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=1914) 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.
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=1914) 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.
Authors: Scott T Micek; Nareg Roubinian; Tim Heuring; Meghan Bode; Jennifer Williams; Courtney Harrison; Theresa Murphy; Donna Prentice; Brent E Ruoff; Marin H Kollef Journal: Crit Care Med Date: 2006-11 Impact factor: 7.598
Authors: Asli Ozdas; Theodore Speroff; L Russell Waitman; Judy Ozbolt; Javed Butler; Randolph A Miller Journal: J Am Med Inform Assoc Date: 2005-12-15 Impact factor: 4.497
Authors: Steven Fishbane; Michael S Niederman; Colleen Daly; Adam Magin; Masateru Kawabata; André de Corla-Souza; Irum Choudhery; Gerald Brody; Maureen Gaffney; Simcha Pollack; Suzanne Parker Journal: Arch Intern Med Date: 2007 Aug 13-27
Authors: Evan W Orenstein; Naveen Muthu; Asli O Weitkamp; Daria F Ferro; Mike D Zeidlhack; Jason Slagle; Eric Shelov; Marc C Tobias Journal: Appl Clin Inform Date: 2019-10-30 Impact factor: 2.342