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Pharmacogenetics
Clinical Decision
Support System
Med_Inf 406
Eric Chavez, Sean Heffernan, David W. West
March 16, 2014
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Chavez, Heffernan, West
Introduction
Background
The Pharmacogenetic Clinical Decision Support System aims to advance personalized
medicine by utilizing preemptive genotyping to help providers make safe and appropriate
medication orders. An underlying assumption is that as pharmacogenetic testing becomes
more and more common in the future, providers will have a patient’s genotype available
before they order medications. The results of pharmacogenetic testing will be stored in a
clinical data repository of the electronic health record (EHR). This data will either be
automatically uploaded to the clinical data repository from laboratory information systems
or from genetic testing vendors, or it will be manually entered into the EHR by staff in a
standardized, coded format. Unlike other lab data which change over time, results of genetic
testing are stable over a patient’s lifetime. The same data can be reused repeatedly to help
providers make decisions about future medication orders. As the field of pharmacogenetics
advances, more actionable information can be determined from the results of these tests.
Genetic testing is becoming less expensive and more common in clinical practice. This type
of testing results in pharmacogenetic data. Pharmacogenetics is the study of how a patient’s
genotype and phenotype affect his individual response to medications. Genetic testing can
provide information about an individual patient’s ability to metabolize medications through
genotyping the cytochrome P450 system. Patients can be classified as rapid metabolizers,
normal metabolizers, or slow metabolizers. When multiple choices of medications exist to
treat a given medical condition, this information can be used to help providers make
decisions about medication selection and dosing. Rapid metabolizers would be expected to
show a lower clinical response to a given medication or to require higher than standard
dosing strategies. Slow metabolizers would be expected to suffer more toxicity and adverse
effects and would need lower than standard doses. Some genetic testing can inform
clinicians of the variability in efficacy of certain medications for a patient based on his
genotype. Pharmacogenetics is believed to be on the verge of revolutionizing personalized
medicine by targeting therapies for individual patients, reducing trial-and-error treatments,
and greatly reducing toxicity and adverse effects. This will help to reduce overall healthcare
costs.
Patients respond to drugs in different ways. There are some groups of patients that will
experience severe adverse effects and there are some groups of patients that will not
respond at all to certain drugs. Factors that may affect response to drugs include age,
gender, disease state, smoking, drug interactions, patient adherence, and genetic factors. It
has been shown that genetic factors may account for 20-95% of individual variation in drug
response (Kalow, 1998). The amount of variation depends on the class of drug and genetic
phenotype of the patient. Pharmacogenetics can provide some explanation of how genetic
variants change drug response (Weinshilboum, 2003).
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Examples of the benefits of personalized medicine using genetic testing include
individualized drug dosing, individualized drug selection, and economic savings. These will
be illustrated below using the examples of warfarin, hepatitis C, and HIV.
Warfarin has a narrow therapeutic index. Inadequate or excessive anticoagulation can lead
to increased risk of adverse cardiovascular event or bleeding complication. Warfarin dosage
is complicated by individual variability and requires regular monitoring to achieve proper
anticoagulation effects. Initial therapy is usually by fixed dosage which is then adjusted
based on the patient’s anticoagulation effect, as measured by international normalized ratio
(INR). Because genetic factors account for 30-35% of the variability of warfarin response,
having genetic test results could lead to a faster time to achieve stable therapeutic dose. In
one study, 46% of patients benefitted from genetically informed drug dosing because they
required lower or higher doses of warfarin than the standard dosage (Klein, 2009). Another
study showed that patients treated with genetically informed dosing had 28% less
hospitalizations after 6 months (Epstein, 2010). The ability to more accurately predict
warfarin dosing by avoiding under-dosing (risk of coagulation) or overdosing (risk of
bleeding) can lead to improved drug efficacy, improved patient safety, and organizational
cost savings.
Genetic testing can help guide decision making when the clinical effect of a drug is expected
to vary according to patient genotype and phenotype. Adverse clinical effects may differ
according to phenotype when slow metabolizers accumulate toxic metabolites of a drug.
Genetic testing can help guide decision making by steering toward alternative drug
treatments or dosing regimens. Patients who are slow metabolizers would benefit from
reduced dosing of the standard drug therapy or from taking a different drug altogether. Fast
metabolizers may benefit from higher than standard dosing regimens. They would be
expected to show less benefit from standard doses. This could delay the efficacy of
treatment which means longer time with the illness. Longer time with the illness means
more patient suffering and dissatisfaction, potentially more time lost due to a decreased
functional state, and more cost to the healthcare system and the overall economy.
An example is chronic Hepatitis C which is treated with PegINF-α-2a or PegINF-α-2b and
ribavirin. Less than half of patients achieve sustained viral repression with the medications.
There is a genetic test that can predict those who will have sustained viral repression based
on a polymorphism in the IL28B gene (Ge, 2009). Test such as these help providers know
who will respond to a given drug therapy. This can save time, save money, and save patients
from adverse drug effects when it is predicted that the benefit is very low.
Avoiding inappropriate drug selection can save money. For example if a more expensive
drug is shown to be less effective in patients with certain genotypes, the cost of trying that
drug may be avoided and alternative medications can be selected. Avoiding ineffective drug
regimens can save money by reducing patient suffering and dysfunction and by speeding
time to recovery. Avoiding serious adverse drug reactions can save money by reducing
hospitalizations or office visits and by reducing patient morbidity. It may be more costeffective to use generic drugs that are associated with adverse effects in some genotypes
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than to use a more expensive drug without the potential adverse effects for everyone, if
genetic testing is used to help select appropriate patients for the generic drug.
An example is in the treatment of HIV with abacavir. It is estimated that 5-8% of patients will
develop a hypersensitivity reaction to the medication in the first 6 weeks of treatment. Rechallenging with the medication after initial treatment may result in worsening of the
reaction or even death. Genotyping can reduce this risk by predicting who will have the
hypersensitivity reaction. In one study, screening patients for the genotype associated with
the reaction and not giving those patients abacavir lead to complete elimination of the
reaction in the study population (Gazzard, 2008). In an analysis of cost-effectiveness, it was
found that genetic testing and treatment with abacavir when appropriate was less costly
than completely avoiding abacavir and using a more expensive alternative medication,
tenofovir (Kauf, 2010).
