Healthcare Intelligent System

Not only as an Information System,

The Next Generation Domain in Healthcare

Sung Soo Kim, MD, PhD

CIO, Yonsei University Health System

Professor, department of Ophthalmology

Yonsei University College of Medicine

HIS • Hospital Information System

• Clinical Information System (CIS)

• Patient Data Management System (PDMS)

• Healthcare Information System • At First, as a Simple electronic Medical Record • Healthcare-ICT convergence • Including Healthcare service & Resource Management

• Not only as a Medical Record, but also as a Total Care.

• Total Solution for Healthcare System Management ⇒ More Faster, Accurate & Proper Healthcare Efficient Healthcare Service in Scale of Economy

Background of Korean Healthcare-ICT

Convergence Very Tough Conditions of

Korean Healthcare System

Korean Healthcare System

•Only One Dominant Healthcare Service Payer • The National Health Insurance Service(NHIS)

• Strong Centralized Healthcare Control System • Health Insurance Review & Assessment Service(HIRA) • Control All about Healthcare Service including Price of Medicine &

Healthcare Products • Restrict Expenditure of National Healthcare Service • Low Fee for Service compared to other OECD Countries

• In Principle, Korean Healthcare Institutes are “Non-Profit” by Law. • should not make “Profits” except for Reinvestment • No Stockholders in Healthcare Institutes.

Three Major Values in Korean Service Business

1. Faster! Faster!

2. Accurate Proper Results!

3. Brand New is Better, Oldies are Boring!

•Korean pursue Fast, Accurate Service by New Technology! Also in Healthcare

Fast, Accurate & Efficient Korean Healthcare Service

• Choice of Korean Healthcare Institute

• ICT Convergence into Total Healthcare Service

• Low Fee for Service ⇒ Need Cost Reduction & Scale of Economy in Service

• Need More Accurate Service for Customer ⇒ Digital Healthcare Technology

• Korean Healthcare ICT Convergence System

Yonsei University Health System Since 1885. The First Westernized Hospital in Korea

Graduate School of Public Health

Severance Hospital, 2005

Gangnam Severance Hospital, 1983

Yongin Severance Hospital Dental Hospital

College of Nursing

College of Medicine College of Dentistry ABMRC

Yonsei University Health System (YUHS)

• Severance Hospital (main)

• Licensed Beds: 2,091(‘13) • Outpatient Visits: 9,023/day(‘13) • Number of Inpatients : 91,235(‘13)

• Gangnam Severance Hospital • Licensed Beds: 805(‘13) • Outpatients visits: 3,386/day, Inpatients : 40,313(‘13)

• Youngin Severance Hospital: 118 beds(‘13), • Outpatients visits: 726/day(’13)

(Total 3,102 beds and 10,320 employee)

Consists of 2 graduate Schools(Public Health and Nursing), and 3 colleges (Medical, Dental and Nursing College)

Consists of 3 General Hospitals and 1 Mental Health Hospital

Yonsei University Health System (YUHS)

• Severance Hospital (main)

• Licensed Beds: 2,322(‘15) • Outpatient Visits: 9,560/day(‘15) • Number of Inpatients : 98,553(‘15)

• Gangnam Severance Hospital • Licensed Beds: 814(‘15) • Outpatients visits: 3,446/day, Inpatients : 38,994(‘15)

• Youngin Severance Hospital: 106 beds(‘15), • Outpatients visits: 712/day(’15)

(Total 3,102 beds and 10,320 employee)

Consists of 2 graduate Schools(Public Health and Nursing), and 3 colleges (Medical, Dental and Nursing College)

Consists of 3 General Hospitals and 1 Mental Health Hospital

• Severance Hospital Opened 2080 beds • Operating 5 Specialized Hospitals

• Full functioned HIS was Required!!! Not only E-MR!

