Clinical decision support for genetically guided personalized medicine: a systematic review

Clinical decision support for genetically guidedpersonalized medicine: a systematic review

Brandon M Welch,1 Kensaku Kawamoto2

ABSTRACTObjective To review the literature on clinical decisionsupport (CDS) for genetically guided personalizedmedicine (GPM).Materials and Methods MEDLINE and Embase weresearched from 1990 to 2011. The manuscripts includedwere summarized, and notable themes and trends wereidentified.Results Following a screening of 3416 articles, 38primary research articles were identified. Focal areas ofresearch included family history-driven CDS, cancermanagement, and pharmacogenomics. Nine randomizedcontrolled trials of CDS interventions for GPM wereidentified, seven of which reported positive results. Themajority of manuscripts were published on or after 2007,with increased recent focus on genotype-driven CDS andthe integration of CDS within primary clinical informationsystems.Discussion Substantial research has been conducted todate on the use of CDS to enable GPM. In a previousanalysis of CDS intervention trials, the automaticprovision of CDS as a part of routine clinical workflowhad been identified as being critical for CDSeffectiveness. There was some indication that CDS forGPM could potentially be effective without the CDS beingprovided automatically, but we did not find conclusiveevidence to support this hypothesis.Conclusion To maximize the clinical benefits arisingfrom ongoing discoveries in genetics and genomics,additional research and development is recommendedfor identifying how best to leverage CDS to bridge thegap between the promise and realization of GPM.

BACKGROUNDGenetically guided personalized medicine (GPM)entails the delivery of individually tailored medicalcare that leverages information about each person'sunique genetic characteristics.1 The promise ofGPM has expanded as advances in genomics haveaccelerated over the past several decades. Thispromise of GPM is that research discoveries willone day lead to medical treatments and therapiesthat are tailored to the individual characteristics ofeach patient, including clinical data, genetic testresults, patient preference, and family healthhistory (FHx). GPM has the potential to increasethe efficacy, quality, and value of healthcareby providing individually optimized prevention,diagnosis, and treatment.2

As ongoing research continues to expand theGPM knowledge base, it has become increasinglyimportant to translate this knowledge into routinehealthcare practice in order to realize the promise ofGPM.3 However, the effective realization of GPMremains very limited.4 While this is partly due to

the need for further evidence of the clinical utilityand cost effectiveness of a genetically guidedapproach to patient care, an important additionalreason is the need for information systemsthat assist in the translation of knowledge frombench to bedside.5 Even without the complexity ofgenetics, it can often take over 15 years to translateresearch from bench to bedside.6 This translationalbottleneck is likely to be an even more significantproblem in GPM for the following reasons.

Limited genetic proficiency of cliniciansMany clinicians receive minimal training in clinicalgenetics. As a result, many physicians lack theconfidence and understanding needed for effectivelyinterpreting and using genetic information in theirclinical practices.7

Limited availability of genetics expertsCurrently, there are about 3000 board-certifiedgenetic counselors8 and approximately 1200medical geneticists practicing in the USA (S. R.DelBusso, American Board of Medical GeneticsAdministrator, October 28, 2011, personalcommunication). The growing utility of geneticinformation is putting an increasing burden onthese professionals. We cannot expect thesegenetics experts to be readily available each timegenetic information should be used to guidemedical treatment. For effective, efficient, andwidespread clinical use, the burden of geneticinterpretation and guidance must be shared by thewider clinical community.

Breadth and growth of genetic knowledge baseThere are currently over 2500 clinical genetic testsavailable to clinicians, encompassing a widebreadth of medical care.9 It is therefore unreason-able to expect a clinician to remember everyappropriate genetic test for a particular conditionin conjunction with test-specific guidelines forordering and interpretation. Compounding thisissue, the continual growth in the knowledge baseand the prospect of full genome sequencing willinevitably overwhelm clinicians' capacities tomanage and leverage this information effectivelyfor GPM unless computerized assistance is providedfor interpreting and acting on this information.Various investigators and leaders have identified

health information technology as being vitalto overcoming these barriers and realizing thepromise of GPM.2 10 In particular, clinical decisionsupport (CDS) has been identified as a criticalenabler of GPM.11 12 CDS entails providing clini-cians, patients, and other healthcare stakeholderswith pertinent knowledge and person-specific

< An additional appendix ispublished online only. To viewthis file please visit the journalonline (http://dx.doi.org/10.1136/amiajnl-2012-000892).1Department of BiomedicalInformatics and Program inPersonalized Health Care,University of Utah, Salt LakeCity, Utah, USA2Department of BiomedicalInformatics and InformationTechnology Services, Universityof Utah, Salt Lake City, Utah,USA

Correspondence toDr Kensaku Kawamoto,Department of BiomedicalInformatics, University of Utah,615 Arapeen Way, Suite 208,Salt Lake City, UT 84092, USA;[email protected]

Received 10 February 2012Accepted 27 July 2012

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388 J Am Med Inform Assoc 2013;20:388–400. doi:10.1136/amiajnl-2012-000892

Published Online First22 August 2012

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information, intelligently filtered or presented at appropriatetimes, to enhance health and healthcare.13 CDS has the capacityto process complex, disparate data and present actionable,standardized, evidence-based recommendations in a way that isusable by a clinician in everyday practice.11 As such, CDS canhelp bridge the gap between the promise and realization of GPM(figure 1). Given the criticality of CDS for realizing the promiseof GPM, and given the lack of a systematic review on this topic,we sought in this paper to assess the history and state of CDSfor GPM through a systematic review of the literature.

METHODSData sources and inclusion criteriaWe searched MEDLINE and Embase from 1990 to 2011 usinga search strategy adapted from previous systematic reviews ofCDS,14 genetic health services,15 and FHx16 (see supplementaryappendix, available online only, for full search strategy). Thefinal literature search was conducted on June 1, 2012. Theinclusion criteria for the review were as follows: English article;human focus; manuscript in peer-reviewed journal; and primaryfocus on the use of computers to deliver genetically guided,patient-specific assessments and/or recommendations tohealthcare providers and/or patients to guide clinical decision-making, as further defined in Box 1.

For all identified references, the authors reviewed titles, indexterms, and available abstracts to determine if the articlesappeared to meet all inclusion criteria. If insufficient informationwas available to make a confident decision at this stage, thearticle was included for full-text retrieval. Each full-text articlewas then reviewed to determine its final inclusion status.

Data abstractionFor each of the articles that met the inclusion criteria listedabove, we abstracted data on the clinical application area, CDStype, genetic information used, primary users, article type, studylocation, CDS purpose, and notable informatics aspects. CDStype was defined as being either stand-alone CDS or integratedCDS. A stand-alone CDS system is a CDS system that exists inisolation from a primary clinical information system containingrelevant patient data, such as an electronic health record (EHR)system. A stand-alone CDS system requires manual data inputbefore a CDS result can be produced. In contrast, an integratedCDS system is integrated with a primary clinical information

system such as an EHR system or a computerized provider orderentry system to aggregate necessary patient-specific informationautomatically and to provide guidance within routine clinicalworkflows. Clinical application area was defined as the clinicaldomain targeted by the CDS intervention. Article type consistedof system description papers and evaluation studies of varioustypes (eg, qualitative evaluation, randomized controlled trial).Genetic information used consisted of FHx, genotype, or both.Primary users were defined as the individuals who primarilyentered information and received the results. Study location wasthe country or region where the research was conducted. CDSpurpose identified the role of the CDS system within thecontext of clinical decision-making. A notable informatics aspectwas also abstracted if a manuscript utilized a methodology thatwas considered to be of potential interest to an informaticsaudience. For intervention studies, additional details regardingthe study size and study outcomes were abstracted.

