The expenditure of computer-related worktime using clinical decision support systems in chronic pain therapy
Hecht et al. BMC Anesthesiology
The expenditure of computer-related worktime using clinical decision support systems in chronic pain therapy
Timm Hecht 0
Anika C. Bundscherer 0
Christoph L. Lassen 0
Nicole Lindenberg 0
Bernhard M. Graf 0
Karl-Peter Ittner 0
Christoph H. R. Wiese 0
0 Department of Anesthesiology, University Medical Centre Regensburg , Franz-Josef-Strauß-Allee 11, D-93053 Regensburg , Germany
Background: Estimate the expenditure of computer-related worktime resulting from the use of clinical decision support systems (CDSS) to prevent adverse drug reactions (ADR) among patients undergoing chronic pain therapy and compare the employed check systems with respect to performance and practicability. Methods: Data were collected retrospectively from 113 medical records of patients under chronic pain therapy during 2012/2013. Patient-specific medications were checked for potential drug-drug interactions (DDI) using two publicly available CDSS, Apotheken Umschau (AU) and Medscape (MS), and a commercially available CDSS AiDKlinik® (AID). The time needed to analyze patient pharmacotherapy for DDIs was taken with a stopwatch. Measurements included the time needed for running the analysis and printing the results. CDSS were compared with respect to the expenditure of time and usability. Only patient pharmacotherapies with at least two prescribed drugs and fitting the criteria of the corresponding CDSS were analyzed. Additionally, a qualitative evaluation of the used check systems was performed, employing a questionnaire asking five pain physicians to compare and rate the performance and practicability of the three CDSSs. Results: The AU tool took a total of 3:55:45 h with an average of 0:02:32 h for 93 analyzed patient regimens and led to the discovery of 261 DDIs. Using the Medscape interaction checker required a total of 1:28:35 h for 38 patients with an average of 0:01:58 h and a yield of 178 interactions. The CDSS AID required a total of 3:12:27 h for 97 patients with an average time of analysis of 0:01:59 h and the discovery of 170 DDIs. According to the pain physicians the CDSS AID was chosen as the preferred tool. Conclusions: Applying a CDSS to examine a patients drug regimen for potential DDIs causes an average extra expenditure of work time of 2:09 min, which extends patient treatment time by 25 % on average. Nevertheless, the authors believe that the extra expenditure of time employing a CDSS is outweighed by their benefits, including reduced ADR risks and safer clinical drug management.
Drug-drug interaction; Clinical decision support systems (CDSS); Adverse drug reaction; Expenditure of time; Chronic pain; Polypharmacy
Pharmacotherapy features prominently in patient treatment
and the number of prescribed drugs has steadily increased
. According to literature estimates the average number is
five prescribed drugs among the elderly patients [2, 3].
However, the growing number of drugs taken by patients is
significantly associated with the risk of drug-drug
interactions (DDIs) [3–5]. These DDIs may lead to adverse drug
reactions (ADR) [6–8]. For example, each year up to 20.000
people die from ADRs in Great Britain’s hospitals ,
including ADRs due to DDIs. DDIs are not only an issue of
polymedication but can also arise from lack of knowledge
resulting in medication errors . In fact, the majority of
medication errors happens at the stage of prescription
[11–14] making DDIs a rather common concern .
ADRs are a serious cause of mortality, morbidity and
costs in the healthcare system, making them a major
burden in healthcare [15–17]. Notably, there are estimates
suggesting that up to 52 % of the ADRs are avoidable .
Thus, strategies to prevent ADRs are of special interest.
Clinical decision support systems (CDSS) are especially
conceived for this task [19, 20] and could positively affect
physicians’ efforts concerning safe drug prescribing and the
detection of DDIs [19–24]. However, not many data are
available about how practicable it is to implement the
usage of CDSSs in a hospital workflow. A major aspect for
the decision to integrate the regular use of a CDSS in a
clinical routine is the required time [11, 25–27]. Therefore,
this study has compared three different CDSSs with
respect to the expenditure of time, performance and
practicability associated with the computer-based analysis of
pharmacotherapies in order to prevent ADRs due to DDIs.
This was a retrospective clinical study in the chronic
pain unit at the university hospital of Regensburg in
Germany. The study population consisted of 113
patients who were undergoing chronic pain therapy at the
chronic pain unit in the years 2012/2013. This setting
was chosen because of the special interest in ADR
resulting from DDIs in multi-morbid and
polymedicated patients. Patient records were viewed,
extracting parameters such as age, body size, gender and weight
for descriptive statistics. Furthermore, pain location and
the duration of disease were recorded. Information
about the patient’s medication was gathered, including
the type and the total amount of drugs that had been
For analyzing each patient’s pharmacotherapy for DDIs,
three CDSS were selected: the two freely available
internet-based interaction check systems: (1) “Apotheken
Umschau” (AU; SCHOLZ Datenbank®, © ePrax AG) and
(2) Medscape (MS; copyright © by WebMD LLC), and the
commercially available (3) CDSS AiDKlinik® (AID;
copyright © Dosing GmbH). Other CDSS are available in
Germany. However, the AU, MS and AID check systems
were chosen as they are representatives of the major types
of CDSS: freely or commercially available, designed for use
by both patients and physicians or by heath care
professionals only, applicable to drugs approved in a specific
country or internationally.
