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PRISM Forum SIG: Clinical Informatics - mining patient-centric data


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Title: PRISM Forum SIG: Clinical Informatics - mining patient-centric data

PRISM Forum SIG Clinical Informatics - mining
patient-centric data
  • 22-Oct-2010

Overview 1
  • Overarching Themes
  • Patient data accumulated by payors and health
    care providers as a part of their routine
    activities will provide a fount of information
    and insight about the activity of pharmaceuticals
    in real-world use
  • Randomized clinical trial including Phase IV
    trials - although still the gold standard for
    ensuring efficacy and safety, is perceived to be
    insufficient to provide an understanding of the
    full spectrum of use and response to
    pharmaceuticals in the marketplace.

Overview 2
  • More Overarching Themes
  • Health care providers and life-science IT
    organizations, are moving aggressively to
    aggregate data they have under their control or
    get access to that data.
  • Payors, both commercial and governmental, and
    regulatory agencies are beginning to mine
    available data to establish, or refute,
    effectiveness and or safety claims.
  • Privacy advocates, and some local and national
    legislators are attempting to make it harder to
    gain access to this data
  • Existing initiatives (HIGHTECH, ARRA) are driving
    the adoption of technology to capture even more
    patient data

Overview 3
  • More Overarching Themes
  • Organizations which develop the capability to use
    this fragmented, heterogeneous data will have a
    huge advantage over competitors, vendors and
    others with whom they do business.
  • This requires
  • The physical infrastructure to support the
  • Access to people with appropriate domain
    knowledge and analytical capabilities
  • Analytical tools

Overview 4
  • More Overarching Themes
  • Without ability to access and utilize this data,
    Pharma companies will be at a distinct
    disadvantage unable to engage effectively in
  • regarding safety or comparative effectiveness
  • With payors to establish support for future
  • Pharma will also lack a potentially critical
    source of data for translational medicine
  • However use of the available data with
    appropriate tools can yield surprising and
    valuable results for all phases of the
    development process.

Steven Labkoff, DeloitteThe Data Gold Rush
Opportunities for the Pharmaceutical Industry
from Healthcare Data
  • There is growing recognition of the potential
    value of patient care data
  • The vast bulk of patient data (prescribing
    information, physicians notes, hospital records,
    lab results, etc) are outside the control of the
    pharma industry
  • Regulators, payors and providers will make
    assessments of safety, formulary placement and
    pricing based on this data
  • The data is
  • Dispersed
  • Heterogeneous and difficult to aggregate
  • Outside the control of pharma
  • Some efforts, both regional and national, are
    aimed at making it more difficult to gain access
    than it already is

Steven Labkoff, Deloitte (2)
  • Those who currently have the data (providers,
    payors) and come major life-science IT
    organizations are building capabilities to market
    the data and/or analytical services.
  • Hiring Informatics and IT talent to create and
    maintain useable data
  • Having access to the data, and the ability to use
    it effectively, provides a tremendous competitive
    advantage in any discussion of efficacy or safety

Kristen Rosati,Coppersmith Schermer
Brockelman, PLCHIPAA Challenges Ahead in Mining
Patient-Centric Data
  • The High Tech Act (HTA) creates incentives for
  • Physicians to adopt electronic health records
  • Communities to create Health Information
  • HTA also includes
  • Prohibition on the sale of private patient data
  • New privacy rules
  • New rules apply to business associates
  • Mandatory breach reporting
  • Civil and criminal penalties for improper release
    of patient data
  • Series of tiered penalties, up to 1.5M
  • State Attorneys general can bring HIPAA actions
  • Opens door to multiple, conflicting
    interpretations of the regulations

Kristen Rosati (2)
  • HTA also
  • limits the way in which providers can
    communicate with patients if they are being paid
    by third parties
  • Requires authorization from patients before data
    can be sold
  • Requires that patients opt-in to authorize the
    storage of samples for research purposes
  • De-identified data and aggregated data are not
    covered under these laws

Kristen Rosati (3)
  • Final regulations based on the laws have not been
  • HHS is holding public meetings to discuss how to
    write the regulations
  • Privacy advocates are actively engaged in
    influencing the discussion

Ken Park, McKinseyExploring the Clinical
Informatics Landscape in Europe, Asia, and Beyond
  • Providers, payors and regulators increasingly are
    doing their own comparative effectiveness and
    safety research using the real-world patient
  • Outside the US the landscape is fragmented. The
    UK and Germany have examples of organizations
    developing data and analytics
  • GPRD and QResearch in the UK
  • AOK and BIPS in Germany
  • These are a mix of public and private
    organizations, with public and private data

Ken Park, McKinsey (2)
  • Other European countries have some capabilities
  • Their focus is often on
  • The specific needs of their patient population
  • Outcomes research based on their particular
    practices and standards of care

