HIMSS Patient Matching
Testing Event
Synthetic Patients and Their Usage at
MiHIN
Jeff Eastman, Ph.D.
Michigan Health Information Networks Shared Services
Where Do We Use Real Health Information?
• Clinicians who are treating their patients need real information
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Every patient’s health information should be quickly retrievable by
anyone authorized to be involved in that patient’s care (especially if
the patient is unconscious or unable to do so themselves)
• Researchers who are attempting to “discover” something new
need real information but they keep it “behind closed doors” to
prevent unauthorized disclosure
• Systems that integrate electronic medical record systems so
that health information can be quickly shared through
interoperable statewide and national networks need real
information
• But not the people who develop them or operate them
Copyright 2014,2015 MiHIN Shared Services. MiHIN
Why Use Synthetic Health Information?
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Some types of data that we share statewide involve millions of “messages”
per week just in Michigan
• Significant risk of unintentional disclosure of real health information
Testing system interoperability with real health data is high risk due to
possible disclosure of information protected by federal laws on privacy
• Especially for information about behavioral health, certain diseases, or
substance use
• Real health data cannot easily be fully “de-identified”
• Good, realistic test data is practically never available in healthcare today
Major risk is wrong people seeing someone’s protected health information
• Software developers, systems integrators & testers need to view test
data to do their jobs
• Test data could be sent to the wrong recipient(s) in high volumes
Risks are much higher during development and testing than any other time
• Dozens of new use cases are waiting to be developed and tested
Copyright 2014,2015 MiHIN Shared Services. MiHIN
How Shared Services support
Statewide Transitions of Care
Alerts &
Notification
Data Sharing
Organization
Data Sharing
Organization
Delivery
Preference
Lookup
Specialist
Primary Care
Patient to Provider
Attribution
Patient
Care
Coordinator
1) Patient goes to hospital, hospital sends message to DSO / MiHIN
2) MiHIN checks patient attribution lists and identifies three providers
3) MiHIN retrieves contact and delivery preference for each provider
4) Notifications are routed to providers based on contact info and preferences
Copyright 2015 Michigan Health Information Network Shared Services
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Empowers Clinical Alerts:
Medication Reconciliation
Animation
MR
Data Sharing
Organization
(DSO)
Data Sharing
Organization
(DSO)
Health
Provider
Directory
Care
Coordinator
Primary Care
Patient to Provider
Attribution
Specialist
1) Patient discharged, hospital sends message to DSO / MiHIN
2) MiHIN checks patient-provider attribution and identifies providers
3) MiHIN retrieves contact and delivery preference for each provider from HPD
4) Medication reconciliation routed to providers based on contact info, preferences
Copyright 2015 Michigan Health Information Network Shared Services
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MiHIN Transitions of Care Service (TOC)
• MiHIN TOC service has been in production since Nov 2013
• 59 Physician Organizations in production
• 2563 hospital & practice organizations in production
• 7925 providers affiliations receiving real-time TOC notifications
• Over 4 million TOC notifications transmitted per week
• 85% of Michigan statewide admissions are shared currently
• 90% of Michigan statewide admissions expected to be shared
by the end of 2015
• Onboarding more hospitals and practices weekly
• Excellent source of provider, organization and affiliation data
• Processing monthly updates to ACRS data sets in production
• Working towards transactional updates
Copyright 2015 Michigan Health Information Network Shared Services
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How is this accomplished today?
National
Plan & Provider
Enumeration
Service
Providers,
Hospitals,
Data Sharing
Organizations
Health Provider
Search Service
Statewide Provider
Directory
Individual NPIs
Organizational NPIs
Multiple Affiliations
Specialties
Provider Index
MCIR
Immunizations
LARA
Licensing
21st century contact info:
Direct addresses
HIE routing & delivery
preferences
State of
Michigan
20th century contact info:
Address, phone, fax
MDCH Data
Hub
HSTR
Meaningful Use
Other
MiHIN
Services
Active Care
Relationship Service
CHAMPS/MMIS
Medicaid
(Patient-Provider
Attributions)
Other
Repositories
Copyright 2015 Michigan Health Information Network Shared Services
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What is coming next?