(1) AU: The AU CDSS was designed for use by both
patients and physicians. Therefore, the information
provided is more detailed and comprehensible also
for users without specific medical knowledge. The
AU CDSS was conceived for use in Germany and
drugs approved in Germany only. It is a tool
providing the users not only with information
about DDIs of a specific pharmacotherapy under
consideration but additionally about double
medication, fitness to drive, drug-food interactions
and cumulative side effects. Up to ten drugs can be
entered and analyzed using both, active agent and
trade name, dose and dosage form. This CDSS
classifies occurring DDI as: (1) Severe interactions
that should be avoided implicitly. These are
marked by the system with a red stop-sign. (2) Severe
or significant interactions, that should be avoided,
marked with a red exclamation mark. (3) Interactions
that are significant but only in some rare cases, marked
with orange exclamation mark. (4) Interactions of
notable but minor relevance, marked with a yellow
exclamation mark. (5) Interactions which most
probably are not existent. Additionally, information
about DDI is given, comprising a summary explaining
the main interaction issue. Furthermore, albeit not
considered in this study, the interaction is outlined in
detail, classifying the interaction, reporting about the
mechanism, time-dependent course, symptoms that
can appear and which dose needs to be considered.
Management of the interaction is recommended
explaining which afflictions and symptoms are to
be looked out for, which laboratory findings are
important and which actions are to be taken. The
CDSS AU drug databank is based on information
approved by the Federal Institute for Drugs and
Medical Devices and is continuously updated albeit
at irregular intervals.
(2) MS: Medscape (MS) is a freely available
internetbased CDSS. It is an internationally well established
tool for simple drug-drug interaction checking. It
was created for English speaking countries and
drugs approved in the USA only. Drugs are entered
by generic name, active agent or trade name. A
dose cannot be entered. The maximum number of
drugs that can be analyzed is 30. A given patient’s
pharmacotherapy is evaluated for DDIs. A short
explanation about interaction issue and mechanism
is provided. Furthermore, the tool indicates relevant
aspects and risks of the patient’s regimen such as
deterioration of renal function or renal dysfunction,
the potential for causing gastrointestinal bleedings
or teratogenic effects for certain drugs given
during pregnancy. The CDSS classifies the DDIs
as: (1) Serious - use alternative. (2) Significant
monitor closely. (3) Minor or non-significant. Of
note, a DDI can be classified as serious, significant
or minor at the same time when the underlying drug
combinations have multiple, different effects.
The clinical information is updated regularly and
represents the expertise and practical knowledge
of top physicians and pharmacists from leading
academic medical centers in the United States
(3) AID: AID, the third evaluated CDSS, is commercially
available and was designed to support healthcare
professionals in a hospital workflow. Drugs are
entered by their generic name, active agent,
trade name and dose. More than 30 drugs can
be analyzed. Besides DDIs, users are informed
about double medication, incompatibility, exceeded
maximal doses and the risk of kidney insufficiency
due to the patient’s pharmacotherapy. Furthermore,
assistance is provided in drug usage during pregnancy.
Interactions found are classified as (1) Clinically
severe interaction, outlined red. (2) Clinically relevant
interaction with potential to harm. (3) Clinically most
probably not relevant interactions, outlined yellow. In
addition, interactions are explained, pointing out risk
patients and potential symptoms. Detailed clinical
management is provided including the suggestion of
alternative drugs, lowering or raising a drug dose or
close monitoring if drug combinations avoided. The
CDSS AID refers to the Pharmindex database
which is updated every 14 days and issued by
the Medizinischen Medien Informations GmbH
(MMI, Neu-Isenburg, Germany).
Pain physician questionnaire
Additionally, the three employed CDSSs were evaluated
and compared by five pain physicians at the University
of Regensburg to highlight the main advantages and
weaknesses of each system. Furthermore, these
physicians were asked to rate main advantages of each System
to determine their preferred CDSS.
To evaluate the expenditure of time using the CDSSs for
analyzing patient’s pharmacotherapy time was taken with
a stopwatch. For AU and MS time measurement involved:
opening the Mozilla Firefox™ (Mozilla Foundation,
Mountain View, California, United States) internet
browser, selecting the CDSS, entering the patient’s
medication, analyzing and printing the results. Printing
the results in MS was eased by a print button whereas
printing in AU was solely possible by text marking. Prior
to their use, bookmarks for the websites of the MS and
AU interaction checkers were attached to the bookmark
library of the internet browser for faster access. This was
neither required nor possible for AID.