Ken Park, McKinsey (3)
  • Building relationships with the existing local
    medical infrastructure is essential in order to
    gain access to patient data
  • Many organizations hesitant, unwilling or unable
    to work directly with Pharma companies
  • Access may be available via
  • Local commercial data providers
  • Local academic researchers

Ken Park, McKinsey (4)
  • In Asia
  • South Korea, Taiwan and Thailand have some
    efforts underway
  • Potential game-changers
  • China
  • Able to dictate medical practice
  • Able and willing to create needed infrastructure
  • Abu Dhabi
  • Used oil wealth to build medical infrastructure
    (including EHRs) from the ground-up
  • Alberta, Canada
  • Regional health care authority driving adoption
    of EHRs

Bill Marder, Thomson ReutersFive Year Trends in
Spending by Disease Results from the MarketScan
  • Bending the Cost Curve
  • An illustration of the use of EMR data to
    identify changes in spending patterns in a large
    patient population.
  • Able to identify major diseases contributing to
    changes in spending.
  • This type of information is critical for a
    provider when negotiating reimbursement rates
    with a payor
  • EMR data from an employer-based insurance pool
    was processed using proprietary software
  • Very difficult to process textual data
  • Provide-centric EMRs do not provide longitudinal
  • There are no standards Some key data (e.g. blood
    pressure, height, weight) may be missing

John Murphy, QuintilesAdvanced Analytic
Concepts A Gamblers Guide to the Drug
Discovery, Development Commercial Universe
  • Model-Based Drug Discovery (MBDD) should be used
    for all phases of development
  • Disease modeling
  • Dose selection
  • Trial design
  • Financial analysis, etc
  • Quintiles has built a data factory to support
  • Data from gt 10,000 clinical trials
  • Commercial data
  • Links to external data from 1000 databases

John Murphy, Quintiles (2)
  • PACeR Partnership to Advance Clinical
    electronic Research
  • Consortium with New York hospitals and medical
    centers , Pharma companies
  • Collect and federate all patient data
  • Use advanced analytics (including neural
    networks) to drive modeling efforts

John Murphy, Quintiles (3)
  • PACeR is a business
  • Using activity as incubator
  • Partners are VC for spin-offs
  • Maintain vocabularies, ontologies, processes
  • Pharma Purchasers will be customers
  • PACeR Clinical Science will do modeling
  • Trial modeling, patient selection, protocol
    validation, safety
  • Hospitals, etc will provide data to answer
  • Franchise model, HPCs will buy into selling data
    based on common model
  • Adopt processes and standards established by
  • Monetary incentives will bring slow followers
  • Make adoption and compliance based on financial

Paul Bleicher, HumedicaHealthcare
InformaticsCreating Value and Defining
  • Different organizations have different uses for
    de-identified data
  • Health Care Organizations
  • Quality management
  • Patient safety
  • Resource management
  • Government
  • Effectiveness and safety research
  • Establishing reimbursement schedules
  • Public health
  • Pharma
  • Clinical research
  • Pharmacovigilance
  • Market research

Paul Bleicher, Humedica (2)
  • Longitudinal data is the holy grail
  • EHR adoption is improving, and already better
    than assumed
  • The problem is the data is
  • Textual, hard to process
  • Not structured for analysis
  • Generated from a variety of platforms and legacy
  • Analytical tools need to improve
  • Users of the data must become comfortable with
    new visualizations and analytic techniques
  • Concerns about data security need to be addressed

Zhaohui (John) Cai, AZ EMR Data Mining for Drug
Safety Challenges and Opportunities
  • EMR Data Mining for Drug Safety
  • Difference between EMR, HER, PHR
  • Who uses for safety?
  • Not many
  • EHR and EMR are created/used by providers
  • PHR is personal
  • EHR/EMR derived/generated by a variety of sources
  • Current system relies on SRS - spontaneous
    reporting, voluntary submission
  • Limitations in quality and timeliness of data
  • EMR has advantages in timeliness and quality
  • Still have issues, comorbidity, dosing, etc.
  • How would that work?

Zhaohui (John) Cai, AZ (2)
  • EMR vs. claims data
  • Claims data may have real-world authenticity
  • Lacks timeliness
  • Claims data may be more complete
  • EMR may be more complete, but limited to the
    information in the providers network
  • Deparsing an EMR can be difficult
  • Practice management systems may have same
    limitations as claims data
  • Data content
  • Demographics, gender, YOB, etc. Other key data
    may be missing
  • NLP needed to process data even for common data

Zhaohui (John) Cai, AZ (3)
  • Limitations of EHR as source
  • Small sample size
  • Migration of legacy data
  • Data missing
  • NLP required
  • Data
  • Variable quality
  • Lack of standards
  • Different coding standards

Zhaohui (John) Cai, AZ (4)
  • Variety of statistical tests available to
    recognize signals
  • Clinical Study techniques may be limited because
    sample size can be large.
  • Multi-variable regression may be useful
  • Challenges
  • How are baselines established?
  • Only available from longitudinal data, inc
    prescribing history.

