On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop

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This was adopted from the Coleman JJ et al.'s article "On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop".

BACKGROUND

Computerized physician( or provider) order entry(CPOE) and Clinical decision support(CDS)

CPOE systems allow users to prescribe using a computer system,reducing the risk of prescribing errors resulting from illegible handwriting or transcription errors. They also have shown to reduce medication errors and adverse drug events(ADEs) in hospitals. [1] [2] [3] [4] [5] [6] CPOE systems often have integrated CDS which has the potential to improve clinicians' decisions through guidance, alerts, and reminders. In principle, clinicians support the idea of CDS alerts in identifying and preventing erroneous or less optimal prescribing. [7] [8] [9] [10]

Alert specificity and sensitivity

In a CDS system,sensitivity is the ability of the system to alert prescribers correctly when patients are risk of experiencing drug-induced harm. The specificity of the CDS system is a measure of it's ability to distinguish between events that cause harm and non-events that will not. Safe alerting systems should have high specificity and sensitivity, present clear information, not unnecessarily disrupt workflow, and facilitate safe and efficient handling of alerts. [11]

Knowledge of alert fatigue in CDS systems

CDS alerts have the potential to cause harm to patients by occurring too frequently. [7] [12] [9] [10] In most systems, majority of the alerts are overridden. [13] [14] [15] [16] Exposure to frequent false alarms can desensitize users so that they ignore and increasingly mistrust alarms. [17] Most of the focus on reducing override rates in CDS systems considers strategies such as the customization of the third party providers' set of alerts, [18] [19] [20] implementation of highly specific algorithms,[15] and use of tiered severity to stratify and reduce the number of interruptive alerts.[21] [22] Other suggested strategies to counteract the alert fatigue include turning off frequently overridden alerts and directing time-dependent drug-drug interaction alerts to nurses.[23] [24] Despite various improvement strategies,alert fatigue continues to occur and frustrate users.[25] To address the issues, European experts on CDS attended a workshop in Birmingham,UK where they agreed on a consensus on the current gaps in the research and the challenges of improving alerting in CDS systems.

METHOD

Researchers with a strong publication record in the field of CDS were identified and were invited to attend a two day workshop in Birmingham,UK in November 2011. The objectives of this workshop were:

  1. to identify key knowledge gaps in the study of CDS-based alerting;
  2. to identify research priorities on CDS-based alerting; and
  3. to identify research methodologies to evaluate alerts

RESULTS

Knowledge gaps in the study of alerts in CDS systems

  1. Sensitivity and specificity of a CDS system
  2. Presentation and personalization of alerts
  3. Timing of alerts
  4. Relevance of the outcome measures in the study of alerts
  5. Measurement of the quality of alerts
  6. Design and firing of alerts/rules
  7. Legal issues- This was discussed in a American context,[26] with particular emphasis on the liability implications of CDS with drug-drug interactions [27] [28]
  8. Human factors and usability

Important research priorities

Determine the optimum sensitivity and specificity of a CDS system in practice

A perfect CDS system would be both 100% specific and 100% sensitive. Current systems tend to have high sensitivity but low specificity.[29] Sensitivities below 100% are risky and may contribute to patient harm, especially for the most injurious events. It is important that the system is able to draw in additional information from beyond the knowledge base(KB) to increase specificity,for example through the integration of individual patient information such as lab values and co-morbidities with information on medicines.[30] [31] [32] [33] The challenge is in ensuring that drug information is accurate,comprehensive and up-to-date,whilst keeping the process manageable in terms of expertise,time and resources. One solution may be the collaborative development and sharing of KBs between countries.[34] [35] However, system quality may differ with regards to different alert categories, and differences when alerting for medications only,as opposed to a combination of medications and patient parameters.[36] [37] By comparing differences in the design of current systems,it may be possible to identify a gold standard on which to base future CPOE systems.

