions.15 Emergency department triage is also an area where nurses are utilizing CDSSs to facilitate emergency triage assessment and categorization.16,17 Nurses are using CDSSs for the management of oral anticoagulation.18 Web-based systems are being developed to assist nurses in the management of cancer pain.12,19

Expert systems are CDSSs that replicate the decisions of human experts. Nurse researchers have been developing and testing ESs to aid nurses in clinical decision making for more than two decades.20 One of the first nursing ESs was designed to assist consultations between advanced practice nurses (APNs) and staff nurses caring for nursing home patients with urinary incontinence.21 Jirapaet22 developed an ES prototype to facilitate nurses' decision making while caring for mechanically ventilated neonates (MVN). The Nurse Computer Decision Support Project (N-CODES) is an ES that provides novice nurses with the decision support assistance of an experienced preceptor, is provided via a handheld computer, and is known as a "pocket preceptor."23

RESEARCH METHODS AND MODELS APPLIED TO EVALUATE CLINICAL DECISION SUPPORT SYSTEMS IN NURSING

Qualitative Methods and Models

Six of 17 studies applied qualitative methods to investigate CDSSs in nursing. In a descriptive phenomenological study, Caelli et al24 explored the necessary data to develop a health promotion CDSS. This pilot study demonstrated that data collection must be broad and should include both observational and descriptive data and incorporate focus group interviews, videotaping, and computer science approaches to data generation for modeling the types of decisions that nurse should engage in for health promotion practice.

Cathain et al15 explored nurses' views of their role as nurse advisor and the use of CDSS software in the National Health Service Direct (a 24-hour telephone advice line in England, Wales, and Scotland). Nurses described both the software and themselves as essential to the clinical decision-making process. The CDSS software was viewed as a safety net that facilitated consistency and guidance when more clinical knowledge was needed relative to the call. Nurses described a dual process of decision making, wherein the nurse as the active decision maker was looking for consensus with the software recommendations but was ready to override the software recommendations, if necessary.

A study by Clark et al14 was undertaken to determine nurses' perceptions of effective implementation strategies for the wound and skin intelligence system: a CDSS for the prediction, prevention, and management of pressure ulcers using CPG. The implementation project was based on Rogers'25Diffusion of Innovation theory and Bandura's26Social Learning Theory. Analysis of identified themes from team meeting minutes, nurses' activity logs, and interviews with senior nurse managers revealed the following barriers to the use of CDSSs for implementing CPG: (1) lack of consistent administrative leadership, (2) time required to learn and implement new guidelines, (3) technological deficiencies of hospital computer systems, and (4) competencies related to learning the computer decision support system. The identified benefits were (1) increased communication between members of the interdisciplinary team, (2) increased likelihood of staff identifying issues related to the management of pressure ulcers, (3)increased use of information related to available resources, and (4) improved consistency of care.

Eley et al17 investigated emergency department nurses' perceptions of the Toowoomba Adult Triage Trauma Tool (TATTT) as a CDSS for nurses in emergency triage. The suitability of various developed simulations as well as patient educational training materials was assessed. Overall, the emergency department nurses made positive comments about the TATTT as a triage tool. All participants believed that the pocket personal computer and training simulation were excellent teaching tools for learning to triage, and all participants believed that the education materials used for training were good. Some comments cautioned that the TATTT could undermine the nurses' triage role by enabling the replacement of nurses with nonnurses for emergency triage.

Im and Chee19 applied mixed methods to analyze an Internet survey and e-mail group discussions of faculty members from 10 countries who were oncology nurses. The purpose of the investigation was to collect data on cancer pain from the perspective of multicultural expert oncology nurses. Findings were then applied in developing computer software to assist oncology nurses in clinical decision making related to cancer pain.

