Evidence-Based Clinical Decision Support

Summary

Background: Clinical decision support (CDS) is a key tool for enabling evidence-based medicine and improving the quality of healthcare. However, effective CDS faces a variety of challenges, including those relating to knowledge synthesis, capture, transformation, localization and maintenance. If not properly addressed, these challenges can limit the effectiveness of CDS, and potentially risk inaccurate or inappropriate interventions to clinicians.

Objectives: (1) To describe an approach to CDS development using evidence as a basis for clinical decision support systems that promote effective care; (2) To review recent evidence regarding the effectiveness of selected clinical decision support systems.

Method: Review and analysis of recent literature with identification of trends and best practices.

Results: The state-of-the-art in CDS has advanced significantly, and many recent trials have shown CDS to be effective, although the results are mixed overall. Issues related to knowledge capture and synthesis, problems in knowledge transformation at the interface between knowledge authors and CDS developers, and problems specific to local CDS design and implementation can interfere with CDS development. Best practices, tools and techniques to manage them are described.

Conclusions: CDS, when used well, can be effective, but further research is needed for it to reach its full potential.

Keywords

Clinical decision support system - practice guideline - evidencebased medicine

 

• References

• 1 Sackett D, Rosenberg WMC, Gray JAM, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn't. BMJ 1996; 312: 71.
• 2 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 (2) 141-5.
• 3 Wright A, Sittig DF, Ash JS, Feblowitz J, Meltzer S, McMullen C. et al. Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems. J Am Med Inform Assoc 2011; 18 (3) 232-42.
• 4 Bouaud J, Seroussi B. Revisiting the EBM decision model to formalize non-compliance with computerized CPGs: results in the management of breast cancer with OncoDoc2. AMIA Annu Symp Proc 2011; 2011; 125-34.
• 5 HINARI. About Hinari. 2012 [cited 2012 December 7, 2012] Available from: http://www.who.int/hinari/about/en/
• 6 Lohr K, Carey T. Assessing “best evidence”: issues in grading the quality of studies for systematic reviews. J Qual Improvement 1999; 25 (9) 470-9.
• 7 Uijen A, van de Lisdonk E. Multimorbidity in primary care: prevalence and trend over the last 20 years. Eur J Gen Pract 2008; 14 (suppl 1) 28-32.
• 8 Shiffman RN, Dixon J, Brandt C, Essaihi A, Hsiao A, Michel G. et al. The Guideline Implementability Appraisal (GLIA): development of an instrument to identify obstacles to guideline implementation. BMC Med Inform Dec Mak 2005; 5: 23.
• 9 Brouwers M, Kho ME, Browman GP, Burgers JS, Cluzeau F, Feder G. et al. AGREE II: Advancing guideline development, reporting and evaluation in healthcare. Can Med Assoc J 2010; 182: 1045-52.
• 10 Shiffman RN, Shekelle P, Overhage JM, Slutsky J, Grimshaw J, Deshpande AM. Standardized reporting of clinical practice guidelines: a proposal from the Conference on Guideline Standardization. Ann Intern Med 2003; 139: 493-8.
• 11 Institute of Medicine. Finding what works in health care: standards for systematic reviews. Washington, DC: National Academies Press; 2011
• 12 IOM (Institute of Medicine). Clinical practice guidelines we can trust. Washington, DC: National Academies Press; 2011
• 13 Brawley O, Byers T, Chen A, Pignone M, Ransohoff D, Schenk M. et al. New American Cancer Society process for creating trustworthy cancer screening guidelines. JAMA 2011; 306: 2495-9.
• 14 Hussain T, Michel G, Shiffman RN. The Yale guideline recommendation corpus: a representative sample of the knowledge content of guidelines. Int J Med Inform 2009; 78: 354-63.
• 15 Shiffman RN, Michel G, Rosenfeld RM, Davidson C. Building better guidelines with BRIDGE-Wiz: development and evaluation of a software assistant to promote clarity, transparency, and implemetability. J Am Med Inform Assoc 2011; 18: 243-50.
• 16 Shiffman RN, Michel G, Krauthammer M, Fuchs NE, Kaljurand K, Kuhn T. Controlled natural language for clinical practice guidelines. In: Fuchs NE. editor Controlled Natural Language 2009. April 28 2009 Marettimo, Italy: CEUR Workshop Proceedings; 2009
