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Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients

  • Pharmacoepidemiology and Prescription
  • Published:
European Journal of Clinical Pharmacology Aims and scope Submit manuscript

Abstract

Purpose

Clinical decision support systems (CDSS) are promoted as powerful screening tools to improve pharmacotherapy. The aim of our study was to evaluate the potential contribution of CDSS to patient management in clinical practice.

Methods

We prospectively analyzed the pharmacotherapy of 100 medical inpatients through the parallel use of three CDSS, namely, Pharmavista, DrugReax, and TheraOpt. After expert discussion that also considered all patient-specific clinical information, we selected apparently relevant alerts, issued suitable recommendations to physicians, and recorded subsequent prescription changes.

Results

For 100 patients with a median of eight concomitant drugs, Pharmavista, DrugReax, and TheraOpt generated a total of 53, 362, and 328 interaction alerts, respectively. Among those we identified and forwarded 33 clinically relevant alerts to the attending physician, resulting in 19 prescription changes. Four adverse drug events were associated with interactions. The proportion of clinically relevant alerts among all alerts (positive predictive value) was 5.7, 8.0, and 7.6%, and the sensitivity to detect all 33 relevant alerts was 9.1, 87.9, and 75.8% for Pharmavista, DrugReax and TheraOpt, respectively. TheraOpt recommended 31 dose adjustments, of which we considered 11 to be relevant; three of these were followed by dose reductions.

Conclusions

CDSS are valuable screening tools for medication errors, but only a small fraction of their alerts appear relevant in individual patients. In order to avoid overalerting CDSS should use patient-specific information and management-oriented classifications. Comprehensive information should be displayed on-demand, whereas a limited number of computer-triggered alerts that have management implications in the majority of affected patients should be based on locally customized and supported algorithms.

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References

  1. Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD, Servi D, Laffel G, Sweitzer BJ, Shea BF, Hallisey R et al (1995) Incidence of adverse drug events and potential adverse drug events. implications for prevention. ADE Prevention Study Group. JAMA 274(1):29–34

    Article  PubMed  CAS  Google Scholar 

  2. Classen DC, Pestotnik SL, Evans RS, Lloyd JF, Burke JP (1997) Adverse drug events in hospitalized patients. excess length of stay, extra costs, and attributable mortality. JAMA 277(4):301–306

    Article  PubMed  CAS  Google Scholar 

  3. Lagnaoui R, Moore N, Fach J, Longy-Boursier M, Begaud B (2000) Adverse drug reactions in a department of systemic diseases-oriented internal medicine: prevalence, incidence, direct costs and avoidability. Eur J Clin Pharmacol 56(2):181–186

    Article  PubMed  CAS  Google Scholar 

  4. Lazarou J, Pomeranz BH, Corey PN (1998) Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 279(15):1200–1205

    Article  PubMed  CAS  Google Scholar 

  5. Pirmohamed M, James S, Meakin S, Green C, Scott AK, Walley TJ, Farrar K, Park BK, Breckenridge AM (2004) Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. Br Med J 329(7456):15–19

    Article  Google Scholar 

  6. Juurlink DN, Mamdani M, Kopp A, Laupacis A, Redelmeier DA (2003) Drug-drug interactions among elderly patients hospitalized for drug toxicity. JAMA 289(13):1652–1658

    Article  PubMed  CAS  Google Scholar 

  7. Juurlink DN, Mamdani MM, Lee DS, Kopp A, Austin PC, Laupacis A, Redelmeier DA (2004) Rates of hyperkalemia after publication of the randomized aldactone evaluation study. N Engl J Med 351(6):543–551

    Article  PubMed  CAS  Google Scholar 

  8. Ammenwerth E, Schnell-Inderst P, Machan C, Siebert U (2008) The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. J Am Med Inform Assoc 15(5):585–600

    Article  PubMed  Google Scholar 

  9. Feldstein AC, Smith DH, Perrin N, Yang X, Rix M, Raebel MA, Magid DJ, Simon SR, Soumerai SB (2006) Improved therapeutic monitoring with several interventions: a randomized trial. Arch Intern Med 166(17):1848–1854