Data analysis and presentationUsing the abstracted attributes, the manuscripts were groupedinto logical categories, primarily according to CDS type andclinical application area. The findings from these manuscriptswere summarized through tables and narrative discussion. Inaddition, notable themes and trends were identified anddiscussed. A quantitative analysis of CDS trials to identifyfeatures predictive of trial outcomes was considered.14 However,due to the limited sample size of CDS trials available, sucha quantitative analysis of potential success factors was notfeasible.

RESULTSThe initial MEDLINE and Embase searches identified 3416potentially relevant articles. During the title and abstract review,82 articles were rejected for not being in English, 504 articleswere rejected because they were not focused on humans, 34articles were rejected for not being a peer-reviewed manuscript,and 2494 articles were rejected because the primary focus of thework was not on the use of computers to deliver geneticallyguided, patient-specific assessments and/or recommendations.The remaining 302 articles underwent full-text review, at whichstage 37 articles were rejected for not being a peer-reviewedprimary research article and 227 articles were rejected becausethe primary focus of the work was not on the use of computers

Figure 1 Clinical decision support(CDS) as bridge overcoming barriers togenetically guided personalizedmedicine.

CDS

Where we are now

Where we want to be

The Promiseof Personalized

Medicine

The Realizationof Personalized

MedicineBarriers -Limited genetic proficiency of clinicians-Limited availability of genetics experts -Growth of genetic knowledge base

-Research Discoveries-Technology Developments

-Safer Healthcare-Improved Outcomes-Reduced Costs

"Bench" "Bedside"17+ years translating research into medical practice(Traditional Path)

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J Am Med Inform Assoc 2013;20:388–400. doi:10.1136/amiajnl-2012-000892 389

to deliver genetically guided, patient-specific care guidance(figure 2). The final set of included manuscripts consisted of 38primary research articles.17e54 The manuscripts included werepublished from 1990 to 2011, with the majority of manuscriptspublished on or after 2007. Provided below is a summary andanalysis of these earlier works, grouped primarily by CDS typeand area of clinical focus.

CDS systems for genetically guided cancer managementGenetically guided cancer management was the focus of 22primary research articles summarized in tables 1e4.17e37 54

These manuscripts include six manuscripts related to the RiskAssessment in Genetics (RAGs) system for providing FHx-drivenCDS (table 1),17e21 54 six manuscripts on other FHx-drivenCDS tools for breast cancer management (table 2),22e27 fourmanuscripts on genotype-driven CDS tools for breast cancermanagement (table 3),28e31 and six additional manuscripts

on GPM CDS tools for non-breast cancer management(table 4).32e37

RAGs system for providing FHx-driven CDSSome of the earliest and most comprehensive research on the useof CDS to support GPM was conducted by Emery55 (table 1),who identified that existing systems were not designed forprimary care and that none provided patient management advicebased on calculated risk. To address this gap, Emery developeda system known as RAGs, which helped general practitioners(GPs) in the UK collect FHx relevant to familial breast, ovarian,and colorectal cancer and provided appropriate managementguidance, primarily regarding guideline-based specialistreferrals.17e19 54 A later extension of the RAGs system wasreferred to as the GRAIDS system.20 21 This body of workincluded several favorable evaluations of these systems,18 19 21

including a cluster randomized controlled trial (RCT) across 45GP teams that found that GRAIDS significantly increased theproportion of patients referred appropriately to the regionalgenetics clinic according to evidence-based practice guidelines.21

Other FHx CDS tools for breast cancer managementBeyond the work of Emery,55 CDS research for GPM has focusedheavily on breast cancer management (table 2). Risk assessmenttools for breast cancer can enable personalized care according toan individual's level of risk.22 23 An RCT conducted in the UKfound that a stand-alone breast cancer CDS tool had limitedimpact due to lack of awareness and use by GPs.24 At the sametime, a stand-alone CDS tool that calculated risks for breastcancer, heart disease, osteoporosis, and endometrial cancer wasshown in an RCT to enhance the effectiveness of geneticcounselors using the system.25 26 Another stand-alone CDSsystem that has been found to be beneficial is HughesRiskApps,which collects relevant FHx information and provides clinicianswith various tools to support the management of patients. Anobservational implementation study of this tool in a communityhospital setting found significant adoption and impact.27

Genotype-driven CDS tools for breast cancer managementSeveral investigators have developed CDS systems that supporttreatment and decision-making once mutations have been iden-tified in the breast cancer (BRCA) genes (table 3). In the UK,Glasspool and colleagues30 31 developed a CDS tool known asREACT (Risks, Events, Actions and their Consequences overTime), which used a graphical timeline display to model real-time changes in lifetime risks as a result of risk-reduction inter-ventions for breast cancer and ovarian cancer. In addition, severalpatient-directed, stand-alone CDS systems have been developedfor improving risk communication and decision-making in breastcancer management based on BRCA genotype.28 29

CDS for other cancersBesides breast cancer, other cancers have been the focus of CDSresearch and development (table 4). Most of this CDS researchfor other cancers has involved colorectal cancer, and in particularLynch syndromeda strongly heritable type of colorectalcancer.32e34 Of note, the RAGs and GRAIDS systems describedearlier supported both breast cancer and colorectal cancermanagement.17e21 54 An additional CDS system investigated forcolorectal cancer management is CRCAPRO, similar toBRCAPRO, which used FHx to identify patients at risk ofhereditary colorectal cancer.33 In addition, a group in theNetherlands developed a CDS intervention to remind patholo-gists to order Lynch syndrome genetic testing among patients

Box 1 Manuscript inclusion criteria

< Definitions:– Healthcare provider ¼ physician, nurse practitioner,physician assistant, registered nurse, or genetic counselor

– Genetic factor ¼ genotype, gene expression profile, and/orfamily health history

< Universal inclusion criteria:– English article– Human focus– Manuscript in peer-reviewed journal

< Additional inclusion criteria (at least one):– Intervention study evaluating the impact of a CDS system inan actual patient care context

For a comparative intervention study, CDS required to bea part of the primary intervention under evaluationExcludes laboratory evaluations or simulation studies

– Methodology article whose primary focus is on how CDSsystems should be designed specifically to support clinicaldelivery of patient-specific assessments and/or recommen-dations guided by genetic factors. Includes systemdescription articles.

Figure 2 Manuscript selection process. CDS, clinical decision support;GPM, genetically guided personalized medicine.

Review


Table1

Sum

maryof

primaryresearch

onCDSsystem

sforcancer-related

GPM

:RAGssystem

forprovidingFH

x-driven

CDS

Citationandnameof

system

(ifapplicable)

Manuscriptsummaryandtrial

details

(ifapplicable)