O'Neill et al23 completed the first trial of N-CODES to determine whether the clinical decision-making model (CDMM) and the novice clinical-reasoning model (NCRM) adequately represent nurses' decision-making processes. Both models have been developed as the theoretical bases for N-CODES, a CDSS to aid novice nurses' decision making in critical care.27 The researchers point out that previous attempts to build comprehensive CDSSs in nursing have disregarded theoretical models of nursing decision making: a point that can be supported from this synthesis. Only three of the 17 articles identified in this synthesis included theoretical models to support research of CDSSs in nursing.12,14,23

In addition to model testing, O'Neill et al23 applied thematic analysis to encode nurses' written responses to questions related to a scenario of a critical care patient developing pneumonia. Focus group analyses determined whether the pneumonia practice map contained in N-CODES was appropriate, complete, and sequenced correctly according to nurses' decision-making styles. Results of the analyses showed that the sequencing of information in the CDMM and NCRM models was appropriate. One interesting point was that nurse users repeatedly asked where the information provided by N-CODES came from, indicating the process of developing the knowledge base needed to be more apparent to the nurse users.

Quantitative Methods and Models

Six of the 17 studies on CDSSs in nursing applied an experimental or quasi-experimental design. In a randomized controlled trial, Fitzmaurice et al18 found that a nurse-led clinic using on-site blood testing and a computer system to direct nurses' decisions on warfarin dose adjustments was as effective for managing patients' anticoagulation as an in-hospital clinic. In the intervention group, nurses met with the patients, drew the patient's blood sample and measured international normalized ratio (INR) levels with on-site equipment, and used the anticoagulation management support system to direct decisions about dosing warfarin. While patients in the nurse-led clinic maintained longer intervals of therapeutic INR values than did the patients treated in the hospital clinic, costs were found to be significantly higher.

In the early 1990s, two studies were completed on the urological nursing information system (UNIS).20,28 This system was designed as an ES to replicate consultations performed by APNs caring for nursing home residents with urinary incontinence.20 Petrucci21 compared the performance of the UNIS with the performance of the APNs, using randomly selected simulated case studies. Questions and recommendations provided by the UNIS and the APNs were organized into subject profiles and then reviewed by a blinded panel of nurse experts. The experts assigned relevance scores to the questions and recommendations provided by the UNIS and the APNs, and scores were then compared between the two groups. There was a significant difference between groups on overall performance, F4,16 = 10.46, P = .01. The UNIS scored higher than did the APNs on four of five simulated cases and also asked significantly more questions (F4,16 = 11.53, P = .01) than the APNs.

The UNIS was also evaluated in a randomized controlled trial conducted in a long-term care setting.28 Patient care units were matched for patient acuity and staffing patterns and randomly assigned to one of three treatment arms. Unit 1 used UNIS for 10 weeks with 2 weeks of user support, unit 2 used UNIS for 10 weeks with continuous user support, and unit 3 (control group) was not exposed to UNIS. A repeated-measures analysis of variance was completed to assess differences between the units on the number of wet occurrences and on measures of nurses' knowledge about incontinence. There was a significant difference favoring the treatment units for fewer number of wet occurrences over time, F2,81 = 34.67, P = .001, and F9,81 = 29.8, P = .001, respectively. Nurses interacting with UNIS scored significantly higher on the knowledge tests overtime, F2,157 = 19.46, P = .001, and F3,157 = 191.22, P = .001, respectively.

Decision aids are electronic devices designed to assist patients in making decisions about their healthcare choices, and there is evidence to support their effectiveness.29 Creating better Health Outcomes by Improving Communications about patients' Expectations (CHOICE) is a handheld computer-based CDSS for preference-based care planning9 that is designed to help nurses elicit patient preferences for functional performance at the bedside. A quasi-experimental, three-group sequential design was applied to investigate the effect of CDSS on nursing care priorities and patients' preferences. In group 1 (the intervention group), nurses elicited patient preferences for functional performance using CHOICE. For group 2, a study nurse elicited patient preferences for functional performance without using CHOICE, and in group 3, patients received usual care. Analysis of group differences for care priorities, patient preferences, and consistency between the two was completed. Nurses using CHOICE developed care plans more consistent with patient preferences, F103,1 = 11.4, P <.001, and improved patient preference achievement, F103,1 = 4.9, P < .05. There was a higher consistency between patient preferences and nursing care priorities in the CHOICE group, r = 0.49, P < .0001.

