Appl Clin Inform 2014; 05(03): 603-611
DOI: 10.4338/ACI-2014-04-RA-0030
Research Article
Schattauer GmbH

Estimation of severe drug-drug interaction warnings by medical specialist groups for Austrian nationwide eMedication

C. Rinner
1   Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna
,
S. K. Sauter
1   Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna
,
L. M. Neuhofer
1   Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna
,
D. Edlinger
1   Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna
,
W. Grossmann
2   Research Group Scientific Computing, University of Vienna
,
M. Wolzt
3   Department of Clinical Pharmacology, Medical University of Vienna
,
G. Endel
4   Main Association of Austrian Social Security Organizations, Vienna, Austria
,
W. Gall
1   Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna
› Institutsangaben
Weitere Informationen

Publikationsverlauf

received: 14. April 2014

accepted: 21. Mai 2014

Publikationsdatum:
19. Dezember 2017 (online)

Summary

Objective: The objective of this study is to estimate the amount of severe drug-drug interaction warnings per medical specialist group triggered by prescribed drugs of a patient before and after the introduction of a nationwide eMedication system in Austria planned for 2015.

Methods: The estimations of interaction warnings are based on patients’ prescriptions of a single health care professional per patient, as well as all patients’ prescriptions from all visited health care professionals. We used a research database of the Main Association of Austrian Social Security Organizations that contains health claims data of the years 2006 and 2007.

Results: The study cohort consists of about 1 million patients, with 26.4 million prescribed drugs from about 3,400 different health care professionals. The estimation of interaction warnings show a heterogeneous pattern of severe drug-drug-interaction warnings across medical specialist groups.

Conclusion: During an eMedication implementation it must be taken into consideration that different medical specialist groups require customized support.

Citation: Rinner C, Sauter SK, Neuhofer LM, Edlinger D, Grossmann W, Wolzt M, Endel G, Gall W. Estimation of severe drug-drug interaction warnings by medical specialist groups for Austrian nationwide eMedication. Appl Clin Inf 2014; 5: 603–611

http://dx.doi.org/10.4338/ACI-04-RA-0030

 
  • References

  • 1 Lee EK, Mejia AF, Senior T, Jose J. Improving Patient Safety through Medical Alert Management: An Automated Decision Tool to Reduce Alert Fatigue. AMIA Annual Symposium proceedings 2010; 2010: 417-421.
  • 2 Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: A systematic review. Archives of Internal Medicine 2003; 163 (12) 1409-1416.
  • 3 Schedlbauer A, Prasad V, Mulvaney C, Phansalkar S, Stanton W, Bates DW, Avery AJ. What evidence supports the use of computerized alerts and prompts to improve clinicians’ prescribing behavior?. Journal of the American Medical Informatics Association: JAMIA 2009; 16 (04) 531-538.
  • 4 Herbek S, Eisl HA, Hurch M, Schator A, Sabutsch S, Rauchegger G, Kollmann A, Philippi T, Dragon P, Seitz E, Repas S. The Electronic Health Record in Austria: a strong network between health care and patients. Eur Surg 2012; 44 (03) 155-163.
  • 5 Makinen M, Rautava P, Forsstrom J, Aarimaa M. Electronic prescriptions are slowly spreading in the European Union. Telemedicine journal and e-health : the official journal of the American Telemedicine Association 2011; 17 (03) 217-222.
  • 6 Schnipper JL, Hamann C, Ndumele CD, Liang CL, Carty MG, Karson AS, Bhan I, Coley CM, Poon E, Tur-chin A, Labonville SA, Diedrichsen EK, Lipsitz S, Broverman CA, McCarthy P, Gandhi TK. Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. Arch Intern Med 2009; 169 (08) 771-780.
  • 7 Patapovas A, Dormann H, Sedlmayr B, Kirchner M, Sonst A, Muller F, Pfistermeister B, Plank-Kiegele B, Vogler R, Maas R, Criegee-Rieck M, Prokosch HU, Burkle T. Medication safety and knowledge-based functions: a stepwise approach against information overload. British journal of clinical pharmacology 2013; 76 (Suppl. 01) 14-24.
  • 8 WHO Collaborating Centre for Drug Statistics Methodology.. ATC/DDD Index 2014. [cited 2014 April]; Available from: http://www.whocc.no/atc_ddd_index.
  • 9 Österreichische Apotheker-Verlagsgesellschaft m.b.. H. Austria-Codex. 2006 [cited 2014 April]; Available from: http://www3.apoverlag.at/dynasite.cfm?dsmid=106234.
  • 10 Bundesministerium für Justiz.. Gesundheitstelematikgesetz 2012 –Elektronische Gesundheitsakte-Gesetz –ELGA-G. Mai. 2013 [cited 2014 April]; Available from: http://bit.ly/1eEp5HM.
  • 11 Froeschl K, Grossman W, Dorda W, Duftschmid G, Gall W, Moschner M. Approaches Towards Health Information. Information day on the EU funded iWebCare project and workshop on e-Europe 2008 p. 9-13.
  • 12 Gall W, Dorda W, Duftschmid G, Endel G, Hronsky M, Neuhofer L, Rinner R, Grossmann G. Krankenhausaufenthalte infolge unerwünschter Arzneimittelereignisse. Proceedings eHealth2013 Vienna. 2013: 31-36.
  • 13 Orrico KB. Sources and types of discrepancies between electronic medical records and actual outpatient medication use. Journal of managed care pharmacy: JMCP 2008; 14 (07) 626-631.
  • 14 Lessing C, Schmitz A, Albers B, Schrappe M. Impact of sample size on variation of adverse events and preventable adverse events: systematic review on epidemiology and contributing factors. Quality & safety in health care. 2010; 19 (06) e24.
  • 15 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. Pharmacoepidemiology and drug safety 2009; 18 (10) 941-947.