Users

and

studylocation

Genetic

inform

ationused

Integrated

with

primaryclinical

inform

ationsystem

CDSpurposeandclinical

focus

Manuscripttype

Notable

inform

aticsaspect

Coulson,2001

17;RAGs

System

descriptionof

RAGs,

which

was

designed

tohelpGPs

build

afamily

pedigree,calculategenetic

risk,

and

obtain

guideline-basedcare

recommendations

GPs

inUK

FHx

No

Assessm

entof

patient

riskand

provisionof

managem

ent

recommendations

forfamilial

breast,ovarian,

and

colorectalcancer

System

description

RAGsuses

PROform

a,an

argumentation-based

technology

forCDS

Emery,

1999

18;RAGs

Aqualitativeevaluationof

RAGs,

which

foundthat

thesystem

was

easy

touse

byGPs

andserved

asan

appropriate

applicationof

inform

ationtechnology

toassist

with

clinicalcare

GPs

inUK

FHx

No

Assessm

entof

patient

riskand

provisionof

managem

ent

recommendations

forfamilial

breast,ovarian,

and

colorectalcancer

Qualitativestudy

RAGsuses

PROform

a,an

argumentation-based

technology

forCDS

Glasspool,2001

54;RAGs

System

descriptionof

RAGs,

which

uses

anargumentationapproach

toassess

genetic

riskandprovidedetailed

qualitativeexplanationwith

referraladvice

GPs

inUK

FHx

No

Assessm

entof

patient

riskand

provisionof

managem

ent

recommendations

forfamilial

breast,ovarian,

and

colorectalcancer

System

description

RAGsuses

PROform

a,an

argumentation-based

technology

forCDS

Emery,

2000

19;RAGs

Com

parativeanalysisof

RAGs,

Cyrillic

(acommercially

availablepedigree

draw

ingsystem

),andpenandpaper

foruseby

GPs.Thisstudydemonstrated

that

RAGssignificantlyimproved

pedigree

accuracy

andproduced

moreappropriate

managem

entdecisionsthan

theothertwo

methods.Furtherm

ore,

92%of

GPs

preferredRAGsto

theothermethods

GPs

inUK

FHx

No

Assessm

entof

patient

riskand

provisionof

managem

ent

recommendations

forfamilial

breast,ovarian,

and

colorectalcancer

Com

parativestudy

RAGsuses

PROform

a,an

argumentation-based

technology

forCDS

Emery,

2005

20;GRAIDS

System

descriptionof

GRAIDS,a

next-generationFH

xCDStool

that

built

onboth

RAGsandCyrillicandprovided

anenhanced

user

interfaceforGPs

toassess

familialcancer

risk

GPs

inUK

FHx

No

Assessm

entof

patient

riskand

provisionof

managem

ent

recommendations

forfamilial

breast,ovarian,

and

colorectalcancer

System

description

Server-basedapplication

that

provides

both

heuristic

andstatistical

riskassessment

Emery,

2007

21;GRAIDS

AclusterRCTof

GRAIDSconducted

across

45GPteam

sintheUK.

GRAIDS

significantlyincreasedthenumberof

referralsto

theregionalgeneticsclinic

(p¼0

.001),with

thereferralsbeing

significantlymorelikelyto

beconsistent

with

referralguidelines

(p¼0

.006).

Moreover,patientsreferred

from

GRAIDSpractices

hadsignificantly

lower

cancer

worry

scores

atthe

pointof

referral(p¼0

.02)

GPs

inUK

FHx

No

Assessm

entof

patient

riskand

provisionof

managem

ent

recommendations

forfamilial

breast,ovarian,

and

colorectalcancer

RCT

Server-basedapplication

that

provides

both

heuristic

andstatistical

riskassessment

CDS,clinicaldecision

support;FH

x,family

health

history;

GP,

generalpractitioner;GPM

,geneticallyguided

personalized

medicine;

GRAIDS,GeneticRiskAssessm

entinan

Intranet

andDecisionSupport;RAGs,RiskAssessm

entinGenetics;

RCT,

random

ized

controlledtrial.

Review


who met certain criteria, one of which was a suspicious FHx.This intervention significantly improved pathologists' recogni-tion of patients at risk of Lynch syndrome.34 Moreover, DrHenry Lynch, for whom Lynch syndrome is named, developeda CDS system for supporting his hereditary cancer consultingservice. This CDS system expedited clinicians' decision-making

processes and resulted in a significant reduction in time spent oncases.32

Similar to the stand-alone CDS systems for breast cancermanagement described earlier,28 29 stand-alone CDS tools havebeen shown to be useful for the management of other types ofcancers, including prostate cancer37 and alcohol-related

Table 2 Summary of primary research on CDS systems for cancer-related GPM: other FHx CDS tools for breast cancer management

Citation andname ofsystem(if applicable)

Manuscript summaryand trial details(if applicable)

Usersand studylocation

Geneticinformationused

Integratedwithprimaryclinicalinformationsystem

CDS purposeand clinicalfocus

Manuscripttype

Notableinformaticsaspect

Tsouskas,199722

Evaluation of a CDS toolthat used patient-specificbreast cancer riskinformation, including FHx,to identify patients at highrisk of breast cancer. Thesystem identified nine outof 10 women with breastcancers in this study

Cliniciansin Europe

FHx No Assessment ofpatient risk forbreast cancer

Validationstudy

Expert systemdeveloped using avariant of the BASICprogramming language

Berry, 200223;BRCAPRO

Evaluation of BRCAPRO,which predicted theprobability of carryinga BRCA mutation based ona patient’s FHx. BRCAPROwas effective in predictingthe probability of carryingthe BRCA mutation

Cliniciansin USA


Validationstudy

Probability calculatedusing Bayesianupdating

Wilson, 200624 RCT of a stand-alone breastcancer CDS tool to guidereferrals in everyday GPpractices. The studyconsisted of 86 GPpractices. The CDS systemdid not result in a statisticallysignificant improvement, duelargely to the limited awarenessand adoption of the tool by GPs

GPs in UK FHx No Assessment ofpatient risk andprovision ofmanagementrecommendationsfor breast cancer

RCT The deployment ofthe CDS system waspurposely pragmaticand did not involveextensive workflowintegration measures

Matloff, 200726 System description of a toolto provide patient-specificpredictions of women’sfuture risks for breastcancer, heart disease,osteoporosis, andendometrial cancer utilizingpersonal and FHx

Geneticcounselorsin USA


Systemdescription

System used aMarkov model

Matloff, 200625 RCT of a CDS tool used bygenetic counselors26 to enablepersonalized risk assessmentand genetic counseling. The trialinvolved 48 cancer-free, post-menopausal women with afirst-degree relative of breast cancerwho were contemplating the useof alternative menopausal therapyoptions. This trial found that patientsin the intervention group hadincreased knowledge and a lower,more accurate perceived risk ofdeveloping breast cancercompared to the control group

Geneticcounselorsin USA


RCT System used aMarkov model

Ozanne, 200927;Hughes RiskApps

System description ofHughesRiskApps and evaluationof its impact at a communityhospital. The CDS systemsignificantly increased thenumber of patients seen forrisk consultation and genetictest ordering. The implementationimproved efficiency in severalways and did not requiresignificant investment incapital or personnel

Cliniciansin USA

FHx No Assessment ofpatient risk andprovision ofmanagementrecommendationsfor breast cancer

Systemdescription;preepostcomparison

Used tablet computersto collect informationfrom patients. UsedHealth Level 7compliantinformation models.

CDS, clinical decision support; FHx, family health history; GP, general practitioner; GPM, genetically guided personalized medicine; RCT, randomized controlled trial.

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cancers.36 These studies included an RCT that showed thata patient-directed, genotype-driven CDS tool for alcohol-relatedcancer risk significantly reduced alcohol consumption bypatients at increased genetic risk.36 These studies, as well as theprevious studies on breast cancer,28 29 showed that patient-directed CDS systems can be clinically useful.

