Clinical decision support alert appropriateness: A review and proposal for improvement

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This article was derived from McCoy AB, Thomas EJ, Krousel-Wood M, and Sittig DF's "Clinical decision support alert appropriateness: A review and proposal for improvement".[1]

INTRODUCTION

Most health care professionals(HCPs) are adopting EHRs with integrated clinical decision support system(CDS)[1] to improve patient safety.[2] [3] Computerized alerts that prompt clinicians about drug-allergy, drug-drug interaction, and drug-disease warnings or provide dosing guidance are most common.[4] [5] Despite promise, CDS implementation in diverse settings have not consistently improved patient outcomes.[6] [7] [8] [9] Alert overrides occur in most organizations and may be a barrier to improve patient and process outcomes[10] and detailed evaluation of the alerts abd provider responses is necessary to determine appropriateness.[11] [1] Efficient approaches to effectively evaluate alert appropriateness are necessary for optimizing patient safety.

CLINICAL DECISION SUPPORT ALERTS

Medication errors,which occur in 4%-6% of orders, can be prevented by computerized provider order entry(CPOE) and CDS.[12] [13] [14] [15] [16] [17] [18] CDS has reportedy contributed to substantial savings.[19] Meaningful Use(MU) objectives also require institutions to implement drug-drug interaction and drug-allergy interaction checks,and track CDS compliance.[2] Multiple CDS approaches exist,including alerts,simple guided-dosing algorithms,order sets,and complex ordering advisors.[20] [21] Alerts are implemented in 61%-78% of hospitals and included in all major commercial EHRs.[4] [5] [21] [22]

EVALUATION OF CLINICAL DECISION SUPPORT ALERTS

Alert overrides

Alert overrides occur in 49%-96% of alerts and is a potential barrier to success.[10] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] Studies concluded that many overrides are justifiable because of the clinical irrelevance of the alert,known patient tolerance for a drug, or documented clinician intention to monitor the patient, indicating a need for institutions to evaluate alerts to prevent alert fatigue.[23] [24] [26] [28] [31] [32] [33] [34] [35]

Alert and response appropriateness

Some alerts are inappropriate,and adhering to the alert advice cause harm to the patient. [1] Detailed evaluations of alert appropriateness are necessary to identify such undesirable,unintended consequences and to institute efforts to reduce such error.[36] [37] [38] The related evaluation methods are labor intense and difficult to replicate for every alert utilized. More efficient,semiautomated evaluation approaches are necessary to understand alert responses and overrides.

Surveillance tools for alert evaluation

Zimmerman et al.[39] displayed retrospective CDS data in a spreadsheet-based dashboard, and Reynolds et al.[40] developed a web-based,graphic dashboard to allow monitoring of order and alert volume. A real-time surveillance dashboard displayed lists of patients receiving high-risk medications,CDS interactions,and detailed patient views to clinical pharmacists to augment decision making.[41]

Methods for improving alerts

Duke and Bolchini[42] developed a model for creating context-aware drug-drug interaction alerts that allowed tailoring alert displays based on relevant patient-specific information,resulting in improved acceptance of the alerts.However,alerts deemed inappropriate in some clinical scenarios should also be suppressed.Unfortunately,there is no perfect method.

A PROPOSAL TO EVALUATE AND IMPROVE THE APPROPRIATENESS OF ALERTS

Predicting inappropriate alerts and responses

To better evaluate and improve CDS alert appropriateness, the authors first propose a retrospective chart review based alert evaluation framework to determine which alerts are relevant and which overrides are justifiable.[1] This approach aims to first identify predictors of alert and response inappropriateness,not merely alert overrides. The authors plan to develop a gold standard for the appropriateness of each alert and clinician response.

Novel metrics for predicting inappropriate alerts and responses

Integrating clinical context can increase alert inappropriateness and improve alert acceptance.[10] [42] The first variable that the authors will incorporate into the model is the indication of an alerted medication,whether entered by the clinician during e-prescribing or inferred from a previously developed knowledge bases(KBs).[43] [44] [45] Additional variables derived from these KBs will be included in the predictive models to determine if additional data improve detection of inappropriate alerts. By considering clinicians as users and alert responses as user-generated content, alert evaluators may adopt reputation metrics to identify inappropriate alerts.

