The Future of Medicine is Computational

Markus Lingman, MD, PhDPhysician Researcher, Salgrenska Academy

Chief Strategist, Consulting Cardiologist, Halland Hospital GroupRegion Halland, Sweden

Philip D. Anderson, MDAssistant Prof. Emergency Medicine, Harvard Medical School

Department of Emergency Medicine, Brigham and Women’s HospitalBoston, Massachusetts USA

Thomas Wallenfeldt

Datadriven Healthcare, CGI

Datadriven Hälsa- och sjukvårdGet more value from data - Make decisions based on facts - Utilize machine learning

Architecture

Evaluations &

Roadmaps

Big Data Analytics

Factory

Analytics design and

visualization

Proof of Concepts and

Proof of Values

Agile BI services Master Data

Management

solutions

Cloud Analytics

solutions

Analytics for Internet

of Things

Big Data system

development

Data migration

projects

BI Competence

Centers

Application

Management Services

Långt liv låg kostnad

Siverskog J, Henriksson M. Eur J Health Econ. Online Feb 2019 3

Hög produktivitet hög kvalitet

Vårdanalys 2019

• 2 academic medical centers, 6 community hospitals, 1 psychiatric hospital

• Spaulding Rehabilitation Network (7 rehab hospitals, skilled nursing and long-term care facilities, 23 outpatient sites)

• Partners Community Physicians Organization (network of over 2,000 physicians and community health centers)

• Partners Urgent Care (outpatient clinics for minor problems)

• Partners HealthCare At Home (visiting nurses, home health aides)

• MGH Institute of Health Professions (graduate school for nursing and allied health professions)

• A principal teaching affiliate of Harvard Medical School

• Committed to patient care, research, teaching and service to the community

• Partners HealthCare is a non-profit organization

An integrated health system serving over 6 million patients in the Massachusetts and New England area:

What is the opportunity in Sweden?

• High performing organizations

• Common challenges

• Innovation oriented

• Lots of high quality data to learn from

• Less fragmented in Sweden

• Nature of the organisation

Challenges• Demographic trends, rising costs

• Complexity and Fragmentation

• Legal, regulatory environment

Evolving population demographics (USA)

1970 (50 years ago) Today 2070 (50 yrs from now) 8

Prevalence of chronic diseases is rising, especially among the elderly…

9

…and this is associated with increased frequency of exacerbations, often requiring hospitalization,

10… which increases healthcare spending

Healthcare delivery is complicated

• Historical trend towards increasing specialization

– Clinical practice

– Research

– Advances in knowledge, technology

• Structural organization of care delivery around specialties and sectors (silos)

• Fragmentation in administrative processes

– Data gathering, analysis

– Quality, performance monitoring

– Budgets, finance

11

How can we radically improve healthcare delivery?• Common Goal: increase value = outcomes/cost at

population level

• Healthcare system policy and decision makers guide improvement through choices about which new structures, processes to implement

• Care providers make diagnostic and treatment decisions for individual patients based on training and experience

• Fragmentation and Complexity complicates decision making

• Better decision making requires deeper insights:

– What effects will it have? (outcome, cost metrics)

– How big will impact be? (assumptions, data)

– Identifying right patients for right care at right time

Our Vision for Data Driven Healthcare

Imprecision medicine --> precision medicine

Imprecision management --> precision management

Data Information Insights Change Follow up

Patient Data Integrity is guarded by a number of regulations

• GDPR• HIPAA• Privacy Shield• Patientdatalagen• Offentlighetsprincipen• Personuppgiftslagen• etc

vs

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Data Driven

Healthcare

• Data Architecture

• Care Delivery Capacity and Cost Accounting

• Data Driven Management, Personalized Precision Medicine

SHAARP / PEC

Need to link clinical, resource and cost data across the entire healthcare system

Production

Clinical

Patients

Capacity

Cost

Primary Care

Emergency Department

In hospital

OutpatientSpecialty Clinic

Prehospital (EMS)

Social Care

Providers

Additional future data sources: • PROMs• Sensor data from home care

monitoring, wearables• Free text structured data

w/ NLP • Genomics data

16

Understanding actual costs of care requires allocation of system budget to units of production (encounters) + unused capacity

• PEC is a simplified, macro version of Time Driven Activity Based Costing (TDABC)

• Accounts for resource costs of unused care

delivery Capacity– Resource availability data (beds, personnel, etc.)

– Productivity assumptions

– Enables cost trend analysis over time without distortions due to fluctuation in resources or production

• Different algorithms for different types of healthcare encounters– Inpatient, outpatient, primary care, emergency

dept, etc.