For AID, the measured process contained: starting the
hospital’s intranet, opening the AID homepage, choosing
the “medi-box” item, entering the drugs, analyzing and
printing the results using the print option. All
interactions found were claimed DDI whether severe or
nonsignificant and irrespective of whether or not the patient
had experienced an ADR. This setup was chosen to
simulate the drug interaction check as practiced under a
normal work routine. Only patient’s regimens with at
least 2 drugs and fitting the criteria of the corresponding
CDSS were analyzed for DDIs.
Definitions of the terms used
Drug-Drug-Interactions (DDI) were defined as reactions
occurring from drugs taken at the same time and
affecting each other. These reactions can be synergistic or
antagonistic and influence the intended therapeutic
effect as well as increase the possibility or the extent of
side effects. The major issue of DDI is the potential to
harm the patient.
Adverse drug events (ADE) comprise all adverse
effects caused by normal as well as improper drug use.
Adverse drug reactions (ADR) are specific ADEs. ADRs
were defined as negative effects caused by the
combination of two or more drugs, during regular use at normal
dose, with the potential to harm a patient.
Medication error was defined as every drug
combination leading to a DDI with potential to harm, even if no
actual harm was done, the DDI was non-significant or
the probability of causing harm was very small.
Statistical analysis and ethics
Data were acquired primarily through the German Pain
Questionnaire. Patient-specific parameters were encoded
by a custom made system and entered in a table created
with the spreadsheet program MS Excel, vs. 2010
(Microsoft Inc., Redmond, USA). For statistical analysis,
data were processed by the statistics program IBM SPSS
for Windows, vs. 20.0 (IBM SPSS Inc., New York, USA).
To analyze statistical significance one-way ANOVA was
performed. Descriptive data are presented as absolute
values or percentages as indicated and partly expressed
as the mean. The study was approved by the Local
Ethics Commission (14-160-0069, Regensburg, Germany).
According to the declaration of Helsinki, data were
To investigate the time expenditure required to check
for DDIs by the use of CDSS and compare their
performance three different CDSS and a cohort of 113
chronic pain patients were employed. Additionally five
pain physicians evaluated each CDSS to highlight
advantages and weaknesses of the different check systems.
Furthermore, pain physicians evaluations were compared
to determine the preferred and most practical CDSS.
Because of the purpose of the study, neither the clinical
significance, nor the relevance or the correctness of the
discovered interactions were considered.
Chronic pain patients
113 records of patients undergoing pain therapy were
analyzed. Descriptive statistics are shown in Table 1.
Five hundred five drugs were prescribed to 110 patients;
three of the former 113 patients had no medication at all.
The minimum number of drugs among the patients with
medication was one prescribed drug, the maximum
number was twenty. The average number of drugs per patient
was 4.59. 142 different types of drugs were prescribed.
Most frequently prescribed drugs were analgesics (35.4 %).
The non-opioid analgesic drugs with the highest
frequencies of prescriptions were Ibuprofen (31.7 %), Metamizol
(19.5 %) and acetylsalicylic acid (ASS) (17.8 %). Tilidin/
Naloxone (30.9 %) and Tramadol (27.2 %) were the
opioid-analgesics with the largest number of prescriptions.
Drugs affecting the cardiovascular system (beta blockers,
diuretics, statins, Ca2+-inhibitors, Angiotensin converting
enzyme inhibitors (ACE-Inhibitors),
Angiotensin-receptorinhibitors (AT1-inhibitors), digoxin, alpha-1-receptor
inhibitors) were the second most prescribed drugs (15.6 %)
followed by antidepressants (9.9 %), anticonvulsives
(6.1 %), proton-pump inhibitors (6.1 %), thyroxine (2.5 %),
supplements (2.1 %), benzodiazepines (1.9 %) and
antidiabetics (1.9 %).
CDSSs - time expenditure
Thirteen patients had just a single prescription.
Therefore, only 97 patient regimens remained for evaluation.
CDSS Apotheken Umschau (AU)
93 pharmacotherapies were checked for DDIs with the
CDSS AU. Due to the limitation of ten possible
entered drugs, three patient’s regimens could not be
evaluated. One regimen could not be evaluated
because the CDSS neither recognized the drug name
“Avamigran” nor its active agents. Evaluating the 93
pharmacotherapies required a total of 3:55:45 h. The
maximum time for analyzing one pharmacotherapy
was 6:22 min for a regimen comprising ten drugs. The
minimum time was 1:02 min for a regimen comprising
two drugs. The average time for analyzing one patient
was 2:32 min.