• 2005. 4. ERP opened (SAP)

• 2005.11. u-Severance(OCS/EMR) opened

2007. JCI certificated Severance Hospital

2010. JCI certificated Gangnam Severance Hospital

• 2011. The First International u-health system between Gangnam Severance Hospital & Vladivostok, Russia

• 2012. 7. Healthcare-ICT JV(H∞H Healthcare) with KT corp.

HIS Requirements in Big Hospital

•For Hospital Management •Stability: Never-Stop System •User Convenience •Cost-Effectiveness

•For Hospital Network & Expansion •Portability •Scalability • Interoperability

Strategies of u-Severance.net Build-Up

• One Source, One Code, One Platform

• Same Structured • Architecture(.Net) & Database (EMR&OCS): MS SQL

• Same Customized of User Interface (UI) • Minimal customizing for Subdivisions

• Central Integrated Support System in Main Datacenter • ERP, GW, CDRS, E-Mail

• Reliable integrity of Clinical Document & DBs

• Unified Clinical DBs: Easy retrieval of individual data • Bases of Big Data Mining

Unified Patients ID Numbers

Severance Hospital Gangnam Severance Hospital

Yongin Severance Hospital Severance Mental Health Hospital

(Range: 8900000 ~ 8999999)

Only One Digit ID Number(7 Digits) for One Patient in YUHS

(Range: 7350000 ~ 8899999) (Range: 9000000 ~ 9500000)

(Range: 9500001 ~ 9990000)

Complete HIE Big scale CDW

Real-time sharing of Data

Satellite Hospital Networking

• Yongin(120 beds) Hospital • Relatively small Hospital compared to Main center

• DB & VDI server in Gangnam SH • App & Interface Server in Yongin SH • Two H/W manager Required

• Cost: ~US $450,000 by Our own Manpower

• 6 Months SI on 2012 • Running without Critical Error through 3 years

Yongin Severance Hospital

Storage (1TB)

Backup NAS Storage

N/W Core Switch

OCS APP Server

OCS/EMR

APP#1

IIS

.Net Framework

COM+

Windows2008R2

TCP/IP

OCS/EMR

DB Server

SQL Server 2005

MSCS

COM+

Windows2003

TCP/IP

AD/SCCM server

환자대기 서버

OCS/EMR

APP#2

IIS

.Net Framework

COM+

Windows2008R2

TCP/IP

Patient Monitor

OCS MIS

Windows2008R2

TCP/IP

Smart Card

DB Server

SQL Server 2008R2

OCS MIS

Windows2008R2

TCP/IP

OCS/EMR App Server (4core*1CPU,4GB RAM) AD/SMS Server

(4core*1CPU,8GB RAM)

L4 Switch L4 Switch

PQ 440 CPU(12), 16GB RAM

Gangnam Severance Datacenter

40Mbps Line

AD/SCCM

IIS

SCCM/WSUS

SQL2008R2

Directory Services

DNS

Windows2008R2

TCP/IP

OCS/EMR

KMI

KMI(certificate)

TCP/IP

OCS/EMR

DB Server

SQL Server 2005

MSCS

COM+

Windows2003

TCP/IP OCS/EMR

Credit Card

Credit Card

TCP/IP

VDI Storage

VDI server

VM 50

Clustering

u-Severance.net ASP Model

VDI Clients

Effects of u-Severance.net

• Running Cost(2004~2011) • Approx. 70 million $US / 7 years • 2012 Budget : 10 million $US for 4 General Hospitals

• Relatively lower cost S/W(MSSQL) & H/W(x86 NT server)

• Sharing of Maintenance • Overall 72 members in YUHS DMIT

2004 2005 2006 2007 2008 2009

$131'974 $162'887 $192'599 $227'178 $252'872 $280'497

$234'529 $274'343

$346'671 $366'226

$404'307 $440'144

$366'503

$437'230

$539'270

$593'404

$657'179

$720'641

u-Severance Open

OPD revenue InPatient Revenue Total Reveneue

Changes in Healthcare Revenue Unit= US$ 1,000

97%↑

OCS EMR

EIP Enterprise Information Portal

u-Severance Domain

YUHS integrated HIS

HDW Health Data WareHouse

PACS

Severance PACS(GE)

