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Transitioning to human-centric care at scaleData & The City - Amsterdam

Rob IJff

Director Healthcare Innovation, Philips Research

November 7, 2016

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Philips HealthTech, portfolio impression

MR, dXR, CT, NM

imaging

Sleep & Respiratory CareImage-guided

interventions

UltrasoundAir Purification

Skincare Male grooming Sonicare & Airfloss

Oral healthcare

Population Health Management

Mother & Childcare

PreventionHealthy living Diagnosis Treatment Home care

Digital Pathology

Healthcare InformaticsPatient Monitoring Health Suite Digital Platform

Informatics: Cardiology, Genomics, Oncology, Neurology

Radiology, Management of Imaging Data

EMR & Personal Health Information Systems

The European healthcare challenge

EU spends around 10% of its GDP on healthcare

Only 3% of healthcare budgets of the 28 EU Member States is spent on prevention

80% of cardiovascular diseases, 90% of diabetes 2 and

50% of cancers are preventable

The European healthcare challenge

EU spends around 10% of its GDP on healthcare

Only 3% of healthcare budgets of the 28 EU Member States is spent on prevention

80% of cardiovascular diseases, 90% of diabetes 2 and

50% of cancers are preventable

EU is fractured into 28 different markets, each with varying healthcare device certification, professional health practice regulation, data privacy, security and data residency laws.

Single Digital market

Perceptions on readiness of Connected health carehttps://www.futurehealthindex.com/report/2016/

The FHI 2016: perception of readiness for Healthcare Transitioning:

• Accessibility of healthcare services

• Integration level of healthcare services

• Adoption of connected care technology in national healthcare systems.

In 13 markets: Patients, HC Professionals, Insurance, Public Policy makers

• Survey of 25,355 patients

• Survey of 2,659 healthcare professionals

• Over 300 qualitative interviews

• Age 18 – 34

• Age 35 – 54

• Age > 54

Future Health Index – some results

Data is proliferating, but data sharing continues to be a challenge

• 74% of patients have to repeat same information to multiple healthcare professionals. • 60% of patients have experienced repeatedly taking the same tests.• 40- 50% of patients and HCP name Health system bureaucracy as an obstacle.

Future Health Index – some results

Different perceptions of patient’s ability to health self management

Data is proliferating, but data sharing continues to be a challenge

• 74% of patients have to repeat same information to multiple healthcare professionals. • 60% of patients have experienced repeatedly taking the same tests.• 40- 50% of patients and HCP name Health system bureaucracy as an obstacle.

• 56% of patients say they have tools to manage their health.

• 69% of patients say they have the knowledge to manage their own health effectively.But just 40% of HCP agree to that.

• 79% of population > 55 years agree to be fully responsible for preventing poor health.This is 66% for population aged 18-34.

Future Health Index – some results

Different perceptions of patient’s ability to health self management

Data is proliferating, but data sharing continues to be a challenge

• 74% of patients have to repeat same information to multiple healthcare professionals. • 60% of patients have experienced repeatedly taking the same tests• 40- 50% of patients and HCP name Health system bureaucracy as an obstacle

Data Trust and Privacy concerns

• 8-22% of patients regard Privacy concerns as barrier for improved connected care• Majority of HCP have concerns about Liability, Trust and Privacy, Security

• 56% of patients have tools to manage their health

• 69% of patients say they have the knowledge to manage their own health effectively.But just over 40% of HCP agree to that.

• 79% of population > 55 years agree to be fully responsible for preventing poor health(66% for population aged 18-34.)

Data unlocked

891 millionradiology studies under management

8,1 million IoT devices connected to the Internet via HealthSuite Cloud

7 millionsleep therapy patients supported

getting a better night’s sleep

21 petabytesof imaging study data managed for healthcare providers

275 million patientstracked with our

patient monitors

last year

135 billion images managed

We already served over

7 million seniors with

our wearable Lifeline service

Creating rich profiles Moving care close to people

Wide dataLongitudinal, over a period

Deep dataIn depth insights

Dense dataPopulation level

Embedded algorithms

Computer aided diagnosis

Clinical Decision Support

Deep Learning pattern recognitionRisk stratificationClassification models

Predictive analytics

Advanced Visualization

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Car Sense Home CarePatient Empowerment

Non-camera, passive sensors Unobtrusive placement24/7 monitoringPattern recognition, outliers Alerts to care teamGo Safe and Fall Prevention

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Empowering patients in diabetes managementBlood Glucose meter, levelsInsulin intakeWeight scaleNutitionDecision supportPrivate messagingOnline community Connect to EMR, Hospital, Primary CareCloud based 24 / 7

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Population health management of chronic patients COPD, CHF, Diabetes

-32%

Hospital Admissions

90%

Patientsatisfaction

• Patient and Staff engagement by education• Development of new pathways • Development of telecare (call) center• Weight scale, Blood pressure meter• Clinical dashboard• Patient user interface optimized for Elderly,

Chronic Disease Mgt

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Primary care diagnosis with tele specialist consult with Philips LumifyPortable Ultrasound Reduces repeat examsReduces in hospital radiology Improves time to diagnosisAccelerates learning

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The cost of critical care is rising and there is a shortage of intensivists in the US.Need: Providing quality care with few resources at a low cost in rural areas

eICU program offers automated monitoring and remote care by specialists to support bedside ICU teams —standardizing high-quality care: 1 intensivist can supplement bedside support for 200 patients

Transforming critical care delivery

-20%

Dischargemoment

Number of ICU Days*

26%

Survival Probability with eICU *

* Lilly CM, et al. A Multi-center Study of ICU Telemedicine Reengineering of Adult Critical Care. CHEST. 2014 Mar; 145(3): 500-7.