CDS for pharmacogenomicsPharmacogenomics, the practice of tailoring drug therapy to thepatient's unique genetic characteristics, can be a complicatedprocess; genetically guided CDS offers a solution for simplifyingthis process. Table 5 summarizes the six primary research articlesidentified on this topic.38e43 These studies include a descriptionand validation of a CDS system for genetically guided treatment ofHIV infections,38 as well as an RCT that found that genotypingcombined with CDS-guided therapy improved outcomes overstandard of care.39 Outside of HIV therapy, other investigatorsfocused on how CDS for pharmacogenomics could be integratedwith primary clinical information systems such as computerizedprovider order entry systems.40 42 43 These studies evaluated

considerations such as developing the underlying pharmacoge-nomics knowledge base,40 representation of genetic information inthe EHR for supporting pharmacogenomics CDS,42 and theavailability of patient data required for pharmacogenomics withinthe EHR.43 The lone stand-alone system for pharmacogenomicsused genotype and clinical data to estimate and graphicallyrepresent a patient's plasma warfarin concentration over time.41

Other CDS systems for GPMTable 6 summarizes the 10 primary research articles that wereneither cancer specific nor focused on pharmacogenomics.44e53

As with CDS for cancer, there has been a substantial focus onFHx-driven CDS for other medical conditions. For example,a tool called GenInfer considered FHx and calculated inheritancerisks for genetic diseases,44 and FHx-driven CDS was included asa part of the National Russian Genetic Register.45 46 Beyondthese system descriptions, recent studies of FHx-driven CDShave focused on impact evaluation, with mixed results.48 49 51

Finally, there were four primary research studies on genotype-driven CDS systems not focused on pharmacogenomics or

Table 3 Summary of primary research on CDS systems for cancer-related GPM: genotype-driven CDS tools for breast cancer management

Citation andname of system(if applicable)






Manuscripttype


Schwartz, 200928 RCT of patient-facing toolthat captured patient-specificinformation and providedtailored content about risks,benefits and managementoptions based on the patients’particular situations. Thisstudy found that among214 BRCA-positive womenwho were initially undecidedabout how to manage theirbreast cancer risk, patientswho used the CDS tool weremore likely to reach a managementdecision (p¼0.001), had decreaseddecision conflict (p¼0.002), andincreased satisfaction (p¼0.002)compared to women who did notuse the CDS tool

Patientsin USA

Genotype No Provision ofmanagementrecommendationsfor breast cancer

RCT CD-ROMbased,patient-directeddecision aid

Hooker, 201129 Longitudinal RCT of patient-facingBRCA decision aid.28 This studyshowed significantly higher cancer-specific distress (p¼0.01) andgenetic testing-specific distress(p¼0.01) among users of thepersonalized decision aid after onemonth. Distress levels between groupswere the same after 12 months

Patientsin USA

Genotype No Provision ofmanagementrecommendationsfor breast cancer

RCT CD-ROMbased,patient-directeddecision aid

Glasspool,200730;REACT

System description of REACT(Risks, Events, Actions and theirConsequences over Time), a breastcancer CDS tool with a graphicaltimeline display to model real-timechanges in lifetime risk as a result ofrisk-reduction interventions such astamoxifen therapy, hormone therapy,and mastectomy

Geneticcounselorsin UK

Genotype No Prediction ofresponse totreatment forbreast andovarian cancer

Systemdescription

Graphicaldisplay of riskchangesdynamicallybased onselectedinterventions

Glasspool,201031;REACT

Qualitative study of REACT by eightgenetic counselors.30 Most counselorsfound REACT effective for genetic riskmanagement, although there wereconcerns related to the tool’s potentialto alter the dynamics of theclinicianepatient interaction

Geneticcounselorsin UK

Genotype No Prediction ofresponse totreatment forbreast andovarian cancer

Qualitativestudy

Graphicaldisplay of riskchangesdynamicallybased onselectedinterventions

CDS, clinical decision support; GPM, genetically guided personalized medicine; RCT, randomized controlled trial; REACT, Risks, Events, Actions and their Consequences over Time.

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Table 4 Summary of primary research on CDS systems for cancer-related GPM: CDS for other cancers

Citation andname of system(if applicable)


Users andstudylocation




Manuscripttype


Evans, 199532 Description of a FHx CDSsystem developed fora hereditary cancer consultingservice. The system collectedFHx information, evaluatedthe FHx for familial riskpatterns, and producedpreliminary risk assessmentand managementrecommendations. Thesystem resulted ina significant reductionin time spent on cases

Hereditarycancerconsultingservice in USA

FHx No Assessment ofpatient risk andprovision ofmanagementrecommendationsfor hereditarycancer

Systemdescription;impactobservation

Expert rule-basedsystem thatmodeled thepattern recognitioncapabilities ofclinical geneticists

Bianchi, 200733;CRCAPRO

Evaluation of CRCAPRO,which used FHx ofcolorectal and endometrialcancers to identify patientswith Lynch syndrome.This study showed thatCRCAPRO has lowsensitivity and specificity

Clinicians in UK FHx No Assessment ofpatient risk forcolorectal cancer

Systemvalidation

Probabilitycalculated usingBayesianupdating

Overbeek,201034

RCT of electronic remindersto pathologists to considerLynch syndrome genetictesting among newlydiagnosed colon cancerpatients based on FHx. TheCDS reminder intervention in12 pathology laboratoriessignificantly improvedpathologists’ recognition ofpatients at risk for Lynchsyndrome (OR 2.8; 95% CI1.1 to 7.0) and increaseduse of genetic testing (OR4.1; 95% CI 1.3 to 13.2)

Pathologists inEurope

FHx Yes Provision ofmanagementrecommendationsfor colorectalcancer

RCT Electronicremindersprovidedthrough healthinformationsystem

Picone, 201135;NeoMark

System description ofNeoMark, a web-basedtool that combined medicalimages, genetic markers,and other patient databefore and after treatmentof oral cavity squamous cellcarcinoma to predictreoccurrence

Clinicians inEurope

Genotype No Assessment ofpatient risk fororal cancer

Systemdescription

Uses a service-oriented, modulararchitecture

Hendershot,201036

RCT of a web-basedgenetic feedbackintervention involving 200college students of Asiandescent. The systemprovided personalizedalcohol-related health riskinformation and feedbackbased on the patient’sgenotype. The tool resultedin significant reductions indrinking (p¼0.02) amongparticipants with thegenotype associated withhigher risk of alcohol-related cancer

Patients inUSA

Genotype No Assessment ofpatient risk;reduction ofrisky behavior(alcoholconsumption)for alcohol-relatedcancer

RCT Web-basedintervention

Wakefield, 201137 System description and pilotusability test of an onlineCDS tool that presented 22men with age and familyhistory-specific prostatecancer risk informationand management recommendations.Most participants preferred thismethod for receiving prostatecancer information

Patients inAustralia/NewZealand

FHx No Assessment ofpatient risk andprovision ofmanagementrecommendationsfor prostatecancer

Systemdescription;pilot usabilitytest

Online decisionaid using aMarkov model

CDS, clinical decision support; FHx, family health history; GPM, genetically guided personalized medicine; RCT, randomized controlled trial.

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cancer.47 50 52 53 These systems included a CDS system thatretrieved genetic, radiological and clinical data from clinicalinformation systems to provide guidance on intracranial aneurismmanagement,52 as well as a portable medical device that inte-grated clinical and genetic data to provide a diagnosis for rheu-matoid arthritis and multiple sclerosis.47 In addition, GeneInsightprovides geneticists and other clinicians with patient-specificgenetic testing reports, as well as notifications regarding updatesto the presumed clinical significance of patients' previously iden-tified genotype.50 Finally, in a survey study, Scheuner andcolleagues53 found that clinicians felt their EHR systems could domuch more to meet their needs related to GPM.

Trend analysisPublication volume on CDS for GPM generally increased overtime, with a majority published since 2007 (figure 3). While all

publications before 2007 focused on stand-alone CDS, 32% ofarticles since 2007 focused on integrated CDS (figure 4). Like-wise, while 13% of manuscripts before 2007 involved the use ofgenotype for CDS, 61% of manuscripts since 2007 have involvedthe use of genotype (figure 5). As noted earlier, a major focus ofthe literature in this domain has been on FHx CDS, pharma-cogenomics, and CDS for cancer management.

