HIMSS Patient Matching Testing Event Synthetic Patients and Their Usage at MiHIN Jeff Eastman, Ph.D....
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HIMSS Patient Matching Testing Event Synthetic Patients and Their Usage at MiHIN Jeff Eastman, Ph.D. Michigan Health Information Networks Shared Services
HIMSS Patient Matching Testing Event Synthetic Patients and
Their Usage at MiHIN Jeff Eastman, Ph.D. Michigan Health
Information Networks Shared Services
Slide 2
Where Do We Use Real Health Information? Clinicians who are
treating their patients need real information Every patients health
information should be quickly retrievable by anyone authorized to
be involved in that patients 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 Confidential Proprietary Restricted
Slide 3
Why Use Synthetic Health Information? 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 someones 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 Confidential Proprietary Restricted
Slide 4
Data Sharing Organization How Shared Services support Statewide
Transitions of Care Copyright 2015 Michigan Health Information
Network Shared Services 4 Patient to Provider Attribution Delivery
Preference Lookup 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 Primary Care
Specialist Care Coordinator Alerts & Notification Patient
Slide 5
Data Sharing Organization (DSO) Data Sharing Organization (DSO)
Empowers Clinical Alerts: Medication Reconciliation Copyright 2015
Michigan Health Information Network Shared Services 5 Patient to
Provider Attribution Health Provider Directory 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 Primary Care Specialist Animation Care
Coordinator MR
Slide 6
MiHIN Transitions of Care Service (TOC) 6 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
Slide 7
7 How is this accomplished today? Health Provider Search
Service Active Care Relationship Service (Patient-Provider
Attributions) Statewide Provider Directory Individual NPIs
Organizational NPIs Multiple Affiliations Specialties 21 st century
contact info: Direct addresses HIE routing & delivery
preferences 20 th century contact info: Address, phone, fax
Statewide Provider Directory Individual NPIs Organizational NPIs
Multiple Affiliations Specialties 21 st century contact info:
Direct addresses HIE routing & delivery preferences 20 th
century contact info: Address, phone, fax Provider Index MCIR
Immunizations MDCH Data Hub HSTR Meaningful Use CHAMPS/MMIS
Medicaid LARA Licensing LARA Licensing Other Repositories State of
Michigan Providers, Hospitals, Data Sharing Organizations
Providers, Hospitals, Data Sharing Organizations National Plan
& Provider Enumeration Service National Plan & Provider
Enumeration Service Other MiHIN Services
Slide 8
Copyright 2015 Michigan Health Information Network Shared
Services 8 What is coming next? Health Provider Search Service
Active Care Relationship Service Statewide Provider Directory
Provider Index MCIR Immunizations MDCH Data Hub HSTR Meaningful Use
CHAMPS/MMIS Medicaid LARA Licensing LARA Licensing Medicaid Member
Portal State of Michigan Providers, Hospitals, Data Sharing
Organizations Providers, Hospitals, Data Sharing Organizations
National Plan & Provider Enumeration Service National Plan
& Provider Enumeration Service Other MiHIN Services Statewide
Consumer Directory
Slide 9
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 quality- based
healthcare delivery and payment. Copyright 2014,2015 MiHIN Shared
Services. MiHIN Confidential Proprietary Restricted
Slide 10
MiHIN Patient Generator: Works Kind of Like a Music Synthesizer
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 10
Slide 11
Real Patients Have Body Systems Copyright 2014,2015 MiHIN
Shared Services. MiHIN Confidential Proprietary Restricted Systems
& Organs get sick Sometimes they get well on their own
Sometimes they dont, thats 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
Slide 12
SimPatients Have Simulated Body Systems Copyright 2014,2015
MiHIN Shared Services. MiHIN Confidential Proprietary Restricted
Cardiovascular Digestive Endocrine Genitourinary Immune
Integumentary Lymphatic Mental Muscular Nervous Reproductive
Respiratory Skeletal
Slide 13
Body Systems Model Real Health States Copyright 2014,2015 MiHIN
Shared Services. MiHIN Confidential Proprietary Restricted 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 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
Slide 14
Qualitative Health States Help Normalize Results Well Dead
Intensive Critical Ill Sick Copyright 2014 MiHIN Shared Services.
MiHIN Confidential Proprietary Restricted As With Likert
Scales
Slide 15
PatientGen Creates Thousands of SimPatients Copyright 2014,2015
MiHIN Shared Services. MiHIN Confidential Proprietary Restricted
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
Slide 16
The Challenge: Improve Clinical Relevance Copyright 2014,2015
MiHIN Shared Services. MiHIN Confidential Proprietary Restricted
Delivery Relevance Measure Relevance Logic Relevance Simulated
Patient Scenarios Must:
Slide 17
The Original Generator Had Some Deficiencies Copyright
2014,2015 MiHIN Shared Services. MiHIN Confidential Proprietary
Restricted Delivery Relevance Measure Relevance Logic Relevance
Simulated Patient Scenarios: Random generation yielded very random
encounters
Slide 18
Clinical Possibility Constraints Key ElementsDescription
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).
Duration 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). Role-Activity Association
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). Range Activities and events can
be associated with rules or parameters (e.g. drugs have associated
dosage ranges, etc.). Mutual Exclusivity An event may not be
permitted or plausible within the presence of another event.
Likelihood of Occurrence 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.) Metadata 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 Proprietary
Restricted
Slide 19
Simulation Goals & Clinical Relevance Simulation produces a
full longitudinal record Clinical relevance is expanded even
further, to cover a patients lifetime (e.g. natural disease
clusters) Represent a patients lifetime Simulation produces a full
record for an episode of care Clinical relevance is applicable to
many more aspects of care (e.g. what happened before and after the
PCI at 90 minutes) Represent a full episode of care Simulation
produces only data elements required for quality measurement
Clinical relevance is limited to clinical quality measure data Test
Software Copyright 2014,2015 MiHIN Shared Services. MiHIN
Confidential Proprietary Restricted Possible Simulation Goals:
Original Model New Model
Slide 20
SimPatients Have Configurable Demographics Copyright 2014,2015
MiHIN Shared Services. MiHIN Confidential Proprietary Restricted
Names Address distributions Gender distributions Age distributions
Race distributions Religion distributions Body Sign distributions
Risk Factor distributions
Slide 21
Signs and Risks Affect Body Systems Copyright 2014,2015 MiHIN
Shared Services. MiHIN Confidential Proprietary Restricted 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
Slide 22
Patient State Drives Diagnoses & Encounters Copyright
2014,2015 MiHIN Shared Services. MiHIN Confidential Proprietary
Restricted 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
Slide 23
The Interactions Can Be Very Complex 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 Confidential Proprietary
Restricted
Slide 24
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 Confidential Proprietary Restricted
Slide 25
PatientGen In The MiHIN Context Copyright 2014,2015 MiHIN
Shared Services. MiHIN Confidential Proprietary Restricted
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 Patient Gen MIDIGATE CQMRR
Tableau
Slide 26
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 Confidential
Proprietary Restricted
Slide 27
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
Confidential Proprietary Restricted
Slide 28
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
Questions? 30 Copyright 2015 Michigan Health Information
Network Shared Services Jeff Eastman, Ph.D. MiHIN Directory
Architect [email protected] http://www.mihin.org