Designing and implementing an interactive alert evaluation dashboard

During previous research, the authors developed a condition-specific,web-based surveillance tool that allowed clinical pharmacist,informatics personnel,and clinicians to review CDS alert responses in the context of patients at high risk of ADEs.[41] [46] They propose to develop and implement InSPECt (Interactive Surveillance Portal for Evaluating Clinical decision support),an EHR-independent dashboard that will permit further assessment of the use of surveillance in evaluating CDS implementations. InSPECt will consist of two types: the alert detail,and the patient detail. After development and validation of InSPECt are complete the authors will collaborate with CDS managers and clinicians at study sites to review alerts which may improve the rate of appropriateness,potentially reduce the rates of overrides. They will then work with other IT staff,clinician leaders,and informatics investigators to design an intervention to improve the alerts and evaluate it's effect.

CONCLUSION

Despite increased implementation of CDS alerts,detailed evaluations occur rarely.The author's proposed research introduces several innovations to address this gap. This can transform alert evaluation processes across healthcare settings,leading to improved CDS,reduced alert fatigue,and increased patient safety.

COMMENTS

Computerized alerts are extremely common in today's healthcare but majority of them are overridden. Detailed evaluation of the alerts and the provider responses is necessary to determine appropriateness. I totally agree with the authors' thesis that alert evaluations are inadequate and their proposed innovations should improve CDS,reduce alert overrides,and improve patient safety.


Related article reviews:

