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Executive Summary
The Transformation Lab at Intermountain Healthcare in Salt Lake City, Utah, openedin 2013 to create transformative patient care solutions through thoughtful design,development, testing, and validation. Funded by forward-looking strategic collabora-tors like Intel, the Lab is well positioned to solve quality and care challenges byquickly and effectively bringing innovative technologies to the patient’s bedside.
Intel has a vision to accelerate precision medicine, making it available to everyone.As part of this vision, Intel is working closely with industry and governments world-wide to understand the key challenges that must be overcome to accelerate science,translate results, and deliver them today. Intel is working with the TransformationLab to understand these challenges and develop tools that integrate genetic dataand accelerate its use at the point of care.
One challenge to quality patient care is a series of gaps in the collection, use, andstorage of data. Family health histories and other patient-entered data rarely make itinto electronic health records (EHRs). Genetic counselors are not integrated withthese systems. With family health histories and pedigrees that are scanned frompaper forms, complete risk assessments cannot be done. Despite the increased useof clinical genetic testing, most test data is not incorporated into EHRs—at least notin a format usable by computers—and therefore is not available for clinical decisionsupport (CDS). The resulting data silos and incomplete EHRs cause incompleterecords for both patients and providers.
This white paper proposes a new patient data-centric model to build applications that:
• Accessmultiple data sources for CDS
• Are integrated into the clinical workflow
• Deliver meaningful and easy-to-understand information
Patient experiences in the healthcaresystem usually involve multiple visitswith multiple providers at multiple facili-ties. Even in a highly wired system suchas Intermountain Healthcare, gaps existin the collection and storage of dataduring this process. It begins with pa-tient-entered data like intake forms andfamily health histories (data gap 1)
Grant M. WoodSenior IT Strategist
Clinical Genetics InstituteIntermountain Healthcare
Prashant ShahHealthcare Architect
Intel Health and Life SciencesIntel Corporation
Kathleen CroweDirector of Data Science
Big Data SolutionsIntel Corporation
Integrating Genetic Data intoClinical Workflow with ClinicalDecision Support Apps
Current Data Gaps that Prevent Precision Medicine
Precision medicine is the customizationof healthcare that accommodates indi-vidual differences as far as possible atall stages in the process—from preven-tion through diagnosis and treatment topost-treatment follow-up.
White PaperHealthcare
to clinical data collected by one providerbut often not available to another. Sadly,EHRs still have not solved the gap be-tween paper-based and electronic-based practices.
The problem only becomes more appar-ent as we add genetic testing to clinicalcare practice. With the increased use ofgenetic testing, genetic counselors are inhigher demand. Yet they experience atremendous data gap. Integrated patientmanagement tools for genetic coun-selors do not exist (data gap 2). The lim-ited family health histories collected byEHRs do not include a full pedigree and,therefore, do not allow for a completerisk assessment.
Most clinical genetic tests are officesend-outs that are not tracked in theEHR or by the lab system (data gap 3). Ifthey are, the results come back as a PDF,not in the form of discrete data for com-puter processing. When the informationgets back to the attending physician, itmay not be in a form that is understand-able or that has readily apparent, clini-cally actionable treatments. Also, becausegenetic test results are stored separately
from other data, they are not availablefor CDS (data gap 4).
Newly discovered knowledge furthercompounds the situation, making in-significant findings today potentially sig-nificant in the future. Clinicians need tobe aware of the historical knowledgegaps and be alerted when patient fol-low-up is needed.
The resulting data silos and incompleteEHRs are holding back the promise ofclinical genetics to improve diagnosisand treatment and for precision medi-cine to become the standard of care.
User-Experience-Driven Design
Intel and the Transformation Lab areworking together to further understandthese gaps and challenges and to de-velop tools that integrate genetic dataand accelerate its use at the point of care.Figure 1 shows the building blocks of agenomics CDS.