CDSS Medscape (MS)
Because the CDSS MS did not recognize various
drugs or their active agents 52 pharmacotherapies
could not be analyzed. Drugs not recognized by the
CDSS MS were Phenprocoumon, Biperiden, Flupirtin,
Tepilta suspension, Tolperison, Glibenclamid, Melperon,
Metamizol, Lipoic acid, Macrogol, Opipramol, Tetrazepam,
Zopiclone, Bromazepam. Moreover, drug combinations
consisting of two drugs, e.g. Ramipril/HCT that had
not been recognized as a combination but had been
recognized when entered individually, were analyzed
each on their own. The CDSS MS required a total of
1:28:35 h for evaluating the remaining 45
pharmacotherapies. 8:04 min was the maximum amount of time
needed to analyze a patient’s regimen that covered 10
drugs. The minimum amount of time for an evaluation
was 0:21 min for a regimen that comprised two drugs.
The average time for analyzing one patient’s regimen
was 1:58 min.
Pain complaints fora
Maximum pain intensitya
Mean pain intensitya
CDSS AID Klinik (AID)
Ninety-seven patient pharmacotheapies were analyzed
with the CDSS AID. All patients with two or more
prescribed drugs fitted the systems criteria. All drugs were
recognized and no patient regimen exceeded the limit of
drugs that can be entered. Analyzing the 97
pharmacotherapies with the CDSS AID required a total of
3:12:27 h. 7.39 min was the maximum amount of time
needed to evaluate one patient’s regimen comprising 20
drugs. The minimum amount of time required to
analyze one patient’s regimen comprising a
pharmacotherapy consisting of two drugs was 0:43 min. The
average time needed to analyze one patient’s regimen was
Table 2 shows the time expenditure of all three CDSSs
CDSS related time expenditure
Employing the CDSS AID was significantly faster than
the use of CDSS AU (p < 0.05).
Most frequently involved drug combinations and drugs
When evaluating 93 pharmacotherapies comprising 446
drugs the CDSS AU discovered 261 DDIs in 63 (67.7 %)
pharmacotherapies. In 30 (32.2 %) patient regimens no
DDI was found by the CDSS AU. The maximum
number of DDIs discovered was 12 in a regimen comprising
ten drugs. The minimum number was one DDI
discovered in a regimen comprising two drugs. The average
number of interactions was 4.14 per patient. 3.8 % were
classified as severe interactions, 36.3 % were severe or
considerable interactions, 37.9 % of the discovered
interactions were classified as only significant in some rare
cases and 16.4 % were of slight relevance. 5.3 % of the
discovered interactions were classified as most probably
not relevant. The most frequently involved drugs were
non-opioids (22 %), antidepressants (14.5 %) and opioids
The CDSS MS discovered 178 DDIs in 38 (84.4 %)
pharmacotherapies comprising 202 drugs within a range of 1
to 17. The average amount of DDIs was 4.71. Of the 178
Table 2 CDSS drug-interaction check – time expenditure
Average time per analyzed regimen
Minimum: 1:02 min
Maximum: 6:22 min
DDIs 6.7 % were classified as severe, 75.2 % as
significant and 17.9 % as minor significant. Concerning all
DDIs discovered by the CDSS MS, non-opioid analgesics
(44.3 %) were the drugs involved most frequently,
followed by beta blockers (7.8 %) and antidepressants
(7.5 %). Only in 7 (15.5 %) of the 45 evaluated patient
regimen no DDI was found.
In analyzing 97 pharmacotherapies comprising 492
drugs, the CDSS AID discovered 170 DDIs within a
range of one to twelve DDIs in 57 (58.7 %)
pharmacotherapies. No DDI’s were found in 40 (41.2 %) of the
evaluated patient regimens. The average number of
interactions was 2.98 per patient. 11.7 % were clinically
severe interactions, 42.9 % of the DDIs were classified as
potentially clinically relevant interactions, and 45.2 %
the discovered DDIs were clinically not relevant and not
further specified. The most common drugs involved in
DDIs according to the CDSS AID were non-opioids
(27.9 %), ACE-inhibitors (12.9 %) and diuretics (11.2 %).
Table 3 lists the drugs and drug combinations which
most frequently contribute to DDIs.
Pain physician questionnaire
All five pain physicians believe that CDSS are important
tools to prevent DDIs and improve medication safety.
Four stated to use CDSSs on a regular basis. One
physician stated to not use computerized check systems
because of the increased workload and lack of time in
hospital workflow. Two physicians have used all three
check systems in the past. Two have only used CDSS
AID and one physician has not used either of them.