Determine whether personalization of alerts will reduce alert fatigue

Customization of the setting in which the system is used could provide an opportunity to eliminate inappropriate alerts and requires further evaluation. This may improve usability and receptivity of CDS alerts. Allowing individual users to personalize the interface design,like in smartphones,of CDS alerts may also reduce alert fatigue. Personalization of alerts may also be done in a automatic way based upon a user's familiarity with certain risk situations

References

  1. ↑ 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, 15(5):585-600
  2. ↑ Hug BL, Witkowski DJ, Sox CM, Keohane CA, Seger DL, Yoon C, Matheny ME, Bates DW: Adverse drug event rates in six community hospitals and the potential impact of computerized physician order entry for prevention
  3. ↑ Reckmann MH, Westbrook JI, Koh Y, Lo C, Day RO: Does computerized provider order entry reduce prescribing errors for hospital inpatients? A systematic review. J Am Med Inform Assoc 2009, 16(5):613-623
  4. ↑ Shamliyan TA, Duval S, Du J, Kane RL: Just what the doctor ordered. Review of the evidence of the impact of computerized physician order entry system on medication errors
  5. ↑ van Doormaal JE, van den Bemt PM, Zaal RJ, Egberts AC, Lenderink BW, Kosterink JG, Haaijer-Ruskamp FM, Mol PG: The influence that electronic prescribing has on medication errors and preventable adverse drug events: an interrupted time-series study
  6. ↑ Wolfstadt JI, Gurwitz JH, Field TS, Lee M, Kalkar S, Wu W, Rochon PA: The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review
  7. ↑ 7.0 7.1 Ko Y, Abarca J, Malone DC, Dare DC, Geraets D, Houranieh A, Jones WN, Nichol WP, Schepers GP, Wilhardt M: Practitioners' views on computerized drug-drug interaction alerts in the VA system
  8. ↑ Ammenwerth E, Jung M: Expectations and barriers versus cxCDSS-CPOE: a European user survey. In Proceedings of the PSIP International Workshop, Sofia, Bulgaria, 23 June 2011. Edited by Beuscart R, Tcharaktchiev D, Angelova G. Shoumen: Incoma; 2011:49-52
  9. ↑ 9.0 9.1 Lapane KL, Waring ME, Schneider KL, Dubé C, Quilliam BJ: A mixed method study of the merits of e-prescribing drug alerts in primary care. J Gen Intern Med 2008, 23(4):442-446
  10. ↑ 10.0 10.1 Weingart SN, Simchowitz B, Shiman L, Brouillard D, Cyrulik A, Davis RB, Isaac T, Massagli M, Morway L, Sands DZ, et al.: Clinicians' assessments of electronic medication safety alerts in ambulatory care
  11. ↑ 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, 13(2):138-147
  12. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named ammenwerth
  13. ↑ Quinzler R, Schmitt SP, Pritsch M, Kaltschmidt J, Haefeli WE: Substantial reduction of inappropriate tablet splitting with computerised decision support: a prospective intervention study assessing potential benefit and harm. BMC Med Inform Decis Mak 2009, 9:30
  14. ↑ 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, 13(2):138-147
  15. ↑ 15.0 15.1 <Seidling HM, Schmitt SP, Bruckner T, Kaltschmidt J, Pruszydlo MG, Senger C, Bertsche T, Walter-Sack I, Haefeli WE: Patient-specific electronic decision support reduces prescription of excessive doses. Qual Saf Health Care 2010, 19(5):e15
  16. ↑ 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, 163(21):2625-2631
  17. ↑ Getty DJ, Swets JA, Pickett RM, Gonthier D: System operator response to warnings of danger: a laboratory investigation of the effects of the predictive value of a warning on human response time
  18. ↑ Resetar E, Reichley RM, Noirot LA, Doherty JA, Dunagan WC, Bailey TC: Strategies for reducing nuisance alerts in a dose checking application. AMIA Annu Symp Proc 2005, 624-628