Patrick Ryan, OMOPInformatics Opportunities for
Exploring the Real-World Effects of Medical
  • Observational Medical Outcomes Partnership
  • FDAAA establishes national network SENTINAL to
    create surveillance for all regulated medical
  • If the data were available
  • What could be done with it?
  • What hypotheses could be generated?
  • How reliable would the results be?
  • What infrastructure is required?
  • Governance
  • How to satisfy all stakeholders
  • FDA
  • payors
  • Providers
  • patients

Patrick Ryan, OMOP (2)
  • OMOP Public/private partnership
  • Conduct research on methodologies to evaluate
    performance of analytical methods to identify
    drug safety issues
  • Data provided by multiple sources hospitals and
    health care systems
  • ETL required significant effort, no one ran into
    insurmountable barriers to converting data to
    common model

Patrick Ryan, OMOP (3)
  • Created common data model
  • Identified ten mature drug classes with
    well-defined safety profiles
  • Created informatics tools (now in public domain)
  • Created processing tools to allow aggregation on
  • Key to success is the creation of aset of
    informatics tools
  • Much current work is one-off and ad hoc
  • Standards both data and analytical are needed
  • Problem with federating disparate data sources
  • Necessary, arduous to develop tools
  • Think such an approach could be done

Patrick Ryan, OMOP (4)
  • Common vocabsularies, NML, MEDRA
  • Change from standard thinking rather than
    tracking one drug at one time, need to track all
    drugs across all available data sources
  • Asked community which questions they would ask if
    they had access to this data
  • Community invited to implement their query
  • Can test results, compare across db
  • See variability in outcomes from db
  • Also able to test sensitivity of tests to initial
  • Compare outcomes to benchmarks, true negatives
    and positives
  • These tools have broader applicability outside

Marsha Wilcox, JJ Using Publically Available
Data to Redefine the Phenotype for Genetic
  • Public data refine phenotype in genetic studies
  • Let data/genetics define what the phenotype
    should be.
  • Mental illness, the phenotype is a surrogate for
    ogran pathology
  • NIH data from dbGAP, metabolic data from
    Framingham Heart Study
  • Is it possible to identify genetic subtypes of
    patients with the same diagnosis, and correlate
    this to newly available data (e.g. imaging)

Marsha Wilcox, JJ (2)
  • Used unusual statistical methods (machine
    learning) to identify subtypes
  • NIMH and VA data
  • Diagnostic algorithms to identify subtypes based
    on qualitative traits from manuals
  • Mapped pos/neg/disorganized symptoms from
    diagnosis and mapped to chromosome map. Found
    symptoms peak on chromosome 5, - symptoms on
    chromosome 12(?)

Marsha Wilcox, JJ (3)
  • Schizophrenia results in loss of tissue, maybe
    disease or treatment with neuroleptics
  • Did imaging on patients soon after early
  • Brain imaging links brain loss to negative traits
  • Also found relationship of HLA region with
    early-onset RA
  • GWAS differentiated obese populations to identify
    subpops at risk for heart disease and not.

Panel Discussion
  • Panel
  • Diego Miralles
  • Ken Park
  • Bill Mardar
  • Where will ClinInf be in 2020?
  • KP Tend to underestimate what can be
    accomplished in 10 years
  • Pharma wants result in 1 year
  • BM some areas will move forward (oncology).
    Still relying on organizations like Partners and
    Intermountain Health
  • DM Pharma will be much smaller, no new drugs.
    DD too long and expensive. Need to bring cost
    structure down, use technology. How clinical
    trials are conducted.
  • Social networking as mechanism for conducting
  • DM mytrust Things will change, capabilitites.
    Have to be able to take advantage of patients
    capabilities MyMedicalInformation (?). Dont
    underestimate consumer-derived data.
  • BM complex negotiations with regulators,
  • KP These may influence pipelines, but wont
    really affect empty piplines. But innovation is

Panel Discussion (2)
  • Will we use Personally Health Records?
  • DM Everyone has failed miserably with PHRs.
    Compare with SanDisk with encoded information.
    Patients cant provide adequate information.
    Must be provided by HCP.
  • BM must lower concern over privacy. Then HCP
    can enter data.
  • KP If youre healthy PHR is useless, if youre
    sick its too late. Its not a privacy issue,
    its just a hassle. MS Vault facilitates upload
    from some centers, but data entry is still too
  • Paul Parallel to financial data? Anyone
    comfortable with putting all their data in Mint?
  • Alistair What about physician expectation and
  • Paul See PatientsLikeMe questions about
    where data was going resulted in many users
    abandoning the site. They didnt want data sold
    to pharma.
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