National
Plan & Provider
Enumeration
Service
Providers,
Hospitals,
Data Sharing
Organizations
Provider Index
Statewide
Consumer
Directory
MCIR
Immunizations
LARA
Licensing
Active Care
Relationship
Service
State of
Michigan
MDCH Data
Hub
Health Provider
Search Service
HSTR
Meaningful Use
Other
MiHIN
Services
Statewide Provider
Directory
CHAMPS/MMIS
Medicaid
Medicaid
Member Portal
Copyright 2015 Michigan Health Information Network Shared Services
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PatientGen – The Goal…
• Advance ability to automatically create large quantities of
realistic health data that is not protected or private
• Accelerate efforts to deploy interoperable healthcare systems
using realistic data for development, testing, and successful
deployment of data sharing “use cases”
• Provide general purpose ability to create a wide variety of
“safe” test data for use cases ranging from:
• Public health reporting (e.g. immunizations, syndromics,
reportable labs, cancer/birth defect/death notifications)
• Transitions of care (admission-discharge-transfers,
medication reconciliations)
• Clinical quality measures (CQMs)
• Accelerate the transformation from volume-based to qualitybased healthcare delivery and payment.
Copyright 2014,2015 MiHIN Shared Services. MiHIN
MiHIN Patient Generator:
Works Kind of Like a Music Synthesizer
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Ability to adjust settings to vary patient populations and outcomes:
• Population demographics (age, gender, race, religion)
• Population names, addresses & contact information
• Synthetic medical systems (providers, practices, hospitals, specialty
organizations)
• Population risk factors (smoking, alcohol, diet, exercise, …)
• Population body signs (BP, Lipids , BMI, Pregnancy, …)
• Morbidity models (diabetes, heart disease, pregnancy, STDs, …)
Can save/share/adjust reusable “patient population” decks
• Urban low income (high childhood obesity), rural, tribal nation,
retired/geriatric
Simulation generates many useful kinds of healthcare data
All healthcare data is synthesized, so no PHI
Copyright 2014 Michigan Health Information Network
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Real Patients Have Body Systems
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Systems & Organs get sick
• Sometimes they get well on their own
• Sometimes they don’t, that’s why we
have doctors
Doctors evaluate health, treat sickness
• History and exam (symptoms, signs)
• Testing
• Diagnosis
• Prevention, treatment
• lifestyle
• Rx
• procedures
•
Thousands of real diagnoses
• Complicated dependencies
• Incomplete understanding
• Way too much to simulate in detail
Copyright 2014,2015 MiHIN Shared Services. MiHIN
SimPatients Have Simulated Body Systems
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Cardiovascular
Digestive
Endocrine
Genitourinary
Immune
Integumentary
Lymphatic
Mental
Muscular
Nervous
Reproductive
Respiratory
Skeletal
Copyright 2014,2015 MiHIN Shared Services. MiHIN
Body Systems Model Real Health States
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Behavior
• ADHD
• Autism
Nervous
• Hemorrhagic Stroke
• Ischemic Stroke
• Diabetic Retinopathy
• Macular Edema
• Proliferative Retinopathy
• Peripheral Neuropathy
• Blindness
Genitourinary
• STDs
• Microalbuminuria
• Gross Proteinuria
• End Stage Renal Disease
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Skeletal
• Lower Extremity
Amputation
Cardiovascular
• Venous Thromboembolism
• Coronary Heart Disease
• Murmur
• Myocardial Infarction
• Atrial Fibrillation
• Lateral Ventricular
Hypertrophy
Reproductive
• Eclampsia
• Abruptio Placentae
• Spontaneous Abortion
• Gestational Diabetes
• Puerperium Complications
And any eCQM Diagnosis
Copyright 2014,2015 MiHIN Shared Services. MiHIN
Qualitative Health States Help Normalize Results
Well
Ill
Dead
As With Likert Scales
Intensive
Sick
Critical
Copyright 2014 MiHIN Shared Services. MiHIN
PatientGen Creates Thousands of SimPatients
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Highly Configurable:
• Patients: Name, address, gender, age, race, religion,
telecom, PCP, practice, specialists & specialty organizations
• Providers: Name, address, gender, age, race, religion,
telecom, NUCC specialty
• Practices: Name, address, telecom, NUCC specialty
• Hospitals: Same as practices plus staff specialists
• Specialty Organizations: Same as practices
• Patient Risk Factors: Diet, exercise, alcohol, smoking, drug
use, promiscuity
Monte Carlo simulation
• Patients age, have children, get sick, get treated, get better,
but ultimately die
• Lots of realistic healthcare data is generated in the process
• More data formats are in the works
Any similarity to real individuals or organizations is purely
coincidental and is a product of random processes
Copyright 2014,2015 MiHIN Shared Services. MiHIN
The Challenge: Improve “Clinical Relevance”
Reflect real-world
patient populations
and care delivery
practices
Adhere to
real-world clinical
possibility
constraints
Logic
Relevance
Simulated Patient
Scenarios Must:
Contain
measure-related
data and events
Copyright 2014,2015 MiHIN Shared Services. MiHIN
The Original Generator Had Some Deficiencies
Simulated Patient
Scenarios:
Adhere to
real-world
clinical
possibility
constraint
s
Logic
Relevance
Reflect real-world
patient populations
and care delivery
practices
Contain
measure-related
data and events
Random generation yielded very random encounters
Copyright 2014,2015 MiHIN Shared Services. MiHIN
Clinical Possibility Constraints
Key Elements
Description
Sequence
Episodes of care have a beginning and an end. Events occur in a specific order (e.g.
