BACKGROUND: Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known. OBJECTIVE: We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care. METHODS: We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites. RESULTS: The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines. CONCLUSION: Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.
BACKGROUND: Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known. OBJECTIVE: We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care. METHODS: We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites. RESULTS: The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines. CONCLUSION: Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.
Entities:
Keywords:
Computer assisted decision making; clinical care; clinical decision support; clinical decision support systems; computerized medical record system; diabetes mellitus; disease management; hospital information system; medicine clinical information system; specific conditions
Authors: Amit X Garg; Neill K J Adhikari; Heather McDonald; M Patricia Rosas-Arellano; P J Devereaux; Joseph Beyene; Justina Sam; R Brian Haynes Journal: JAMA Date: 2005-03-09 Impact factor: 56.272
Authors: Adrian H Zai; Richard W Grant; Greg Estey; William T Lester; Carl T Andrews; Ronnie Yee; Elizabeth Mort; Henry C Chueh Journal: J Am Med Inform Assoc Date: 2008-04-24 Impact factor: 4.497
Authors: Victor M Montori; Sean F Dinneen; Colum A Gorman; Bruce R Zimmerman; Robert A Rizza; Susan S Bjornsen; Erin M Green; Sandra C Bryant; Steven A Smith Journal: Diabetes Care Date: 2002-11 Impact factor: 19.112
Authors: Patrick J O'Connor; Joann M Sperl-Hillen; William A Rush; Paul E Johnson; Gerald H Amundson; Stephen E Asche; Heidi L Ekstrom; Todd P Gilmer Journal: Ann Fam Med Date: 2011 Jan-Feb Impact factor: 5.166
Authors: Jesse C Crosson; Pamela A Ohman-Strickland; Karissa A Hahn; Barbara DiCicco-Bloom; Eric Shaw; A John Orzano; Benjamin F Crabtree Journal: Ann Fam Med Date: 2007 May-Jun Impact factor: 5.166
Authors: Christopher A Harle; Julie DiIulio; Sarah M Downs; Elizabeth C Danielson; Shilo Anders; Robert L Cook; Robert W Hurley; Burke W Mamlin; Laura G Militello Journal: Appl Clin Inform Date: 2019-09-25 Impact factor: 2.342
Authors: Hala K El Mikati; Lisa Yazel-Smith; Randall W Grout; Stephen M Downs; Aaron E Carroll; Tamara S Hannon Journal: Appl Clin Inform Date: 2020-05-13 Impact factor: 2.342