DISCUSSIONSummary of findingsIn order to learn from past research efforts and to guide futureresearch into the use of CDS to enable GPM, we conducteda systematic review of the literature. Through a literature searchspanning from 1990 to 2011, we screened 3416 manuscripts andincluded 38 primary research articles. A majority of thesemanuscripts was published from 2007 to 2011, with an

Table 5 Summary of primary research on CDS systems for pharmacogenomics

Citation and nameof system(if applicable)






Manuscripttype


Pazzani, 199738;CTSHIV

System description of the CTSHIVCDS program which manages HIVgenome data and makes virus-specific therapeuticrecommendations

Cliniciansin USA

HIVgenotype

No Provision ofmanagementrecommendationsfor HIV

Systemdescription

Uses a backwardchaining expertsystem

Tural, 200239;RetroGram

RCT of genotyping accompaniedby RetroGram, which ranked drugsuitability based on the HIVgenotype. This study showedthat genotyping combined withRetroGram use improved HIVtherapy outcomes over standardof care (p<0.05)

Cliniciansin Europe

HIVgenotype

No Provision ofmanagementrecommendationsfor HIV

RCT Contains approximately200 rules based on thescientific literature

Swen, 200840 Description of how the Royal DutchAssociation for the Advancement ofPharmacy developed guidelines forthe use of genetic information fordrug prescribing and integratedthese guidelines into automateddrug prescription and medicalsurveillance systems fornationwide use

Clinicians andpharmacistsin Europe

Genotype Yes Alert on gene-druginteractions forpharmacogenomics

Systemdescription

Recommendationsincorporated into theG-standard, an electronicdrug database usedfor CDS

Bon Homme,200841

System description of prototypeCDS tool for personalized warfarintherapy that combined genetic andclinical data to estimate the requiredwarfarin dose and the patient’splasma warfarin concentration

Cliniciansin USA

Genotype No Therapeutic doseguidance forwarfarin

Systemdescription

Provides a graphicaldisplay of estimatedplasma warfarinconcentration over time

Deshmukh,200942

This study compared the use of asingle nucleotide polymorphismdata model to the use of an alleledata model for CDS computationin an EHR system. While therewere statistically significantdifferences in computation time,this did not translate into significantdifferences in the overall clinicianordering time

Clinicians andpharmacistsin USA

Genotype Yes Alert on geneedruginteractions forpharmacogenomics

Comparativestudy ongenotype datarepresentation

CDS rules developedwithin the CernerEHR environment

Overby, 201043 This study found that thePharmacogenomics KnowledgeBase was a good source forpharmacogenomics knowledgeand that sufficient clinical dataexisted in the local EHR system tosupport 50% of the pharmacogenomicknowledge in drug labels that arecapable of being expressed as CDSrules

Cliniciansin USA

Genotype Yes Provision of therapyguidance forpharmacogenomics

Feasibilitystudy

The MINDscape EHRsystem was used inthe study

CDS, clinical decision support; CTSHIV, Customized Treatment Strategies for HIV; EHR, electronic health record; RCT, randomized controlled trial.

Review


Table6

Sum

maryof

primaryresearch

onGPM

CDSsystem

sforotherconditions

Citationandnameof

system

(ifapplicable)

Manuscriptsummaryandtrialdetails

(ifapplicable)

Users

and

studylocation

Genetic

inform

ationused

Integrated

with

primaryclinical

inform

ationsystem

CDSpurposeand

clinical

focus

Manuscripttype

Notable

inform

aticsaspect

FHx-driven

CDSsystem

s

Harris,1990

44;GenInfer

System

descriptionof

theGenInferprogram,

which

used

FHxinform

ationalongwith

other

inheritance

factorsto

calculategenetic

risks

and

probabilitiesof

inheritance

Clinicians

inUSA

FHx

No

Assessm

entof

patient

riskforinherited

disease

System

description

Based

onPearl’s

algorithm

forfusion

andpropagationin

aprobabilistic

beliefnetwork

Kobrinskii,

1997

45and

Kobrinsky,

1998

46;National

Russian

GeneticRegister

Descriptionof

theinform

ationsystem

used

byRussia’sfederalgeneticscenter

tomanage

patientsacross

Russiain

need

ofgeneticscare.

Thissystem

supportedpedigree

creation,

cytogenetic

analysis,riskassessment,and

inform

ationsupport

Geneticsspecialists

inEurope

FHx

No

Assessm

entof

patient

riskforinherited

disease

System

description

Utilized

both

server–client

andlocaldeploymentmodels

Orlando,

2011

48;MeTree

System

descriptionof

MeTree,

atool

that

evaluatesFH

xandprovides

managem

ent

recommendations

regardingvarious

heritable

conditionsforpatientsandclinicians.Also

provides

theprotocol

foraplannedevaluationof

thetool

inNorth

Carolinaprimarycare

clinics

Patientsand

clinicians

inUSA

FHx

No

Assessm

entof

patient

riskandprovisionof

managem

ent

recommendations

for

inherited

disease

System

description;

evaluationprotocol

description

Patient-driven

application

that

provides

CDSas

aprintout

Rubinstein,

2011

49;

CDCFamily

Healthware

RCTwith

3284

participants

oftheCDCFamily

Healthwaretool,which

provides

personalized

screeningrecommendations

formultipleheritable

conditionsbasedon

FHx.

Bothinterventionand

controlgroups

show

edimproved

adherenceto

screeningrecommendations

comparedto

the

baselinetim

eperiod,

buttherewas

nosignificant

difference

betweentheinterventionandcontrol

groups

Patientsin

USA

FHx

No

Assessm

entof

patient

riskandprovisionof

managem

ent

recommendations

for

inherited

disease

RCT

Apatient-directed,

web-based

tool

Wells,2007

51;

PRED

ICTCVD-5

Systemdescriptionofareal-timeCDSsystem

that

pulledclinicaldata

from

theEH

Rto

calculate

cardiovascular

diseaseriskandproviderisk

managem

entrecommendations.Aretrospective

analysisfoundthatincludingthepatients’ethnicity

andFH

xinto

theriskassessmentprocess

substantially

increasedthenumberof

patients

eligiblefordrug

treatm

entandlifestyle

managem

ent

Clinicianin

Australia

andNew

Zealand

FHx

Yes

Assessm

entof

patient

riskforheartdisease

System

description;

retrospectiveanalysis

Integrated

with

theMedTech

practicemanagem

entsystem

Genotype-driven

CDSsystem

s

Iavindrasana,2008

52;

@neurIST

System

descriptionof

@neurIST,

aCDSsystem

which

collectsgenetic

data,radiologicaldata,and

clinicaldata

from

clinicalinform

ationsystem

sto

provideCDSregardingintracranialaneurisms

Clinicians

inEurope

Genotype

Yes

Provisionof

managem

ent

recommendations

for

intracranialaneurism

System

description

Usesaservice-

oriented,

standards-basedapproach

Kalatzis,2009

47

System

descriptionof

apoint-of-careportable

medicaldevice

that

integrates

clinicaldata

with

genetic

data

obtained

from

aminiature

diagnostic

system

toproduceadiagnosisforrheumatoid

arthritisandmultiplesclerosis

Clinicians

inEurope

Genotype

No

Diagnostic

assistance

forarthritisandmultiple

sclerosis

System

description

Acombinationof

artificial

neuralnetworks,decision

trees,andsupportvector

machineswas

foundto

have

thebest

performance

Continued

Review


increasing shift in focus from FHx CDS to genotype-drivenCDS, and from stand-alone CDS to integrated CDS. There havebeen nine RCTs of CDS interventions for GPM, but most CDSinterventions for GPM have not yet been rigorously assessed fortheir clinical impact.

