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Contemporary EHRs: How Standards Support Global Research and EducationLynn Johnson, PhD, University of Michigan
Heiko Spallek, PhD, DMD, MSBA, University of Sydney
Carlos Gonzalez-Cabezas, DDS, MSD, PhD University of Michigan
Mark Genuis, PhD, ICE Health Systems
Agenda
• Introductions• The Big Picture about Data and EHRs (Spallek)• ICDAS/ICCNS (Gonzalez-Cabezas)• Caries Standards in Action (Genuis)• Discussion
Collaboration for Health IT
Disclosures
Johnson• No financial compensation• COI/COC files with U-MSpallek• No financial compensation• Unrestricted travel support to attend ADEA/AADR/IADR in 2017 & 2018• Outside Earnings Declaration filed with UsydGonzalez-Cabezas• No financial compensationGenuis• CEO of ICE Health Systems
The Big Picture about Data and EHRs
Heiko Spallek, PhD, DMD, MSBA, University of Sydney
Dentists Trust Dentists
The Big Picture about Data and EHRs
Heiko Spallek, PhD, DMD, MSBA, University of SydneyIdeasOpportunities of Big DataTrends in HITRequirements for AI in HealthcarePatient PortalsEHR as BarrierPower of Standards: e.g. SNODDSLearning Health System
1854: What can we discover from data?
“Broad Street Cholera Outbreak”
164 Years Fast Forward: Trends in Health IT
• Interoperability • Blockchain • Artificial Intelligence
https://www.healthdatamanagement.com/opinion/three-rising-technologies-that-will-impact-healthcare-in-2018
“There is palpable excitement at the interface of biology, psychology, engineering, sensor technology, computation and therapeutics”
Dr. Jeffrey Flier, Dean of the Harvard Medical School
http://blogs.wsj.com/experts/2015/02/17/what-harvard-medical-schools-dean-learned-in-silicon-valley/
What can be done with Big Data?
Mayo Clinic (59,000 employees) + UnitedHealth Group ($122 billion corporation) + Optum Labs :
$300m research study over 5 years : repeated in hours , same result
https://www.optum.com/optumlabs/research-principles.html
JASON Report on AI (January 2018)
Progress in AI accelerates because:• Frustration with the existing systems• Ubiquity of networked smart devices• Comfort with at-home services
“without access to high quality, reliable data, the promise of AI will not be realized”
Artificial Intelligence for Health and Health CareJSR-17-Task-002
JASON, The MITRE Corporationhttps://www.healthit.gov/sites/default/files/jsr-17-task-002_aiforhealthandhealthcare12122017.pdf
JASON Recommendations
• Capturing smartphone data• Integrating social and environmental data
Artificial Intelligence for Health and Health CareJSR-17-Task-002
JASON, The MITRE Corporationhttps://www.healthit.gov/sites/default/files/jsr-17-task-002_aiforhealthandhealthcare12122017.pdf
Pathogen sequencing MinION; Oxford Nanopore Technologies)
JASON: Critical Missing Data Gaps
“AI application development requires training data, and will perform poorly when significant data streams are absent.“
What do we collect and aggregate in dentistry? • Scale? • Quality? • Social and environmental data?• Biospecimen?• Standardization? Artificial Intelligence for Health and Health Care
JSR-17-Task-002JASON, The MITRE Corporation
https://www.healthit.gov/sites/default/files/jsr-17-task-002_aiforhealthandhealthcare12122017.pdf
Data Availability in an HIT Ecosystem
FAIR: Findability, Accessibility, Interoperability, ReusabilityWilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship.
Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016)
Potential data sources for HIT ecosystem• Electronic Health Records, including electronic medical and dental records, genetic data, proteomics
data, microbiome data, bio-specimen data, etc. • Registry data across multiple disciplines • Patient-generated heath data (PGHD), e.g. home monitoring, seamless integration of PGHD into EHR • Precision medicine • Diagnostics • Socio-economic data (e.g. Census) • Urban planning data • Traffic data • Sensor data • Health Insurances (claims data) • Public Health data (surveys) • Environmental data (workplace, bio surveillance, air quality data, UV radiation, Radon, water
fluoridation) • Education data, including health behaviour interventions in schools and communities • Dietary data, food consumption, supplements
What data can we collect?Classes of data1. structured in traditional databases2. unstructured, e.g. images, video, voice, GIS3. Internet of Things (IoT)
https://www.apple.com/researchkit/ https://www.apple.com/healthcare/
Internet of Medical Things (IoMT)
3rd generation AliveCor Heart Monitor http://www.alivecor.com/home
Internet of Dental Things (IoDT)Kolibree: http://www.kolibree.com/en/
Screen Shot 2015-02-21 at 6.47.53 PM
Data = Asset ?
• Digital data generated globally in 2002: 5 terabytes• 5 terabytes now generated every two days• 90% of the world’s information generated in just the past two years
IBM 2016
• Upward trend in data generation• < 5% of useful data is analyzed to generate information & derive knowledge
Data is a strategic asset with great potential and should be treated and managed as such.
Data Availability and Us e: P roductivity C ommis s ion Inquiry R eport, No. 82, 31 March 2017, http://www.pc.gov.au/inquiries/completed/data-acces s/report/data-access .pdf
Analyzing qualitative data
• Qualitative data = hows & whys• Transform qualitative -> quantitative: shallow
shadow of original form, e.g., toothache• Humans explain context when communicating• Computers get context during design from
engineers
Patient Portals
e.g., Australia
Australian Digital Health Agency
EHRs as a Barrier
What EHRs Do Wrong• Alerts and reminders (alert fatigue)• Data entry (tedious, redundant)• Incompleteness• Data overload (note bloat)• Poor navigability
Electronic Health Records (EHR)
What EHRs Do Right • B illing• Legibility• Availability• R es ult reporting• Order entry• Alerts and reminders
Dental Records:WOR N—write once read never
Electronic Medical Record (EMR) Electronic Dental Record (EDR)
Integration for• Safe and efficient patient care, e.g. allergies, medication• health profession education based on interprofessional
education principles• biomedical research that acknowledges that the mouth is part of
the bodyIf you want to bring healthy lives and healthy mouths together, you also need to bring EMRs and EDRs together! Sample research questions• Is maintaining a full dentition important for older patient?• Does pre-chemo dental therapy help? • What role does the dentition play in dialysis outcomes? • Does a healthy dentition improve overall health?
Standards
e.g., SNODDS
Credit for next slides:
Elsbeth Kalenderian, DDS, MPH, PhDChair, Department of Preventive and Restorative Dental Sciences Leland A. & Gladys K. Barber Distinguished Professor in Dentistry University of California, San Francisco, School of DentistrySan Francisco, CA
Visiting ProfessorHarvard School of Dental MedicineBoston, MA
Need for Structured/Standardized Diagnoses
Courtesy A. Acharya
Integrated Terminologies
Promote Quality of Care
• Created in 2009
• Developed by dentists, informatics experts, researchers and academics from Harvard, UCSF, UT Houston and ACTA
• $7+ million US government funded grant
• Implemented at 22+ American and European dental institutions
• Harmonized with ADA’s SNODENT into SNODDS
SNODDS
Vision of a Learning Health System
Learning Health System (LHS)
Health systems--at any level of scale--become learning systems when they can, continuously and routinely, study and improve themselves
Perspective: Jan 3, 2013“C ode R ed and B lue — S afely Limiting Health C are’s G DP Footprint”
Arnold Mils tein, M.D., M.P .H.