    Article  PubMed  Google Scholar 

  10. Kaushal R, Kern LM, Barron Y, Quaresimo J, Abramson EL (2010) Electronic prescribing improves medication safety in community-based office practices. J Gen Intern Med 25(6):530–536

    Article  PubMed  Google Scholar 

  11. Kaushal R, Shojania KG, Bates DW (2003) Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 163(12):1409–1416

    Article  PubMed  Google Scholar 

  12. Smith DH, Perrin N, Feldstein A, Yang X, Kuang D, Simon SR, Sittig DF, Platt R, Soumerai SB (2006) The impact of prescribing safety alerts for elderly persons in an electronic medical record: an interrupted time series evaluation. Arch Intern Med 166(10):1098–1104

    Article  PubMed  Google Scholar 

  13. Eslami S, de Keizer NF, Abu-Hanna A (2008) The impact of computerized physician medication order entry in hospitalized patients—a systematic review. Int J Med Inform 77(6):365–376

    Article  PubMed  Google Scholar 

  14. van Doormaal JE, van den Bemt PM, Zaal RJ, Egberts AC, Lenderink BW, Kosterink JG, Haaijer-Ruskamp FM, Mol PG (2009) The influence that electronic prescribing has on medication errors and preventable adverse drug events: an interrupted time-series study. J Am Med Inform Assoc 16(6):816–825

    Article  PubMed  Google Scholar 

  15. Wolfstadt JI, Gurwitz JH, Field TS, Lee M, Kalkar S, Wu W, Rochon PA (2008) The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. J Gen Intern Med 23(4):451–458

    Article  PubMed  Google Scholar 

  16. Olvey EL, Clauschee S, Malone DC (2010) Comparison of critical drug-drug interaction listings: the Department of Veterans Affairs medical system and standard reference compendia. Clin Pharmacol Ther 87(1):48–51

    Article  PubMed  CAS  Google Scholar 

  17. Abarca J, Malone DC, Armstrong EP, Grizzle AJ, Hansten PD, Van Bergen RC, Lipton RB (2004) Concordance of severity ratings provided in four drug interaction compendia. J Am Pharm Assoc 44(2):136–141

    Article  Google Scholar 

  18. Vonbach P, Dubied A, Krahenbuhl S, Beer JH (2008) Evaluation of frequently used drug interaction screening programs. Pharm World Sci 30(4):367–374

    Article  PubMed  Google Scholar 

  19. van Doormaal JE, Mol PG, van den Bemt PM, Zaal RJ, Egberts AC, Kosterink JG, Haaijer-Ruskamp FM (2008) Reliability of the assessment of preventable adverse drug events in daily clinical practice. Pharmacoepidemiol Drug Saf 17(7):645–654

    Article  PubMed  Google Scholar 

  20. Smithburger PL, Kane-Gill SL, Benedict NJ, Falcione BA, Seybert AL (2010) Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings. Ann Pharmacother 44(11):1718–1724

    Article  PubMed  CAS  Google Scholar 

  21. Vonbach P, Dubied A, Beer JH, Krahenbuhl S (2007) Recognition and management of potential drug-drug interactions in patients on internal medicine wards. Eur J Clin Pharmacol 63(11):1075–1083

    Article  PubMed  Google Scholar 

  22. van der Sijs H, Mulder A, van Gelder T, Aarts J, Berg M, Vulto A (2009) Drug safety alert generation and overriding in a large Dutch University Medical Centre. Pharmacoepidemiol Drug Saf 18(10):941–947

    Article  PubMed  Google Scholar 

  23. Thomson Reuters DRUG-REAX SYSTEM. Available at: http://thomsonreuters.com/products_services/healthcare/healthcare_products/a-z/drug_reax_system

  24. Pharmavista Interactions (2011). Available at: http://www.pharmavista.ch

  25. ID Berlin ID PHARMA CHECK. Available at: http://www.id-berlin.de

  26. Albengres E, Le Louet H, Tillement JP (1998) Systemic antifungal agents. drug interactions of clinical significance. Drug Saf 18(2):83–97