DICOM Non-DICOM

Cardiac PACS(Infinit)

Dental PACS (Ty)

Gangnam PACS (GE)

Severance OCS

Gangnam OCS

Yongin OCS

Dental OCS Mobile(iOS)

CDRS CDSS

Order

RIS

LIS

GW

PM/PA EDI

HR/MM/FICO/PS/FM/PM

SAP ERP

ABC

SEM(Strategic Enterprise Management)

BSC(Balanced Scorecard)

E-Mail

EIS

MIS

ESS/MSS

Web CRM(Customer

Relationship Management)

U-CTMS

Data Capacity & Usage

Category Allocated

Capacity (TB) Available Capacity (GB) Monthly Usage (GB)

EMR 19.7 5,526 170

OCS DB 11.2 3,531 90

ERP DB 4.2 807 20

BW DB 9.3 982 90

GW DB 1 417 7.2

Exchange DB 11.1 4697 48

CDR 3.6 1,897 21

Subtotal 60.1 17,857 446.2

GE PACS 710 250,000 4,500

Cardiovascular PACS

200 50,000 1,000

Dental PACS 7.8 3,900 65

Subtotal 917.8 303,900 5,565

Total 977.9 321,757 6,011.2

(20 Feb 2014)

Data Capacity & Usage

Category Allocated

Capacity (TB) Available Capacity (GB) Monthly Usage (GB)

EMR 22.2 4,743 184

OCS DB 15.5 5,878 121

ERP DB 4 527 22

BW DB 10.6 6,229 98

GW DB 1.3 478 18

Exchange DB 11.8 3,496 163

CDR 3.6 1,437 41

Subtotal 69 22,788 647

GE PACS 720 98,000 6,000

Cardiovascular PACS

400 200,000 1,000

Dental PACS 7.8 2,800 65

Subtotal 1,127.8 300,800 7,065

Total 1,196.8 323,588 7,712

(18 Oct 2015)

Data Capacity & Usage

Category Allocated

Capacity (TB) Available Capacity (GB) Monthly Usage (GB)

EMR 22.8 2,883 193

OCS DB 14.9 3,938 133

ERP DB 5.2 4,114 32

BW DB 10.6 5,464 56

GW DB 1.3 357 28

Exchange DB 11.8 3,094, 163

CDR 3.6 1,204 55.7

Subtotal 70.2 17,960 660

GE PACS 720 50,000 6,500

Cardiovascular PACS

400 144,000 1,000

Dental PACS 21 7,000 70

Subtotal 1,141 201,000 7,570

Total 1,2112 218,960 8,231

(24 June 2016)

First Visit

Patients

Follow-up

Patients Total

Yearly 470,856 1,996,817 2,467,673

Monthly Average

39,238 166,401 205,639

Daily Average

1,722 7,301 9,023

Number of Outpatients Visiting YUHS

*Operated by One Unified HIS Solution *Maximum Daily Visiting Patients : 11,158 (25 Sep 2013) *** Registered Patients: 5,853,780 (20 Feb 2014) 3,725,008(Severance H) 1,788,680(Gangnam SH) 326,372(Yongin SH), 13,720(Gwangju SH)

(Jan 2013 - Dec 2013)

First Visit

Patients

Follow-up

Patients Total

Yearly 519,678 2,609,914 3,129,592

Monthly Average

43,307 217,493 260,799

Daily Average

1,904 9,560 11,464

Number of Outpatients Visiting YUHS

*Operated by One Unified HIS Solution *Maximum Daily Visiting Patients : 11,877 (3 Sep 2015) *** Registered Patients: 6,624,619 (10 Jul 2016) 4,221,252(Severance H) 2,041,781(Gangnam SH) 361,586(Yongin SH),