Through a user-experience study (UX),Intel and Intermountain will seek to dis-cover how healthcare professionalsmake clinical decisions about breast
Contents
Current Data Gaps that PreventPrecision Medicine . . . . . . . . . . . . . . . . .1
User-Experience-Driven Design . . . .2
CDS-Driven Clinical Applications . . .3
Individual Care Applications . . . . . .3
Data Integration for Precision Medicine . . . . . . . . . . . . . . . . . . . . . . . .4
Model Components . . . . . . . . . . . . . .4
Population-Based Data as a CDS Source . . . . . . . . . . . . . . . . . . . . . . . . . .4
Conclusion . . . . . . . . . . . . . . . . . . . . . . . .5
Figure 1 Genomics CDS Building Blocks, Component View
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Integrating Genetic Data Into Clinical Workflow with Cliical Decision Support Apps
Figure 2 Clinical Workflow Requiring New Model Components
When genetic test results are available, atreatment recommendation applicationcan provide the best course of treat-ment for the patient, including
• The most effective drugs
• Treatments to avoid due to a lack ofclinical effectiveness based on thepatient’s genetic profile
In the case of a positive breast cancerbiopsy, if the genetic test is positive forthe BRCA gene mutation, the patientmay be at risk for a second breast can-cer. In such a situation, the patient maychoose a mastectomy (removal ofbreast) instead of a lumpectomy (re-moval of a discrete lump of breast tis-sue), thus impacting her breast cancertreatment. The application may alsomake treatment recommendations forother cancer risks such as removal of anovarian tumor. For some conditions, itmay be advisable to avoid radiation ex-posure. Thus, the treatment plan shouldbe personalized to the patient.
cancer, including what information theyrequire and why. The study will also tryto understand how healthcare workersassign trust to and validate information.This approach will involve in-depth in-terviews, shadowing, and think-aloudsessions. An important aspect of thiswork will be to insert the delivery ofmeaningful data at the right juncture ofthe clinical workflow through clinical ap-plications driven by CDS.
CDS-Driven Clinical Applications
Individual-Care Applications
Individual-care applications are embed-ded in a care workflow to assist geneticcounselors and other clinicians in mak-ing decisions. They combine compre-hensive, structured data including:
• Family health histories
• Pedigrees
• Imaging
• Screenings
• Past clinical data
When coupled with customized clinicalguidelines, this information can formthe foundation for a personalized careplan for the patient. It may include useof a risk assessment application to offera risk profile of the patient for one ormore inherited diseases, as well as torecommend genetic testing for the pa-tient or family.
An order entry assistance applicationcan suggest the appropriate tests to de-termine multiple cancer risks or othergenetic diseases. For example, a patientwith a positive breast cancer biopsy andBRCA gene mutation is also at risk forovarian cancer. Knowing whether shecarries the BRCA gene mutation maychange her ovarian-cancer screeningschedule. Such genetic testing cangreatly shorten the diagnostic processand help provide timely treatment.
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Integrating Genetic Data Into Clinical Workflow with Cliical Decision Support Apps
Integrating Genetic Data Into Clinical Workflow with Cliical Decision Support Apps
Data Integration for Precision Medicine
Developing a data architecture beginsby collecting and storing both patientand clinical data in a centralized (orlinked) repository. Next, combining clini-cal, family health history, and genetic/genomic data will lead to the creation ofnew query methodologies, new insightsinto clinical correlations of these datasources, and the generation of new careguidelines and protocols.
Model Components
Figure 2 shows the components of a clini-cal workflow model. Using breast canceras an example, the following componentsneed to be built to integrate genetic datainto the clinical workflow:
1. Notification App (not on diagram).Upon reaching a certain age, apatient is notified by an EHR-gener-ated message that she shouldschedule a mammogram.
2. Patient Data App. Prior to hermammogram appointment, thepatient fills out an electronic intakeform for initial screening.
3. Patient Data App. Prior to hermammogram appointment, thepatient fills out an electronic familyhealth history with a full pedigreefor further screening.
4. Data Store. Data from apps in num-bers 2 and 3 are stored in central-ized risk-screening and familyhealth history databases (data gap1). First data gap is filled, as thisdata will be available to the newCDS application.
5. CDS App. Risk-assessment appschedules high-risk patient visitwith genetic counselor.
6. Clinical App. Patient goes throughfurther screening with a geneticcounselor and, with doctorapproval, genetic testing is ordered.
