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

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PRISM Forum SIG Clinical Informatics - mining
patient-centric data

• 22-Oct-2010

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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.

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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

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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
  activities
• Access to people with appropriate domain
  knowledge and analytical capabilities
• Analytical tools

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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
  discussions
• regarding safety or comparative effectiveness
• With payors to establish support for future
  development
• Pharma will also lack a potentially critical
  source of data for translational medicine
  efforts.
• However use of the available data with
  appropriate tools can yield surprising and
  valuable results for all phases of the
  development process.

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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

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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

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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
  Exchanges
• 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
  peryearpertype
• State Attorneys general can bring HIPAA actions
• Opens door to multiple, conflicting
  interpretations of the regulations

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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

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Kristen Rosati (3)

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

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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
  data.
• 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
  sources

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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

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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

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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

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Bill Marder, Thomson ReutersFive Year Trends in
Spending by Disease Results from the MarketScan
Database

• 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
  data
• There are no standards Some key data (e.g. blood
  pressure, height, weight) may be missing

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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
  MBDD
• Data from gt 10,000 clinical trials
• Commercial data
• Links to external data from 1000 databases

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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

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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
  questions
• Franchise model, HPCs will buy into selling data
  based on common model
• Adopt processes and standards established by
  PACeR
• Monetary incentives will bring slow followers
  along
• Make adoption and compliance based on financial
  benefit

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Paul Bleicher, HumedicaHealthcare
InformaticsCreating Value and Defining
Challenges

• 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

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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
  systems
• 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

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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
  provided
• EMR has advantages in timeliness and quality
• Still have issues, comorbidity, dosing, etc.
• How would that work?

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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
  types

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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

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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.

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Patrick Ryan, OMOPInformatics Opportunities for
Exploring the Real-World Effects of Medical
Products

• Observational Medical Outcomes Partnership
• FDAAA establishes national network SENTINAL to
  create surveillance for all regulated medical
  products
• 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

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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

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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
  population/subpopulation
• 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

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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
  parameters
• Compare outcomes to benchmarks, true negatives
  and positives
• These tools have broader applicability outside
  safety.

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Marsha Wilcox, JJ Using Publically Available
Data to Redefine the Phenotype for Genetic
Studies

• 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)

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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(?)

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Marsha Wilcox, JJ (3)

• Schizophrenia results in loss of tissue, maybe
  disease or treatment with neuroleptics
• Did imaging on patients soon after early
  diagnosis
• 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.

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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
  trials
• 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,
  reducing
• KP These may influence pipelines, but wont
  really affect empty piplines. But innovation is
  unpredictable.

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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
  hard.
• Paul Parallel to financial data? Anyone
  comfortable with putting all their data in Mint?
• Alistair What about physician expectation and
  behavior?
• Paul See PatientsLikeMe questions about
  where data was going resulted in many users
  abandoning the site. They didnt want data sold
  to pharma.