(Jan 2015 - Dec 2015)

Pop. of Switzerland 2014 8,236,600

Paradigm Shift

• Rising of Far Advanced ICT Technology

⇒ Change of Concept & Paradigm in Traditional Healthcare

1. Healthcare Continuum & Healthcare Web • Smart Mobile Technology

2. Healthcare Big Data as a New Potent Resource • Machine Learning & Artificial Intelligence

3. Healthcare Intelligent System

Healthcare 3.0

• Healthcare-ICT Convergence

• Advancement in Web

• Patient Consumer

• Leading by

• Hospital MD Consumer

• Treatment Prevention

• Waiting Real-time

• Place

• Stand-alone Networking

• Health Information

• MDs Consumer(SNS, PHR)

Delloite, Healthcare 3.0, 2013

Severance Life Tag Project

• NFC integrated Tag for Chronic Illness, Silver Care, and Emergency Situations

BOM Project in Malawi, Africa Cataract Surgery in Mobile Surgery Unit

Integrating & Connecting Everything

into

C-Health.Net

Other Country Healthcare Service Network Systems

Smart Hospital

His.Net

Mobile Healthcare

Unit

Home Healthcare

u-health & Visiting

Outdoor

LifeTag & Wearable

ContinuativeHealthcare.Net YUHS

3rd Party Healthcare Service Network Systems

Mobile Private Clinics

Private Clinics

Data & System Management

By Cloud Technology

Healthcare Big Data

• Big Data in Nature • Real-time Accumulation of Data

• Big Volume, Growing as time goes on

• Big Complexity of Analysis

• Big Numbers of Sources (HIS & C.Health.net)

• Importance of “Time” & “Experience”

• Integration with Other Big Data • Social & Cultural

• Financial

• Climate & Environment

S.Kim’s Publication with BiDa

1. Rim TH, Lee CS, Lee SC, Kim DW, Kim SS. INTRAVITREAL RANIBIZUMAB THERAPY FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION AND THE RISK OF STROKE: A National Sample Cohort Study. Retina. 2016 Jun 23. [Epub ahead of print] PubMed PMID: 27341664.

2. Rim TH, Oh J, Kang SM, Kim SS. Association between retinal vein occlusion and risk of heart failure: A 12-year nationwide cohort study. Int J Cardiol. 2016 Aug 15;217:122-7. doi: 10.1016/j.ijcard.2016.04.174. Epub 2016 May 3. PubMed PMID: 27179901.

3. Rim TH, Lee CS, Lee SC, Kim S, Kim SS, Epidemiologic Survey Committee Of The Korean Ophthalmological Society. Association between Previous Cataract Surgery and Age-Related Macular Degeneration. Semin Ophthalmol. 2016 Apr 29:1-8. [Epub ahead of print] PubMed PMID: 27128789.

4. Rim TH, Han J, Choi YS, Hwang SS, Lee CS, Lee SC, Kim SS. Retinal Artery Occlusion and the Risk of Stroke Development: Twelve-Year Nationwide Cohort Study. Stroke. 2016 Feb;47(2):376-82. doi: 10.1161/STROKEAHA.115.010828. Epub 2016 Jan 7. PubMed PMID: 26742801.

5. Rim TH, Kim DW, Kim SE, Kim SS. Factors Associated with Cataract in Korea: A Community Health Survey 2008-2012. Yonsei Med J. 2015 Nov;56(6):1663-70. doi: 10.3349/ymj.2015.56.6.1663. PubMed PMID: 26446652; PubMed Central PMCID: PMC4630058.

6. Rim TH, Choi M, Yoon JS, Kim SS. Sociodemographic and health behavioural factors associated with access to and utilisation of eye care in Korea: Korea Health and Nutrition Examination Survey 2008-2012. BMJ Open. 2015 Jul 16;5(7):e007614. doi: 10.1136/bmjopen-2015-007614. PubMed PMID: 26185177; PubMed Central PMCID: PMC4513532.

