powering medical research with data: the research analytics adoption model

Proprietary and Confidential © 2015 Health Catalystwww.healthcatalyst.com

July 22, 2015

Powering Medical Research With Data: The Research Analytics Adoption Model

© 2015 Health Catalystwww.healthcatalyst.comProprietary and Confidential 2

Why are we here?

• Sick and healthy patients alike want better care. Research helps determine what we mean by better care.

• There is incredible waste in research. We can greatly reduce this waste with data and analytics.

• The future: precision medicine aims to deliver the right care to right patient at the right time

© 2015 Health Catalystwww.healthcatalyst.comProprietary and Confidential3

To get to Precision Medicine• Improve research – identify underlying molecular causes of

disease and refine treatments

• Improve care delivery – deliver to care guidelines and adapt

• Create better coordination between care delivery and research

3

Time

Measured in Weeks or Months

Habit of allFront-line Clinicians at

Every FacilityNew Clinical or Operational Best Practice Knowledge Discovered

Measured in Years


Agenda

• Review Research Process

• Review Roadblocks to Efficient Research

• Present Research Analytics Adoption Model

• Conclusion


Agenda



• Present Research Analytics Adoption Model

• Conclusion


Research ProcessHypothesis Generation

• Previous research• Current literature• Exploratory analysis

Cohort Exploration

• How many patients match my criteria?

• Do I have enough patients to do my study?

Grant Application

• Data from cohort exploration should be used

• Historical recruitment data should be used if positive

IRB Application

• Regulatory requirement• Identify population, protocol, and

data needs

Patient Recruitment

• Who are my patients?• Where can I find them?• How will I collect their consent

Data Collection• Prospective data collection

(questionnaire)• Retrospective data collection

(data pull)• Approved by healthcare

organization

Data Analysis• Statistics• Advanced tools for

genomics• Needs to be secure

Publication• Conclusions are compiled• Manuscript submitted• Nobel Prize received

Translate to Clinical Practice

• How can this discovery now be used to treat patients?

• Work closely with care delivery system


Hypothesis Generation

Previous research

Current literature

Exploratory analysis

Cohort Exploration

How many patients match my criteria?

Do I have enough patients to do my study?

Grant Application

Data from cohort exploration should be used

IRB Application

Regulatory requirement

Identify population, planned interventions, and data

Patient Recruitment

Who are my patients?

Where can I find them?

How will I collect their consent

Data CollectionProspective data collection

(questionnaire)

Retrospective data collection (data pull)

Approved by healthcare organization

Data Analysis

Statistics

Advanced tools for genomics

Needs to be secure

Publication

Conclusions are compiled

Manuscript submitted

Nobel Prize received


How can this discovery now be used to treat patients?

Work closely with care delivery system

Research Process – Roadblocks (Waste)

Technical Insufficient Exploratory

Tools

OrganizationalNo process for release of data

Technical Lack of single source for dataInsufficient self-service tools

Insufficient tools to support data release process

OrganizationalInstitutional restrictions

TechnicalInsufficient data and tools to

find patients

OrganizationalSlow IRB

TechnicalInsufficient Exploratory Tools

Insufficient tools to support IRB process

OrganizationalLack of ‘deployment

system’Research not aligned with

care improvement initiatives

Technical Insufficient Exploratory

Tools

TechnicalInsufficient Exploratory

Tools

TechnicalInsufficient analysis

tools/platform

OrganizationalInsufficient skillset

OrganizationalLack of support for

manuscript preparation.


Research Process - Other Considerations

Data Integration

Operational ReportingProspective data collection

(questionnaire)



Technical Too much time spent cobbling operational

reports. Takes away from research

Sharing DataProspective data collection

(questionnaire)



Technical Data coordinating centers often lack infrastructure

for efficient sharing across sites

Data IntegrationProspective data collection

(questionnaire)



Technical Experimental data siloed

and limited ability to combine with clinical


Agenda



• Present Analytics Adoption Model

• Conclusion


Level 0 Manual dataset generation

• Data is delivered through operational analysts

• Research data requests typically prioritized very low

• Often no set research process or infrastructure

• Result: frustrated analysts and frustrated researchers


Subject Area Data Marts

Linking & StandardizationCommon Linkable Identifiers, Patients, Labs, Encounters, Diagnoses, Medications, etc.

