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Appl Clin Inform 2020; 11(03): 487-496
DOI: 10.1055/s-0040-1714276
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Clinician Perceptions of Timing and Presentation of Drug-Drug Interaction Alerts

Kate E. Humphrey
1   Patient Safety and Quality, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States
,
Maria Mirica
2   General Internal Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
,
Shobha Phansalkar
3   Division of General Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
,
Al Ozonoff
4   Precision Vaccines Program, Division of Infectious Diseases, Boston Children’s Hospital, Boston, Massachusetts, United States
5   Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States
,
Marvin B. Harper
6   Emergency Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States
› Author Affiliations Funding Funding was supported through grant funds issued by The Risk Management Foundation of the Harvard Medical Institutions (CRICO/RMF).
Further Information

Publication History

21 February 2020

11 June 2020

Publication Date:
22 July 2020 (online)

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Abstract

Objective Alert presentation of clinical decision support recommendations is a common method for providing information; however, many alerts are overridden suggesting presentation design improvements can be made. This study attempts to assess pediatric prescriber information needs for drug–drug interactions (DDIs) alerts and to evaluate the optimal presentation timing and presentation in the medication ordering process.

Methods Six case scenarios presented interactions between medications used in pediatric specialties of general medicine, infectious disease, cardiology, and neurology. Timing varied to include alert interruption at medication selection versus order submission; or was noninterruptive. Interviews were audiotaped, transcribed, and independently analyzed to derive central themes.

Results Fourteen trainee and attending clinicians trained in pediatrics, cardiology, and neurology participated. Coders derived 8 central themes from 929 quotes. Discordance exists between medication prescribing frequency and DDI knowledge; providers may commonly prescribe medications for which they do not recognize DDIs. Providers wanted alerts at medication selection rather than at order signature. Alert presentation themes included standardizing text, providing interaction-specific incidence/risk information, DDI rating scales, consolidating alerts, and providing alternative therapies. Providers want alerts to be actionable, for example, allowing medication discontinuation and color visual cues for essential information. Despite alert volume, participants did not “mind being reminded because there is always the chance that at that particular moment (they) do not remember it” and acknowledged the importance of alerts as “essential in terms of patient safety.”

Conclusion Clinicians unanimously agreed on the importance of receiving DDI alerts to improve patient safety. The perceived alert value can be improved by incorporating clinician preferences for timing and presentation.

Keywords

drug interactions - electronic health records - health personnel alert fatigue - medication alert systems

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed by the Boston Children's Hospital Institutional Review Board.


 

