Use of order sets in inpatient computerized provider order entry systems: A comparative analysis of usage patterns at seven sites

Abstract

Background Many computerized provider order entry (CPOE) systems include the ability to create electronic order sets: collections of clinically-related orders grouped by purpose. Order sets promise to make CPOE systems more efficient, improve care quality and increase adherence to evidence-based guidelines. However, the development and implementation of order sets can be expensive and time-consuming and limited literature exists about their utilization. Methods Based on analysis of order set usage logs from a diverse purposive sample of seven sites with commercially- and internally-developed inpatient CPOE systems, we developed an original order set classification system. Order sets were categorized across seven non-mutually exclusive axes: admission/discharge/transfer (ADT), perioperative, condition-specific, task-specific, service-specific, convenience, and personal. In addition, 731 unique subtypes were identified within five axes: four in ADT (S=4), three in perioperative, 144 in condition-specific, 513 in task-specific, and 67 in service-specific. Results Order sets (n=1,914) were used a total of 676,142 times at the participating sites during a one-year period. ADT and perioperative order sets accounted for 27.6% and 24.2% of usage respectively. Peripartum/labor, chest pain/Acute Coronary Syndrome/Myocardial Infarction and diabetes order sets accounted for 51.6% of condition-specific usage. Insulin, angiography/angioplasty and arthroplasty order sets accounted for 19.4% of task-specific usage. Emergency/trauma, Obstetrics/Gynecology/Labor Delivery and anesthesia accounted for 32.4% of service-specific usage. Overall, the top 20% of order sets accounted for 90.1% of all usage. Additional salient patterns are identified and described. Conclusion We observed recurrent patterns in order set usage across multiple sites as well as meaningful variations between sites. Vendors and institutional developers should identify high-value order set types through concrete data analysis in order to optimize the resources devoted to development and implementation.

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