Skip to main content
Log in

Potential drug related problems detected by electronic expert support system in patients with multi-dose drug dispensing

  • Research Article
  • Published:
International Journal of Clinical Pharmacy Aims and scope Submit manuscript

Abstract

Background Drug related problems (DRPs) are frequent and cause suffering for patients and substantial costs for society. Multi-dose drug dispensing (MDDD) is a service by which patients receive their medication packed in bags with one unit for each dose occasion. The clinical decision support system (CDSS) electronic expert support (EES) analyses patients’ prescriptions in the Swedish national e-prescription repository and provides alerts if potential DRPs are detected, i.e. drug–drug interactions, duplicate therapy, drug-disease contraindications, high dose, gender warnings, geriatric, and paediatric alerts. Objective To analyse potential DRPs in patients with MDDD, detected by means of EES. Setting A register study of all electronically stored prescriptions for patients with MDDD in Sweden (n = 180,059) March 5–June 5, 2013. Method Drug use and potential DRPs detected in the study population during the 3 month study period by EES were analysed. The potential DRPs were analysed in relation to patients’ age, gender, number of drugs, and type of medication. Main outcome measure Prevalence of potential DRPs measured as EES alerts. Results The study population was on average 75.8 years of age (±17.5, range 1–110) and had 10.0 different medications (±4.7, range 1–53). EES alerted for potential DRPs in 76 % of the population with a mean of 2.2 alerts per patient (±2.4, range 0–27). The older patients received a lower number of alerts compared to younger patients despite having a higher number of drugs. The most frequent alert categories were drug–drug interactions (37 % of all alerts), duplicate therapy (30 %), and geriatric warnings for high dose or inappropriate drugs (23 %). Psycholeptics, psychoanaleptics, antithrombotic agents, anti-epileptics, renin-angiotensin system agents, and analgesics represented 71 % of all drugs involved in alerts. Conclusions EES detected potential DRPs in the majority of patients with MDDD. The number of potential DRPs was associated with the number of drugs, age, gender, and type of medication. A CDSS such as EES might be a useful tool for physicians and pharmacists to assist in the important task of monitoring patients with MDDD for potential DRPs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Juurlink DN, Mamdani M, Kopp A, Laupacis A, Redelmeier DA. Drug-drug interactions among elderly patients hospitalized for drug toxicity. JAMA. 2003;289(13):1652–8.

    Article  PubMed  CAS  Google Scholar 

  2. Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA. 1998;279(15):1200–5.

    Article  PubMed  CAS  Google Scholar 

  3. Strandell J, Wahlin S. Pharmacodynamic and pharmacokinetic drug interactions reported to VigiBase, the WHO global individual case safety report database. Eur J Clin Pharmacol. 2011;67(6):633–41.

    Article  PubMed  CAS  Google Scholar 

  4. Jonsson AK, Spigset O, Tjaderborn M, Druid H, Hagg S. Fatal drug poisonings in a Swedish general population. BMC Clin Pharmacol. 2009;9:7.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Tache SV, Sonnichsen A, Ashcroft DM. Prevalence of adverse drug events in ambulatory care: a systematic review. Ann Pharmacother. 2011;45(7–8):977–89.

    Article  PubMed  Google Scholar 

  6. Westerlund T, Gelin U, Pettersson E, Skarlund F, Wagstrom K, Ringbom C. A retrospective analysis of drug-related problems documented in a national database. Int J Clin Pharm. 2013;35(2):202–9.

    Article  PubMed  CAS  Google Scholar 

  7. Wester K, Jonsson AK, Spigset O, Druid H, Hagg S. Incidence of fatal adverse drug reactions: a population based study. Br J Clin Pharmacol. 2008;65(4):573–9.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Salvi F, Marchetti A, D’Angelo F, Boemi M, Lattanzio F, Cherubini A. Adverse drug events as a cause of hospitalization in older adults. Drug Saf. 2012;35(Suppl 1):29–45.

    Article  PubMed  Google Scholar 

  9. Topinkova E, Baeyens JP, Michel JP, Lang PO. Evidence-based strategies for the optimization of pharmacotherapy in older people. Drugs Aging. 2012;29(6):477–94.

    Article  PubMed  Google Scholar 

  10. Marcum ZA, Handler SM, Wright R, Hanlon JT. Interventions to improve suboptimal prescribing in nursing homes: a narrative review. Am J Geriatr Pharmacother. 2010;8(3):183–200.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Strandell J, Bate A, Lindquist M, Edwards IR. Drug-drug interactions—a preventable patient safety issue? Br J Clin Pharmacol. 2008;65(1):144–6.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Jonsson AK, Hakkarainen KM, Spigset O, Druid H, Hiselius A, Hagg S. Preventable drug related mortality in a Swedish population. Pharmacoepidemiol Drug Saf. 2010;19(2):211–5.