patient experiences chest pain, before diagnosed of heart attack, before angioplasty is
performed).
Activities span typical lengths of time which can be represented as a minimum and
maximum, or average duration (e.g. an angioplasty procedure takes 60-90 min).
Activities may be constrained to a specific role, via regulation or policy (e.g. diagnoses
are made by physicians, advanced practice nurses, or physician assistants).
Duration
Role-Activity
Association
Range
Mutual Exclusivity
Likelihood of
Occurrence
Metadata
Activities and events can be associated with rules or parameters (e.g. drugs have
associated dosage ranges, etc.).
An event may not be permitted or plausible within the presence of another event.
Events are associated with an expected frequency (e.g. infants born full term have a
high chance of survival, patients admitted for a traumatic injury are unlikely to be
admitted against their will, etc.)
Activities or events may produce, or may require specific information as metadata (e.g.
patients have an associated age, gender & race).
Copyright 2014,2015 MiHIN Shared Services. MiHIN Confidential –
Simulation Goals & Clinical Relevance
Possible
Simulation
Goals:
Represent a
Represent a full
episode of care patient’s lifetime
Test Software
Simulation
produces only
data elements
required for quality
measurement
Simulation
produces a full
record for an
episode of care
Simulation
produces a full
longitudinal record
Clinical relevance
is limited to clinical
quality measure
data
Clinical relevance
is applicable to
many more
aspects of care
(e.g. what
happened before
and after the PCI
at 90 minutes)
Clinical relevance
is expanded even
further, to cover a
patient’s lifetime
(e.g. natural
disease clusters)
Original Model
New Model
Copyright 2014,2015 MiHIN Shared Services. MiHIN
SimPatients Have Configurable Demographics
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Names
Address distributions
Gender distributions
Age distributions
Race distributions
Religion distributions
Body Sign distributions
Risk Factor distributions
Copyright 2014,2015 MiHIN Shared Services. MiHIN
Signs and Risks Affect Body Systems
• Body Signs
• Measurable values that have trajectories over patient lifetimes
e.g. blood pressure, HbA1c, BMI, Cholesterol …
• Signs can represent chronic conditions such as hypertension,
diabetes, obesity, hyperlipidemia
• Quantized & normalized using Likert scales (1-5)
• Initial values drawn from configurable prevalence data
• Risk Factors
• Patients have risk factors such as smoking, diet, alcohol use,
drug use, promiscuity
• Risks can affect the trajectories of signs & the likelihoods of
complications
• Patient risks initially drawn from configurable prevalence data
• As patients age, they acquire risks drawn from incidence data
Copyright 2014,2015 MiHIN Shared Services. MiHIN
Patient State Drives Diagnoses & Encounters
• Diagnoses (CQM measure diagnoses)
• Patient state is calculated based upon systems, risks & signs
• Incidence & prevalence likelihoods based upon published
medical studies and experience (e.g. Framingham)
• System health state changes drive diagnosis and encounters
• Most likely diagnosis can be computed from risks & signs when
not specified by organ, system or risk logic
• Quality Measures
• Sampled from most likely diagnosis measures
• Drive patient encounters via scripted CAT-I event sequences
embodying clinical knowledge
• Patient Encounters
• Produce CQM reports, ADT events & ACRS Care Teams
• Outcomes can influence signs & risks to close the feedback
loop and improve longitudinal histories
Copyright 2014,2015 MiHIN Shared Services. MiHIN
The Interactions Can Be Very Complex
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Diet & Exercise risks influence BMI, Lipid & HbA1c signs
Alcohol, Drug & Promiscuity risks influence Pregnancy incidence
Alcohol & Diet risks influence pregnancy complication incidence
Alcohol & Promiscuity risks influence STD incidence
Smoking risk + Pregnancy, BMI, BP, Lipids & HbA1c signs influence