• Build innovative clinical decisionsupport tools
The model can be built as a template forany clinical care process, disease, ormedical condition. To achieve the goalof interoperability, standards will beused where available. The solutions inthis model should work with any datacollection tool, so they can be imple-mented by other healthcare institutions.
Population-Based Data as a CDS Source
Knowledge can be discovered throughpowerful, population-based applica-tions by combining clinical, familyhealth history, and risk data. Risk algo-rithms and models can be developedbased on historical case data. And tar-geted interventions can be made basedon the risk profile.
Intermountain data scientists will use theIntel® Analytics Toolkit for Apache Hadoop*software to explore new data relationships inthe combined data set to formulate hypothe-sis and develop new risk algorithms of thecombined data sets available at Intermoun-tain. The Intel Analytics Toolkit is a platformthat unifies entity based machine learningwith an end-to-end graph processingpipeline. It includes powerful algorithms foruncovering relationships hidden in big dataand lowers the barrier of entry to at-scalemachine learning over graph data (Figure 3).
Large population databases—such asthe Utah population database that haspedigree, outcomes, and claims data—can be used in conjunction with clinicaldata to assess patients who may be atrisk. While genetic mutations play a partin disease manifestations, it is also clearthat the environment and social factorsalso contribute. Having the right data ina population health database will enablediscovery of these environmental andsocial factors and the importance theyplay. Models can be created based onhistoric treatment and outcomes andthen applied in practice to suggest thebest course of treatment.
Also, optional family testing isdecided (data gap 2). The seconddata gap is filled, as clinicians andgenetic counselors are now inte-grated into the EHR.
7. Clinical App. Genetic/genomic test-ing is ordered with relevant clinicalinformation for the lab.
8. Clinical App. Genetic/genomic test-ing tracked by EHR. [Data Gap 3]Third data gap is filled as this datawill be integrated into the EHR forclinical review by all healthcareproviders.
9. Data Store. Test result narrative andcodified results are stored in central-ized database (data gap 4). Fourthdata gap is filled, as this data will beavailable to new CDS application.
10. CDS App. New CDS applicationpulls data from combined clinical,family health history, andgenetic/genomic repositories, pro-viding the latest knowledge forresults review and recommendedtreatment.
11. Knowledge Collection App andData Store. Current and newly vali-dated risk algorithms, test orderingguidance, and clinical treatmentrules are stored in a knowledge-base. Updates to this knowledgefeed back into the screening, risk-assessment and CDS applications.
12. For the Future. When a whole-genome-sequence data repositorycomes online, #7, #8 and #10 willbecome one application.
This model not only fills the gaps andintegrates genetic counseling, it alsohelps find the combination that providesopportunities to:
• Discover new connections betweenthe data
• Develop and validate new riskalgorithms
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Integrating Genetic Data Into Clinical Workflow with Cliical Decision Support Apps
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Conclusion
Intermountain’s Transformation Lab andIntel have collaborated to overcome thechallenges associated with accessingand utilizing genetic data in healthcaresettings, and to develop tools that accel-erate its use at the point of care.
The result is a new four-part genetic-data solution:
1. A centralized genetic/genomic datarepository that is linked to all clini-cal and patient data
2. Access for all clinicians to EHRs,including genetic counselors
• Build innovative clinical decisionsupport tools
This model will be built as a template forany clinical care process, disease, ormedical condition. To achieve the goal ofinteroperability, standards will be usedwhere available. The model will allow forthe incorporation of additional datatypes relevant to the clinical domains asinputs into clinical decision support ap-plications.
For more information onhealthcare solutions, visitwww.intel.com/healthcare orwww.intermountainhealthcare.org.
3. Genetic test data that is incorpo-rated into EHRs in a computer con-sumable format
4. Novel and interoperable CDS tools
This concept leads to new insights intocorrelations of clinical, family health his-tory, and genetic/genomic data sources.These insights are the generation ofnew care guidelines and protocols thatwill improve clinical outcomes. By fillingthese gaps and better integrating ge-netic counseling, the combination pro-vides opportunities to:
• Discover new connections betweenthe data
• Develop and validate new risk algorithms
Figure 3 Development of new Risk Algorithms using Intel® Analytics Toolkit for Apache Hadoop* Software
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Integrating Genetic Data Into Clinical Workflow with Cliical Decision Support Apps