7. Rim TH, Lee CS, Lee SC, Chung B, Kim SS; Epidemiologic Survey Committee of the Korean Ophthalmological Society. Influence of visual acuity on suicidal ideation, suicide attempts and depression in South Korea. Br J Ophthalmol. 2015 Aug;99(8):1112-9. doi: 0.1136/bjophthalmol-2014-306518. Epub 2015 Mar 2. PubMed PMID: 25733526.

Health Big Data : Future

Why We Take an Attention?!!

Combined with

Machine Learning & Deep Learning

Quantum computer(HW)

Network Speed : 1G wireless & 40G Wired~

Cluster Computing & Cloud Technology(SW)

정보(情報)

Data Information Intelligence Knowledge Wisdom Consciousness

Now, To be Intelligent!

• DIKW Hierarchy(Pyramid), Jennifer Rowley, 2007

Healthcare INTelligent System HINTs

•Concept of Next Generation Digital Health System

•Healthcare ICT System integrated with • Active Advisory Support • Beyond hospital Networking : IoT & Smart mobile Health • Real-Time update of Informatics & Research • Continuous Verifying Evidence & Meta-analysis

• Mining New Possibilities in Healthcare • Precision Medicine

• Personalized Medicine

• Robotics

HINTs Requirement • Multicenter Participation with High speed Network

• Cloud Platform • Cluster Computing • Distributed Data Processing System • Cloud storage & security system

• Components • Multicenter Sharing Healthcare Information System • Domestic & Global

• Health Big Data based Data Warehouse(HDW) • Multicenter EMR • Genome Cloud

• Machine & Deep learning based Analytics & Reporting

• Almost Technology is Available Now.

5 Staging of HDW Health Data Warehouse

EMPI (Enterprise

Master Patient Index)

Landing Reporting Data Stores\Marts Staging Sources

Mu

lti

Faci

lity

Da

ta

Clinical Data Mart

Reporting & Analytics

Standard Reporting

Ad-Hoc Reporting

Structured Reporting

Unstructured Reporting

Clinical Data Repository EMR

EHR

CPOE

ET

L\H

L7

Provider Client

Connect ODS

(Clinical Operational

Administrative Data) Provider Client System

Data Warehouse

Subject Area Data Marts & Cubes

Business Dimensional

Views

ET

L

ET

L

Data De-identification masking D

ata

Im

po

rt

Ex

po

rt E

TL

Provider Client Connect Reporting

System HIE for select transactional clinical data (ESB)

Parser

Relational Databases for Priority Use

Cases Reporting &

Analytics

Hadoop

Claims Health

Informatics

HL

7

ETL Claims (External

)

HL

7

Unstructured

Other external

Other

Data Warehouse

Extract, Transform, Load (ETL)

Analytical Reporting

Dynamic Dashboards

CDSS

Stage 1: Self Service Reporting 환경 구성을 위해 모든 기간계 (Clinical OLTP) 데이터를 Clinical Data Repository에 적재함

Stage 2: Clinical Data Mart를 primary data source로 하는 통합 Ambulatory Self Service Reporting 환경을 위해 EMR 데이터 를 활용하여 Clinical Data Mart를 구성함

Stage 3: Provider Client Connect로 원무 데이터를 통합하여 집단건강관리 보고/분석 체계를 구축함 (환자&의료진 포탈을 통합 포함)

Stage 4: 보고 템플릿 (예산/근로생산성 보고, 원가 계산/서비스 라인 보고, 누수 방지 보고) 이 내장된 통합 Relational Database와 보고 및 분석 Solution Suite을 도입함

Stage 5: 재무, 운영, 진료 (입원/외래 등) 데이터의 저장, 표준화, 집합화를 위한 Provider Client Enterprise DW 구축을 진행함

1

2

3

4

5

Analysis & Reporting

• CDRS : Report Table Only without Analysis

Analysis & Reporting

• HP Autonomy, MS PowerBI

Machine Learning & Deep Learning..