Strengths and limitationsAs one important strength of this study, as far as we are aware,this work represents the first systematic review on CDS forGPM. As such, it contributes an important perspective ona topic that has the potential to have significant impacts in bothclinical medicine and biomedical informatics. As a secondstrength, this systematic review was based on search strategiesrefined through previous systematic reviews on relatedtopics.14e16 Third, we searched Embase in addition toMEDLINE, so as to provide greater coverage of the internationalliterature. Finally, in addition to providing a summary of rele-vant manuscripts, this review provides insights and trend anal-yses that show how this scientific field has developed over timeand where the field appears to be headed moving forward.In terms of limitations, this study does not provide a quanti-

tative meta-analysis of the impact of CDS interventions forGPM. However, such a meta-analysis was not possible due tothe limited number of outcome studies in this field and theheterogeneous nature of the various interventions and clinicaldomains. Second, we only included manuscripts written inEnglish, which may have led to some relevant manuscripts beingexcluded that were written in a different language. Third, somerelevant 2011 articles may not have been indexed by the time ofour literature search and therefore erroneously excluded.However, a literature search update in June 2012 added less than1% to the number of articles we had previously retrievedthrough March 2012, which suggests that this risk is low.Finally, there is a potential for publication bias with regard tothe clinical trials included, in which studies with successfuloutcomes were more likely to be published than studies withunsuccessful outcomes. There was a potential indication of sucha bias, in that seven of nine RCTs evaluated (77%) reportedpositive results, whereas the expected rate of positive resultswould more typically be in the range of approximately 60%.56

However, given the limited sample size, the observed discrep-ancy may simply be due to chance. Moreover, as discussed next,the high rate of successful interventions may be partly explainedby the fact that use of many of these systems was required bythe study protocol, which improved the systems' likelihood ofuse and impact.

Consistency of trial findings with expected outcomesIn a previous systematic review of CDS RCTs, we identified theautomatic provision of CDS as a part of routine clinical work-flow to be a critical predictor of the success or failure of CDSinterventions (adjusted OR of 112.1, p<0.00001).14 Whileautomatic provision of CDS was not a guarantee of success inthis systematic review, a lack of this feature was associated withnegative outcomes in all cases, generally due to the lack of use ofthe system.14 Moreover, a later RCT specifically evaluating theimportance of automatic provision of CDS directly confirmedthis finding.57

On initial examination, the results of the present systematicreview seemed to contradict this finding, as we found severalRCTs in which stand-alone CDS interventions for GPM were notprovided automatically as a part of routine clinical workflow butresulted in positive improvements in clinical practice.21 25 28 29 36 39

However, in all but one of these RCTs,21 use of the CDS systemTable6

Continued

Citationandnameof

system

(ifapplicable)

Manuscriptsummaryandtrialdetails

(ifapplicable)

Users

and

studylocation

Genetic

inform

ationused

Integrated

with

primaryclinical

inform

ationsystem

CDSpurposeand

clinical

focus

Manuscripttype

Notable

inform

aticsaspect

Scheuner,2009

53

Asurvey

ofhealth

professionals,geneticsexperts,

andEH

Rdevelopers

regardingtheability

ofEH

Rsystem

sto

document,organize,anduseFH

xand

genetic

inform

ation

Clinicians

inUSA

FHx;

genotype

Yes

Assessm

entof

patient

riskandprovisionof

managem

ent

recommendations

for

genetically-guided

personalized

medicine

Survey

EHRsweregenerally

perceived

aslackingtheability

tosupportgenomicmedicine

Aronson,2011

50;

GeneInsight

System

descriptionof

GeneInsight,aplatform

that

provides

patient-specific

genetic

testingreportsas

wellas

notifications

whenthepresum

edclinical

significanceofgenetic

variantschange

forpatients

who

have

been

previouslytested

Geneticists

andother

clinicians

inUSA

Genotype

No

Provisionof

patient-

specificgenetic

testing

reports;

notificationof

changesin

clinical

significanceof

genetic

variants

System

description

Isregistered

with

theFood

andDrugAdm

inistrationas

aclassIexem

ptmedicaldevice

CDC,Centers

forDisease

Control

andPrevention;

CDS,clinicaldecision

support;EH

R,electronic

health

record;FH

x,family

health

history;

GPM

,geneticallyguided

personalized

medicine;

RCT,

random

ized

controlledtrial.

Review


was mandated by the study protocol, which was an exclusioncriterion in the previous systematic review that identifiedthe critical importance of the automatic provision of CDS.14

Therefore, we believe it is premature to draw the conclusionthat automatic provision is not important when providing CDSfor GPM, as it is possible that the same CDS interventions thatled to positive results in the studies included would not haveled to positive results if use of the system was not mandatedby the study protocol, due to lack of awareness and use of thetool. With regard to other, less critical success factors identifiedin the previous systematic review of CDS interventions,14 wedid not find any trends that contradicted those findings.However, the sample size of available CDS trials was too smallin this study to allow for any meaningful analysis of these otherfactors.

Of note, in the RCT of the GRAIDS system for FHx-basedCDS, the system did have a positive impact, even though its usewas not mandated by the study protocol and the system was

not automatically provided as a part of routine clinical work-flow.14 However, the use and impact of this system may havebeen the result of exceptional circumstances specific to the studycontext and unlikely to be available in a routine clinical practicesetting. In particular, in the RCT of the GRAIDS system,designated clinicians were recruited at each practice, receivedextensive training on GRAIDS, and managed all patients in thepractice expressing concern regarding their breast or colorectalcancer FHx.21 This type of resource-intensive deploymentstrategy may not be feasible outside the context of a researchstudy, as demonstrated in another RCT of a stand-alone breastcancer CDS tool, which had limited impact due largely to thelack of awareness and adoption of the tool by clinicians.24

Therefore, while more evidence is needed before a solid conclu-sion can be drawn, we found no conclusive evidence that CDSfor GPM is unique in terms of the intervention features requiredfor successful outcomes.

Assessment of current research state and required researchIn recent years, CDS has been proposed as a promising approachto realizing the promise of GPM.10e12 58e65 However, we iden-tified only 38 primary research articles published from 1990 to2011 on the design, implementation, use, and evaluation of CDSsystems to support genetically guided patient care, whichamounts to approximately 1.7 articles per year. Even in the yearwith the most publications on this topic (2011), we identifiedonly six primary research articles. In particular, we identifiedonly nine RCTs of the impact of CDS systems for GPM, sevenarticles focused on CDS integrated with primary clinical infor-mation systems, and 16 articles involving the use of genotype todrive CDS. Furthermore, few groups have demonstrated howgenotype-driven CDS can be integrated into clinical settings andclinical information systems in a scalable, standards-based, andeffective manner.40 43 52 53

Given the tremendous volume of research being conducted inthe discovery of novel personalized medicine diagnostics andtherapeutics, we feel that much more research is required onhow CDS can and should be leveraged to take these discoveriesand to implement them in routine clinical practice. For example,even for FHx-driven CDS, which is perhaps the most well-

Figure 3 Publications included per year.

Figure 4 Publications focused on stand-alone versus integrated clinicaldecision support (CDS).

Figure 5 Publications focused on family health history (FHx)-drivenversus genotype-driven clinical decision support.

Review


established area of research with regard to CDS for GPM, therehas been limited research on the optimal use of FHx-driven CDStools beyond hereditary cancer management. Indeed, given thelimited literature available on any one topic, we feel it would bepremature to consider any aspect of CDS for GPM to be fullymature and not in need of any further research.