PEC

Human Resources data

Financial dataPatient

Encounter data

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Production Clinical Patients Providers Capacity Cost

SHAARP/PEC Enables Patient Focused, Data Driven Care

SHAARP / PEC

Opportunity Identification

• Variability analysis, Benchmarking

• Systematic vs. issue based

Prioritize Initiatives

• ROI Analysis

• Value Matrices

Evidence Based Guidelines

• Monitor implementation at the whole population level

• Optimize patient level compliance

Machine Learning-based AI Predictive Modeling

• Predict healthcare events of interest

• Prevention, care optimization

Personalized Precision Medicine for Patients Data Driven Management for Healthcare Systems

Results• Real World Evidence (RWE) studies

• Applied AI predictive modeling

• System efficiency, productivity

Anticoagulation in Atrial Fibrillation: Deeper Insights from Linking Data across Multiple Sources

• Nearly 70 strokes could potentially have been prevented, 9% of the total acute strokes

• Cost to the system

– € 580K inpatient

– € 4.8M in total

CHADS2Vasc Received

Anticoagulation

(%)

Did not receive

Anticoagulation

(%)

≤ 1 (anticoagulation not

indicated) 581 (46%) 688 (54%)

≥ 2 (anticoagulation

indicated) 7,229 (80%) 1,793 (20%)

Problem: Many patient with atrial-fibrillation are not anticoagulated, leading to preventable strokes

Solution: Develop a feedback mechanism to providers by linking prescription to EHR data

Personalized Precision Medicine for Patients

Palliative Care

ED Cost 5%

Inpatient Cost73%

Outpatient Cost 15%

Primary Care Cost7%

Care Utilization in Last Year of Life (2016)

Problem: many terminal patients who would benefit from palliative care do not receive it, leading to invasive and expensive inpatient care, which does not change their outcomes, may worsen quality of life

Solution: AI machine learning can more accurately identify these patients for referral to palliative care

0

5000

10000

15000

20000

25000

1357911131517192123252729313335373941434547495153

SEK

Week before death

Average Weekly Care Utilization in Last Year of Life

Personalized Precision Medicine for Patients

Net effects resulting from Data Driven Strategic Planning in Region Halland

Initiatives implemented based on data driven strategic planning

• ED Observation

• Inpatient discharge planning

• CHF pathway

• Joint replacement

• Others.

Results within the Hospital System

• Reduced hospital bed days equivalent to 2-3 hospital wards across system

• Occupancy levels remained stable on inpatient wards

• 30-day all cause readmission rate went down by 11%

• 25% increase in patients discharged before noon

• Overall hospital LOS down 18%

• Hospital admissions down by 8%

• Admission rates from ED to hospital decreased 18%

• Population growth of 4%

• 7% increase in patient arrivals to ED

Initial net annual savings: 80-100 million SEK/yr = 1% total health system budget

Next Steps• Strategies for assembling larger virtual data

sets

• Distributed analytics

• Secondary use of data sets

The Nightingale initiative

Data platform for public good that will drive

innovations in research at the intersection of

medicine and data science.

Funding: Schmidt Futures,

Lead: Sendhil Mullainathan (University of Chicago), Ziad Obermeyer (UC Berkeley).

Objective: Bring together high-dimensional, cutting-edge medical datasets, with the goal ofenabling the best researchers in the world to push forward the boundaries of medical science using data from partners - a select group of leading US health systems and national governments.

Value created: this work will create a new field: ‘computational medicine’ that uses data, in combination with sophisticated algorithms, to answer fundamental questions in medicine.

Distributed analytic approaches• Opal/Enigma (MIT)

• Secure multi-party computation

• Homomorphic encryption

Distributed analyticsGoal - Efficient distributed machine learning with preserved privacy

…Medical

DB 1

Medical

DB 2

Medical

DB N

Machine learning

task (at HH)

Privacy wall (e.g. Hospital)

Metadata communication that preserves privacy Courtesy of prof Mattias Ohlsson

Technology: “BASE Jumping” Distributed Analytics for

Secure Network Collaboration

• The Problem: – high return on big data healthcare analytics requires

access to massive whole population complete healthcare data sets;

– however, regulatory frameworks restrict data sharing between provider organizations

• The Solution: – Network of healthcare provider organizations running

SHAARP/PEC; each secure their own data

– Carry out desired analyses on entire virtual data lake using GDPR-compliant encrypted computational strategies

– Patient level data from provider organizations are not viewed or shared with other organizations

– Only aggregated (deidentified) results are shared with others

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Phase 2: SHAARP/PEC International Analytic Network

Distributed Analytics

Regional Healthcare System 1

Municipality Care System

2

Regional Healthcare System 3

Municipality Care System

4

Regional Healthcare System 5

Municipality Care System

6

• Whole system / whole population international “virtual” healthcare data set – SHAARP/PEC based standardized data model used

by all provider organizations

– Provider organizations control their own data

– “Base Jumping” supports distributed analytics of provider organizations primary data

• Provider organizations get – access to international benchmarking,

– best practices approaches in healthcare system management

– More accurate machine learning-based AI predictive modeling

• Academic organizations get– Access to massive, world class, research grade

population health data set

SHAARP/PEC virtual data lake

Summary • Data in healthcare is the new oil, but need to be compliant

with regulatory frameworks

• Preliminary work in RH shows that data driven healthcare improves outcomes and reduces costs

• Common data model is necessary for broader collaboration

• Collaboration can be based on– Anonymization – linking data, combined data sets

– Not anonymizing, not linking – distributed analytics