• Purification of Complex Healthcare Big Data • Removal of Bias in Health Big Data

• Make Dataset for AI Training

• AI Learning Cycle System • AI teaching AIs

SEverance Health Informatics SEHI Project Roadmap

• Stage 0. Consulting & Trial by Expert Companies

• ISP Consulting by H∞H Healthcare, 2013

• Big-Cen Med at Gangnam Severance Hospital, 2015

• Stage 1. Infra Build-up & CDW Extended : 2016 • Query & Real-time Listing for Basic statistics

• Big Data Oriented Data Integrating

• ERP & Research Data

• Unstructured Data Mining

• Stage 2. Integrated HDW(Total DW for Healthcare) : 2018 • Combined with Cloud Platform

• Stage 3. Automated Analysis & Reporting (CDSS & CP): 2020

• Stage 4. HDW-based AI Teaching System: 2023

• Stage 5. Artificial Intelligence integrated HDW(HINTS, SEHI Final) • 2025 **Real Healthcare Oracle by Deep Learning Machine

Turning Point…

Rising of Healthcare AI

IBM Watson

DeepMind Health

Healthcare AI

IBM Watson

• Since 1957 / real AI 2014

• Collaboration : Memorial Sloan Kettering

• AI learning with 0.6M cases & 2M Articles

• Starting with Big Data Analysis • NLP(Natural language processing)

• Unstructured Information Management Architecture (UIMA)

• Medical Concept Extraction Tooling

• Health Language Medical Terminology Management

• IBM Industry Solution Services Healthcare Annotators Assets

• Commercial Applicable Now. • Manipal Hospital(India), Bumrungrad

International Hospital (Thailand)

• SK C&C for NLP(Korea)

DeepMind Health

• Since 2015

• Collaboration : NHS

• MOU with NHS

• 5 year plan for AI into NHS

• AI Learning of 1.6 M NHS Pts’ Data

• improvements in clinical outcomes, patient safety and cost reductions

• Streams & HARK app

In the Era of Healthcare AI

• Evidence based Medicine • Real-time update of Evidence(Adopted) Medicine

• Different Concept of Stats • Evidence from Sample vs. Status by Near Total Population

• Randomized Case Control vs. Big Data based Retrospective Study

• Virtual Simulation of New Knowledge • New findings in Big Data applied on Genomics

• Life long e-Cohort by C.Health.Net

Structure of HINTs

WWW OCS

EMR EIP Enterprise Information Portal

Hopital Information System

PACS

Severance PACS(GE)

DICOM Non-DICOM

Cardiac PACS(Infinit)

Dental PACS (Ty)

Gangnam PACS (GE)

Severance OCS

Gangnam OCS

Yongin OCS

Dental OCS Mobile(iOS)

Order

RIS

LIS

GW

PM/PA EDI

HR/MM/FICO/PS/FM/PM

SAP ERP

ABC

SEM(Strategic Enterprise Management)

BSC(Balanced Scorecard)

E-Mail

EIS

MIS

ESS/MSS

U-CTMS

HDW Health Data WareHouse

CDRS CDSS & CP

Machine Learning & Deep Learning System

Cloud Infrastructure

HINTS : Different Perspectives

• Healthcare : Take Care of Human, Not Data & Material

• Design of HINTs • Augmented Healthcare Service by • Collective Intelligence of Healthcare Experts & Patients

• Became More Wise, Not smart

• Make Different Derivatives • Intellectual Properties • New Knowledge derived Business

• Consciousness

Healthcare Intelligence derived Process

The future is already here. It's just unevenly distributed…

William Gibson, Neuromancer 1984

And Not Fully Integrated Yet!

With the love of God, free humankind from disease and suffering.

Mission of Severance Hospital

Thank You for Your Time!