ContentPopulation Definitions (800+), Hierarchies, Comorbidities, Risk Stratification, Attribution

Source Marts

EMR

EMR Financial IDEA BioBank Clinical TrialsResearch Registries

Enabler for all Levels > 0: Data Warehouse

Financial IDEA BioBank Clinical TrialsResearch Registries

e.g. Epic, CernerNextGen

e.g. EPSi, Peoplesoft,

Lawson12

OperationsResearchQuality

(Custom data collection tool)


Level 1 De-identified tools and data marts

• De-identified applications minimize data access roadblocks

• Used for preparatory to research activities

• Can provide a starting point for data analysis


Level 2 Delivery of customized data sets


(questionnaire)



Technical Siloed data


(questionnaire)



Technical Insufficient tools to

support data release process


(questionnaire)



Technical Insufficient self-service

tools


(questionnaire)



Organizational No process for release of

data

• Clear guidelines established for data release• Appropriate data stewards involved• Dedicated research analysts who understand

institutional data, regulatory rules, institutional rules

• Datasets delivered in agile, consultative manner• Tools to facilitate data request workflow


Level 3 Study recruitment facilitated by EDW

• Provide tools that allow an investigator to define a population of eligible patients

• Improve death status using external data, if necessary• Provide up-to-date scheduling information to recruiters

via mobile device• Provide option for electronic consent• Capture ‘do not contact’ requests• Coordinate patient approaches to prevent ‘study fatigue’

Patient RecruitmentProspective data collection

(questionnaire)



Technical Insufficient data and tools

to find patients

Patient RecruitmentProspective data collection

(questionnaire)



Organizational Institutional restrictions


Level 4 Research-specific data collection is centralized

• Move away from Excel and Access as data collection tools

• Provide infrastructure to support self-service data collection form creation/administration

• Data collection tool should pull from and push to EDW• Pull master data• Push collected data into EDW

• Apply appropriate security to the tool as well as EDW extract


(questionnaire)



Data Analysis

Statistics

Advanced tools for genomics

Needs to be secure

TechnicalSiloed data

Technical Insufficient analysis

tools/platform


Level 5 Automated reporting of research operations

• Pull key research systems into EDW• Clinical trials management system• Patient recruiting system• Electronic IRB system

• Provide key metrics• How many active studies do we have?• How many of our patients are enrolled in trials?• How many of our patients do not want to be involved in

research?

Operational ReportingProspective data collection

(questionnaire)



Technical Too much time spent cobbling operational

reports. Takes away from research


Level 6 Biobank/Genomic data integration

• Leverage incredible richness of clinical data set with biological inventories and data• Easily answer question: How many bio samples do I have for

female COPD patients over the age of 65 across all repositories?

• Provide a platform for genomic discovery• Easily answer question: What populations are enriched for a

given set of gene variants?• Easily answer question: What gene variants are enriched for a

given population?• Be aware of regulations on genomic data

Data IntegrationProspective data collection

(questionnaire)



Technical Experimental data siloed

and limited ability to combine with clinical


Level 7 Multi-site data sharing

• Provide as much automated extracts to national registries as possible

• Coordinating centers leverage data warehousing techniques for data collection

• Coordinating centers have automated intake or federation of data from participating sites

• Coordinating centers provide exploratory tools to analyze combined data set

Sharing DataProspective data collection

(questionnaire)



Technical Data coordinating centers often lack infrastructure

for efficient sharing across sites


Level 8 Translational Analytics




OrganizationalLack of ‘deployment

system’




OrganizationalResearch not aligned with

care improvement initiatives

• Care delivery using analytic tools for population health/care management

• Research aligned with care delivery priorities• Research leverages cohorts, outcome measures from

care improvement while adding in new data• Discoveries from research quickly turned back to care

improvement analytics for deployment across the system


Translational Research Registries: Discovery

Heart Failure Data Mart

Heart Failure SAM(De-Identified)

