Playing with FHIR IR How to Exploit the EHR Mark L Braunstein, MD - - PowerPoint PPT Presentation

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Playing with FHIR IR How to Exploit the EHR Mark L Braunstein, MD - - PowerPoint PPT Presentation

Aug 23, 2023 449 likes •1.14k views

Playing with FHIR IR How to Exploit the EHR Mark L Braunstein, MD Professor of the Practice School of Interactive Computing Georgia Institute of Technology Visiting Research Fellow E-Health Centre, CSIRO Variable Results Australia ranks

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Playing with FHIR IR

How to Exploit the EHR

Mark L Braunstein, MD Professor of the Practice School of Interactive Computing Georgia Institute of Technology Visiting Research Fellow E-Health Centre, CSIRO

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” “ Australia ranks highest

  • n Administrative

Efficiency and Health Care Outcomes, and is among the top-ranked countries on Care Process and Access

Commonwealth Fund (2017)

Variable Results

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Common Trends

https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-healthcare-and-life-sciences-predictions-2020.pdf

Aging Population More Chronic Disease Increased Costs

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In Including Australia

https://www.aihw.gov.au/reports/australias-health/australias-health-2018/contents/table-of-contents

Australian Institute

  • f Health and

Welfare 2018

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Systems Not Designed for Chronic Care

https://www.mja.com.au/journal/2007/187/2/care-patients-chronic-disease-challenge-general-practice

Medical Journal of Australia

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Poor Care Coordination

https://www.commonwealthfund.org/publications/surveys/2015/dec/2015-commonwealth-fund-international-survey-primary-care-physicians

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Lack of Continuity

96 92 90 85 81 66 65 64 60 28

UK NETH NZ SWE AUS US NOR CAN SWIZ GER

https://www.commonwealthfund.org/publications/surveys/2015/dec/2015-commonwealth-fund-international-survey-primary-care-physicians

In Practice Nurses or Case Managers

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Variable Use of f In Informatics

30 11 53 24 80 11 57 60 10 20 30 40 50 60 70 80 90 Email Record Sharing Australia NZ SWIZ US

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Healthcare Professionals Want Digital Health

Activity Current ently using ng a compute uter, smart phone ne or tablet et % Not using, g, but inter eres ested in using g a compute uter, , smart phone e or tablet et % Not interested ed in using ng a compute uter, smart phone ne or tablet et for this activity % Sharing health records with my patients 25 59 7 Transferring prescriptions to the pharmacy 25 56 8 Providing interactive decision-making support 32 53 6 Communicating with patients before or after consultations 33 49 7 Sharing health records with other practitioners 43 45 4

Top 5 activities health professionals want to use digital technologies to help better support them to deliver health services

Courtesy Australian Digital Health Agency

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EHRs Have “Issues”

https://medicomp.com/whats-the-most-frustrating-about-ehrs/

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Goals: IO IOM

safe, effective, patient-centered, timely, efficient, equitable

Learning Health System

https://www.ahrq.gov/professionals/systems/learning-health-systems/index.html

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Necessary ry In Informatics Substrate

EMR Adoption Analytics Access to POC Open Interoperability

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Variable EHR Readiness

http://www.oecd.org/els/health-systems/health-statistics.htm

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Current EHR Limitations

No standards regarding discrete data No integrated communication Not user-friendly Big data but not smart data Lack of regulation

https://www.beckershospitalreview.com/healthcare-information-technology/the-problem-with-ehrs-5-complaints-from-cios.html

Remember these are we proceed Hospital CIOs

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What to Do?

Repair? Replace?

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In Innovate!

“Fostering third party apps creates a market where innovations compete with each other for purchase and use by providers (and patients), thus reducing dependency on updates and specific functions made by an EHR vendor.”

  • -Ken Mandl, Josh Mandel, Isaac Kohane

https://www.sciencedirect.com/science/article/pii/S2405471215000046

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How?

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It It Works Quantitatively

https://research2guidance.com/325000-mobile-health-apps-available-in-2017/

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Proof of Effectiveness is Lacking but …

11 of 23 randomized controlled trials showed a meaningful effect on health or surrogate outcomes attributable to apps … the overall evidence of effectiveness was of very low quality … pilot studies … only

  • ne has progressed to a large clinical trial.

https://www.nature.com/articles/s41746-018-0021-9

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… Mostly “Siloed” Apps

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Ext xtend the Phone App Model to EHRs?