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Digital pathology

Ultrafast slide scanner of tissue samplesDigital processing, archiving Case manager Workflow supportSupporting cancer research with Deep Learning

An Acute Care episode is a high care intensity stage in the Continuum of Providing Care.- Patient’s pre-event status can provide rich context to the current acute episode to be used for better outcomes.- Better understanding of the post-event status can speed up recovery, prevent readmissions

Deep Learning and Machine Learning techniques used to extract meaning from noisy data, enabling deterioration detection, clinical pathways guidance, and evidenced-based medicine. Advanced data visualization is used to facilitate insights, consultation and decision making by Physiciains, Nurses

Deep & Dense Data based Analytics for Acute Care

2000-2010

ICU Datasets of 100-10k patients, single hospitalStructured data: selected vital signs, medications, etc., ~10’s of parameters

Basic algorithm development, implementations of agreed standards, high false alarm rates

2010-2015

ICU Datasets of 50k-2M patients, multiple hospitalsStructured data: all vital signs, medications, lab values, etc., ~100’s of parameters

Advanced algorithm development, improved generalization to new environments, workflow integration

2015-2025

Datasets spanning Home, ED, ICU and more, 2M-10M patients, dozens of hospitals, structured and unstructured data: free text notes, claims, medications, etc., 1000’s of parameters

Algorithms are being personalized to the patient, considering patient history and likely therapy responses. Hospitals monitor and optimize care in real time, bringing evidence based medicine to the bedside.

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AI Maturity

Pote

nti

al t

o D

isru

pti

on

Aerospace

Cars

RetailBank, Ins

Cons SW

Enterprise SW

Semiconductor

Cons Electron

Acceleration of Artificial Intelligence

Source Zinnov, nnovations for the next decadehttp://zinnov.com/author/admin/

1950 Introduction Turing Test

1960 first AI programo play Tic Tac Toe

1997: IBM Deep blueDefeats Gary Kapsparov

2011 WatsonJeopardy champion

2015: Deep Mind’s self trained AI beats human players in 29 out of 49 Atari games

1943 Neural Networks (threshold logic, McCulloch, Pitts)

1995 Random Decision Forest (Ho)

>2000: Big Data Models

1950 Bayesian Models

Methodologies since the 50’s

1965 Deep learning(Ivakhnenko, Lapa)

J

Global distribution in # of AI start ups and $ invest

January 20, 201620

Source Zinnov, nnovations for the next dcasehttp://zinnov.com/author/admin/

J

Acute care clinicians challenged to assimilate large quantities of multimodal data in order to make therapy decisions. Data mining and machine learning methodology to critical care large databases to create algorithms for early alerts, risk stratification, therapy decision support.

Predictive analytics in acute care

Waveforms: ECG, EEG, Capnography, PPG…

Blood pressures, heart rate, cardiac output, tissue oxygenation, intracranial pressure, …

Medical record, nursing notes

Lab data

Imaging

Medication Types & Amount

Mechanical Ventilation: End tidal CO2, airway pressures, oxygen levels, …

Early detection of deterioration

Predictive algorithms for • Hemodynamic Instability• Acute Kidney Injury• Acute Respiratory Distress Syndrome

Therapy decision support

• Search existing database for similar patient cohort to derive therapy recommendations.

• Track patient response to therapy• When to wean off of therapy

Resource management

• Rank patient population by acuity• Estimate length of stay

-68%

ICU transfers from General

Ward

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Exploring - Heart Valve replacement procedures tailored to patient contextClinical pathway and protocol enriched by personalized treatment approach

Image Guided TherapyVolcano’s (Philips) portfolio of intravascular imaging and measurement devices and measurement catheters with Philips’ imaging solutions

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Patient and Professionals

Engagement Empowerment

TechnologyIntelligent sensing, diagnostic, therapy systemsCollaboration & workflow optimizationInformatics & artificial intelligence, cloud technologyIndustrialization of Healthcare

PharmaPayers

Research GP

Home care HospitalCaregivers

Transformation to human-centric networked care at scaleHigh Touch, High Tech

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Patient and Professionals

Engagement Empowerment

TechnologyIntelligent sensing, diagnostic, therapy systemsCollaboration & workflow optimizationInformatics & artificial intelligence, cloud technologyIndustrialization of Healthcare

HealthCareEvidence based Personalized medicineNew clinical and health pathways, incl preventionNetworked care delivery, Accountable Care Organization

ModelsInnovation by Co-creationNew data governance and managementRevenue sharing based on value outcome, late ROI

PharmaPayers

Research GP

Home care HospitalCaregivers

Transformation to human-centric networked care at scaleTransformation of a Societal system

www.linkedin.com/in/robijff