Main advantages stated by the pain physicians were that
AU is freely available, is easy to handle and has s
selfexplanatory layout. Disadvantages of the AU CDSS are
that certain drugs are not recognized and too many
irrelevant drug-drug interactions are highlighted. The
explanations of potential DDIs are relatively long and
written for laymen. Furthermore, no medication plans
can be created and saved. Therefore, the drug regimens
have to be reentered each time an interaction check is
Minimum: 0:43 min
Maximum: 7:39 min
Minimum: 0:21 min
Maximum: 8:04 min
CDSS Cinical decision suport sytem, AID CDSS AiDKlinik®, AU CDSS Apotheken Umschau, MS CDSS Medscape
Table 3 Drugs and drug combinations most frequently involved in DDIs
Detecting CDSS (interactions in total) Drug combinations
AU (261) Severe (3.8 %) ASS-Ibuprofen
Severe/Considerable (36.3 %)
Significant (rare) (37.9 %)
Not relevant (5.3 %)
Minor Significant (17.9 %)
Potentially clinically relevant (42.9 %)
CDSS Cinical decision suport sytem, AID CDSS AiDKlinik®, AU CDSS Apotheken Umschau, MS CDSS Medscape, HCT Hydrochlorothiazide, ASS Acetylsalicylic acid
required. Additionally, minimal errors in drug spelling
are not tolerated.
According to the pain physicians, advantages of the
CDSS Medscape are its free availability, quick drug
input, reasonable rating and concise explanation of
interactions found. The main disadvantage was stated to be
the fact that Medscape was created for English-speaking
countries and drugs approved in USA only. This makes
drug input for German drug names difficult and even
impossible for drugs that are approved in Germany
but not in the USA. Furthermore, no medication
plan can be created and saved, forcing the physician
to reenter all drugs every time changes are made to
a patient’s drug regimen and when a DDI check is
required. Minor errors in drug name spelling are not
Main advantages were stated to be the easy handling
and rapid drug entry with tolerance for spelling errors of
drug names. A large data base helps to select all entered
drugs. Ratings of the DDIs found are reasonable and
explanations of the potential interactions are concise.
Additionally, links to further sources of information about
the drugs entered are provided. Drug plans can be
created and saved. Patients regimens are clearly represented
According to the pain physicians, the main
disadvantages of the CDSS AID are that it is available only at
institutions which purchased the CDSS, and that the
data entry is time consuming. For example, each
drug has to be entered with dose and instructions
The immediate aim of this study was to determine the
time needed to analyze pharmacotherapies of chronic
pain patients for DDIs, using three different CDSSs. On
a wider scale, the results of this trial serve to evaluate
the usability of such tools in a daily routine.
We found that analyzing patient regimens employing
the CDSS AU required on average 2:32 min detecting
4.14 interactions. The CDSS MS required 1:58 min for
analyzing a pharmacotherapy detecting 4.71 DDIs in
average. Using the CDSS AID to analyze
pharmacotherapies required 1.59 min detecting 2.98 interactions on
The time needed to evaluate a patient’s
pharmacotherapy, the DDIs found, as well as the drugs involved in the
DDIs all depended on the particular CDSS used. For
example, data entry differs among the CDSSs. While drugs
are entered with dose and trade or generic name in the
CDSSs AU and AID, no dose is required in MS, which
can be time saving during drug entry. However, the fact
that many German trade names were not recognized by
the CDSS MS had an adverse effect on time expenditure.
Similarly, the fact that the user had to look up the active
agent before entering a patient’s regimen for drugs that
were not known to the CDSS increased time expenditure
and effort. Furthermore, even minor errors in the
spelling of a drug name or active agent made it impossible
for the CDSS MS and CDSS AU search engine to find
the correct drug. This makes data entry difficult,
especially when the real drug name is unknown. On the
other hand, the CDSS AU automatically offers the
option to enter additional information about patient
diseases which requires extra time even if just declining the
request. Moreover, the CDSS AU has not implemented a
print option. This definitely prolonged the
documentation because the desired information had to be extracted
and printed using another program.
The way the results of the DDI check were provided
by the three CDSSs differed as well. Both, the CDSS MS
and AID delivered assorted information with more
severe interactions listed first. In contrast, the CDSS AU
did not sort the discovered interactions by degree of
severity. This made evaluation of the results more difficult
and the search for desired information more time
consuming. Moreover, the number of the discovered DDIs
as well as their classification varies between the check
systems. The CDSS MS and AID each classify DDIs
based on three degrees of severity, whereas the CDSS
AU utilizes five. The number of DDIs discovered by the
CDSS AU and the five categories of classification may
lead to reduced awareness because too much
information is provided [28, 29].
Concerning the usability of the CDSS MS there is an
additional restriction because it was primarily designed
for use in Anglo-American countries. Accordingly, in
53.6 % of the evaluated patient regimens, the CDSS MS
did not recognize one or more drugs of the patient’s
pharmacotherapies including some of the most
frequently used analgesics in Germany. Even though the
CDSS MS is a simple and easy to use tool for analyzing
pharmacotherapies, it is of limited use for evaluating
pharmacotherapies of chronic pain patients in German
language area. In summary, when taking into account all
different aspects, the CDSS AID turned out to be the
fastest CDSS and proved superior in handling compared
to the two other CDSS because it is especially tailored
for a hospital workflow and to ease a physician’s work.