  19. ↑ Resetar E, Reichley RM, Noirot LA, Dunagan WC, Bailey TC: Implementing daily dosing rules using a commercial rule base. AMIA Annu Symp Proc 2006, 1073
  20. ↑ Reichley RM, Seaton TL, Resetar E, Micek ST, Scott KL, Fraser VJ, Dunagan WC, Bailey TC: Implementing a commercial rule base as a medication order safety net. J Am Med Inform Assoc 2005, 12(4):383-389
  21. ↑ Paterno MD, Maviglia SM, Gorman PN, Seger DL, Yoshida E, Seger AC, Bates DW, Gandhi TK: Tiering drug-drug interaction alerts by severity increases compliance rates. J Am Med Inform Assoc 2009, 16(1):40-46
  22. ↑ Anton C, Nightingale PG, Adu D, Lipkin G, Ferner RE: Improving prescribing using a rule based prescribing system. Qual Saf Health Care 2004, 13(3):186-190
  23. ↑ van der Sijs H, Lammers L, van den Tweel A, Aarts J, Berg M, Vulto A, van Gelder T: Time-dependent drug-drug interaction alerts in care provider order entry: software may inhibit medication error reductions. J Am Med Inform Assoc 2009, 16(6):864-868
  24. ↑ van der Sijs H, Aarts J, van Gelder T, Berg M, Vulto A: Turning off frequently overridden drug alerts: limited opportunities for doing it safely. J Am Med Inform Assoc 2008, 15(4):439-448
  25. ↑ Perna G: Clinical alerts that cried wolf. As clinical alerts pose physician workflow problems, healthcare IT leaders look for answers. Healthc Inform 2012, 29(4):18, 20
  26. ↑ 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):2310-2317
  27. ↑ 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):2310-2317
  28. ↑ Ridgely MS, Greenberg MD: Too many alerts, too much liability: sorting through the malpractice implications of drug-drug interaction clinical decision support. St Louis Univ J Health Law Pol 2012, 5:257-296
  29. ↑ Weingart SN, Simchowitz B, Padolsky H, Isaac T, Seger AC, Massagli M, Davis RB, Weissman JS: An empirical model to estimate the potential impact of medication safety alerts on patient safety, health care utilization, and cost in ambulatory care. Arch Intern Med 2009, 169(16):1465-1473
  30. ↑ Troiano D, Jones MA, Smith AH, Chan RC, Laegeler AP, Le T, Flynn A, Chaffee BW: The need for collaborative engagement in creating clinical decision-support alerts. Am J Health Syst Pharm 2013, 70(2):150-153
  31. ↑ Coleman JJ, Nwulu U, Ferner RE: Decision support for sensible dosing in electronic prescribing systems. J Clin Pharm Ther 2011, 37(4):415-419
  32. ↑ Ferner RE, Coleman JJ: An algorithm for integrating contraindications into electronic prescribing decision support. Drug Saf 2010, 33(12):1089-1096
  33. ↑ Seidling HM, Storch CH, Bertsche T, Senger C, Kaltschmidt J, Walter-Sack I, Haefeli WE: Successful strategy to improve the specificity of electronic statin-drug interaction alerts. Eur J Clin Pharmacol 2009, 65(11):1149-1157
  34. ↑ Böttiger Y, Laine K, Andersson ML, Korhonen T, Molin B, Ovesjö ML, Tirkkonen T, Rane A, Gustafsson LL, Eiermann B: SFINX-a drug-drug interaction database designed for clinical decision support systems. Eur J Clin Pharmacol 2009, 65(6):627-633
  35. ↑ Kawamoto K, Hongsermeier T, Wright A, Lewis J, Bell DS, Middleton B: Key principles for a national clinical decision support knowledge sharing framework: synthesis of insights from leading subject matter experts. J Am Med Inform Assoc 2013, 20(1):199-207
  36. ↑ van der Sijs H, Bouamar R, van Gelder T, Aarts J, Berg M, Vulto A: Functionality test for drug safety alerting in computerized physician order entry systems. Int J Med Inform 2010, 79(4):243-251
  37. ↑ van Doormaal JE, Rommers MK, Kosterink JGW, Teepe-Twiss IM, Haaijer-Ruskamp FM, Mol PGM: Comparison of methods for identifying patients at risk of medication-related harm. Qual Saf Health Care 2010, 19:1-5

[Category: Reviews] [Category: CDS]

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