An ES prototype to assist nurses' decisions while caring for MVN was developed by Jirapaet.22 A quasi-experimental, single-group, pretest-posttest design was used to determine the impact of the ES-MVN on neonatal nurses' clinical judgment and information access capabilities. Using convenience sampling (N = 16), neonatal nurses working in a tertiary-care hospital were enrolled in the study. Prior to the intervention, all nurses completed a paper-and-pencil questionnaire, which evaluated their perceptions of clinical judgment and information access capabilities. The intervention involved training the nurses on the use of the ES-MVN with 10 case simulations. Nurses then completed a posttest questionnaire to reassess their perceptions of clinical judgment and information access capabilities. Paired t test on prescores and postscores showed a significant increase in nurses' performance scores of diagnoses and management care with the ES-MVN, t(15) = 17.21, P = .0001 (two-tailed). After the nurses used the ES-MVN, their scores for perceptions of their information access capability and clinical judgment ability were significantly higher than those before use of the ES-MVN, t(15) = 6.91, P = .0001, and t(15) = 17.53, P = .0001, respectively.

Zielstorff et al13 developed the Pressure Ulcer Prevention and Management System (PUPMS) to assist nurses with individualized guideline-based treatment plans for patients who have, or are at risk for, pressure ulcers. Experimental and nonexperimental protocols were developed to determine (1) if the system was acceptable to instructional and content experts, (2) if the system improved nurses' knowledge about pressure ulcer management and clinical decision-making skills, and (3) if the system was acceptable for use by clinicians. To determine whether the CDSS affected nurses' knowledge and clinical decision-making skills, experimental and control groups were recruited from two nursing units (N = 39). Both groups completed a pretest to determine baseline knowledge of pressure ulcer management. To test for improvements in decision making, three computer-based simulation programs pertaining to pressure ulcer prevention and treatment were developed. The PUPMS was implemented on the experimental unit for 21 weeks. Results found that a 21-week exposure to the CDSS had no effect on nurses' knowledge or clinical decision making related to pressure ulcer prevention.

Five of the 17 studies on CDSS in nursing applied a nonexperimental design. In addition, Zielstorff et al13 evaluated the instructional adequacy of PUPMS by having three registered nurses with expertise in pressure ulcer management and instructional technology complete a modified version of the Underwood Software Evaluation Tool.30 To determine end user satisfaction, nurses completed a 12-item questionnaire31 designed to measure computer end user satisfaction. The system received an overall positive rating from all 15 nurse experts and users. In addition, qualitative analysis of written comments and face-to-face interviews with the nurses from the experimental unit were reported and substantiated the overall acceptance of the CDSS.

In an observational study, Dong et al16 evaluated the accuracy of the eTRIAGE decision support system. Consecutive patients presenting to a large urban tertiary care emergency department were assessed by triage nurses and again by study nurses using the eTRIAGE decision support tool. Triage score distribution and agreement between both triage methods were analyzed by an expert panel. Nurses' triage scores showed lower agreement than did eTRIAGE tool scores. The variability in nurses' memory-based triage scores would be expected on the basis of level of experience; however, a CDSS applies the same rules consistently to every patient every time.

Nurses' acceptance of the decision support computer program for cancer pain management was evaluated among 122 oncology nurses.12 A feminist perspective was used as a theoretical guide for the study with the assumption that nurses' acceptance of CDSSs is related to both their continuous interaction with their environment and biases reflecting their view of the world. In this study, the sociodemographic and professional backgrounds of the nurses (including sex and ethnicity) were viewed as significant characteristics to structure their acceptance of the CDSS. Nurses' acceptance of the CDSS was assessed using the Questionnaire for User Interaction Satisfaction.32 Data were analyzed using descriptive and inferential statistics, including analysis of variance and correlation analysis. There were significant differences in the total scores of user satisfaction by sex, religion, ethnicity, job title, and specialty. The results suggest that nurses do welcome decision support systems and that sociodemographic and professional characteristics should be considered in development.

Stroud et al33 completed a descriptive correlational survey to determine the prevalence and patterns of use of personal digital assistants (PDAs) by nurse practitioner students and faculty, examine relationships between patterns of use of PDAs and demographic characteristics, and describe patterns of use of PDAs that support evidence-based practice. The majority of participants used PDAs and had been doing so for no more than a year. Use was higher among men than women, and respondents reported using PDAs most days of the week. Most participants related that PDA use supported clinical decision making.

