Best Practices in Clinical Decision Support

Summary

Background: Evidence demonstrates that clinical decision support (CDS) is a powerful tool for improving healthcare quality and ensuring patient safety. However, implementing and maintaining effective decision support interventions presents multiple technical and organizational challenges.

Purpose: To identify best practices for CDS, using the domain of preventive care reminders as an example.

Methods: We assembled a panel of experts in CDS and held a series of facilitated online and in-person discussions. We analyzed the results of these discussions using a grounded theory method to elicit themes and best practices.

Results: Eight best practice themes were identified as important: deliver CDS in the most appropriate ways, develop effective governance structures, consider use of incentives, be aware of workflow, keep content current, monitor and evaluate impact, maintain high quality data, and consider sharing content. Keys themes within each of these areas were also described.

Conclusion: Successful implementation of CDS requires consideration of both technical and socio-technical factors. The themes identified in this study provide guidance on crucial factors that need consideration when CDS is implemented across healthcare settings. These best practice themes may be useful for developers, implementers, and users of decision support.

Keywords

Clinical decision support systems - clinical governance - computerized medical record systems - hospital information systems

 

Conflicts of interest

None of the authors has a conflict of interest to declare with respect to the content of this manuscript.

• References

• 1 Hillestad R. et al. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood). 2005; 24 (05) 1103-1117.
• 2 U.S. House of Representatives and Senate. American Recovery and Reinvestment Act of 2009.. 2009 [cited 2009 May 18]; Available from: http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf
• 3 U.S. Department of Health and Human Services.. Health Information Technology (IT) Recovery Program. 2009 [cited 2009 May 18]; Available from: http://www.hhs.gov/recovery/programs/index.html#Health
• 4 Garg AX. et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293 (010) 1223-1238.
• 5 Bates DW. et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 1998; 280: 1311-1316.
• 6 Kawamoto K. et al. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330: 765.
• 7 Linder JA. et al. Electronic health record use and the quality of ambulatory care in the United States. Arch Intern Med 2007; 167: 1400-1405.
• 8 Koppel R. et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 2005; 293: 1197-1203.
• 9 Han YY. et al. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 2005; 116: 1506-1512.
• 10 Del Beccaro MA. et al. Computerized provider order entry implementation: no association with increased mortality rates in an intensive care unit. Pediatrics 2006; 118: 290-295.
• 11 Ash JS, Bates DW. Factors and forces affecting EHR system adoption: report of a 2004 ACMI discussion. J Am Med Inform Assoc 2005; 12: 8-12.
• 12 Greenes RA. Clinical decision support: the road ahead. Boston, MA: Elsevier Academic Press; 2007
• 13 Osheroff JA. et al. Improving outcomes with clinical decision support: an implementers’ guide. Chicago: HIMSS; 2005
• 14 Osheroff JA. et al. A roadmap for national action on clinical decision support. J Am Med Inform Assoc 2007; 14 (02) 141-145.
• 15 Wyatt J, Spiegelhalter D. Evaluating medical expert systems: what to test and how?. Medical informatics = Medecine et informatique 1990; 15 (03) 205-217.
• 16 Chaudhry B. et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144: 742-752.
• 17 Osheroff JA. et al. A roadmap for national action on clinical decision support. J Am Med Inform Assoc 2007; 14: 141-145.
• 18 Bates DW. 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: 523-530.
• 19 Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 2003; 163: 1409-1416.
• 20 Kaushal R. et al. Return on investment for a computerized physician order entry system. J Am Med Inform Assoc 2006; 13: 261-266.
• 21 Kadmon G. et al. Computerized order entry with limited decision support to prevent prescription errors in a PICU. Pediatrics 2009; 124: 935-940.
• 22 Sittig DF. et al. Grand challenges in clinical decision support. J Biomed Inform 2008; 41: 387-392.
• 23 Ash JS, Stavri PZ, Kuperman GJ. A consensus statement on considerations for a successful CPOE implementation. J Am Med Inform Assoc 2003; 10: 229-234.
• 24 Ash J. Organizational factors that influence information technology diffusion in academic health sciences centers. J Am Med Inform Assoc 1997; 4: 102-111.
• 25 Lorenzi NM, Kouroubali A, Detmer DE, Bloomrosen M. How to successfully select and implement electronic health records (EHR) in small ambulatory practice settings. BMC medical informatics and decision making 2009; 9: 15.
• 26 Yoon-Flannery K. et al. A qualitative analysis of an electronic health record (EHR) implementation in an academic ambulatory setting. Informatics in primary care 2008; 16: 277-284.
• 27 Shekelle PG, Morton SC, Keeler EB. Costs and benefits of health information technology. Evidence report/technology assessment 2006; 132: 1-71.
• 28 Brokel JM, Harrison MI. Redesigning care processes using an electronic health record: a system’s experience. Joint Commission journal on quality and patient safety / Joint Commission Resources 2009; 35: 82-92.
• 29 Terry AL. et al. Implementing electronic health records: Key factors in primary care. Canadian family physician Medecin de famille canadien 2008; 54: 730-736.
• 30 Simon SR. et al. Electronic health records: which practices have them, and how are clinicians using them?. Journal of evaluation in clinical practice 2008; 14: 43-47.
• 31 Campbell EM. et al. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2006; 13: 547-556.
• 32 AMIA.. Clinical Decision Support (CDS): Morningside Initiative. [cited 2009 May 18]; Available from: http://www.amia.org/inside/initiatives/cds
• 33 Lin JH, Haug PJ. Exploiting missing clinical data in Bayesian network modeling for predicting medical problems. Journal of biomedical informatics 2008; 41: 1-14.
• 34 Poissant L, Tamblyn R, Huang A. Preliminary validation of an automated health problem list. AMIA Annual Symposium proceedings / AMIA Symposium 2005: 1084.
• 35 Wright A. et al. Creating and sharing clinical decision support content with Web 2.0: Issues and examples. J Biomed Inform 2009; 42: 334-346.
• 36 Glaser B, Strauss A. The Discovery of Grounded Theory. Chicago: Aldine Publishing Company; 1967
• 37 Sittig DF, Teich JM, Osheroff JA, Singh H. Improving clinical quality indicators through electronic health records: it takes more than just a reminder. Pediatrics 2009; 124: 375-377.
• 38 Dexter PR. et al. Inpatient computer-based standing orders vs physician reminders to increase influenza and pneumococcal vaccination rates: a randomized trial. JAMA 2004; 292: 2366-2371.
• 39 Wright A, Goldberg H, Hongsermeier T, Middleton B. A description and functional taxonomy of rule-based decision support content at a large integrated delivery network. J Am Med Inform Assoc 2007; 14: 489-496.
• 40 Wagner MM, Hogan WR. The accuracy of medication data in an outpatient electronic medical record. J Am Med Inform Assoc 1996; 3: 234-244.
• 41 Szeto HC. et al. Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. The American journal of managed care 2002; 8: 37-43.