References

  1. ↑ 1.0 1.1 1.2 1.3 McCoy AB,Thomas EJ, Krousel-Wood M, and Sittig DF. Clinical decision support alert appropriateness: A review and proposal for improvement. Ochsner J. 2014 Summer; 14(2): 195–202.
  2. ↑ 2.0 2.1 Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010 Aug 5;363(6):501–504. Epub 2010 Jul 13.
  3. ↑ Bates DW, O'Neil AC, Boyle D, et al. Potential identifiability and preventability of adverse events using information systems. J Am Med Inform Assoc. 1994 Sep-Oct;1(5):404–411
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  5. ↑ 5.0 5.1 Kuperman GJ, Bobb A, Payne TH, et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc. 2007 Jan-Feb;14(1):29–40. Epub 2006 Oct 26.
  6. ↑ Gurwitz JH, Field TS, Rochon P, et al. Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting. J Am Geriatr Soc. 2008 Dec;56(12):2225–2233.
  7. ↑ Strom BL, Schinnar R, Bilker W, Hennessy S, Leonard CE, Pifer E. Randomized clinical trial of a customized electronic alert requiring an affirmative response compared to a control group receiving a commercial passive CPOE alert: NSAID—warfarin co-prescribing as a test case. J Am Med Inform Assoc. 2010 Jul-Aug;17(4):411–415
  8. ↑ Tamblyn R, Reidel K, Huang A, et al. Increasing the detection and response to adherence problems with cardiovascular medication in primary care through computerized drug management systems: a randomized controlled trial. Med Decis Making. 2010 Mar-Apr;30(2):176–188. Epub 2009 Aug 12.
  9. ↑ Strom BL, Schinnar R, Aberra F, et al. Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial. Arch Intern Med. 2010 Sep 27;170(17):1578–1583
  10. ↑ 10.0 10.1 10.2 van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006 Mar-Apr;13(2):138–147. Epub 2005 Dec 15
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  16. ↑ Ammenwerth E, Schnell-Inderst P, Machan C, Siebert U. The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. J Am Med Inform Assoc. 2008 Sep-Oct;15(5):585–600. Epub 2008 Jun 25
  17. ↑ van Rosse F, Maat B, Rademaker CM, van Vught AJ, Egberts AC, Bollen CW. The effect of computerized physician order entry on medication prescription errors and clinical outcome in pediatric and intensive care: a systematic review. Pediatrics. 2009 Apr;123(4):1184–1190
  18. ↑ Bates DW, Teich JM, Lee J, et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc. 1999 Jul-Aug;6(4):313–321
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  21. ↑ 21.0 21.1 Wright A, Sittig DF, Ash JS, 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 May 1;18(3):232–242. Epub 2011 Mar 17
  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 Sep-Oct;16(5):637–644. Epub 2009 Jun 30
  23. ↑ 23.0 23.1 Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. Physicians' decisions to override computerized drug alerts in primary care. Arch Intern Med. 2003 Nov 24;163(21):2625–2631
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  25. ↑ Persell SD, Dolan NC, Friesema EM, Thompson JA, Kaiser D, Baker DW. Frequency of inappropriate medical exceptions to quality measures. Ann Intern Med. 2010 Feb 16;152(4):225–231
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  29. ↑ saac T, Weissman JS, Davis RB, et al. Overrides of medication alerts in ambulatory care. Arch Intern Med. 2009 Feb 9;169(3):305–311
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  31. ↑ 31.0 31.1 Swiderski SM, Pedersen CA, Schneider PJ, Miller AS. A study of the frequency and rationale for overriding allergy warnings in a computerized prescriber order entry system. J Patient Saf. 2007 Jun;3(2):91–96.
  32. ↑ 32.0 32.1 Litzelman DK, Tierney WM. Physicians' reasons for failing to comply with computerized preventive care guidelines. J Gen Intern Med. 1996 Aug;11(8):497–499.
  33. ↑ van der Sijs H, van Gelder T, Vulto A, Berg M, Aarts J. Understanding handling of drug safety alerts: a simulation study. Int J Med Inform. 2010 May;79(5):361–369. Epub 2010 Feb 19
  34. ↑ Sittig DF, Krall MA, Dykstra RH, Russell A, Chin HL. A survey of factors affecting clinician acceptance of clinical decision support. BMC Med Inform Decis Mak. 2006 Feb 1;6:6
  35. ↑ Russ AL, Zillich AJ, McManus MS, Doebbeling BN, Saleem JJ. Prescribers' interactions with medication alerts at the point of prescribing: A multi-method, in situ investigation of the human-computer interaction. Int J Med Inform. 2012 Apr;81(4):232–243. Epub 2012 Jan 31
  36. ↑ Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc. 2007 Oct;11:26–30
  37. ↑ Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005 Mar 9;293(10):1197–1203
  38. ↑ Bloomrosen M, Starren J, Lorenzi NM, Ash JS, Patel VL, Shortliffe EH. Anticipating and addressing the unintended consequences of health IT and policy: a report from the AMIA 2009 Health Policy Meeting. J Am Med Inform Assoc. 2011 Jan-Feb;18(1):82–90
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  40. ↑ Reynolds G, Boyer D, Mackey K, Povondra L, Cummings A. Alerting strategies in computerized physician order entry: a novel use of a dashboard-style analytics tool in a children's hospital. AMIA Annu Symp Proc. 2008 Nov;6:1108
  41. ↑ 41.0 41.1 McCoy AB, Cox ZL, Neal EB, et al. Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury: a randomized, controlled trial. Appl Clin Inform. 2012 Jan 1;3(2):221–238. Epub 2012 Jun 13
  42. ↑ 42.0 42.1 Duke JD, Bolchini D. A successful model and visual design for creating context-aware drug-drug interaction alerts. AMIA Annu Symp Proc. 2011; 2011:339–348. Epub 2011 Oct 22
  43. ↑ Wright A, Chen ES, Maloney FL. An automated technique for identifying associations between medications, laboratory results and problems. J Biomed Inform. 2010 Dec;43(6):891–901. Epub 2010 Sep 25
  44. ↑ 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 Annu Symp Proc. 2011; 2011:888–894. Epub 2011 Oct 22
  45. ↑ McCoy AB, Wright A, Laxmisan A, 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 Sep-Oct;19(5):713–718. Epub 2012 May 12
  46. ↑ Waitman LR, Phillips IE, McCoy AB, et al. Adopting real-time surveillance dashboards as a component of an enterprise wide medication safety strategy. Jt Comm J Qual Patient Saf. 2011 Jul;37(7):326–332
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