Finally, Chin et al34 published findings from their most recent evaluation of the N-CODES system. Mixed methods were applied in this descriptive study. Usability, navigation, and nurse satisfaction of an N-CODES prototype were evaluated by a sample (n = 10) of nurses. The participants were led through a research protocol, which consisted of a series of eight tasks to solve patient problems while using N-CODES. The researchers recorded participant progress through the protocol on an observation data-recording sheet. Investigators recorded the time to complete the tasks, any difficulties in navigating screens, participant comments, and nonverbal behaviors, such as evidence of frustration. Participants also completed a 15-item usability Likert-type scale and were asked to provide additional feedback.

Time to complete the eight tasks ranged from 21 to 48 minutes. Results of the usability questionnaire indicate that nurses agreed/strongly agreed to all positively phrased questions. Overall, qualitative data yielded positive comments from the nurses. The participants agreed that the program would be helpful to both new nurses and experienced nurses. Nurses were also positive in their responses about the system and potential to improve clinical decision making. In following the work being done by these nurse researchers, it is evident that nurses are receptive to using CDSS to facilitate clinical decision making, and systems such as N-CODES have great potential to improve the process and outcomes of nursing care.

APPLICATION OF CLINICAL DECISION SUPPORT SYSTEMS FOR EVIDENCE-BASED PRACTICE IN NURSING

Evidence-adaptive systems incorporate research findings within a computer knowledge base that delivers evidence-based recommendations to the nurse at the point of care.7 Based on this synthesis, six CDSSs were identified as being developed to promote evidence-based practice in nursing. Much of the literature on the use of computers to promote evidence-based practice has focused on efforts to increase the use of CPG among physicians.4,35,36 Strategies for the CPG implementation most likely to be effective among physicians are reminder systems, academic detailing, the use of combined interventions,4 and interventions that deliver patient-specific advice at the time and place of a consultation.35 The use of CDSSs to facilitate the use of CPG has been proposed to bridge the gap between evidence and practice,7 and there is evidence that CDSSs improve practitioner performance.37,38 While it cannot be assumed that these strategies would transfer to nursing, we can conclude from this synthesis that nurses are receptive to the use of CDSSs to aid in evidence-based clinical decision making.15

Emergency department nurses have positively viewed the use of a CDSS to support triage decisions and reported that the system increased their self-confidence with decision making.17 Nurses have identified evidence-adaptive CDSS benefits as improved interdisciplinary communication, increased access to information on best practice, and increased consistency in quality of care. System and personal barriers to CDSS use were noted as a lack of administrative support, time required to learn and implement new technology, and deficiencies in the electronic medical record, which must be overcome for successful implementation in nursing practice.14 Finally, there is some support that evidence-adaptive CDSSs may be effective in assisting nurses with guideline-adherent care14 and improving patient outcomes.18

CONCLUSION

The CDSS nursing science remains in its infancy and significantly lags behind the progress made by medicine. In a recent review of the medical literature, Garg et al38 identified 100 CDSSs and only two were specific for clinical decision making in nursing.18,21 Multiple methods are being applied to evaluate CDSSs in nursing. Most studies have been focused on the impact the system has on the quality of nurses' decision making and clinical actions, usability, integration with workflow, and the quality of clinical advice offered. Two studies were identified that investigated the systems' cost-effectiveness and ability to help improve patient outcomes.18,21 In addition, there has been little research on theoretical models to support the development of CDSSs in nursing. Only three of 17 studies applied a theoretical framework. Clark et al14 based their implementation project on Rogers'25Diffusion of Innovation theory and Bandura's26Social Learning Theory. Im and Chee12 applied a feminist framework to guide their research on nurses' acceptance of a CDSS for cancer pain management. Only O'Neill et al27 tested a theoretical framework developed specifically to understand nurses' decision making and for application in developing a CDSS for nursing. The effectiveness of CDSSs in translating findings from research into practice is mixed, with one investigation providing evidence of improving nurses' compliance with established guidelines14 and another investigation13 indicating no improvement in nurses' knowledge or clinical decision making with the use of a CDSS.