    Article  PubMed  Google Scholar 

  13. Black AD, Car J, Pagliari C, Anandan C, Cresswell K, Bokun T, et al. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med. 2011;8(1):e1000387.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bates DW, Cohen M, Leape LL, Overhage JM, Shabot MM, Sheridan T. White paper—reducing the frequency of errors in medicine using information technology. J Am Med Inform Assoc. 2001;8(4):299–308.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  15. Cresswell KM, Bates DW, Sheikh A. Ten key considerations for the successful implementation and adoption of large-scale health information technology. J Am Med Inform Assoc. 2013;20(e1):e9–13.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Huckvale C, Car J, Akiyama M, Jaafar S, Khoja T, Bin Khalid A, et al. Information technology for patient safety. Qual Saf Health Care. 2010;19(Suppl 2):i25–33.

    Article  PubMed  Google Scholar 

  17. Appari A, Carian EK, Johnson ME, Anthony DL. Medication administration quality and health information technology: a national study of US hospitals. J Am Med Inform Assoc. 2012;19(3):360–7.

    Article  PubMed  PubMed Central  Google Scholar 

  18. McKibbon KA, Lokker C, Handler SM, Dolovich LR, Holbrook AM, O’Reilly D, et al. The effectiveness of integrated health information technologies across the phases of medication management: a systematic review of randomized controlled trials. J Am Med Inform Assoc. 2012;19(1):22–30.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ojeleye O, Avery A, Gupta V, Boyd M. The evidence for the effectiveness of safety alerts in electronic patient medication record systems at the point of pharmacy order entry: a systematic review. BMC Med Inform Decis Mak. 2013;13:69.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Devine EB, Hansen RN, Wilson-Norton JL, Lawless NM, Fisk AW, Blough DK, et al. The impact of computerized provider order entry on medication errors in a multispecialty group practice. J Am Med Inform Assoc. 2010;17(1):78–84.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Saverno KR, Hines LE, Warholak TL, Grizzle AJ, Babits L, Clark C, et al. Ability of pharmacy clinical decision-support software to alert users about clinically important drug–drug interactions. J Am Med Inform Assoc. 2011;18(1):32–7.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wong K, Yu SK, Holbrook A. A systematic review of medication safety outcomes related to drug interaction software. J Popul Ther Clin Pharmacol. 2010;17(2):e243–55.

    PubMed  Google Scholar 

  23. Kuperman GJ, Bobb A, Payne TH, Avery AJ, Gandhi TK, Burns G, et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc. 2007;14(1):29–40.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223–38.

    Article  PubMed  CAS  Google Scholar 

  25. Ucar A. Assessment of clinical relevance of alerts from EES used at an emergency department. Uppsala: Uppsala University; 2011 (In Swedish).

    Google Scholar 

  26. Hovstadius B, Astrand B, Petersson G. Dispensed drugs and multiple medications in the Swedish population: an individual-based register study. BMC Clin Pharmacol. 2009;9:11.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Rosholm JU, Bjerrum L, Hallas J, Worm J, Gram LF. Polypharmacy and the risk of drug–drug interactions among Danish elderly. A prescription database study. Dan Med Bull. 1998;45(2):210–3.

    PubMed  CAS  Google Scholar 

  28. Hovstadius B, Hovstadius K, Astrand B, Petersson G. Increasing polypharmacy—an individual-based study of the Swedish population 2005–2008. BMC Clin Pharmacol. 2010;10:16.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Astrand E, Astrand B, Antonov K, Petersson G. Potential drug interactions during a three-decade study period: a cross-sectional study of a prescription register. Eur J Clin Pharmacol. 2007;63(9):851–9.

    Article  PubMed  Google Scholar 

  30. Johnell Klarin. The relationship between number of drugs and potential drug–drug interactions in the elderly: a study of over 600,000 elderly patients from the swedish prescribed drug register. Drug Saf. 2007;30(10):911–8.

    Article  PubMed  Google Scholar 

  31. Espinosa-Bosch M, Santos-Ramos B, Gil-Navarro MV, Santos-Rubio MD, Marin-Gil R, Villacorta-Linaza P. Prevalence of drug interactions in hospital healthcare. Int J Clin Pharm. 2012;34(6):807–17.