The field of pharmacogenetics is rapidly developing. The majority of practicing physicians
lack any formal training in the field, and most do not know how to incorporate this
information into their practices to improve safety and outcomes for patients (Baars, 2005). It
would be impossible for providers to memorize all of this information as more and more
clinically relevant information and actionable guidelines are added. The pharmacogenetics
clinical decision support system can help to solve this problem by using a just-in-time tool
that brings actionable recommendations to providers at the point of care. This system will
integrate into the computerized provider order entry (CPOE) module of the EHR and make
recommendations to alter drug choice or alter drug dose when providers attempt to write
medication orders. In order to reduce alert fatigue the system will issue alerts only when
strong clinical guidelines exist in the pharmacogenetics literature.
There are few studies that report on the development and implementation of
pharmacogenetic clinical decision support systems. In 2009 Deshmukh, et al. studied two
different methods of presenting genetic test results to a Cerner clinical decision support
system. They used the CYP2C9 gene and used a data model of either single nucleotide
polymorphisms (SNP) or alleles as the discreet test reports. Both models worked for the
clinical decision support system (CDSS). The allele-based system was easier to write decision
rules for, and the SNP-based system allowed for retention of more discreet data that could
possibly be used and represented in different ways in the future. Overby (2010) reported on
the feasibility of collecting and standardizing pharmacogenetic data from various sources,
turning that data into computable form, and writing decision rules based on the data. In
2012 Overby, et al. expanded on their previous work and described a prototype of semiactive and active CDSS using translated pharmacogenetic data to drive rules-based decisions
in a Cerner EHR environment. The semi-active component allowed providers to look up
genetic testing information about a specific drug while the active component offered
recommendations for therapy change or dose adjustments when providers attempted to
order a medication. Bell (2014) et al. describe an active CDSS in which genetic test results
are captured and encoded in the EHR problem list. Alerts were generated when providers
attempted to prescribe a medication that matched a high risk phenotype in the problem list.
In a review article, Welch and Kawamoto (2013) report on six different studies that use
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genomic data as part of CDSS to guide medication management for warfarin and
antiretroviral medications.
Stakeholders, Goals, and Objectives
For the purposes of this project we will assume that our target healthcare organization is a
large multispecialty medical center with both inpatient and outpatient services. The
stakeholders for the project are visualized in “onion-ring” format in figure 1. The innermost
circle contains the product which is the pharmacogenetics clinical decision support system.
The next circle contains the business system which are those stakeholders who do the work
in developing the product and who have operational roles. The next circle contains the
business who are the stakeholders who will benefit directly from the product. The
outermost circle contains the wider environment of stakeholders who will be indirectly
affected by the product.
The Clinical Decision Support Committee is the main driving force for the CDS initiative. The
committee may or may not include several of the other key stakeholders listed below. The
committee is responsible for developing the goals and objectives for the CDS system and for
evaluating the science behind the system and the evidence that it will provide benefit for
patients and for the organization. The committee solicits feedback from key stakeholders to
ensure that the project is feasible. Overall, the committee guides the development,
implementation, evaluation, and monitoring of the CDSS. It is important for the CDS
committee to engage all stakeholders through every phase of the CDS lifecycle.
Clinical leadership is necessary to evaluate the need for the CDSS and the medical evidence
to support the system. Clinical leaders need to be involved in every stage of the project
development and implementation to give feedback on how provider workflows are effected
and how patient care is impacted. Notable among the clinical leaders is the Physician
Champion who plays a key role in the development and implementation of the CDSS. Usually
the champion is well respected among his/her peers, has excellent communication skills,
remains active clinically, and helps to grow support by spreading enthusiasm.
Technical leadership is necessary to translate the clinical ideas into a functional operating
plan in the computer information system. It will be necessary to involve members of the EHR
and CPOE committees in the technical leadership since this CDSS uses the EHR and CPOE
system as its point of interaction with providers. A vendor representative will be included to
share ideas about problems and solutions that other organizations have faced when
implementing similar CDS systems.
The administrative leadership is ultimately responsible for approving the CDS project. They
are responsible for determining whether or not the CDSS meets the goals of the
organization, for evaluating the return on investment, and for fundraising or budgeting for
the project. Administrative leadership will be indirectly affected as it is hoped that outcomes
will improve and cost savings can be realized for the organization.
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Genetic testing labs will need to ensure that results of genetic testing can be automatically
integrated into the EHR and CPOE systems
End users are the front line clinicians who will be using the CDSS on a daily basis. They
should be active in all phases of the project to ensure buy-in and adoption. End users can
give valuable feedback during various stages of the project to make adjustments for work
flow in order to help the CDSS achieve success. Resistors and detractors among the end user
group should be included in the planning process so that potential problems can be
addressed early.
Patients may be involved during the project design and evaluation to give feedback and to
help spread support for the system. They should be made aware of the potential benefits
and risks of the new CDSS. It is hoped that ultimately patients will be the primary
beneficiaries of the new system with improved outcomes and safety.
Figure 1. Stakeholders
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Organizational goals and clinical goals of the project have to do with improving patient lives,
improving efficiency of physician workflows, and saving healthcare dollars. These goals are
displayed in figure 2. More specific objectives for the project are shown in figure 3. These
objectives may be used as the metrics for a CDSS evaluation program.
Figure 2. Organizational and clinical goals.
Improve
patient safety,
outcomes, and
satisfaction
• Minimize harmful drug effects
• Optimize treatment for individual patients
• Reduce length of patient morbidity and loss of
function by selecting effective drug regimens
more rapidly
Improve
physician
effectiveness
and efficiency
• Make more effective decisions in regards to
drug selection and dosing
• Predict optimal dosing for drugs with a narrow
therapeutic index
• Avoid trial-and-error treatments
Reduce
healthcare
costs
• Develop cost-effective treatment by selecting
the most effective and appropriate drug for
each individual patient
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Figure 3. Specific objectives.
Improve
patient
safety
reduce the number of adverse drug events
Improve
dosing
regimens
Decrease from standard dosage or select alternative medication for
patients with slow metabolizer phenotypes
Select most
effective
therapy
Increase the number of orders written for drugs shown to be effective for
an individual patient’s genotype
Improve
treatment
response
times
Improve the time to achieve stable therapeutic dosing for drugs with
narrow therapeutic windows (i.e. warfarin)
reduce the number of hospitalizations and office visits due to adverse
drug events
Increase from standard dosage or select alternative medication for
patients with rapid metabolizer phenotypes
Reduce the number of orders written for drugs shown not to be effective
for an individual patient’s genotype
Improve time to achieve reduction or resolution of patient symptoms by
more rapidly selecting the most effective treatment regimen
Information System Inventory
The pharmacogenetics clinical decision support model shown below in figure 4 represents
current and future system states, which will easily generalize to other clinical environments,
allow providers to enter metabolizer status when ordering a drug, and support reporting
and interpreting genetic test results.