Neurological & Cardiovascular system morbidities & mortalities
• Eyes: diabetic retinopathy leading to blindness
• Kidneys: diabetic renal disease leading to kidney failure
• Peripheral nerves: diabetic neuropathy leading to amputations
• Heart: coronary heart disease leading to Afib & AMI
• Brain: hemorrhagic, ischemic stroke
• Circulatory: vascular disease, venous thromboembolism
• PatientGen approximates these interactions to produce more
credible, but not epidemiologically accurate patient life histories
Copyright 2014,2015 MiHIN Shared Services. MiHIN
Patient Gen Today
• 28 important conditions are modeled with credible precision
• Incidence & prevalence models are based upon published
medical studies and Internet based estimates
• All 2014 EH and EP CAT-I measure reports can be produced
• Event scripting can be specified using Cypress Bonnie tools
• Simulations can be done at multiple resolutions, from hourly
to monthly iterations (weekly is default)
• Populations are limited only by available memory
• Standard MiHIN patient and provider personas have been
coded and participate in each simulation run
• Providers are patients too: they age, retire, die and are
replaced as needed by the hospitals and practices they serve
Copyright 2014,2015 MiHIN Shared Services. MiHIN
PatientGen In The MiHIN Context
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PatientGen Can Produce
 Patient Care Teams (attribution)
 ADTs
 CAT-I CQMs
 Newborn Screenings
 Death notifications
 FHIR Resources
 Immunizations
 Reportable Labs
 Syndromics
 CCDs
From simulated patients undergoing
simulated health state changes in a
controlled but random manner, based
upon real-world probabilities
With No Protected Health Information
Copyright 2014,2015 MiHIN Shared Services. MiHIN
Patient Gen
MIDIGATE™
CQMRR
Tableau
Remaining Challenges
• Improvements in modeling of medical conditions to improve
breath of coverage and longitudinal clinical relevance
• Additional body signs and risk factors needed for longitudinal
clinical relevance
• Implementation of actual treatment outcomes to reduce
subsequent morbidity risks for effective treatment regimens
• Better integration with Bonnie for scripting
• More complete output of FHIR resources
• Additional HL-7 messages (e.g. Immunizations, Syndromics
Surveillance, Reportable Labs, Cancer, HIV)
• Implementation of symptoms to support CCD messages
• User interface development to simplify configuration and
execution
Copyright 2014,2015 MiHIN Shared Services. MiHIN
The Open Source Option Is Under Consideration
• Open Source means more hands, eyes and energy to
improve quality & advance PatientGen capabilities
across multiple fronts
• Open Source means more organizations can benefit
from simulated healthcare data
• We favor Apache-style meritocracy for organization &
team roles
• Successful open source projects require continuity of
leadership and direction
• MiHIN is seeking external funding sources to provide this
leadership
Copyright 2014,2015 MiHIN Shared Services. MiHIN
FHIR Database Contents
• 367 Encounters – model Active Care Relationships
• 235 Patients – synthetic patients
• Michigan demographic profiles
• Includes 16 MiHIN standard “personas”
• 284 Practitioners – synthetic PCPs and specialists
• 46 Organizations – synthetic hospitals & practices
• 2215 Bundles – groups of related patients
• One gold standard patient
• 24 perturbations of that patient
• 1-8 of patient fields randomly perturbed
• 3 random perturbations at each level
FHIR Resources And Linkages
Bundle
(2215)
Patient Matching
Bundles
Patient
Patient
Patient
Patient w/
Encounter
(367)
Perturbations
patientId
patientId
Patient
(235)
practitionerId
Practitioner
(284)
organizationId
Organization
(46)
Patient-Provider-Organization Attribution Encounters
Questions?
Jeff Eastman, Ph.D.
MiHIN Directory Architect
eastman@mihin.org
http://www.mihin.org
Copyright 2015 Michigan Health Information Network Shared Services
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