• 17 Shahar Y, Young O, Shalom E, Mayaffitt A, Mosko-vitch R, Hessing A. et al. editors. The digital electronic guideline library (DeGeL): a hybrid framework for representation and use of clinical guidelines. Proc Symposium on Computerized guidelines and Protocols 2004. Prague, CZ: IOS Press; 2004. http://www.ncbi.nlm.nih.gov/pubmed/15537218
• 18 Balas EA, Boren SA. Managing clinical knowledge for health care improvement. Yearbook of Medical informatics 2000. Stuttgart, Germany: Schattauer; 2000. p. 65-70.
• 19 Ohno-Machado L, Gennari JH, Murphy SN, Jain NL, Tu SW, Oliver DE. et al. The guideline interchange format: a model for representing guidelines. J Am Med Inform Assoc 1998; 5: 357-72.
• 20 Patel VL, Allen VG, Arocha JF, Shortliffe EH. Representing clinical guidelines in GLIF: individual and collaborative expertise. J Am Med Inform Assoc 1998; 5: 467-83.
• 21 ASTM International. Standard specification for Guideline Elements Model version 3 (GEM III)-document model for clinical practice guidelines. Conshohocken, PA: 2012
• 22 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 (5) 637-44.
• 23 Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L. 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 (6) 523-30.
• 24 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a sytematic review of trials to identify features critical to success. BMJ 2005; 330: 765-72.
• 25 Osheroff JA, Pifer EA, Teich JM, Sittig DF, Jenders RA. Improving outcomes with clinical decision support: an implementer's guide. Chicago, IL: HIMSS; 2005
• 26 Lamy J-B, Ebrahiminia V, Riou C, Seroussi B, Bouaud J, Simon C. et al. How to translate therapeutic recommendations in clinical practice guidelines into rules for critiquing physician prescriptions? methods and application to five guidelines. BMC Med Inform Decis Mak 2010; 10: 31.
• 27 Lomotan E, Hoeksema L, Edmonds D, Ramirez-Garnica G, Shiffman R, Horwitz L. Evaluating the use of a computerized decision support system for asthma by pediatric pulmonologists. Int J Med Inform 2012; 81: 57-65.
• 28 Elson RB, Connelly DP. Computerized decision support systems in primary care. Primary Care 1995; 22: 365-84.
• 29 Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2001
• 30 Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR. et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med 2012; 157 (1) 29-43.
• 31 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 2005; 330 (7494) 765.
• 32 Garg AX, Adhikari NKJ, McDonald H, Rosas-Arellano MP, Devereaux P, Beyene J. et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes. JAMA 2005; 293 (10) 1223-38.
• 33 Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998; 280 (15) 1339-46.
• 34 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 ofmedical care. Ann Intern Med 2006; 144 (10) 742-52.
• 35 Shojania KG, Jennings A, Mayhew A, Ramsay CR, Eccles MP, Grimshaw J. The effects of on-screen, point of care computer reminders on processes and outcomes of care. Cochrane Database Syst Rev 2009 (3) CD001096.
• 36 Jaspers MW, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Inform Assoc 2011; 18 (3) 327-34.
• 37 Cheung A, Weir M, Mayhew A, Kozloff N, Brown K, Grimshaw J. Overview of systematic reviews of the effectiveness of reminders in improving healthcare professional behavior. Systematic reviews 2012; 1 (1) 36.
• 38 Nieuwlaat R, Connolly SJ, Mackay JA, Weise-Kelly L, Navarro T, Wilczynski NL. et al. Computerized clinical decision support systems for therapeutic drug monitoring and dosing: a decision-maker-researcher partnership systematic review. Implementation science: IS 2011; 6: 90.
• 39 Hemens BJ, Holbrook A, Tonkin M, Mackay JA, Weise-Kelly L, Navarro T. et al. Computerized clinical decision support systems for drug prescribing and management: a decision-maker-researcher partnership systematic review. Implementation science: IS 2011; 6: 89.
• 40 Roshanov PS, You JJ, Dhaliwal J, Koff D, Mackay JA, Weise-Kelly L. et al. Can computerized clinical decision support systems improve practitioners' diagnostic test ordering behavior? A decision-maker-researcher partnership systematic review. Implementation science: IS 2011; 6: 88.
• 41 Souza NM, Sebaldt RJ, Mackay JA, Prorok JC, Weise-Kelly L, Navarro T. et al. Computerized clinical decision support systems for primary preventive care: a decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. Implementation science: IS 2011; 6: 87.
• 42 Roshanov PS, Misra S, Gerstein HC, Garg AX, Sebaldt RJ, Mackay JA. et al. Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review. Implementation science: IS 2011; 6: 92.