GenomicsData Mart

De-identificationToolkit

• Cohort• Outcomes• Process Metrics• Focus: Care Improvement

• Integrated Genomics Data

• Cohort• Outcomes• Process Metrics• Experimental Data• Focus: Discovery


Translational Research Registries


Heart Failure Study(Identified)

RecruitmentToolkit


• Cohort• Outcomes• Experimental data• Includes genomic data prospectively collected for study• Prospectively collected questionnaire data• Focus: Translational

with IRB

approval

Questionnaire Genomics


Translational Research Registries: Back to Clinical Practice


Heart Failure Study(Identified)

RecruitmentToolkit


• Cohort• Outcomes• Experimental data• Includes genomic data prospectively collected for study• Prospectively collected questionnaire data• Focus: Translational

with IRB

approval

Questionnaire Genomics

Include new evidence into Care Improvement focused data mart


Research Analytics Adoption ModelLevel 8 Translational Analytics

Learning health system routinely transitions research data into care delivery guidelines. Variation in patient care is measured, but deliberate and personalized.

Level 7 Multi-site data sharingInfrastructure to participate in multi-site studies and research registries. Coordinating centers use automated tools to collect and combine data from different sites.

Level 6 Biobank/Genomic data integrationBiobank and experimental genomic data are integrated with the EDW. These repositories are no longer limited by data collected at time of sample acquisition.

Level 5 Automated reporting of research operationsSystem-wide research metrics regarding finances and accrual are easily generated and used by researchers. Analytics supporting research billing compliance are provided to the health system.

Level 4 Research-specific data collection is centralized

Data collection tools designed for research can feed directly into the data warehouse. Tools can be pre-populated with approved data from the data warehouse.

Level 3 Study recruitment facilitated by EDWCohort criteria, scheduling data, treating physician data are combined to facilitate study recruitment. Recruitment contact lists are centrally managed to prevent study fatigue.

Level 2 Delivery of customized data sets including clinical notes

Dedicated research analysts deliver custom data sets to researcher. NLP analysis of notes. IRB is engaged in designing data access workflow. IRB templates are designed specifically for data set definition. Workflow tools support approval process.

Level 1 De-identified tools and data martsDe-identified tools allow cohort counting/exploration through user interfaces. De-identified views into the data warehouse provide platform for deeper discovery and hypothesis generation

Level 0 Manual dataset generation Operational analysts carve out small fraction of time for research dataset generation. Still a lot of spreadsheet-based manual chart abstraction.


Research Analytics Strategy(a starting point)

Time


Data Warehouse Considerations for Research

• Critical to provide both de-identified and identified access paths

• Warehouse must provide security model that allow this kind of access

• Organization needs to devise policies that enable access for appropriate users

• Ability to ingest wide variety of research data must be quick and easy


Conclusion

• Research-Clinical alignment is a strategic initiative, requires executive engagement and support

• Use the model to evaluate current capabilities

• Turn the model on its side for a starter strategy

• Please email me with feedback! [email protected]

• I mean it!

• THANK YOU for your time today!

mailto:[email protected]

Healthcare Analytics Summit 15

Here’s a sneak preview …Industry-leading Speakers

Jim CollinsBest-selling author of Good to Great, Great by Choice, Built to Last, and How the Mighty Fall

Ed CatmullCo-founder of PixarPresident of Pixar and Walt Disney Animation Studios

Daryl MoreyHouston RocketsGeneral Manager and Managing Director of Basketball Operations

Amir RubinStanford Health CarePresident and CEO

Timothy G. Ferris, MD, MPHPartners HealthCareSenior Vice President ofPopulation Health Management

Timothy Sielaff, MD, PhD, FACSAllina HealthChief Medical Officer

Summit highlights3-day AgendaWe’ve increased the time of this year’s summit to allow for more sessions, topics, and networking.

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powering medical research with data: the research analytics adoption model

Healthcare

data patient recruitment

grant application data

historical recruitment

health catalyst

data needs patient recruitment

powering medical research

right care

better care