Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

Summary

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.

 

MissingLinkText

Keywords

Computer assisted decision making - hospital information system - clinical decision support systems - computerized medical record system - clinical decision support - medicine clinical information system - clinical care - disease management - specific conditions - diabetes mellitus

 

 

MissingLinkText

Conflicts of interest

The authors declare that they have no conflicts of interest in the research presented.

• References

• 1 American Recovery and Reinvestment Act of 2009 Pub L. No. 111-5.
• 2 Blumenthal D, Tavenner M. The ,,meaningful use“ regulation for electronic health records. N Engl J Med 2010; 363 (06) 501-504.
• 3 Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc 2007; 14 (02) 141-145.
• 4 Zai AH, Grant RW, Estey G, Lester WT, Andrews CT, Yee R. et al. Lessons from implementing a combined workflow-informatics system for diabetes management. Ibid 2008; 15 (04) 524-533.
• 5 Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E. et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of internal medicine 2006; 144 (010) 742-752.
• 6 Goud R, de Keizer NF, ter Riet G, Wyatt JC, Hasman A, Hellemans IM. et al. Effect of guideline based computerised decision support on decision making of multidisciplinary teams: cluster randomised trial in cardiac rehabilitation. BMJ (Clinical research ed.) 2009; 338: b1440.
• 7 Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J. et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293 (010) 1223-1238.
• 8 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ (Clinical research ed.) 2005; 330 7494 765.
• 9 Weber V, Bloom F, Pierdon S, Wood C. Employing the electronic health record to improve diabetes care: a multifaceted intervention in an integrated delivery system. Journal of general internal medicine 2008; 23 (04) 379-382.
• 10 Montori VM, Dinneen SF, Gorman CA. et al. The impact of planned care and a diabetes electronic management system on community-based diabetes care: the Mayo Health System Diabetes Translation Project. Diabetes care 2002; 25 (011) 1952-1957.
• 11 Mullan RJ, Montori VM, Shah ND. et al. The diabetes mellitus medication choice decision aid: a randomized trial. Archives of internal medicine 2009; 169 (017) 1560-1568.
• 12 O’Connor PJ, Sperl-Hillen JM, Rush WA. et al. Impact of electronic health record clinical decision support on diabetes care: a randomized trial. Annals of family medicine 2011; 9 (01) 12-21.
• 13 Crosson JC, Ohman-Strickland PA, Hahn KA. et al. Electronic medical records and diabetes quality of care: results from a sample of family medicine practices. Annals of family medicine 2007; 5 (03) 209-215.
• 14 Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS. et al. Grand challenges in clinical decision support. Journal of biomedical informatics 2008; 41 (02) 387-392.
• 15 Center for Studying Health System Change: Tracking Report No.. 18: Results from the Community Tracking Study [Internet]. c2007 [cited 2009 Oct 30]. Available: http://www.hschange.com/CONTENT/941.
• 16 CDS Consortium: CDSC Overview Document [Internet].. c2008 [cited 2009 Jul 1]. Available from: http://www.allhealth.org/chcrep/1024docs_ww/CDSCOverview.pdf.
• 17 Bates DW, Kuperman GJ, Wang S. et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530.
• 18 Wright A, Sittig DF. A four-phase model of the evolution of clinical decision support architectures. International journal of medical informatics 2008; 77 (010) 641-649.
• 19 Waitman LR, Miller RA. Pragmatics of implementing guidelines on the front lines. J Am Med Inform Assoc 2004; 11 (05) 436-438.
• 20 Wright A, Sittig DF, Ash JS, Sharma S, Pang JE, Middleton B. Clinical decision support capabilities of commercially-available clinical information systems. J Am Med Inform Assoc 2009; 16 (05) 637-644.
• 21 Graham R, Mancher M, Wolman DM, Greenfield S, and Steinberg E. Editors Committee on Standards for Developing Trustworthy Clinical Practice Guidelines. Institute of Medicine. ,,Clinical Practice Guidelines We Can Trust.“ Washington DC: National Academy of Sciences; 2011