These findings match the results of the implemented
questionnaire and the qualitative comparison of the
three CDSS by five pain physicians. All pain physicians
chose the CDSS AID as preferred interaction check
system. Especially the reasonable number of interactions
found and the concise descriptions of DDIs make this
CDSS popular among the physicians. The features that
allow to create a medication plan that can be saved and
that provide additional drug information make it easy to
integrate this tool and the necessary interaction checks
into a physicians work routine. Both other CDSSs turned
out to be less practicable for different reasons.
According to the pain physicians the CDSS AU seems
to be a tool more suitable for patients than
physicians. Too many of the discovered potential
interactions are irrelevant and explanations of potential
DDIs are too long. The freely available CDSS Medscape
was found to be fast and sufficient with respect to
identifying relevant interactions and to providing an
adequate amount of additional information.
Unfortunately, many drug regimens could not be evaluated
for DDIs with this tool making it impossible for
physicians to exclusively rely on this check system in a
daily work routine.
The expenditure of time for analyzing patients’
pharmacotherapies is of great interest especially in view of
the decreasing amount of time physicians have for
patient treatment. According to a German study, general
practitioners spend on average 8 min per patient
assuming a number of 45 patients per day . A two minute
drug interaction check would extend the average patient
treatment time by 25 %. However, this would apply only
to patients who actually receive multiple drug
prescriptions. Therefore, the overall increase in temporal effort
and work-load for a physician is likely to be considerably
smaller. Importantly, a modest increase in treatment
time must be set in relation to the large numbers of
DDIs that were actually detected in the patient’s
pharmacotherapies and the obvious necessity to avert ADRs
which in principal are preventable [6, 14, 18]. Keeping in
mind that ADRs are a serious cause of morbidity,
mortality and costs in healthcare [15–17] it seems
more reasonable to spend a rather small amount of
time for a DDI check than unnecessarily risking
patient welfare or to invest a definitely greater amount
of time and money in order to deal with the effects of
ADRs. Indeed, just correcting a medication error in
hindsight is more time consuming than an interaction
check in advance .
The study setup reflects the performance of CDSSs in an
actual hospital workflow. However, especially with
respect to the check systems AID and AU only basic
functions were applied. For example, aside from the
prescribed drugs, no additional information was added to
specify the interaction check. This could increase time
expenditure in practice. In this regard, the CDSS AID
provides an advantage as it is integrated into a
computerized physician order entry system. Thus, additional
patient information could easily be obtained and combined
with the interaction check by the system increasing the
accuracy of interaction checks . This could lead to
time savings compared to the internet-based CDSS
where additional parameters and patient information
have to be entered separately by the user. It must be
mentioned, though, that the DDIs found by the different
CDSSs were not examined for their correctness, their
relevance, or their actual ADR risk. For example, the
CDSS AU identified the drug combination
TramadolPregabalin as a severe risk for DDIs. Yet, the
combination Tramadol-Pregabalin is part of current guidelines
for pain therapy. This indicates that not every
CDSSbased recommendation should be adopted without
additional scrutiny which in turn can be time
consuming. However, this example shows that mistakes
in patient treatment can also occur from the use of
CDSSs  and suggests that prior to implementation as
treatment routine a CDSS should be carefully evaluated
not only with respect to time expenditure but also for
In this study, patient regimens were entered
sequentially in a single session by one person and a clear
training effect was observed. This made data input
significantly faster towards the final entries. However,
increased time expenditure and other difficulties
encountered especially during the process of getting acquainted
with a new computer program could be of practical
relevance. They could act as deterrents and provoke first
time users to avoid the application of a CDSS. Moreover,
the analysis of pharmacotherapies by the use of a CDSS
was not only highly dependent on the examiner’s
computer skills but also on his/her prior pharmacologic
knowledge. For example knowing trade names and
corresponding active agents ease both extracting
information from patient records and entering the regimen into
From a technical point of view, the use of an
internetbased CDSS may lead to differences in time expenditure
due to location-/user-dependent differences in
connection speed. Furthermore, the CDSSs were evaluated only
for the expenditure of time arising from analyzing
pharmacotherapies. The CDSSs were not evaluated
concerning their sensitivity and the relevance of the discovered
DDIs. However, in view of the observed discrepancies
among the different CDSSs concerning the detected
DDIs and the groups of drugs involved, this would be of
further interest in future research.
There are many reports that show that CDSSs
undoubtedly are of relevance in preventing DDIs and their
consequences. In our current study we determined the
amount of time required for the use of a CDSS and
compared their performance and practicability. Our
results show that the commercially available CDSS AID is
the preferred check system of pain physicians.