The CDS will 1) integrate genetic testing results into the Clinical Data Repository for use in
the existing CPOE system and 2) store the results from genetic labs in the CDS Knowledge
Base where rules will be applied for specifying results and preparing them for presentation
to users via the Clinical System Interface.
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Figure 4. Clinical Decision Support Model
The EHR Application Environment supports the functional capabilities of the decision
support system. Discrete data are stored within the Clinical Data Repository and the Clinical
User can interact with the Clinical System Interface to view data and order medication
(CPOE), which may trigger decision support implemented in the repository. Interventions
and offered medication choices display to the user through the Clinical System Interface.
The Clinical Decision Support Module implements the user interface features, providing
methods for transforming inputs (genetic test results or medications being ordered) into
patient-specific output (pharmacogenomics links to e-resources or alert messages).
Imbedding the decision support tool in the EHR will allow the use of pharmacogenomics
knowledge in making prescribing decisions.
System Inventory Needed Acquisitions
 One File System for the Result Specification XML Repository
 Two Databases (one each) for the Pharmacogenomic Knowledge Base and CDS
Knowledge Base
 Two application servers for deploying the new databases
 One Rules Engine for retrieval and calculation of genetic tests and related
medication options
 One web server for linking the CDS to the vendor’s e-resources

Workstations
o 10 new workstations to be deployed across Ambulatory, Inpatient,
Behavioral Health, Laboratory, and Pharmacy Services
o Note: Although the setting for deploying the CDS is a large urban hospital,
the intended user base of the software is limited, so the number of new
workstations needed is minimal
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

Personnel
o Physicians
o Lab Technicians
o Pharmacists
o Nurse Practitioners
o Software Administrators
o IT Technicians
o Vendor Liaison
Software
o System Interface Software
o Database Deployment Software
o Rules Design/Maintenance Software
o Web Services Software
Intervention Selection and Workflow Opportunities
Medication dosing has drawn the attention of clinical decision support systems for decades,
especially after the Institute of Medicine (IOM) published its report, “To Err is Human” in
1999 in which medication errors were most prominent among all types of errors which
occur in the health care setting (Kohn, 1999). The implementation of computerized
physician order entry (CPOE), even in its simplest form, circumvents errors related to
legibility, but as a change in workflow, there has been concern in the literature that other
types of errors not previously seen are being introduced.
Since the IOM published its report, clinical decision support around dosing has become more
and more sophisticated. This support is predicated on the enhancement in CPOE to require
granular elements of the order to be entered. These elements include dose, dose unit,
frequency, and route at a minimum. Third party expert content providers have emerged to
provide dosing support for virtually any FDA approved medications where granular CPOE has
been implemented.
Dose range checking is the most basic type of decision support, essentially alerting the
prescriber if the single or daily dosage falls outside of a standard range. Condition-based
defaults and weight-based dosing are other forms of clinical decision support that start to
take other clinical factors into account when providing support. Lab studies such as
creatinine can be incorporated to provide further dosing support.
Genetic studies that help predict metabolism and/or effectiveness of certain drugs is a new
and growing field that shows great promise for being integrated into clinical decision
support systems. Many physicians are now ordering these tests to assist their dosing
decisions.
The proposed CDSS will integrate genetic testing results into the clinical data repository in a
manner that will effectively provide support at the point of order entry with the goal of
achieving a therapeutic, but non-toxic level of medication. Genetic tests are an excellent
example of “patient-level” data in that the results do not change over time. Family history
and birth history are other examples of patient-centered data. Once the data is captured, it
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does not need to be recaptured. It does need to be readily accessible at the point of care.
Therefore, avoiding duplication of genetic testing would be an additional benefit of the
proposed CDSS, though not its primary objective.
A number of system features will be important to integrate this information into medication
dosing decisions. The results originating in the genetic labs need to be normalized into a
value that can guide and alert dosing. The medication repositories supplied by third party
domain expert systems need to include this relationship with genetic testing in a manner
similar to age, weight, and renal function based dosing elements. Finally, the health care
organization’s EHR needs to support and integrate these various elements into a useful
presentation to the prescriber on pharmacogenetic-based clinical guidelines.
Change Management Plan
Clinical decision support systems ultimately strive to modify and align behaviors to best
practices that will achieve optimal outcomes. Attempts at behavior change often encounter
resistance. The likelihood that behavior change will occur tends to correlate positively to
the perceived value of the change and negatively to the pain of adoption. The change
management plan will address both of these principles.
To boost the perceived value proposition prior to implementation, data will be gathered that
helps define the opportunity to improve care. This will entail statistics on the frequency of
medication orders for which genetic data could alter management. One can then apply
knowledge about the frequency of genetic phenotypes in the population to infer the
frequency with which medication dosing could be enhanced to avoid lack of efficacy due to
under dosing or toxicity due to overdosing.
To mitigate the pain of adoption, the effort required to incorporate the genetic information
into the dosing decision needs to be easy to find and presented in a manner that can be
quickly converted into a dosing modification. Reports that are composed of free text, even
if presented in tabular form, need to be stored in relevant granular data elements. The
correct genetic elements need to be connected to the drugs for which a given gene will have
an impact. The connection must classify the effect of the gene, and then offer to alter the
dosing accordingly. In cases where the genetic data suggests a pure lack of efficacy, the
connection between the genetic phenotype and the drug should lead to an offering of
alternatives that would have greater efficacy.
Post-implementation efforts can continue to try to reinforce the value proposition. This can
take the form of queries that report the frequency of adoption of modified dosing in
accordance with genetic data. This information can be trended and shared as an
organization quality improvement objective, with more detailed “drill-down” data to
demonstrate inter-provider or inter-department variability. Areas of success can be
examined closely to see if practices exist that could be replicated to areas of lower success.
The opportunity to offer rationales as to why suggested dosing was not accepted will serve
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to enhance clinical decision support if other variables can be introduced that improves
accuracy.
A continued iterative process that focuses on enhancing one or both of these principles
(increased value proposition, decreased pain of adoption) should serve to solidify the
change in the organization as a whole.
