Development of a clinical decision support system for renal dose adjustment and
integration into a drug information system with computerized decision support
Schmitt SPW1, Kaltschmidt J1, Bertsche T1 ,Wetter T2, Haefeli WE1
1Department of Internal Medicine VI, Clinical Pharmacology and Pharmacoepidemiology, University Hospital Heidelberg, Germany
2Department of Medical Informatics, University Heidelberg, Germany
Between 14% and 21% of all inpatients have significant
renal impairment [1-3] 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 (extrarenally excreted
fraction of a dose) 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 rise 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
pre-requisites.
DosingExperts guides clinicians as a component of the
CPOE-system (see fig. 2).
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 (fig. 3).
Moreover, the suggestions are linked to the available
galenic formulations on the market and their divisibility
and immediate background explanation is offered upon
request (fig. 4).
Fig. 2: Medication before dose adjustment (CPOE).
Fig. 3: By DosingExpert (CCDS) generated suggestions
for a dose adjusted medication showing alerts and
alternatives.
Fig. 4: Example of a background explanation with
references for the active agent aciclovir.
A web service as part of DosingExpert allows future
sharing of this knowledge base using Simple Object
Access Protocol (SOAP).
Retrospective “in vitro” evaluation of outpatients
prescriptions (N1=1382, 49 sections of the University
Hospital of Heidelberg between Aug 15th, 2006 and Aug
25th 2006) showed that in 11.7% cases body weight
was missing and had to be estimated (fig. 5).
Fig. 5: Result for N1=1382 outpatiens prescriptions.
Subsequent DosingExpert pointed out that for 19% of all
drugs with renal active agents prescribed to patients with
renal impairment dose adjustment would be necessary
(fig. 6).
Fig. 6: Result for N2 = 443 drugs prescribed to outpatients
with renal impairment.
Objective
Starting with a database for renal drug dose adjustment
([4] and 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
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 in real-time with the SAP
i.s.h.med hospital information system 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.
Methods
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 within the actual
workflow of the institution. External systems can be
linked to further improve clinical decision support using
the standardized communication protocol SOAP.
Conclusion
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Mekala A, Song J, Komaroff AL, Bates WD. Guided medication dosing for
inpatients with renal insufficiency. JAMA 2001;286:2839.
[2] Falconnier AD, Haefeli WE, Schoenenberger RA, Surber C, Martin-Facklam
M. Drug dosage in patients with renal failure optimized by immediate
concurrent feedback. J Gen Intern Med 2001;16:369-75.
[3] Nash IS, Rojas M, Hebert P, Marrone SR, Colgan C, Fisher LA, Caliendo G,
Chassin MR. Reducing Excessive Medication Administration in Hospitalized
Adults With Renal Dysfunction. Am J Med Qual 2005;20:64-9.
[4] Bertsche T, Haefeli WE. Individualisierte Arzneimitteltherapie bei Patienten
mit Niereninsuffizienz. Pharm Ztg 2006;151:718-23
[5] Wong NA, Jones HW. An analysis of discharge drug prescribing amongst
elderly patients with renal impairment. Postgrad Med J 1998;74:420-2.
[6] Classen DC et al. Adverse drug events in hospitalized patients. Excess
length of stay, extra costs, and attributable mortality. JAMA 1997;277:301-6.
[7] Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Luf MN, Boyle D,
Leape L. The impact of computerized physician order entry on medication
error prevention. JAMIA 1999;6:313-21.
[8] Kaltschmidt J, Gallin S, Haefeli WE. Essential functional requirements for an
effective electronic drug information system in a hospital. Int J Clin
Pharmacol Ther 2004;42:615.
[9] Dettli L. The kidney in pre-clinical and clinical pharmacokinetics. Jpn J Clin
Pharmacol Ther 1984;15:241-54.
[10] Kaluza T, Hartge F, Kaltschmidt J, Wetter Th, Haefeli WE. Developing a
webbased quality assurance tool for active agent and drug interaction
knowledge bases of AiDKlinik; student research project 2006.
Literature
GMDS-2006
MI-30 A177
We successfully developed a knowledge-based system
named DosingExpert for renal dose adjustment and
integrated it into the drug information system AiDKlinik of
the University Hospital of Heidelberg (see fig. 1 - 4).
Fig. 1: Three level architecture of DosingExpert
Fulfilling the above criteria, this system comprehensively
individualizes therapy with more than 650 active
ingredients. As an ongoing strategically planned process
experts can update this knowledge base using a newly
developed web based system [10].
Results