Irrespectively of which CDSS is chosen, however, CDSS
implementation would notably extend the time for patient
treatment. In the authors’ opinion, the impact of this
extra expenditure of time is outweighed by the
longterm patient benefits including reduced ADR risks,
morbidity and mortality. Therefore the authors recommend
using CDSSs on a routine basis in clinical drug
management during chronic pain therapy.
The authors assure that there are no relations with any company, whose
products are mentioned in the article, or with any company that sells a
competing product. CHRW, CLL, KPI, NL declare, that they received financial
support for lectures and events of the following companies: Grünenthal,
Hexal, Janssen-Cilag, MSD, Mundipharma, Pfizer, Takeda, Teva. This financial
support did not have any influence on the outcome of this study.
TH, KPI, and CHRW participated in designing the study. TH and CHRW
participated in collecting and entering the data. NL, CLL, AB supported in
editing the manuscript. BMG co-wrote the manuscript and added important
comments to the paper. All authors read and approved the final manuscript.
1. Linjakumpu T , Hartikainen S , Klaukka T , Veijola J , Kivelä SL , Isoaho R. Use of medications and polypharmacy are increasing among the elderly . J Clin Epidemiol . 2002 ; 55 ( 8 ): 809 - 17 .
2. Boyd CM , Darer J , Boult C , Fried LP , Boult L , Wu AW . Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance . JAMA . 2005 ; 294 ( 6 ): 716 - 24 . doi:10.1001/jama.294.6. 716 .
3. Stewart RB , Cooper JW . Polypharmacy in the aged . Practical solutions. Drugs Aging . 1994 ; 4 ( 6 ): 449 - 61 .
4. Macedo AF , Alves C , Craveiro N , Marques FB . Multiple drug exposure as a risk factor for the seriousness of adverse drug reactions . J Nurs Manag . 2011 ; 19 ( 3 ): 395 - 9 . doi:10.1111/j.1365- 2834 . 2011 .01216.x.
5. Jose J , Rao PG . Pattern of adverse drug reactions notified by spontaneous reporting in an Indian tertiary care teaching hospital . Pharmacol Res . 2006 ; 54 ( 3 ): 226 - 33 . doi:10.1016/j.phrs. 2006 .05.003.
6. Obreli-Neto PR , Nobili A , de Oliveira Baldoni A , Guidoni CM , de Lyra Júnior DP , Pilger D , et al. Adverse drug reactions caused by drug-drug interactions in elderly outpatients: a prospective cohort study . Eur J Clin Pharmacol . 2012 ; 68 ( 12 ): 1667 - 76 . doi:10.1007/s00228- 012 - 1309 -3.
7. Du Souich P. In human therapy, is the drug-drug interaction or the adverse drug reaction the issue ? Can J Clin Pharmacol . 2001 ; 8 ( 3 ): 153 - 61 .
8. Bucşa C , Farcaş A , Cazacu I , Leucuta D , Achimas-Cadariu A , Mogosan C , et al. How many potential drug-drug interactions cause adverse drug reactions in hospitalized patients? Eur . J Intern Med . 2013 ; 24 ( 1 ): 27 - 33 . doi:10.1016/j.ejim. 2012 .09.011.
9. Pirmohamed M , James S , Meakin S , Green C , Scott AK , Walley TJ , et al. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients . BMJ. 2004 ; 329 ( 7456 ): 15 - 9 . doi:10.1136/ bmj.329.7456.15.
10. Ko Y , Malone DC , Skrepnek GH , Armstrong EP , Murphy JE , Abarca J , et al. Prescribers' knowledge of and sources of information for potential drugdrug interactions: a postal survey of US prescribers . Drug Saf . 2008 ; 31 ( 6 ): 525 - 36 .
11. Bates DW , Boyle DL , Vander Vliet MB , Schneider J , Leape L. Relationship between medication errors and adverse drug events . J Gen Intern Med . 1995 ; 10 ( 4 ): 199 - 205 .
12. von Laue NC , Schwappach DL , Koeck CM . The epidemiology of preventable adverse drug events: a review of the literature . Wien Klin Wochenschr . 2003 ; 115 ( 12 ): 407 - 15 .
13. Kanjanarat P , Winterstein AG , Johns TE , Hatton RC , Gonzalez-Rothi R , Segal R. Nature of preventable adverse drug events in hospitals: a literature review . Am J Health Syst Pharm . 2003 ; 60 ( 17 ): 1750 - 9 .
14. Lövborg H , Eriksson LR , Jönsson AK , Bradley T , Hägg S. A prospective analysis of the preventability of adverse drug reactions reported in Sweden . Eur J Clin Pharmacol . 2012 ; 68 ( 8 ): 1183 - 9 . doi:10.1007/s00228- 012 - 1237 -2.