System Design
The pharmacogenetic CDSS enables the use of pharmacogenetic data each time a relevant
medication is prescribed as part of pre-emptive testing and stores the results in the EHR
before a drug is prescribed. Accordingly, the pharmacogenetic CDSS advances personalized
medicine by addressing the inherited variation in drug responses by maximizing the drug
efficacy and minimizing toxicity for individual patients. The CDSS can also reduce costs
associated with inappropriate drug treatments or serious adverse drug reactions.
Included in the System Design is a discussion of the:
1. Components of the System Architecture, which specifies the functional
requirements of the CDSS
2. Pharmacogenetic Use Case for Clinical Assessment and Genetic Testing
3. Conceptual Model for the Pharmacogenetic CDS in terms of Client/Server and WebBased implementations
Components of System Architecture
The components of the CDSS architecture are best represented by a specification of its
functional requirements. Below in figure 5 is a depiction of the system’s functions and the
interaction between processes and outputs.
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Figure 5. System architecture.
Pharmacogenomics CDS Functional Requirements
Triggers
Data Elements
Interventions
Offered
Choices
PGx eResources
Events that
cause a
decision
support tule
to be invoked
Used by a
rule to make
inferences
Actions a
decision
support
module can
take
Order Entered
Lab Result Stored
Lab Result
Drug List
Diagnosis
Age
Gender
History
Show Guidelines
Pick Lists
Data Entry Template
Cancel Current Order
Defer Warning
Override Rule/Keep Order
Edit Problem List
Notify Logs
Deriving CDS
Rules
FDA Info on Genomic
Biomarkers
Evidence Based Synopses
CPIC Guidelines
Article Searches
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Pharmacogenetics Use Case
The use case below shows how personal health and family health history information is
exchanged with genetic testing information between consumers and clinicians based on two
scenarios:
1. Clinical Assessment
2. Genetic Testing
PGx CDS Use Case
Perspective / Rule
Clinician
Clinical Assessment
Testing Lab
1. Construct Medical History
2. Evaluate Relevant Genetic Test
Scenarios
3. Order Test
1. Receive Test Orders
2. Prepare for Test
Genetic Testing
3. Perform Test
4. Develop / Transmit Lab Result
5. Provide Supplemental Information
4. Receive Lab Tests
5. Perform Interpretation
6. Care Planning
These scenarios demonstrate how genetic test results are represented in EHR components
of a pharmacogenetic CDSS and the three phases of performing genomic tests:
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1. Pre-Analytic Phase to determine which genetic test is appropriate
2. Analytic Phase where samples are analyzed
3. Post-Analytic Phase where results are reported and interpreted
Intervention Specification
The pharmacogenetic CDSS is an alert-type intervention. Triggering occurs when a provider
uses a CPOE system to prescribe a medication for a patient. The trigger is data-driven since a
query will be performed in the clinical data repository to determine if genetic testing results
exist for the patient. If such results are available, then a query will be performed to match
the drug to a known drug-gene pair in the pharmacogenomics knowledge base.
Decision rules in the CDSS will be based on clinical practice guidelines that are published by
the Clinical Pharmacogenetics Implementation Consortium (CPIC). Founded in 2009, the
CPIC is a group of organizations that develops and publishes clinical practice guidelines for
actionable prescribing decisions based on the results of genetic testing (Caudle, 2014). CPIC
guidelines are written for drug-gene pairs where evidence clearly shows that genetic
variations affect efficacy or risk and where prescribing recommendations such as changing
dose or changing drug can be made. CPIC guidelines are written in accordance with Institute
of Medicine standards for the development of clinical practice guidelines.
The CDSS will use the following logic (see figure 6). If genetic test results are available for the
patient and a guideline exists for a drug-gene pair, then a modal alert box will immediately
be displayed for the prescriber in the CPOE user interface. The alert will inform the
prescriber of the patient’s genotype or phenotype and the expected variation in efficacy or
risk of adverse effect for the drug. Based on data from the guideline contained in the
pharmacogenomics knowledge base, the alert will offer specific advice for dosage
adjustment or alternative drug treatment as appropriate. An infobutton will be included in
the alert box which will link to more detailed information and evidence for the
recommended changes in treatment. The prescriber will then have an option of canceling
the order, modifying the order, or overriding the alert and proceeding with the order. When
overriding an order the provider will need to justify the override by selecting a reason from a
dropdown list. If no genetic test results exist for the patient, if there are no drug-gene
matches, or if there are no important actionable clinical practice guidelines, then no alert
will be triggered and workflow will proceed as normal. This logic step will help to prevent
alert fatigue by presenting providers with an alert only when actionable guidelines exist for a
drug-gene pair.
Because the field of pharmacogenetics is rapidly changing, the pharmacogenomics
knowledge base of the CDSS will need to be updated with new practice guidelines from the
CPIC at frequent intervals. We will begin by updating these data sources every three
months.
The CDSS will help the organization to meet the stated goals and objectives by helping
providers avoid prescribing medications for patients when a known risk exists based on
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genotype. Adverse drug reactions will be avoided by lowering doses or by choosing
alternative medications. Cost savings can be realized by avoiding adverse drug reactions, by
avoiding the prescription of drugs known not to be effective for a particular genotype, and
by selecting medications known to be more effective for a particular genotype.
Figure 6. Pharmacogenetic Clinical Decision Support System Logic
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User Interface
According to a report from the HIMSS Usability Task Force (2009) user interface design can
be a key component for ensuring that clinical decision support systems and other health
information technology products meet their intended goals of improving patient care and
safety. This is because a well-designed user interface will enhance provider adoption and
lead to routine clinical use. The design of a CDSS must be satisfying to the user, must be
effective in helping the user do his work, and must be effective at guiding the user towards
making the recommended decision. It is wise to include end users in the planning stages of
interface design so that changes can be made in an iterative fashion in order to maximize
acceptance. The HIMSS Task Force notes several key principles for interface design. The
interface should be simple and concise and present information in a way that is natural and
familiar to the target end user. The design should aim to minimize the cognitive load of the
user who is usually a provider under time pressure with multiple demands on his attention.
Efficient interactions with a minimum number of steps to complete tasks will enhance user
satisfaction. The design should provide feedback along the way in order to allow a user to
learn the system, especially since opportunities for formal training can often be limited.