RECOMMENDATION FOR FUTURE RESEARCH

Based on the findings of this metasynthesis, more research is needed in developing CDSSs specifically to facilitate nurses' decision making in patient care. Advances in programming and knowledge base modeling continue to improve the functionality, usability, and usefulness of CDSSs. As these technological advances are incorporated into the development of new CDSSs for nursing practice, research is needed to establish system architectures that are most adaptable for decision making in nursing practice. The use of computers to aid in nurses' decision making is a new and exciting area for the nurse theorist and one that is just beginning to be explored. Additional theoretical models for the development and testing of CDSSs in nursing are needed. More research is necessary to determine whether CDSSs offer an effective strategy for translating evidence from research into nursing practice. This is an important area for nursing research, as recommended by the Committee on the Quality of Health Care in America's Crossing the Quality Chasm, strategies for improving the quality of healthcare in the US. Among key findings were recommendations for the use of information technology to improve access to information and to support evidence-based decision making.6

This metasynthesis indicates that there is a significant gap in the knowledge on nurses' use of CDSSs to enhance evidence-based practice. Evidence-based protocols are typically developed as paper-based tools, which are difficult to integrate with the clinicians' workflow. In addition, paper format precludes the ability to effectively and efficiently evaluate variance and to empirically link clinical processes with patient outcomes. The development and testing of CDSSs that simultaneously inform and guide nurses on prevention, patient education, and self-management interventions is proposed as research to advance the field of CPG implementation in nursing.