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Harvard’s SMART

https://apps.smarthealthit.org/

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Georgia Tech’s HDAP

http://www.hdap.gatech.edu/apps/

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Cerner/Epic …

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Even the US Government!

https://bluebutton.cms.gov/

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What Will Happen Next xt?

“These apps will give new life to data entered into EHRs and other health IT platforms by providing the ability to visualize risks, trends, and trajectories; mash up clinical records with external data sources; and deliver decision support to clinicians and patients during and between encounters.”

  • -Ken Mandl, Josh Mandel, Isaac Kohane

https://www.sciencedirect.com/science/article/pii/S2405471215000046

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A Better EHR?

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Ju Juxly Timeline

No standards regarding discrete data No integrated communication Not user-friendly Big data but not smart data

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Ju Juxly Trends

No standards regarding discrete data No integrated communication Not user-friendly Big data but not smart data

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Coordinated, , Continuous Care

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Within Cerner

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More Complete Patient View

EHR Data Patient Generated Data

No standards regarding discrete data No integrated communication Not user-friendly Big data but not smart data

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Outcome Prediction

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How?

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HL7 Timeline

FHIR: V 3.0.1 April 19, 2017 … Messaging (lab test results) Model Driven (patient record summaries) 2018?

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A Common Data Model

No standards regarding discrete data No integrated communication Not user-friendly Big data but not smart data

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Condition: Human View

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Condition: Machine View

73211009 Remember me!

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Uniform API

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“Just Like” Amazon!

http://hapi.fhir.org/baseDstu3/Condition?code=SNOMED-CT|73211009 https://www.amazon.com.au/s/ref=nb_sb_noss?url=search- alias%3Daps&field-keywords=size+10+ladies+blue+sweater

Population level query

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Condition Specific Charting

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Medication Reconciliation

RxNorm for different names Multiple dispensings One entry

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Clinical In Insights

No standards regarding discrete data No integrated communication Not user-friendly Big data but not smart data

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Lower Cost

No standards regarding discrete data No integrated communication Not user-friendly Big data but not smart data

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Precision Medicine

No standards regarding discrete data No integrated communication Not user-friendly Big data but not smart data

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What About Your Patients?

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Patient Controlled Health Record

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EHR Connected Mobile Apps

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Over 500 Collaborating Hospitals/Clinics (J (June)

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Sharing Models

Opt Out - Centralized Opt In - Federated

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Secondary ry Use?

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Chart Review

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Search Results

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Drill Down: Conditions/Medications

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Drill Down: Notes

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Research

“a historic effort to gather data from

  • ne million or more people living in

the United States to accelerate research and improve health. By taking into account individual differences in lifestyle, environment, and biology, researchers will uncover paths toward delivering precision medicine.”

https://allofus.nih.gov/

FHIR App

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Emory ry Artificial In Intelligence Sepsis Expert (A (AISE)

Trained on 31,000 Emory ICU patients Validated on 52,000 MIMIC III patients Third International Consensus Definitions for Sepsis (Sepsis-3) 65 features (variables) calculated on hourly basis Can predict sepsis 4 hours in advance (ROC of .85)*

*https://www.ncbi.nlm.nih.gov/pubmed/29286945

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DeepAISE on FHIR

Decline Improvement Emory AISE Score Philips DRS Score: Higher predicts readmission

Submitted to AMIA 2018

Text messaging

  • r the eICU application

Drag/ Drop Automatic

No standards regarding discrete data No integrated communication Not user-friendly Big data but not smart data

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eICU team adjudicates warnings

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Medical Education on FHIR?

We are partnering with UQ ITEE and Faculty of Medicine to offer an experimental course to explore the potential of using FHIR to digitize case based learning.

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Diabetes Case Study

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Burn Case Study

Nathan and is alert and oriented with a GCS of 15

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Nathan’s % total body surface area (%TBSA) of burn is calculated using a Lund-Browder chart to be 62% with 59% full thickness burns, 2% deep dermal and 1% partial thickness burns

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Want to Try ry It It Yourself?

http://cs6440.gatech.edu/

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mark.braunstein@csiro.au