The Determination of Relevant Goals and Criteria Used to Select an Automated Patient Care Information System: A Delphi Approach

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Abstract
Objectives: To determine the relevant weighted goals and criteria for use in the selection of an automated patient care information system (PCIS) using a modified Delphi technique to achieve consensus. Design: A three-phase, six-round modified Delphi process was implemented by a ten-member PCIS selection task force. The first phase consisted of an exploratory round. It was followed by the second phase, of two rounds, to determine the selection goals and finally the third phase, of three rounds, to finalize the selection criteria. Results: Consensus on the goals and criteria for selecting a PCIS was measured during the Delphi process by reviewing the mean and standard deviation of the previous round's responses. After the study was completed, the results were analyzed using a limits-of-agreement indicator that showed strong agreement of each individual's responses between each of the goal determination rounds. Further analysis for variability in the group's response showed a significant movement to consensus after the first goal-determination iteration, with consensus reached on all goals by the end of the second iteration. Conclusion: The results indicated that the relevant weighted goals and criteria used to make the final decision for an automated PCIS were developed as a result of strong agreement among members of the PCIS selection task force. It is therefore recognized that the use of the Delphi process was beneficial in achieving consensus among clinical and nonclinical members in a relatively short time while avoiding a decision based on political biases and the “groupthink” of traditional committee meetings. The results suggest that improvements could be made in lessening the number of rounds by having information available through side conversations, by having other statistical indicators besides the mean and standard deviation available between rounds, and by having a content expert address questions between rounds.