HIMSS Patient Matching Testing Event Synthetic Patients and Their Usage at MiHIN Jeff Eastman, Ph.D....

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 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

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

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

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

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

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

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

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

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

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

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

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

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

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:

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

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

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

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

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

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

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

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

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

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

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

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) Bundle (2215) Patient (235) Patient (235) Practitioner (284) Practitioner (284) Organization (46) Organization (46) Encounter (367) Encounter (367) Patient Patient w/ Perturbations Patient-Provider-Organization Attribution Encounters Patient Matching Bundles patientId practitionerId organizationId

HIMSS Patient Matching Testing Event Synthetic Patients and Their Usage at MiHIN Jeff Eastman, Ph.D....

Documents

Transcript of HIMSS Patient Matching Testing Event Synthetic Patients and Their Usage at MiHIN Jeff Eastman, Ph.D....