In a review on the topic of interface design, Horsky et al. (2012) recommend that
consistency between the CDSS and the larger EHR be maintained. This includes visual
formats, terminology, nomenclature, and processes. They recommend presenting advice
with short explanations and links to more detailed evidence. This way, users will develop
trust in the decision support system over time since they will know where recommendations
are coming from rather than being confronted with a “black box” that issues advice. Clinical
decision support systems should offer guidance and recommendations rather than
commands. Physician are much more likely to follow recommendations from decision
support systems when they are offered alternatives to carry out rather than a hard stop or a
recommendation against a particular action. Recommendations should be written in
succinct and unambiguous language that can be understood after reading it in one pass.
Alerts should be contained on one page with the shortest amount of text possible followed
by an actionable item.
The interface design of the pharmacogenetics CDSS will follow the principles outlined above
as well as the four A’s recommended by Kanstrup (2011): “Attention, All in one, At a glance,
and At hand.”
As mentioned in the intervention specification section above, the CDSS will engage when a
provider uses the CPOE system to write an order for a medication. Input to the CDSS from
the provider will come directly from the CPOE. If an alert is triggered based on the decision
support system logic, a modal alert box will be immediately displayed. This alert box will
have a visual design that is consistent with the rest of the EHR with the exception that it will
be of a different color in order to draw “attention.”
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The alert box will contain all relevant information about the drug-gene pair and clinical
guideline recommendations “all in one” box “at a glance.” Verbiage will be minimized to
present the provider with a succinct explanation. Hyperlinks and infobuttons will be included
in the alert box so that providers can immediately have access to more detailed information
regarding the drug-gene pair and the clinical guidelines if they wish. Immediately following
the recommendations, providers will have “at hand” the ability to override, modify, or
cancel the order.
If providers chose to override the order, they will be asked to justify the override using a
drop down list which will include the following reasons: “patient previously tolerates this
therapy,” “dose or therapy adjustment already made,” “guideline not relevant for this
patient,” “genetic testing is erroneous,” “risks and benefits have been considered/patient
will be carefully monitored,” and “other.” Providers will also be able to enter free text into
the override justification box. This information can be used to later fine-tune the sensitivity
of alerts.
If providers choose to modify the order, they will be taken back to the CPOE screen with the
original drug order information previously entered intact. Providers may then change the
dose or change the drug. If they choose to cancel the order, they will be taken back to the
CPOE screen with a blank medication order entry form.
The following are two illustrations of the user interface.
Example 1: The provider attempts to order abacavir for a patient via the CPOE. The CDSS
searches the clinical data repository and finds that genetic test results exists for the
patient. The CDSS then searches the pharmacogenetics knowledge base for abacavir and
finds there is a related drug-gene pair, abacavir and HLA-B. It then looks for HLA-B test
results for the patient. If those test results exist, it then looks for a clinical guideline for
abacavir/HLA-B. It finds that clinical guidelines do exists. If the patient has the HLAB*57:01 allele then the clinical guideline from CPIC recommends alternative therapy due
to high risk for hypersensitivity reaction.
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Chavez, Heffernan, West
The clinical decision support system will then display an alert box shown in figure 7.
Figure 7. Example clinical decision support alert box for abacavir.
PGx Medication Alert
This patient has genetic test results
indicating the presence of the HLA-B*57:01
genotype. Patients with this genotype are at
significantly increased risk of
hypersensitivity reaction with Abacavir.
There are strong recommendations to avoid
Abacavir for this patient.
Cancel Order
Modify Order
Override Recommendation
More Information
OK
The hyperlink labeled “more information” in the alert box will link to the clinical guidelines
describing the relationship between abacavir and HLA-B and the evidence regarding risk of
hypersensitivity reaction and recommendation to avoid using the medication in patients
with this genotype.
The provider may then chose to override (for example if the patient has already been
taking abacavir with no problems). If he doesn't override, then he may cancel the order
or modify it.
Example 2: The provider attempts to order codeine for a patient. The CDSS searches for genetic
test results. It then searches for codeine and finds the drug-gene pair codeine and CYP2D6. It
then looks for CYP2D6 results for the patient and looks for clinical guidelines for Codeine/CYP2D6.
The system then reports the clinical guideline recommendations based on the patient's phenotype
as shown in figure 8.
20
Chavez, Heffernan, West
Figure 8. Example clinical decision support alert box for codeine.
PGx Medication Alert
This patient has genetic test results
indicating a poor metabolizer phenotype for
CYP2D6. The patient is likely to have
greatly reduced morphine formation after
administration of Codeine leading to
insufficient pain relief. Avoid using Codeine
due to lack of efficacy. Consider alternative
analgesics such as morphine or a nonopioid. Tramadol should also be avoided for
this patient.
Cancel Order
Modify Order
Override Recommendation
More Information
OK
Conceptual Model for the Pharmacogenetics CDSS
Based on the System Inventory, the summary below of each model component is organized
by method of implementation: Client/Server or Web-based.
Client/Server Based
The CDSS EHR Application Environment supports the functional capabilities that were
evaluated by the group and shown in the Components of System Architecture diagram.
Discrete data elements are captured within the Clinical Data Repository. The clinical user can
interact with the Clinical System Interface to view data elements and perform clinical tasks,
e.g., ordering a medication. Such events will then trigger decision support to fire. Triggers
are implemented in the Clinical Data Repository. Interventions and Offered Choices are
displayed to the clinical user within the CDSS Clinical System Interface.
Web-Based
The Clinical Decision Support (CDS) Module implements the IF-THEN rules that are defined in
the Rules/Execution Engine and based on each patient’s genotype data combined with
phenotype data stored in the CDS Knowledge Base.
The database and rules together relate actionable genotype and phenotype pairs to
genome-informed advice messages. If defined rules are met, CDS is delivered in real-time at
the point of care through the EHR.
21
Chavez, Heffernan, West
The CDS Module also implements the User Interface features (information, warnings,
recommendations) and provides the methods for transforming input parameters (i.e., data
about genetic test results and the medication being ordered) into a specific output (i.e.,
Pharmacogenetic links to e-Resources or an Alert message).
Glossary of Terms
Clinical Data Repository
Consolidates data from multiple clinical sources and contains lab results, patient
demographics, pharmacy information, radiology reports, admission / discharge summaries
code vocabularies, progress notes, etc.