• 43 Sahota N, Lloyd R, Ramakrishna A, Mackay JA, Prorok JC, Weise-Kelly L. et al. Computerized clinical decision support systems for acute care management: a decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. Implementation science: IS 2011; 6: 91.
• 44 Romano MJ, Stafford RS. Electronic health records and clinical decision support systems: impact on national ambulatory care quality. Arch Intern Med 2011; 171 (10) 897-903.
• 45 Samal L, Linder JA, Lipsitz SR, Hicks LS. Electronic health records, clinical decision support, and blood pressure control. Am J Manag Care 2011; 17 (9) 626-32.
• 46 Litvin CB, Ornstein SM, Wessell AM, Nemeth LS, Nietert PJ. Adoption of a clinical decision support system to promote judicious use of antibiotics for acute respiratory infections in primary care. Int J Med Inform 2012; 81 (8) 521-6.
• 47 Litvin CB, Ornstein SM, Wessell AM, Nemeth LS, Nietert PJ. Use of an Electronic Health Record Clinical Decision Support Tool to Improve Antibiotic Prescribing for Acute Respiratory Infections: The ABX-TRIP Study. J Gen Intern Med. 2012 Epub 2012/11/03.
• 48 Foy R, Eccles MP, Hrisos S, Hawthorne G, Steen N, Gibb I. et al. A cluster randomised trial of educational messages to improve the primary care of diabetes. Implementation science: IS 2011; 6: 129.
• 49 Tamblyn R, Eguale T, Buckeridge DL, Huang A, Hanley J, Reidel K. et al. The effectiveness of a new generation of computerized drug alerts in reducing the risk of injury from drug side effects: a cluster randomized trial. J Am Med Inform Assoc 2012; 19 (4) 635-43.
• 50 Carroll AE, Biondich P, Anand V, Dugan TM, Downs SM. A randomized controlled trial of screening for maternal depression with a clinical decision support system. J Am Med Inform Assoc. 2012 Epub 2012/06/30.
• 51 Dykes PC, Carroll DL, Hurley A, Lipsitz S, Benoit A, Chang F. et al. Fall prevention in acute care hospitals: a randomized trial. JAMA 2010; 304 (17) 1912-8.
• 52 Beeckman D, Clays E, Van Hecke A, Vanderwee K, Schoonhoven L, Verhaeghe S. A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: A two-armed randomized controlled trial. Int J Nurs Stud. 2012 Epub 2012/10/06.
• 53 Wright A, Feblowitz J, Maloney FL, Henkin S, Bates DW. Use of an Electronic Problem List by Primary Care Providers and Specialists. J Gen Intern Med. 2012 Epub 2012/03/20.
• 54 Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, Ramelson HZ. et al. A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. J Am Med Inform Assoc 2011; 18 (6) 859-67.
• 55 Wright A, Maloney FL, Feblowitz JC. Clinician attitudes toward and use of electronic problem lists: a thematic analysis. BMC Med Inform Decis Mak 2011; 11: 36.
• 56 Wright A, Poon EG, Wald J, Feblowitz J, Pang JE, Schnipper JL. et al. Randomized controlled trial of health maintenance reminders provided directly to patients through an electronic PHR. J Gen Intern Med 2012; 27 (1) 85-92.
• 57 Wright A, Bates DW, Middleton B, Hongsermeier T, Kashyap V, Thomas SM. et al. Creating and sharing clinical decision support content with Web 2.0: Issues and examples. J Biomed Inform 2009; 42 (2) 334-46.
• 58 Wright A, Sittig DF, Ash JS, Bates DW, Feblowitz J, Fraser G. et al. Governance for clinical decision support: case studies and recommended practices from leading institutions. J Am Med Inform Assoc 2011; 18 (2) 187-94.
• 59 Phansalkar S, Desai AA, Bell D, Yoshida E, Doole J, Czochanski M. et al. High-priority drug-drug interactions for use in electronic health records. J Am Med Inform Assoc 2012; 19 (5) 735-43.
• 60 Wright A, Sittig DF. A four-phase model of the evolution of clinical decision support architectures. Int J Med Inform 2008; 77 (10) 641-9.
• 61 Middleton B. The clinical decision support consortium. Stud Health Technol Inform 2009; 150: 26-30.
• 62 Kawamoto K. Open CDS. 2012 [cited 2012 November 30] Available from: http://www.opencds.org/
• 63 Kawamoto K, Del Fiol G, Lobach DF, Jenders RA. Standards for scalable clinical decision support: need, current and emerging standards, gaps, and proposal for progress. Open Med Inform J 2010; 4: 235-44.
• 64 Blumenthal D, Tavenner M. The "meaningful use" regulation for electronic health records. New Engl J Med 2010; 363 (6) 501-4.
• 65 Jones SS, Heaton P, Friedberg MW, Schneider EC. Today's 'meaningful use' standard for medication orders by hospitals may save few lives; later stages may do more. Health Aff (Millwood) 2011; 30 (10) 2005-12.