  • References

  • 1 Kirkendall E, Kouril M, Dexheimer J. , et al. Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records. J Am Med Inform Assoc 2017; 24 (02) 295-302
    CrossrefPubMedGoogle Scholar
  • 2 Saxena K, Lung BR, Becker JR. Improving patient safety by modifying provider ordering behavior using alerts (CDSS) in CPOE system. AMIA Ann Symp Proc 2011; 2011: 1207-1216
    PubMedGoogle Scholar
  • 3 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 (04) 439-448
    CrossrefPubMedGoogle Scholar
  • 4 Bryant AD, Fletcher GS, Payne TH. Drug interaction alert override rates in the Meaningful Use era: no evidence of progress. Appl Clin Inform 2014; 5 (03) 802-813
    Article in Thieme ConnectPubMedGoogle Scholar
  • 5 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
    CrossrefPubMedGoogle Scholar
  • 6 Edrees H, Amato MG, Wong A, Seger DL, Bates DW. High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: override appropriateness and adverse drug events. J Am Med Inform Assoc 2020; 27 (06) 893-900
    CrossrefPubMedGoogle Scholar
  • 7 Sirajuddin AM, Osheroff JA, Sittig DF, Chuo J, Velasco F, Collins DA. Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support. Effective CDS is essential for addressing healthcare performance improvement imperatives. J Healthc Inf Manag 2009; 23 (04) 38-45
    PubMedGoogle Scholar
  • 8 Miller K, Mosby D, Capan M. , et al. Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support. J Am Med Inform Assoc 2018; 25 (05) 585-592
    CrossrefPubMedGoogle Scholar
  • 9 Wong A, Amato MG, Seger DL. , et al. Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit. BMJ Qual Saf 2018; 27 (09) 718-724
    CrossrefPubMedGoogle Scholar
  • 10 Seidling HM, Phansalkar S, Seger DL. , et al. Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support. J Am Med Inform Assoc 2011; 18 (04) 479-484
    CrossrefPubMedGoogle Scholar
  • 11 Seidling HM, Klein U, Schaier M. , et al. What, if all alerts were specific - estimating the potential impact on drug interaction alert burden. Int J Med Inform 2014; 83 (04) 285-291
    CrossrefPubMedGoogle Scholar
  • 12 Phansalkar S, Zachariah M, Seidling HM, Mendes C, Volk L, Bates DW. Evaluation of medication alerts in electronic health records for compliance with human factors principles. J Am Med Inform Assoc 2014; 21 (e2): e332-e340
    CrossrefPubMedGoogle Scholar
  • 13 Baysari MT, Lowenstein D, Zheng WY, Day RO. Reliability, ease of use and usefulness of I-MeDeSA for evaluating drug-drug interaction alerts in an Australian context. BMC Med Inform Decis Mak 2018; 18 (01) 83
    CrossrefPubMedGoogle Scholar
  • 14 Cho I, Lee J, Han H, Phansalkar S, Bates DW. Evaluation of a Korean version of a tool for assessing the incorporation of human factors into a medication-related decision support system: the I-MeDeSA. Appl Clin Inform 2014; 5 (02) 571-588
    Article in Thieme ConnectPubMedGoogle Scholar
  • 15 Marcilly R, Ammenwerth E, Vasseur F, Roehrer E, Beuscart-Zéphir MC. Usability flaws of medication-related alerting functions: a systematic qualitative review. J Biomed Inform 2015; 55: 260-271
    CrossrefPubMedGoogle Scholar
  • 16 Glassman P, Miller CE. Effect of preventive dentistry training program for caregivers in community facilities on caregiver and client behavior and client oral hygiene. N Y State Dent J 2006; 72 (02) 38-46
    PubMedGoogle Scholar
  • 17 Ancker JS, Edwards A, Nosal S, Hauser D, Mauer E, Kaushal R. ; with the HITEC Investigators. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak 2017; 17 (01) 36
    CrossrefPubMedGoogle Scholar
  • 18 van der Sijs H, Mulder A, van Gelder T, Aarts J, Berg M, Vulto A. Drug safety alert generation and overriding in a large Dutch university medical centre. Pharmacoepidemiol Drug Saf 2009; 18 (10) 941-947
    CrossrefPubMedGoogle Scholar
  • 19 Ivanovska V, Rademaker CM, van Dijk L, Mantel-Teeuwisse AK. Pediatric drug formulations: a review of challenges and progress. Pediatrics 2014; 134 (02) 361-372
    CrossrefPubMedGoogle Scholar
  • 20 Glaser BG. Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Mill Valley, CA: Sociology Press; 1978
    Google Scholar
  • 21 Maxwell JA. Qualitative Research Design: An Interactive Approach. Vol. 41. Thousand Oaks, CA: Sage Publications, Inc.; 1996
    Google Scholar
  • 22 Yin RK. Case Study Research: Design and Methods. 4th ed. Thousand Oaks, CA: Sage; 2009
    Google Scholar
  • 23 Aitken LM, Mardegan KJ. “Thinking aloud”: data collection in the natural setting. West J Nurs Res 2000; 22 (07) 841-853
    CrossrefPubMedGoogle Scholar
  • 24 Olson G, Duffy S, Mack R. New Methods in Reading Comprehension Research. Hillsdale, NJ: Erlbaum; 1984
    Google Scholar
  • 25 Miles MB, Huberman AM. Qualitative data analysis: an expanded sourcebook. Thousand Oaks, CA: Sage; 1994
    Google Scholar
  • 26 Strauss A, Corbin J. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Thousand Oaks, CA: Sage Publications, Inc.; 1998
    Google Scholar
  • 27 Glaser B, Strauss A. The Discovery of Grounded Theory: Strategies for Qualitative Research. Mill Valley, CA: Sociology Press; 1967
    Google Scholar
  • 28 Crabtree BF, Miller WL. Doing Qualitative Research. 2nd ed. Thousand Oaks, CA: Sage Publications; 1999
    Google Scholar
  • 29 Reason J. Understanding adverse events: human factors. Qual Health Care 1995; 4 (02) 80-89
    CrossrefPubMedGoogle Scholar
  • 30 Treiber LA, Jones JH. Devastatingly human: an analysis of registered nurses' medication error accounts. Qual Health Res 2010; 20 (10) 1327-1342
    CrossrefPubMedGoogle Scholar
  • 31 Payne TH, Hines LE, Chan RC. , et al. Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. J Am Med Inform Assoc 2015; 22 (06) 1243-1250
    CrossrefPubMedGoogle Scholar
  • 32 Russ AL, Zillich AJ, McManus MS, Doebbeling BN, Saleem JJ. A human factors investigation of medication alerts: barriers to prescriber decision-making and clinical workflow. AMIA Ann Symp Proc 2009; 2009: 548-552
    PubMedGoogle Scholar
  • 33 Miller A, Moon B, Anders S, Walden R, Brown S, Montella D. Integrating computerized clinical decision support systems into clinical work: a meta-synthesis of qualitative research. Int J Med Inform 2015; 84 (12) 1009-1018
    CrossrefPubMedGoogle Scholar
  • 34 Lobach DF. The road to effective clinical decision support: are we there yet?. BMJ 2013; 346: f1616
    CrossrefPubMedGoogle Scholar
  • 35 Trumbo CW. Heuristic-systematic information processing and risk judgment. Risk Anal 1999; 19 (03) 391-400
    PubMedGoogle Scholar
  • 36 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; 14 (01) 29-40
    CrossrefPubMedGoogle Scholar
  • 37 Garg AX, Adhikari NK, McDonald H. , et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293 (10) 1223-1238
    CrossrefPubMedGoogle Scholar
  • 38 Paterno MD, Maviglia SM, Gorman PN. , et al. Tiering drug-drug interaction alerts by severity increases compliance rates. J Am Med Inform Assoc 2009; 16 (01) 40-46
    CrossrefPubMedGoogle Scholar
  • 39 Kearns GL, Abdel-Rahman SM, Alander SW, Blowey DL, Leeder JS, Kauffman RE. Developmental pharmacology--drug disposition, action, and therapy in infants and children. N Engl J Med 2003; 349 (12) 1157-1167
    CrossrefPubMedGoogle Scholar
  • 40 Rödle W, Wimmer S, Zahn J. , et al. User-centered development of an online platform for drug dosing recommendations in pediatrics. Appl Clin Inform 2019; 10 (04) 570-579
    Article in Thieme ConnectPubMedGoogle Scholar
  • 41 Fiks AG, Jimenez ME. The promise of shared decision-making in paediatrics. Acta Paediatr 2010; 99 (10) 1464-1466
    CrossrefPubMedGoogle Scholar
  • 42 Ko Y, Malone DC, Skrepnek GH. , et al. Prescribers' knowledge of and sources of information for potential drug-drug interactions: a postal survey of US prescribers. Drug Saf 2008; 31 (06) 525-536
    CrossrefPubMedGoogle Scholar