    Article  PubMed  CAS  Google Scholar 

  27. Hempfling W, Grunhage F, Dilger K, Reichel C, Beuers U, Sauerbruch T (2003) Pharmacokinetics and pharmacodynamic action of budesonide in early- and late-stage primary biliary cirrhosis. Hepatology 38(1):196–202

    Article  PubMed  CAS  Google Scholar 

  28. Haueis P, Greil W, Huber M, Grohmann R, Kullak-Ublick GA, Russmann S (2011) Evaluation of drug interactions in a large sample of psychiatric inpatients: a data interface for mass analysis with clinical decision support software. Clin Pharmacol Ther 90(4):588–596

    Article  PubMed  CAS  Google Scholar 

  29. Taylor LK, Tamblyn R (2004) Reasons for physician non-adherence to electronic drug alerts. Stud Health Technol Inform 107(Pt 2):1101–1105

    PubMed  Google Scholar 

  30. Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS (2003) Physicians’ decisions to override computerized drug alerts in primary care. Arch Intern Med 163(21):2625–2631

    Article  PubMed  Google Scholar 

  31. Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ, Blumenfeld B, Recklet EG, Bates DW, Gandhi TK (2006) Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc 13(1):5–11

    Article  PubMed  Google Scholar 

  32. Tamblyn R, Huang A, Taylor L, Kawasumi Y, Bartlett G, Grad R, Jacques A, Dawes M, Abrahamowicz M, Perreault R, Winslade N, Poissant L, Pinsonneault A (2008) A randomized trial of the effectiveness of on-demand versus computer-triggered drug decision support in primary care. J Am Med Inform Assoc 15(4):430–438

    Article  PubMed  Google Scholar 

  33. Isaac T, Weissman JS, Davis RB, Massagli M, Cyrulik A, Sands DZ, Weingart SN (2009) Overrides of medication alerts in ambulatory care. Arch Intern Med 169(3):305–311

    Article  PubMed  Google Scholar 

  34. Weingart SN, Simchowitz B, Shiman L, Brouillard D, Cyrulik A, Davis RB, Isaac T, Massagli M, Morway L, Sands DZ, Spencer J, Weissman JS (2009) Clinicians’ assessments of electronic medication safety alerts in ambulatory care. Arch Intern Med 169(17):1627–1632

    Article  PubMed  Google Scholar 

  35. Hansten PD, Horn JR, Hazlet TK (2001) ORCA: OpeRational ClassificAtion of drug interactions. J Am Pharm Assoc (Wash) 41(2):161–165

    CAS  Google Scholar 

  36. Frölich T, Zorina O, Fontana AO, Kullak-Ublick GA, Vollenweider A, Russmann S (2011) Evaluation of medication safety in the discharge medication of 509 surgical inpatients using electronic prescription support software and an extended operational interaction classification. Eur J Clin Pharmacol 67:1273–1282

    Google Scholar 

  37. Guzek M, Zorina OI, Semmler A, Gonzenbach RR, Huber M, Kullak-Ublick GA, Weller M, Russmann S (2011) Evaluation of drug interactions and dosing in 484 neurological inpatients using clinical decision support software and an extended operational interaction classification system (Zurich Interaction System). Pharmacoepidemiol Drug Saf 20(9):930–938

    PubMed  Google Scholar 

  38. Kucher N, Koo S, Quiroz R, Cooper JM, Paterno MD, Soukonnikov B, Goldhaber SZ (2005) Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med 352(10):969–977

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

The physicians at the Department of Internal Medicine at the University Hospital Zurich are gratefully acknowledged for their time and effort spent in discussing and improving the patients’ pharmacotherapy.

Financial support and conflict of interest statement

The work presented in this manuscript was carried out independently by the authors and with general resources available at the Department of Clinical Pharmacology. IC, ME, and GKU are involved in the development of prescribing software, but they have no financial associations to the CDSS studied here. All authors declare that they have no conflict of interest regarding the work presented here.

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Correspondence to Stefan Russmann.

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Fritz, D., Ceschi, A., Curkovic, I. et al. Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients. Eur J Clin Pharmacol 68, 1209–1219 (2012). https://doi.org/10.1007/s00228-012-1241-6

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  • DOI: https://doi.org/10.1007/s00228-012-1241-6

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