singapore healthcare landscape - analytics

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  • Advanced Analytics in Healthcare

    Singapore Healthcare Landscape -Analytics

  • 2

    IHiS: Who We Are

    VISIONINNOVATION

    INTEGRITY

    PASSION

    TRUST & RESPECT

    DYNAMISM

    To be the trusted technology partner

    in healthcare

    To lead and deliver technology for

    excellence in healthcare

    IHiS architects and manages highly integrated award-winning systems across Singapores public healthcare sector.

  • 3

    IHiS: What We Do

    supporting

    40,000Healthcare users

    6 19 Hospitals8 National

    Specialty Centers

    18 Polyclinics

    Clusters Agency

    > 780Systems andInterfaces

    Professionals

    1,200

  • 4

    IHiS: What We Do

    Applications

    Architecture & Innovation

    TechnologyManagement

    HCLOUD

    network services

    Data

    Cent re

    CLUSTER INFRASTRUCTURE

    Security

    Services

    BUSINESS SYSTEMS

    Patient Systems

    Healthcare Analytics

    Development Centre

    Diagnostics

    CPSS2

    S

    C

    M

    Pharmacy

    National Architecture Office

    Innovation

    Office

  • 5

    IHiS: Our Customers

  • 6

    Singapore Healthcare Landscape

    Primary Care

    - 18 Polyclinics (20%)

    -1,500 private medical clinics (80%)

    Secondary & Tertiary Care

    - 9 Restructured Hospitals & 8 national specialty centres (80%)

    - 10 Private Hospitals (20%)

    Step Down and Long Term Care

    - People Sector (70%)

    - Community Hospitals , nursing homes , Day care etc.

  • 7

    Singapore - Healthcare 2020

    Regional Health Systems

    Team-based care disease management models

    Community Health Assist Scheme (CHAS)

    Better Insurance Scheme

    More drugs to be subsidised

    Powering Up the Intermediate & Long-Term Care Sector

  • 8

    Digital Disruption Wave

  • 9

    Preventive Predictive Personalized Participatory

    Risk assessment and preventive treatment

    Genome analysis to predict risk of diseases and devise personalized treatment

    Greater accessibility to healthcare with wearables and online resources

    Data monitoring and collection by patients to mange their well being and make informed decisions

    Digital Disruption in Healthcare 4P

  • 10

    Everybody is talking about Big Data

    Source: Big data; The Next Frontier for Innovation, Competition and Productivity McKinsey Global Institute Report & comScore

    ,Radical Group

  • 11

    AlphaGo vs Lee Sedol

  • 12

    What is next after AlphaGo?

  • 13

    Google is Not Alone

  • 14

    Big Data the BUZZ

    Many people buy an expensive camera thinking it will make them a

    better photographer, when really better photographers have better

    cameras.

    In Healthcare we are

    struggling with small

    volume of data itself

  • 15

    Why Healthcare Analytics is SO HOT ?

  • 16SOURCE INFORMATION WEEK, MARCH 2013, HEALTHCARE ORGANIZATIONS GO BIG FOR ANALYTICS.

    How relevant is Analytics to Healthcare ?

  • 17

    Relevant yet difficult to evangelize

    We dont have data readily available for analysis

    We dont have enough funds/resources for analytics projects

    I want big data

    What do I do with the analytics output

    Which technology should I adopt

    IT is responsible for project

  • 18

    Operational Transformation

    Objective - Operational Excellence

    Approach Focus on business as usual

    decision that will be repeated frequently

    Outcome Enable capability

    to harness analytics in operations

  • 19

    People

    System

    Operations

    Ecosystem of Feedback Loop

    Process Generates data

    Analytics Insight

    Execution

    Inculcate the data driven culture

    through people, operation and

    technology

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  • 20

    Collaboration is the Key

    What information has been recorded

    Where is the data

    How to get the data

    How to deploy the model

    What is the problem

    What are the likely causes

    What do we need from the model

    How should we use the model

    How to frame the problem in modeling languages

    What models to use

    What variables to include integrate information

    from IT professional and medical professionals

    How to assess the model performance

    IT Professionals

    Medical Professionals

    Data Scientists

    Knowledge of the data

    Knowledge of the problem

    Knowledge of the technique

    Analytics Solutions

    Relevant

    Accurate

    Easy to deploy

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