15. Nazer LH , Eljaber R , Rimawi D , Hawari FI . Adverse drug events resulting in admission to the intensive care unit in oncology patients: Incidence, characteristics and associated cost . J Oncol Pharm Pract . 2013 ; 19 ( 4 ): 298 - 304 . doi:10.1177/1078155212465995.
16. Pinzón JF , Maldonado C , Díaz JA , Segura O. Costos directos e impacto sobre la morbimortalidad hospitalaria de eventos adversos prevenibles a medicamentos en una institución de tercer nivel de Bogotá (Direct costs and hospital morbimortality impact from preventable adverse drug events) . Biomedica . 2011 ; 31 ( 3 ): 307 - 15 . doi:10.1590/S0120- 41572011000300003 .
17. Bates DW , Spell N , Cullen DJ , Burdick E , Laird N , Petersen LA , et al. The costs of adverse drug events in hospitalized patients . Adverse Drug Events Prevention Study Group. JAMA . 1997 ; 277 ( 4 ): 307 - 11 .
18. Hakkarainen KM , Hedna K , Petzold M , Hägg S. Percentage of patients with preventable adverse drug reactions and preventability of adverse drug reactions - a meta-analysis . PLoS ONE . 2012 ; 7 ( 3 ), e33236. doi:10.1371/ journal.pone.0033236.
19. van Doormaal JE , van den Bemt PM , Zaal RJ , Egberts AC , Lenderink BW , Kosterink JG , et al. The influence that electronic prescribing has on medication errors and preventable adverse drug events: an interrupted time-series study . J Am Med Inform Assoc . 2009 ; 16 ( 6 ): 816 - 25 . doi:10.1197/ jamia.M3099.
20. Fritz D , Ceschi A , Curkovic I , Huber M , Egbring M , Kullak-Ublick GA , et al. Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients . Eur J Clin Pharmacol . 2012 ; 68 ( 8 ): 1209 - 19 . doi:10.1007/s00228- 012 - 1241 -6.
21. Wright A , Feblowitz J , Phansalkar S , Liu J , Wilcox A , Keohane CA , et al. Preventability of adverse drug events involving multiple drugs using publicly available clinical decision support tools . Am J Health Syst Pharm . 2012 ; 69 ( 3 ): 221 - 7 . doi:10.2146/ajhp110084.
22. Kaushal R , Shojania KG , Bates DW . Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review . Arch Intern Med . 2003 ; 163 ( 12 ): 1409 - 16 . doi:10.1001/ archinte.163.12.1409.
23. Classen DC , Pestotnik SL , Evans RS , Burke JP . Computerized surveillance of adverse drug events in hospital patients . JAMA . 1991 ; 266 ( 20 ): 2847 - 51 .
24. Ing Lorenzini K , Reutemann B , Samer CF , Guignard B , Bonnabry P , Dayer P , et al. Quel programme informatique de détection des interactions médicamenteuses néfastes? (Comparison of four drug interaction screening programs) . Rev Med Suisse . 2012 ; 8 ( 358 ): 1978 - 82 .
25. Bates DW , Kuperman GJ , Wang S , Gandhi T , Kittler A , Volk L , et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality . J Am Med Inform Assoc . 2003 ; 10 ( 6 ): 523 - 30 . doi:10.1197/jamia.M1370.
26. Rabøl LI , Anhøj J , Pedersen A , Pedersen BL , Hellebek AH . Beslutningsstøtte til elektronisk patientmedicinering: reduceres forekomsten af medicineringsfejl? (Clinical decision support: Is the number of medication errors reduced?) . Ugeskr Laeg . 2006 ; 168 ( 48 ): 4179 - 84 .
27. Lyons A , Richardson S. Clinical decision support in critical care nursing . AACN Clin Issues . 2003 ; 14 ( 3 ): 295 - 301 .
28. Kesselheim AS , Cresswell K , Phansalkar S , Bates DW , Sheikh A. Clinical decision support systems could be modified to reduce 'Alert Fatigue' while still minimizing the risk of litigation . Health Affairs . 2011 ; 30 ( 12 ): 2310 - 7 . doi:10.1377/hlthaff.2010.1111.
29. Zorina OI , Haueis P , Greil W , Grohmann R , Kullak-Ublick GA , Russmann S. Comparative performance of two drug interaction screening programmes analysing a cross-sectional prescription dataset of 84,625 psychiatric inpatients . Drug Saf . 2013 ; 36 ( 4 ): 247 - 58 . doi:10.1007/s40264- 013 - 0027 -9.
30. Grobe T , Dörming H , Schwartz F. Barmer GEK Arztreport. http:// presse.barmer-gek.de/barmer/web/Portale/Presseportal/Subportal/ Infothek/Studien-und-Reports/Arztreport/Arztreport- 2010 /PDFArztreport,property=Data.pdf. 2010 .