…U.S. health care needs to adopt new work methods, outlined in the Institute of Medicine’s vision for a learning health system…
A Health System That Can Learn
• Every patient’s characteristics and experience are available for study
• Best practice knowledge is immediately available to support decisions
• Improvement is continuous through ongoing study
• This happens routinely, economically and almost invisibly
• All of this is part of the culture
Charles P. Friedman, PhDJosiah Macy, Jr. Professor of Medical EducationChair, Department of Learning Health Sciences
Professor of Information and Public HealthUniversity of Michigan
How can we accelerate 17 years to 17 months?C onverting data cemeteries to sources of knowledge
Screen Shot 2015-02-21 at 6.47.53 PM
Data FAIRness ➔ Knowledge FAIRness
• Deliberate and evolutionary process of infrastructure co-production in which the full spectrum of stakeholders are directly engaged.
• Purposefully collected data outside of care experience can be important components of the learning process.
Friedman, Rubin, Sullivan: Toward an Information Infrastructure for Global Health Improvement, IMIA May 2017: https://imia.schattauer.de/en/contents/archive/issue/special/manuscript/27496.html
Atul Gawande on the potential of information for health
kinds of information that matter to your health and well-being over time, information about the state of ...• your internal systems, e.g. imaging, lab-test results• your living conditions, e.g. housing, environment• the care you receive, e.g. medications, treatments• your behaviors, e.g. sleep, exercise
“The potential of this information is so enormous it is almost scary.”
Atul Gawande: The Heroism of Incremental Care, The New Yorker, 2017, http://www.newyorker.com/magazine/2017/01/23/the-heroism-of-incremental-care
What patient care experiences have you encountered that might have been
improved if care providers or patients had had access to an EHR that used standards to integrate data from other health care
systems?
Dental Caries Recordsas an Example
Carlos Gonzalez-Cabezas, DDS, MSD, PhDUniversity of Michigan
Dental Caries
• Treat the Disease (dental caries): detect lesions, diagnose activity, identify etiologic factors, identify risk, treat, evaluate
• Treat the consequences of the disease (e.g., cavities)
Clinical Decision Making
Terminology:
The consensus was to separate out three key terms:• Lesion detection• Lesion assessment• Caries diagnosis
International Consensus Workshop-Caries Clinical Trials Consensus
Statements
ICDASInternational Caries Detection and Assessment System
ICDAS•Backer Dirks 1951 >•Marthaler 1966 >•WHO 1979•Pitts & Fyffe 1988• Ismail 1992•Ekstrand, Ricketts & Kidd 98 •Fyffe et al 2000•Nyvad 2001 •And many, many more (see Systematic Reviews NIH CDC and ICW-CCT)
The Evidence Base in Caries Detection and AssessmentReliable inclusion of clinical visual enamel & dentine caries detection is not new
Since 2002
ADA Caries Classification System
ICCMS™4DCaries Management
DETECT & Assess
DECIDE
DO
DETERMINE
Patient-level Caries Risk
Caries Staging & Activity
Appropriate Tooth Preserving Caries
Prevention & Control Interventions
Personalised Care Plan: Patient & Tooth Levels
(HISTORY)
(MANAGEMENT)
(DECISION MAKING)
(CLASSIFICATION & INTRA-ORAL RISK)
The International Caries Classification and
Management System is a health outcomes focused
system that aims to maintain health and
preserve tooth structure. It uses a simple form of
the ICDAS Caries Classification model to stage caries severity and assess lesion activity in
order to derive an appropriate,
personalised, preventively based, risk-
adjusted, tooth preserving Management
Plan.
Risk-basedRecall
interval
Caries Standards in Action
Mark Genuis, PhD, ICE Health System
Adhering to & Encouraging Standards
49
ECMA 262, 3rd edition
Integrations
50
...And more to come...
Glick Medical Support System
How can the ICE Health EHR improve caries research and subsequently patient
care?
What CARIES patient care experiences have you encountered that might have
been improved if care providers or patients had had access to an EHR that used standards to integrate data from
other health care systems?
What standards could help you in your research? How might it help?
Contemporary EHRs: How Standards Support Global Research and Education
Lynn Johnson, [email protected] Spallek, [email protected] Carlos Gonzalez-Cabezas, [email protected] Genuis, [email protected]