In looking forward, we believe that the largest loomingresearch challenge in terms of CDS for GPM will be the devel-opment of effective approaches to manage and utilize wholegenome sequence data in the clinical setting. The pursuit of low-cost whole genome sequencing has been a priority research areafor many years, such that sequencing costs may be reduced toa level amenable to routine clinical use in the near future.66

While sequencing technologies continue to advance, the infor-matics capabilities to apply whole genome sequencing data toclinical practice is still in its infancy.67 Indeed, in our systematicreview, we did not find a single primary research articleaddressing this topic. Therefore, we recommend the prioritiza-tion and resourcing of this area of research by the scientificcommunity. In particular, to realize the full clinical potential ofwhole genome sequence data, we believe that approaches willneed to be developed for providing advanced CDS capabilitiesthat are integrated with clinical information systems andprovided automatically as a part of routine clinical workflow.

CONCLUSIONThe promise of GPM is growing with the recent advances anddiscoveries in genomics research. With this growth also comesthe growing need for translating such discoveries into everydayclinical care, so that we are able to realize the promises of GPM.CDS has the potential to bridge this gap between the promiseand realization of GPM. By systematically reviewing the litera-ture in this field and by identifying gaps in required research, wespeculate that this paper will assist with efforts to leverage CDSto enable GPM at scale.

Contributors BMW and KK both contributed to the design and conduct of the study,as well as the preparation of the manuscript.

Funding This study was funded by grant K01HG004645 from the US National HumanGenome Research Institute, the University of Utah Department of BiomedicalInformatics, and the University of Utah Program in Personalized Health Care. Thefunding sources played no role in the study design, in the collection, analysis andinterpretation of data, in the writing of the manuscript, or in the decision to submit themanuscript for publication. The views expressed in this manuscript are those of theauthors alone and do not necessarily reflect the official views of the National HumanGenome Research Institute or of the University of Utah.

Competing interests BMW is the founder and owner of SGgenomics, Inc., whichdeveloped ItRunsInMyFamily.com, a patient-centered FHx tool. KK is serving asa consultant to Inflexxion on a project funded by the National Institute on Drug Abuseto develop CDS capabilities for mental healthcare. KK receives royalties for a DukeUniversity-owned CDS technology for infectious disease management known asCustomID that he helped develop. KK was formerly a consultant for Religent, Inc. anda co-owner and consultant for Clinica Software, Inc., both of which providecommercial CDS services, including through use of a CDS technology known asSEBASTIAN that KK developed. KK no longer has a financial relationship with eitherReligent or Clinica Software.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement Individuals interested in the raw data and analyses maycontact the corresponding author to obtain such data.

REFERENCES1. U.S. Department of Health and Human Services. Glossary Of Terms For Personalized

Health Care Website. http://www.hhs.gov/myhealthcare/glossary/glossary.html(accessed Aug 2012).

2. Abrahams E, Ginsburg GS, Silver M. The personalized medicine coalition: goals andstrategies. Am J Pharmacogenomics 2005;5:345e55.

3. Khoury MJ, McBride CM, Schully SD, et al. The scientific foundation for personalgenomics: recommendations from a National Institutes of HealtheCenters forDisease Control and Prevention multidisciplinary workshop. Genet Med2009;11:559e67.

4. Offit K. Personalized medicine: new genomics, old lessons. Hum Genet2011;130:3e14.

5. Fernald GH, Capriotti E, Daneshjou R, et al. Bioinformatics challenges forpersonalized medicine. Bioinformatics 2011;27:1741e8.

6. Balas EA, Boren SA. Managing clinical knowledge for health care improvement.Yearbook of Medical Informatics 2000: Patient-Centered System. Stuttgart, Germany:Schattauer, 2000:65e70.

7. Rosas-Blum E, Shirsat P, Leiner M. Communicating genetic information: a difficultchallenge for future pediatricians. BMC Med Educ 2007;7:17.

8. American Board of Genetic Counseling, Inc. About ABGC.http://www.abgc.net/About_ABGC/GeneticCounselors.asp (accessed Aug 2012).

9. GeneTests: Growth of Laboratory Directory. Seattle: University of Washington. http://www.ncbi.nlm.nih.gov/projects/GeneTests/static/whatsnew/labdirgrowth.shtml(accessed Aug 2012).

10. Glaser J, Henley DE, Downing G, et al. Personalized Health Care Workgroup of theAmerican Health Information Community. Advancing personalized health care throughhealth information technology: an update from the American Health InformationCommunity’s Personalized Health Care Workgroup. J Am Med Inform Assoc2008;15:391e6. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC130085/

11. Kawamoto K, Lobach DF, Willard HF, et al. A national clinical decision supportinfrastructure to enable the widespread and consistent practice of genomic andpersonalized medicine. BMC Med Inf Decis Mak 2009;9:17.

12. Downing GJ, Boyle SN, Brinner KM, et al. Information management to enablepersonalized medicine: stakeholder roles in building clinical decision support. BMCMed Inf Decis Mak 2009;9:44.

13. Osheroff JA, Teich JM, Middleton B, et al. A roadmap for national action on clinicaldecision support. J Am Med Inform Assoc 2007;14:141e5.

14. Kawamoto K, Houlihan CA, Balas EA, et al. Improving clinical practice using clinicaldecision support systems: a systematic review of trials to identify features critical tosuccess. BMJ 2005;330:765.

15. Scheuner MT, Sieverding P, Shekelle PG. Delivery of genomic medicine for commonchronic adult diseases: a systematic review. JAMA 2008;299:1320e34.

16. Wilson BJ, Qureshi N, Santaguida P, et al. Systematic review: family history in riskassessment for common diseases. Ann Intern Med 2009;151:878e85.

17. Coulson AS, Glasspool DW, Fox J, et al. RAGs: a novel approach to computerizedgenetic risk assessment and decision support from pedigrees. Methods Inf Med2001;40:315e22.

18. Emery J, Walton R, Coulson A, et al. Computer support for recording andinterpreting family histories of breast and ovarian cancer in primary care (RAGs):qualitative evaluation with simulated patients. BMJ 1999;319:32e6.

19. Emery J, Walton R, Murphy M, et al. Computer support for interpreting familyhistories of breast and ovarian cancer in primary care: comparative study withsimulated cases. BMJ 2000;321:28e32.

20. Emery J. The GRAIDS Trial: the development and evaluation of computer decisionsupport for cancer genetic risk assessment in primary care. Ann Hum Biol2005;32:218e27.

21. Emery J, Morris H, Goodchild R, et al. The GRAIDS Trial: a cluster randomisedcontrolled trial of computer decision support for the management of familial cancerrisk in primary care. Br J Cancer 2007;97:486e93.

22. Tsouskas LI, Liaros A, Tzitzikas J, et al. The use of an expert system ofcomposite risk factors in breast cancer screening. Stud Health Technol Inform1997;43:859e63.

23. Berry DA, Iversen ES Jr, Gudbjartsson DF, et al. BRCAPRO validation, sensitivity ofgenetic testing of BRCA1/BRCA2, and prevalence of other breast cancersusceptibility genes. J Clin Oncol 2002;20:2701e12.

24. Wilson BJ, Torrance N, Mollison J, et al. Cluster randomized trial of a multifacetedprimary care decision-support intervention for inherited breast cancer risk. Fam Pract2006;23:537e44.

25. Matloff ET, Moyer A, Shannon KM, et al. Healthy women with a family history ofbreast cancer: impact of a tailored genetic counseling intervention on risk perception,knowledge, and menopausal therapy decision making. J Womens Health2006;15:843e56.

26. Matloff ET, Shannon KM, Moyer A, et al. Should menopausal women at increasedrisk for breast cancer use tamoxifen, raloxifene, or hormone therapy? A frameworkfor personalized risk assessment and counseling. J Cancer Educ 2007;22:10e14.