    Article  PubMed  Google Scholar 

  32. Johnell K, Fastbom J. Multi-dose drug dispensing and inappropriate drug use: a nationwide register-based study of over 700000 elderly. Scand J Prim Health Care. 2008;26(2):86–91.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Sjoberg C, Edward C, Fastbom J, Johnell K, Landahl S, Narbro K, et al. Association between multi-dose drug dispensing and quality of drug treatment—a register-based study. PLoS One. 2011;6(10):e26574.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wallerstedt SM, Fastbom J, Johnell K, Sjoberg C, Landahl S, Sundstrom A. Drug treatment in older people before and after the transition to a multi-dose drug dispensing system—a longitudinal analysis. PLoS One. 2013;8(6):e67088.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  35. Sinnemaki J, Sihvo S, Isojarvi J, Blom M, Airaksinen M, Mantyla A. Automated dose dispensing service for primary healthcare patients: a systematic review. Syst Rev. 2013;2:1.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Gerber A, Stollenwerk B, Lauterbach KW, Stock S, Buscher G, Rath T, Lungen M. Evaluation of multi-dose repackaging for individual patients in long-term care institutions: savings from the perspective of statutory health insurance in Germany. Int J Pharm Pract. 2008;16:387–94.

    Article  Google Scholar 

  37. Sjoberg C, Ohlsson H, Wallerstedt SM. Association between multi-dose drug dispensing and drug treatment changes. Eur J Clin Pharmacol. 2012;68(7):1095–101.

    Article  PubMed  Google Scholar 

  38. Coxe S, West SG, Aiken LS. The analysis of count data: a gentle introduction to poisson regression and its alternatives. J Pers Assess. 2009;91(2):121–36.

    Article  PubMed  Google Scholar 

  39. Lao CK, Ho SC, Chan KK, Tou CF, Tong HH, Chan A. Potentially inappropriate prescribing and drug–drug interactions among elderly Chinese nursing home residents in Macao. Int J Clin Pharm. 2013;35(5):805–12.

    Article  PubMed  Google Scholar 

  40. Johnell K, Weitoft GR, Fastbom J. Sex differences in inappropriate drug use: a register-based study of over 600,000 older people. Ann Pharmacother. 2009;43(7):1233–8.

    Article  PubMed  Google Scholar 

  41. Olsson J, Bergman A, Carlsten A, Oke T, Bernsten C, Schmidt IK, et al. Quality of drug prescribing in elderly people in nursing homes and special care units for dementia: a cross-sectional computerized pharmacy register analysis. Clin Drug Investig. 2010;30(5):289–300.

    Article  PubMed  CAS  Google Scholar 

  42. Ruggiero C, Lattanzio F, Dell’Aquila G, Gasperini B, Cherubini A. Inappropriate drug prescriptions among older nursing home residents: the Italian perspective. Drugs Aging. 2009;26(Suppl 1):15–30.

    Article  PubMed  Google Scholar 

  43. Wahab MS, Nyfort-Hansen K, Kowalski SR. Inappropriate prescribing in hospitalised Australian elderly as determined by the STOPP criteria. Int J Clin Pharm. 2012;34(6):855–62.

    Article  PubMed  Google Scholar 

  44. Heikkila T, Lekander T, Raunio H. Use of an online surveillance system for screening drug interactions in prescriptions in community pharmacies. Eur J Clin Pharmacol. 2006;62(8):661–5.

    Article  PubMed  Google Scholar 

  45. Roughead EE, Kalisch LM, Barratt JD, Gilbert AL. Prevalence of potentially hazardous drug interactions amongst Australian veterans. Br J Clin Pharmacol. 2010;70(2):252–7.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Seidling HM, Schmitt SP, Bruckner T, Kaltschmidt J, Pruszydlo MG, Senger C, et al. Patient-specific electronic decision support reduces prescription of excessive doses. Qual Saf Health Care. 2010;19(5):e15.

    PubMed  CAS  Google Scholar 

  47. Lindblad CI, Hanlon JT, Gross CR, Sloane RJ, Pieper CF, Hajjar ER, et al. Clinically important drug-disease interactions and their prevalence in older adults. Clin Ther. 2006;28(8):1133–43.

    Article  PubMed  CAS  Google Scholar 

  48. Coleman JJ, van der Sijs H, Haefeli WE, Slight SP, McDowell SE, Seidling HM, et al. On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop. BMC Med Inform Decis Mak. 2013;13:111.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors would like to express their gratitude to Abdul Aziz Ali for advice on statistical analyses.

Funding

eHealth Agency, Medical Products Agency, Linnaeus University.

Conflicts of interest

The authors have no conflict of interest. At the time of the study, two of the authors (BL and BE) were employed at the government agency, the eHealth agency, managing the EES.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hammar Tora.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tora, H., Bo, H., Bodil, L. et al. Potential drug related problems detected by electronic expert support system in patients with multi-dose drug dispensing. Int J Clin Pharm 36, 943–952 (2014). https://doi.org/10.1007/s11096-014-9976-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11096-014-9976-z

Keywords

Navigation