Pharmacogenetic Knowledge Base
Sponsored by NIH (pharmaKB.org), this knowledge base is the curator of research
information on genetic variations in drug responses, e.g., how genetic factors influence a
patient’s response to drugs
Genomics Vendor Knowledge Base
Contains genotyping and genetic testing results for patients
CDS Knowledge Base
Uses an inference engine to facilitate presentations of drug interactions, dosing suggestions,
alerts, and diagnoses by applying rules to patient data
Knowledge Engineering
Knowledge acquisition
While the interaction relationship within a drug-gene pair is likely to remain stable once
established, the quantity of relevant genotypes is growing rapidly. It would be beyond the
scope of our healthcare organization to try and directly acquire and maintain a compendium
of these pairs from primary pharmacogenetic literature. Fortunately, as previously noted,
the Clinical Pharmacogenetics Implementation Consortium (CPIC) represents a collaborative
effort of significant academic breadth which composes clinical guidelines for drug-gene pairs
in a format that complies with Institute of Medicine (IOM) standards. The “acquisition”
process in this case is actually a selection process where the organization chooses the
guidelines that are considered relevant for its needs. It may be tempting at first to think
about a wholesale adoption of guidelines, but in the long run, a process of “taking
ownership” by selecting and applying the guideline is an important one to aid in the
adoption of these new guidelines.
The Clinical Decision Support Committee (CDSC) for the organization is central to have the
organization take ownership of these guidelines. They would be able to assess the needs of
the organization and leverage the resources necessary to approve and oversee a robust
22
Chavez, Heffernan, West
process of acquisition and adoption. There would be merit in considering a subcommittee
for this activity to handle the anticipated volume of new guidelines emanating from CPIC.
Knowledge representation
Taking “ownership” of a drug-gene pair guideline from the CPIC will require more than a
simple title/description of the relationship. These relationships need to be parsed into a
consistent set of elements which will facilitate their incorporation in to the organization’s
CDSS. The proposed structure can be seen in Appendix A using a table of drug gene pairs
where three layers are inferred. The top layer represented by the first two columns in green
are the drug-gene pair descriptions. The last three columns in white (Phenotype, Guideline
Recommendation, Recommendation Type, etc.) would have a potential many-to-one
relationship with the pair as indicated by many pairs being seen more than once. The
Genotype column in yellow has a many-to-one relationship with the Phenotype
recommendations since a given phenotype could derive from multiple genotypes.
Ultimately these columns would correlate with data entry points for the clinical decision
support system to allow the elements to present in the user interface in the method
previously described. Other meta-data will also be helpful to capture including the date
approved by the CDSC and the primary organizational researcher. A future review data
could also be entered to facilitate reporting and queuing of guidelines needing review and
renewal. These future reviews would occur at a pre-determined interval to determine if
new literature has presented any refinements or recommendations, perhaps around lab
monitoring.
Evaluation
The Pharmacogenetics Clinical Decision Support System is a relatively new model and
requires both validation and verification. The validation phase will be a robust testing
program before implementation. This testing will be handled in three segments called unit
testing, scenario testing and integrated testing.
1. Unit Testing: During this phase of testing, each drug-gene pair result is tested by
having the particular genetic result entered on a test patient in a test domain. The
corresponding drug component would then be entered to validate that the clinical
decision support that fires works as designed. It should offer accurate information
about how to respond. The cycle here is relatively simple, but the volume of cycles
is directly tied to the volume of drug-gene pairs being implemented.
2. Scenario Testing: During this phase of testing, a testing script is used to elicit
specific scenarios that may challenge the robustness of the CDSS. A sample testing
script is attached in Appendix B and would likely be enhanced by the project team
to add additional scenarios desired by stakeholders.
23
Chavez, Heffernan, West
3. Integrated Testing: Unit testing will occur within the confines of the CDSS and the
EHR. Integrated testing will engage all of the necessary systems to test the entire
flow of the CDSS. This will include the addition of relevant drug-gene pairs to the
relevant knowledgebase, interfacing of gene results from the laboratory
information system to the EHR and completion of medication order including
transmittal via e-prescribing. The goal is to detect unintended consequences to
related downstream systems.
Verification will commence after implementation and will utilize a defined set of metrics to
assess impact. There will be pre-verification analysis to determine a baseline for metrics of
interest:




Medication ordering frequencies for each medication where a drug-gene pair exists
Dosing variability for each medication where a drug-gene pair exists
Adverse events for each medication where a drug-gene pair exists
Frequency and count of dose changes, including discontinuation and starting, for
each medication where a drug-gene pair exists
The baseline metrics above will be continued after implementation, and once the CDSS is
implemented, the following metrics will be added:





Alert frequency for absence of relevant gene testing
Percentage of corresponding medications prescribed without prior genotype
information
Alert frequency for therapeutic modification due to relevant drug-gene pair
Response frequencies to alert (override, change medication, alter dosing)
Percentage of medications prescribed where dosing incorporated suggested dose
modification
Specific drug-gene pairs may utilize metrics of enhanced efficacy. In the case of warfarin
based products, median time to therapeutic effect as measured by Prothrombin Times (PT)
and their related INR. Not all medications will have this type of a metric available.
Discussion
Several previous studies have reported on the feasibility of incorporating pharmacogenetic
data into CDSS and some have reported on the deployment of such data in CDSS in pilot
studies or in a limited scope with decision support for a particular drug or class of drugs (Bell
2014, Bielinski 2014, Deskhmukh 2009, Goldspell 2013, Overby 2010 and 2012). Here we
have presented ideas for a wider scope of pharmacogenetic CDS. Given the underlying
assumption that patients will already have the results of genetic testing available in the
24
Chavez, Heffernan, West
clinical data repository (preemptive genotyping), this system will have limited value to
providers and patients in organizations where the prevalence of such genetic testing is low.
It is expected that in the future genetic testing will be less expensive and more ubiquitous.
As this occurs, this CDSS will be at the ready to help make use of that data for the benefits of
those patients who have had genetic testing done.
The interpretation of genetic testing can be confusing, especially for providers with limited
training and limited time. Evaluating the vast amount of research in pharmacogenetics is a
daunting task. For this reason, our system employs guidelines that have already been vetted
and approved by the CPIC.