27. Ozanne EM, Loberg A, Hughes S, et al. Identification and management of women athigh risk for hereditary breast/ovarian cancer syndrome. Breast J 2009;15:155e62.

28. Schwartz MD, Valdimarsdottir HB, DeMarco TA, et al. Randomized trial ofa decision aid for BRCA1/BRCA2 mutation carriers: impact on measures of decisionmaking and satisfaction. Health Psychol 2009;28:11e19.

29. Hooker GW, Leventhal KG, DeMarco T, et al. Longitudinal changes in patientdistress following interactive decision aid use among BRCA1/2 carriers: a randomizedtrial. Med Decis Making 2011;31:412e21.

30. Glasspool DW, Oettinger A, Smith-Spark JH, et al. Supporting medical planning bymitigating cognitive load. Methods Inf Med 2007;46:636e40.

31. Glasspool DW, Oettinger A, Braithwaite D, et al. Interactive decision support for riskmanagement: a qualitative evaluation in cancer genetic counselling sessions.J Cancer Educ 2010;25:312e16.

Review


32. Evans S, Lynch HT, Fusaro RM. Clinical results using informatics toevaluate hereditary cancer risk. Proc Annu Symp Comput Appl Med Care1995:834e8.

33. Bianchi F, Galizia E, Bracci R, et al. Effectiveness of the CRCAPRO program inidentifying patients suspected for HNPCC. Clin Genet 2007;71:158e64.

34. Overbeek LI, Hermens RP, van Krieken JH, et al. Electronic reminders forpathologists promote recognition of patients at risk for Lynch syndrome: cluster-randomised controlled trial. Virchows Arch 2010;456:653e9.

35. Picone M, Steger S, Exarchos K, et al. Enabling heterogeneous data integration andbiomedical event prediction through ICT: the test case of cancer reoccurrence. AdvExp Med Biol 2011;696:367e75.

36. Hendershot CS, Otto JM, Collins SE, et al. Evaluation of a brief web-based geneticfeedback intervention for reducing alcohol-related health risks associated withALDH2. Ann Behav Med 2010;40:77e88.

37. Wakefield CE, Watts KJ, Meiser B, et al. Development and pilot testing of an onlinescreening decision aid for men with a family history of prostate cancer. Patient EducCouns 2011;83:64e72.

38. Pazzani MJ, See D, Schroeder E, et al. Application of an expert system in themanagement of HIV-infected patients. J Acquir Immune Defic Syndr Hum Retrovirol1997;15:356e62.

39. Tural C, Ruiz L, Holtzer C, et al. Clinical utility of HIV-1 genotyping and expert advice:the Havana trial. AIDS 2002;16:209e18.

40. Swen JJ, Wilting I, de Goede AL, et al. Pharmacogenetics: from bench to byte. ClinPharmacol Ther 2008;83:781e7.

41. Bon Homme M, Reynolds KK, Valdes R Jr, et al. Dynamic pharmacogenetic modelsin anticoagulation therapy. Clin Lab Med 2008;28:539e52.

42. Deshmukh VG, Hoffman MA, Arnoldi C, et al. Efficiency of CYP2C9 genetic testrepresentation for automated pharmacogenetic decision support. Methods Inf Med2009;48:282e90.

43. Overby CL, Tarczy-Hornoch P, Hoath JI, et al. Feasibility of incorporating genomicknowledge into electronic medical records for pharmacogenomic clinical decisionsupport. BMC Bioinformatics. 2010;11(Suppl. 9):S10.

44. Harris NL. Probabilistic belief networks for genetic counseling. Comput MethodsPrograms Biomed 1990;32:37e44.

45. Kobrinskii BA, Tester IB, Demikova NS, et al. A concept of the federal computersystem for families with genetic diseases and its practical implementation. BiomedEng 1997;31:172e4.

46. Kobrinsky B, Tester I, Demikova N, et al. A multifunctional system of the nationalgenetic register. Stud Health Technol Inform 1998;52:121e5.

47. Kalatzis FG, Giannakeas N, Exarchos TP, et al. Developing a genomic-based point-of-care diagnostic system for rheumatoid arthritis and multiple sclerosis. Conf ProcAnnu Int Conf IEEE Eng Med Biol Soc; 2e6 September 2009, Minneapolis,Minnesota, 2009:827e30.

48. Orlando LA, Hauser ER, Christianson C, et al. Protocol for implementation of familyhealth history collection and decision support into primary care using a computerizedfamily health history system. BMC Health Serv Res 2011;11:264.

49. Rubinstein WS, Acheson LS, O’Neill SM, et al. Clinical utility of family history forcancer screening and referral in primary care: a report from the Family HealthwareImpact Trial. Genet Med 2011;13:956e65.

50. Aronson SJ, Clark EH, Babb LJ, et al. The GeneInsight Suite: a platform to supportlaboratory and provider use of DNA-based genetic testing. Hum Mutat2011;32:532e6.

51. Wells S, Kerr A, Broad J, et al. The impact of New Zealand CVD risk chartadjustments for family history and ethnicity on eligibility for treatment (PREDICT CVD-5). NZ Med J 2007;120:U2712.

52. Iavindrasana J, Lo Iacono L, Muller H, et al. The @neurIST project. Stud HealthTechnol Inform 2008;138:161e4.

53. Scheuner MT, De Vries H, Kim B, et al. Are electronic health records ready forgenomic medicine? Genet Med 2009;11:510e17.

54. Glasspool DW, Fox J, Coulson AS, et al. Risk assessment in genetics: a semi-quantitative approach. Stud Health Technol Inform 2001;84:459e63.

55. Emery J. Computer support for genetic advice in primary care. Br J Gen Pract1999;49:572e5.

56. Jaspers MW, Smeulers M, Vermeulen H, et al. Effects of clinical decision-supportsystems on practitioner performance and patient outcomes: a synthesis ofhigh-quality systematic review findings. J Am Med Inform Assoc 2011;18:327e34.Epub 2011 Mar 21.

57. van Wyk JT, van Wijk MA, Sturkenboom MC, et al. Electronic alerts versus on-demand decision support to improve dyslipidemia treatment: a cluster randomizedcontrolled trial. Circulation 2008;117:371e8.

58. Ullman-Cullere MH, Mathew JP. Emerging landscape of genomics in theelectronic health record for personalized medicine. Hum Mutat 2011;32:512e16.

59. Snyderman R, Yoediono Z. Prospective care: a personalized, preventative approachto medicine. Pharmacogenomics 2006;7:5e9.

60. Samwald M, Stenzhorn H, Dumontier M, et al. Towards an interoperableinformation infrastructure providing decision support for genomic medicine. StudHealth Technol Inform 2011;169:165e9.

61. Hoffman MA. The genome-enabled electronic medical record. J Biomed Inform2007;40:44e6.

62. Hoffman MA, Williams MS. Electronic medical records and personalized medicine.Hum Genet 2011;130:33e9.

63. Ginsburg GS, Willard HF. Genomic and personalized medicine: foundations andapplications. Transl Res 2009;154:277e87.

64. Downing GJ. Policy perspectives on the emerging pathways of personalizedmedicine. Dialogues Clin Neurosci 2009;11:377e87.

65. Chan IS, Ginsburg GS. Personalized medicine: progress and promise. Annu RevGenomics Hum Genet 2011;12:217e44.

66. Cordero P, Ashley EA. Whole-genome sequencing in personalized therapeutics. ClinPharmacol Ther 2012;2:51.

67. Ali-Khan SE, Daar AS, Shuman C, et al. Whole genome scanning: resolvingclinical diagnosis and management amidst complex data. Pediatr Res2009;66:357e63.

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Clinical decision support for genetically guided personalized medicine: a systematic review

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