gms | German Medical Science

51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (gmds)

10. - 14.09.2006, Leipzig

Development of a clinical decision support system for renal dose adjustment and integration into a drug information system with computerized decision support

Meeting Abstract

  • Simon Schmitt - Innere Medizin VI, Klinische Pharmakologie und Pharmakoepidemiologie, Universitätsklinikum, Heidelberg
  • Jens Kaltschmidt - Innere Medizin VI, Klinische Pharmakologie und Pharmakoepidemiologie, Universitätsklinikum, Heidelberg
  • Thilo Bertsche - Innere Medizin VI, Klinische Pharmakologie und Pharmakoepidemiologie, Universitätsklinikum, Heidelberg
  • Thomas Wetter - Abt. Medizinische Information, Universität, Heidelberg
  • Walter Emil Haefeli - Innere Medizin VI, Klinische Pharmakologie und Pharmakoepidemiologie, Universitätsklinikum, Heidelberg

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (gmds). 51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Leipzig, 10.-14.09.2006. Düsseldorf, Köln: German Medical Science; 2006. Doc06gmds177

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Veröffentlicht: 1. September 2006

© 2006 Schmitt et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Objective

Between 14% and 21% of all inpatients have significant renal impairment [1], [2], [3] and approximately one in seven drugs is mainly excreted through the kidneys [4] requiring dose adjustment (usually dose reduction) in renal failure. Parameters such as the patient’s weight, age, gender, current renal function and drug specific parameters such as the Q0-value are required to define the dose and must be considered in the prescription process together with information on availability, price, and divisibility of tablets. Presently, such dose adjustment is only accomplished in 33% of all cases [1], [2], [5] leading to excess length of stay, risk of adverse reactions [2], and excess drug costs of about 14%. Computerized clinical decision support (CCDS) systems can reduce medication errors, costs, and length of stay [1], [6], [7] but most of them lack the possibility to carry out all steps required to tailor drug therapy to the patients’ needs and to concurrently consider all factors influencing a prescription. The aim of this project was thus to build an electronic system fulfilling most of these prerequisites.

Materials and Methods

Starting with a database for renal drug dose adjustment ([4] and http://www.dosing.de/) created by the Department of Internal Medicine VI, Clinical Pharmacology and Pharmacoepidemiology we developed a knowledge-based system named DosingExpert and integrated it into an existing web-based drug information system ([8] and http://www.aidklinik.de/) with computerized physician order entry (CPOE). The database contains peer reviewed drug dosage information about pharmacologically active agents, renal and adverse drug effects, tablet divisibility, and current drug market data (the latter based on Gelbe Liste Pharmindex). Inpatients’ master data and laboratory test results are exchanged real-time with the SAP i.s.h.med hospital information system in place in our institution. Patients’ estimated or measured current renal function [9] is combined with newly developed algorithms to determine the best medication, adjust dosage and regimen, and avoid potential harm.

Results

We successfully developed a knowledge-based system for renal dose adjustment and integrated it into the drug information system of the University Hospital of Heidelberg (see fig. 1 [Fig. 1]). Fulfilling the above criteria, this system comprehensively individualizes therapy with more than 650 active ingredients. The severity and urgency of all suggestions is expressed in intuitive colour codes and explicit recommendations to discontinue a drug, adapt its dosing regimen, or change to a therapeutic alternative are clearly displayed. Moreover, the suggestions are linked to the available galenic formulations on the market and their divisibility and immediate background explanation is offered upon request. A web service as part of DosingExpert allows future sharing of this knowledge base using Simple Object Access Protocol (SOAP).

Conclusion

DosingExpert demonstrates that numerical data and a large amount of symbolic information of various types can be seamlessly integrated into an online decision support that safeguards physicians’ prescriptions. Because prescription data are available as a control before introduction of the tool, effects on costs, prescription behaviour, clinical workflow, and patient outcome can now be evaluated. Further external systems can be linked to further improve clinical decision support using the standardized communication protocol SOAP.


References

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