The pharmacogenetics CDSS has been designed keeping in mind the three pillars of the CDS
Roadmap outlined by the American Medical Informatics Association as reviewed by Lyman
(2010). “Best knowledge available when needed” means that this system presents only CPIC
approved guidelines with actionable decision support to providers at the point of care when
they attempt to prescribe a medication. This fits into the standard workflow and should
acceptable for providers. “High adoption and effective use” means that alerts will be kept to
a minimum in order to prevent alert fatigue. Scientific evidence of each recommendation
will be available to help build trust and support for the system. “Continuous improvement of
knowledge and CDS methods” means that the CDSS will be evaluated regularly and refined
according to provider and organization needs. The knowledge base will be updated at
frequent intervals using information from the CPIC guidelines. Alerts will be fine-tuned
based on the number and reasons for overrides.
Scalability is an important factor for the CDSS as pharmacogenetic knowledge is constantly
evolving. Utilizing decision rules based on the CPIC guidelines will help to ensure scalability
in the future because staff responsible for maintaining the CDS knowledge base and decision
rules will be able to find this information in one place. New information can be taken from
the CPIC and quickly integrated into the CDSS.
Although this system was design to engage prospectively at the point of care when providers
attempt to prescribe medications, it could be expanded. The system could potentially be
used retrospectively to scan all patient data including active medications and genetic testing
results. If the system finds patients who have been prescribed medications which have a
drug-gene match and an actionable clinical guideline, it could inform the prescribing
physician via email about the guideline. The provider could then make an adjustment to
therapy or dosing if clinically relevant. The system could also be employed as a research
tool. Providers could access the system to search for drug-gene matches for patients prior to
accessing CPOE to prescribe. This may offer some benefit in that providers can discuss
treatment options with patients, considering risks and benefits.
As providers and healthcare organizations strive to offer ever more sophisticated treatment
plans based on personalized medicine, the pharmacogenetics clinical decision support
system will be an essential tool. It will help providers by gathering and presenting
information that is relevant to an individual patient at the point of care. It will help
25
Chavez, Heffernan, West
disseminate to providers the vast amount of pharmacogenetic data available and therefore
aid in the more rapid adoption of clinical guidelines based on pharmacogenetic data. This
system has the potential to greatly improve patient safety and reduce healthcare costs.
26
Chavez, Heffernan, West
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Appendix A. Pharmacogenetics Knowledge Base Table
Drug
Gene
clopidogrel
CYP2C19
clopidogrel
CYP2C19
Genotype
Phenotype
Guideline Recommendation
*1/*2,
*1/*3,
intermediate
consider alternative therapy such as prasugrel or
ticagrelor due to risk of reduced platelet inhibition and
increased risk of bleeding and adverse cardiovascular
*2/*17
metabolizer
*2/*2,
events
consider alternative therapy such as prasugrel or
ticagrelor due to significant risk of reduced platelet
inhibition and severely increased risk of bleeding
*2/*3, *3/*3 poor metabolizer adverse cardiovascular events
Type of
Modification
Recommendation
factor
Lab
Dose Type
therapy change
therapy change
standard loading dose, reduce maintenance dose by 25%
phenytoin
CYP2C9
*1/*2, *1/*3 low metabolizer
phenytoin
CYP2C9
*2/*2,
very low
*2/*3, *3/*3 metabolizer
*1/*1,
*1/*2,
warfarin
CYP2C9
amitriptyline
CYP2D6
amitriptyline
CYP2D6
due to risk for adverse effects such as ataxia, nystagmus,
dysarthria and sedation
dose change
standard loading dose, reduce maintenance dose by 50%
due to risk for adverse effects such as ataxia, nystagmus,
dysarthria and sedation
dose change
dose reduction of warfarin is recommended due to risk
0.75 Maintenance
0.5 Maintenance
of bleeding and cardiovascular events, it is
*1/*3,
low metabolizer
recommended to use a dosing algorithm
*1/*1xN,
ultrarapid
avoid tricyclic antidepressant due to lack of efficacy,
*1/*2xN
*4/*10,
metabolizer
intermediate
consider alternative drug not metabolized by CYP2D6
therapy change
consider 25% reduction in dosing due to increased risk of
*5/*41
metabolizer
side effects
avoid tricyclic antidepressant due to high risk of side
effect, consider alternative drug not metabolized by
*5/*5, *4/*6 poor metabolizer CYP2D6, if tricyclic is used consider 50% dose reduction
dose change
dose change
0.75 All
0.75 All
*4/*4,
*4/*5,
amitriptyline
CYP2D6
*1/*1xN,
ultrarapid
avoid codeine use due to potential for toxicity, consider
alternative opiate
avoid codeine use due to lack of efficacy, consider
therapy change
codeine
CYP2D6
*1/*2xN
*4/*4,
metabolizer
therapy change
codeine
CYP2D6
*4/*5,
poor metabolizer alternative opiate
capecitabine
DPYD
DPYD*2A/*2 complete DPD
A
deficiency
decreased DPD
avoid capecitabine due to risk of severe or fatal drug
toxicity
therapy change
start with 50% dose reduction then titrate dose based on
capecitabine
DPYD
DPYD*1/*2A activity
toxicity
fluorouracil
DPYD
DPYD*2A/*2 complete DPD
A
deficiency
fluorouracil
DPYD
decreased DPD
DPYD*1/*2A activity
avoid fluorouracil due to risk of severe or fatal drug
toxicity
therapy change
start with 50% dose reduction then titrate dose based on
toxicity
dose change
therapy change
dose change
0.5 All
0.5 All
DPYD*2A/*2 complete DPYD
tegafur
tegafur
DPYD
DPYD
A
deficiency
avoid tegafur due to risk of severe or fatal drug toxicity
decreased DPYD
start with 50% dose reduction then titrate dose based on
DPYD*1/*2A activity
toxicity
therapy change
dose change
0.5 All
Monitoring
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Chavez, Heffernan, West
avoid rasburicase, contraindicated due to risk of severe
rasburicase
G6PD
abacavir
HLA‐B
*57:01
positive
hemolysis
avoid abacavir due to significantly increased risk of
therapy change
positive
hypersensitivity
therapy change
avoid alluprinol due to significant risk of severe
allopurinol
HLA‐B
HLA‐B*58:01 high risk
cutaneous adverse reaction
therapy change
avoid carbamazepine if patient is naïve due to significant
risk of cutaneous adverse reactions, if patient has been