REFERENCES

1. Pravikoff DS, Tanner AB, Pierce ST. Readiness of U.S. nurses for evidence-based practice. Am J Nurs. 2006;105(9):40-51.
2. Greco PJ, Eisenberg JM. Changing physicians' practices. N Engl J Med. 1993;329:1271-1273.
3. Grimshaw JM, Shirran L, Thomas R, et al. Changing provider behavior: an overview of systematic reviews of interventions. Med Care. 2001;39(suppl 2):II-2-II-45.
4. Davis DA, Taylor-Vaisey A. Translating guidelines into practice. A systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. Can Med Assoc J. 1997;157(4):408-416.
5. McGlynn EA, Asch SM, Adams J, et al. The quality of health care delivered to adults in the United States. N Engl J Med. 2003;348:2635-2645.
6. Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy of Science; 2001.
7. Sim I, Gorman P, Greenes RA, et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc. 2001;8(6):527-534.
8. Thompson CB, Ryan S, Baggs J. Testing of a computer-based decision support system in an acute care hospital. In: Hovenga EJS, Hannah KJ, McCormick KA, Ronald JS, eds. Proceedings of the Fourth International Conference on Nursing Use of Computers in Information Science. Vol 4. Heidelberg-Berlin, Germany: Springer-Verlag; 1991:769-776.
9. Ruland CM. Handheld technology to improve patient care: evaluating a support system of preference-based care planning at the bedside. J Am Med Inform Assoc. 2002;9:192-201.
10. Chang B, Hirsh M. Knowledge acquisition and knowledge representation in a rule-based expert system. Comput Nurs. 1991;9(5):174-178.
11. Bradburn C, Zeleznikow J, Adams A. FLORENCE: synthesis of case-based and model-based reasoning in a nursing care planning system. Comput Nurs. 1993;11(1):20-24.
12. Im E, Chee W. Nurses' acceptance of the decision support computer program for cancer pain management. Comput Inform Nurs. 2006;24(2):95-104.
13. Zielstorff RD, Barnett GO, Fitzmaurice JB, et al. A decision support system for prevention and treatment of pressure ulcers based on AHCPR guidelines. Proc AMIA Annu Fall Symp. 1996;4:562-566.
14. Clark HF, Bradley C, Whytock S, Handfield S, van der Wal R, Gundry S. Pressure ulcers: implementation of evidence-based nursing practice. J Adv Nurs. 2005;49(6):578-590.
15. Cathain AO, Sampson FC, Munro JF, Thomas KJ, Nicholl JP. Nurses' views of using computerized decision support software in NHS Direct. J Adv Nurs. 2004;45(3):280.
16. Dong SL, Bullard MJ, Meurer DP, et al. Emergency triage: comparing a novel computer triage program with standard triage. Acad Emerg Med. 2005;1:502-507.
17. Eley D, Desley H, Wollaston A, Miller P, McKay M, Wollaston J. Triage nurse perceptions of the use, reliability and acceptability of the Toowoomba Adult Triage Trauma Tool (TATTT). Accid Emerg Nurs. 2005;1:54-60.
18. Fitzmaurice DA, Hobbs FD, Murray ET, Holder RL, Allan TF, Rose PE. Oral anticoagulation management in primary care with the use of computerized decision support and near-patient testing: a randomized controlled trial. Arch Intern Med. 2000;160(15):2343-2348.
19. Im E, Chee W. Decision support computer program for cancer pain management. Comput Inform Nurs. 2003;21(1):12-21.
20. Meyer KE, Sather-Levine B, Laurent-Bopp D, Gruenewald D, Nichol P, Kimmerle M. The impact of clinical information systems research on the future of advanced practice nursing. Adv Pract Nurs Q. 1996;2(3):58-64.
21. Petrucci K. Studies of a Prototype Nurse Expert System: Urological Nursing Information System [dissertation]. Baltimore, MD: University of Maryland; 1990.
22. Jirapaet V. A computer expert system prototype for mechanically ventilated neonates: development and impact on clinical judgment and information access capabilities of nurses. Comput Nurs. 2001;19(5):194-203.
23. O'Neill ES, Dluhy NM, Hansen AS, Ryan JR. Coupling the N-CODES system with actual nurse decision-making. Comput Inform Nurs. 2006;24(1):28-34.
24. Caelli K, Downie J, Caelli T. Toward a decision support system for health promotion in nursing. J Adv Nurs. 2003;42(2):170-180.
25. Rogers EM. Diffusion of Innovation. 4th ed. New York, NY: The Free Press; 1995.
26. Bandura AJ. Social Learning Theory. Englewood Cliffs, NJ: Prentice-Hall; 1977.
27. O'Neill ES, Dluhy NM, Chin E. Modeling novice clinical reasoning for a computerized decision support system. J Adv Nurs. 2005;49(1):68-77.
28. Petrucci K, Petrucci P, Canfield K, McCormick KA, Kjerulff K, Parks P. Evaluation of UNIS: urological nursing information systems. In: Proceedings of the 15th Annual Symposium on Computer Applications in Medical Care. Vol 15. New York, NY: McGraw-Hill; 1992:43-47.
29. O'Connor AM, Rostrom A, Fiset V, et al. Decision aids for patients facing health treatment or screening decisions: a systematic review. BMJ. 1999;319(7212):731-734.
30. Underwood SM. Measuring the validity of computer-assisted instructional media. In: Strickland OL, Waltz CF, eds. Measurement of Nursing Outcomes. Vol 2. New York, NY: Springer; 1988:295-313.
31. Doll WJ, Torkzadeh G. The measurement of end-user computing satisfaction. MIS Q. 1988;12:259-274.
32. Chin JP, Diehl VA, Norman KL. Development of an instrument measuring user satisfaction of the human-computer interface. CHI `88 Conference Proceedings: Human Factors in Computing Systems. New York, NY: Association for Computing Machinery; 1988:213-218.
33. Stroud SD, Erkel AE, Smith CA. The use of personal digital assistants by nurse practitioner. J Am Acad Nurse Pract. 2005;17(2):67-75.
34. Chin EF, Sosa M, O'Neill ES. The N-CODES project moves to user testing. Comput Inform Nurs. 2006;24(4):214-219.
35. Grimshaw JM, Russel IT. Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet. 1993;342:1317-1322.
36. Rousseau N, McColl E, Newton J, Grimshaw J, Eccles M. Practice based, longitudinal, qualitative interview study of computerized evidence based guidelines in primary care. BMJ. 2003;32:1-8.
37. Johnson ME, Langton KB, Haynes RB, Mathien A. Effects of computer-based clinical decision support systems on clinical performance and patient outcome: a critical appraisal of research. Arch Intern Med. 1994;120:135-142.
38. Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes. JAMA. 2005;293(10):1223-1238.
Keywords:

Computer-assisted decision making; Decisions support systems; Evidence-based medicine; Information systems; Practice guidelines

© 2008 Lippincott Williams & Wilkins, Inc.