• 66 Marcotte L, Seidman J, Trudel K, Berwick DM, Blumenthal D, Mostashari F. et al. Achieving meaningful use of health information technology: a guide for physicians to the EHR incentive programs. Arch Intern Med 2012; 172 (9) 731-6.
• 67 Centers for Medicare & Medicaid Services. Final Rule: Medicare and Medicaid Programs; Electronic Health Record Incentive Program — Stage 2. 2012 [cited 2012 Nov 30] Available from: http://www.gpo.gov/fdsys/pkg/FR-2012-09-04/pdf/2012-21050.pdf
• 68 S&I Framework. Health eDecisions Homepage. 2012 [cited 2012 Nov 30] Available from: http://wiki.siframework.org/Health+eDeci-sions+Homepage
• 69 Karsh B-T. Clinical Practice Improvement and Redesign: How Change in Workflow Can Be Supported by Clinical Decision Support (AHRQ Publication No. 09-0054-EF). 2009 [cited 2012 Nov 30] Available from: http://healthit.ahrq.gov/images/jun09cdsworkflow/09_0054_ef.html
• 70 Karsh BT, Weinger MB, Abbott PA, Wears RL. Health information technology: fallacies and sober realities. J Am Med Inform Assoc 2010; 17 (6) 617-23.
• 71 Zhang J, Walji MF. TURF: toward a unified framework of EHR usability. J Biomed Inform 2011; 44 (6) 1056-67.
• 72 Feblowitz JC, Wright A, Singh H, Samal L, Sittig DF. Summarization of clinical information: a conceptual model. J Biomed Inform 2011; 44 (4) 688-99.
• 73 McCoy AB, Wright A, Laxmisan A, Ottosen MJ, McCoy JA, Butten D. et al. Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications. J Am Med Inform Assoc 2012; 19 (5) 713-8.
• 74 Laxmisan A, McCoy AB, Wright A, Sittig DF. Clinical Summarization Capabilities of Commercially-available and Internally-developed Electronic Health Records. Appl Clinl Inform 2012; 3 (1) 80-93.
• 75 McCoy AB, Wright A, Laxmisan A, Singh H, Sittig DF. A prototype knowledge base and SMART app to facilitate organization of patient medications by clinical problems. AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium. 2011; 2011: 888-94.
• 76 Horsky J, Schilf GD, Johnston D, Mercincavage L, Bell D, Middleton B. Interface design principles for usable decision support: A targeted review of best practices for clinical prescribing interventions. J Biomed Inform 2012; 45 (6) 1202-16.
• 77 Zachariah M, Phansalkar S, Seidling HM, Neri PM, Cresswell KM, Duke J. et al. Development and preliminary evidence for the validity of an instrument assessing implementation of human-factors principles in medication-related decision-support systems--I-MeDeSA. J Am Med Inform Assoc 2011; 18 Suppl 1 i62-72.
• 78 Ash JS, Sittig DF, Guappone KP, Dykstra RH, Richardson J, Wright A. et al. Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study. BMC Med Inform Decis Mak 2012; 12: 6.
• 79 Saleem JJ, Haggstrom DA, Militello LG, Flanagan M, Kiess CL, Arbuckle N. et al. Redesign of a computerized clinical reminder for colorectal cancer screening: a human-computer interaction evaluation. BMC Med Inform Decis Mak 2011; 11: 74.
• 80 McGreevey 3rd JD. Order sets in electronic health records: principles of good practice. Chest 2013; 143 (1) 228-35.
• 81 Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc 2007; 26-30.
• 82 Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2006; 13 (5) 547-56.
• 83 Embi PJ, Leonard AC. Evaluating alert fatigue over time to EHR-based clinical trial alerts: findings from a randomized controlled study. J Am Med Inform Assoc 2012; 19 (e1) e145-e8.
• 84 Murphy DR, Reis B, Sittig DF, Singh H. Notifications received by primary care practitioners in electronic health records: a taxonomy and time analysis. Am J Med 2012; 125 (2) 209e1-7.
• 85 Landman AB, Takhar SS, Wang SL, Cardoso A, Kosowsky JM, Raja AS. et al. The hazard of software updates to clinical workstations: a natural experiment. J Am Med Inform Assoc. 2013 Epub 2013/03/16.
• 86 McCoy AB, Waitman LR, Lewis JB, Wright JA, Choma DP, Miller RA. et al. A framework for evaluating the appropriateness of clinical decision support alerts and responses. J Am Med Inform Assoc 2012; 19 (3) 346-52.
• 87 Kesselheim AS, Cresswell K, Phansalkar S, Bates DW, Sheikh A. Clinical decision support systems could be modified to reduce 'alert fatigue' while still minimizing the risk of litigation. Health Aff (Millwood) 2011; 30 (12) 2310-7.
• 88 Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS. et al. Grand challenges in clinical decision support. J Biomed Inform 2008; 41 (2) 387-92.