on carbamazepine therapy for longer than 3 months
carbamazepine
HLA‐B
peginterferon alfa‐2a
IFNL3
*15:02
CT or TT
carrier
unfavorable
simvastatin
SLCO1B1
TC
consider dose reduction due to increased risk of
intermediate risk myopathy
simvastatin
SLCO1B1
CC
*1/*2,
azathioprine
TPMT
high risk
*1/*3A,
*1/*3B,
high risk
*3A/*3A,
*2/*3A,
azathioprine
TPMT
without reaction may consider continued use
consider alternative therapy due to lack of efficacy
consider dose reduction due to increased risk of
myopathy, consider routine creatine kinase monitoring
consider initial dose reduction of 30‐70% or target dose
and titrate based on tolerance
consider alternative therapy due to risk of
therapy change
therapy change
dose change
0.25 All
dose change
0.5 All
dose change
0.5 All
myelosupression, if azathioprine is used drastically
extremely high
risk
reduce dose by 10‐fold and consider three times a week
dosing instead of daily dosing
therapy change
0.1 All
high risk
consider initial dose reduction of 30‐70% or target dose
and titrate based on tolerance
dose change
0.5 All
*2/*3A,
extremely high
used drastically reduce dose by 10‐fold and consider
*3C/*3A,
*1/*2,
risk
three times a week dosing instead of daily dosing
dose change
0.1 All
*1/*3A,
*1/*3B,
*3A/*3A,
high risk
consider initial dose reduction of 30‐50% or target dose
and titrate based on tolerance
dose change
0.5 All
*3C/*3A,
*3C/4,
*1/*2,
mercaptopurine
TPMT
*1/*3A,
*1/*3B,
*3A/*3A,
mercaptopurine
TPMT
thioguanine
TPMT
*2/*3A,
extremely high
used drastically reduce dose by 10‐fold and consider
thioguanine
TPMT
*3C/*3A,
risk
three times a week dosing instead of daily dosing
dose change
reduce initial dose by 30% for patients receiving dose of
more than 250 mg/m^2 due to risk of myelosupression
0.1 All
irinotecan
UGT1A1
UGT1A1*28
high risk
and arrhythmia
0.7 >250 mg/m^2
dose change
dose reduction of warfarin is recommended due to risk
of bleeding and cardiovascular events, it is
warfarin
VKORC1
GG, GA, AA
low metabolizer
recommended to use a dosing algorithm
dose change
0.75 All
CPK
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Chavez, Heffernan, West
Appendix B. Scenario Testing Script
Step
1
Team
User/ Dept
prescriber
Workflow Step
order corresponding
medication for
drug/gene pair
2
prescriber
order corresponding
medication for
drug/gene pair
3
prescriber
order corresponding
medication for
drug/gene pair
4
prescriber
5
prescriber
order corresponding
medication for
drug/gene pair where
2 genes relevant to
the medication are
gene result returns
with pre-existing order
for medication
prescriber
order corresponding
medication for
drug/gene pair
prescriber
order corresponding
medication for
drug/gene pair
prescriber
order corresponding
medication for
drug/gene pair
prescriber
order corresponding
medication for
drug/gene pair where
2 genes relevant to
the medication are
gene result returns
with pre-existing order
for medication
prescriber
Actual
Outcome
Testing Step/Process
Expected Outcome
Test Patient 1 (no pre-existing prescriber alerted to absence
result) - order coumadin of any gene testing and
OUTPATIENT
advised to order as results
could affect dosing and/or
Test Patient 2 (pre-existing
Prescriber completes order for
gene result with genotype that medication without any alert
does not affect dosing and/or
efficacy) - order coumadin Test Patient 3 (pre-existing
prescriber alerted to presence
gene result with genotype that of genotype that affects
does affect dosing and/or
dosing and/or efficacy with
efficacy) - order coumadin recommended modification
Test Patient 4 (2 pre-existing prescriber sees both alerts
gene results with genotype
that affect dosing and/or
efficacy of single medication) order coumadin Test Patient 3 (pre-existing
prescriber alerted to presence
gene result with genotype that of genotype that affects
does affect dosing and/or
dosing and/or efficacy with
efficacy) - refill/reorder
recommended modification
Test Patient 1 (no pre-existing prescriber alerted to absence
result) - order coumadin of any gene testing and
INPATIENT
advised to order as results
could affect dosing and/or
Test Patient 2 (pre-existing
Prescriber completes order for
gene result with genotype that medication without any alert
does not affect dosing and/or
efficacy) - order coumadin Test Patient 3 (pre-existing
prescriber alerted to presence
gene result with genotype that of genotype that affects
does affect dosing and/or
dosing and/or efficacy with
efficacy) - order coumadin recommended modification
Test Patient 4 (2 pre-existing prescriber sees both alerts
gene results with genotype
that affect dosing and/or
efficacy of single medication) order coumadin - INPATIENT
Test Patient 3 (pre-existing
prescriber alerted to presence
gene result with genotype that of genotype that affects
does affect dosing and/or
dosing and/or efficacy with
efficacy) - refill/reorder
recommended modification
Pass/
Fail
Tester
Date
Tested
Comments
or Issues
pharmacist
6
7
dispense
corresponding
medication for
drug/gene pair
order corresponding
medication for
drug/gene pair
Test Patient 1 (no pre-existing pharmacist alerted to absence
result) - dispense coumadin - of any gene testing and
INPATIENT
advised to order as results
could affect dosing and/or
pharmacist
Test Patient 2 (pre-existing
pharmacist completes
gene result with genotype that dispense for medication
does not affect dosing and/or without any alert
efficacy) - dispense coumadin pharmacist
order corresponding Test Patient 3 (pre-existing
pharmacist alerted to
medication for
gene result with genotype that presence of genotype that
drug/gene pair
does affect dosing and/or
affects dosing and/or efficacy
efficacy) - dispense coumadin - with recommended
pharmacist
order corresponding Test Patient 4 (2 pre-existing pharmacist sees both alerts
medication for
gene results with genotype
drug/gene pair where that affect dosing and/or
2 genes relevant to
efficacy of single medication) the medication are
dispense coumadin pharmacist
gene result returns
Test Patient 3 (pre-existing
pharmacist alerted to
with pre-existing order gene result with genotype that presence of genotype that
for medication
does affect dosing and/or
affects dosing and/or efficacy
efficacy) - refill/redispense
with recommended
coumadin - INPATIENT
modification
result abstractor result entry
enter genotype with incorrect result component will only
string
allow validated strings
medical
records historical
Enter patient reported
no alert fires
assistant
medication
medication (Coumadin) on