Resilient Healthcare

by IT support

IDU-D 2018

OrangeTechLab inc.

Jun Miyazaki Ph.D.

Introduction

• To be ready for unexpected incidents of pandemic or paramedic situations in developing countries

• Because these situations will affect and be spread globally to all over the world.

• In this presentation, we propose a resilient health care approach utilizing several IT technologies.

• IT technologies are statistical analysis, process mining, and finally AI based analysis.

Rise of Resilient Systems and engineering

• Resilient Systems and Engineering has been

started since early 21st centuries.

• The objective of the systems and engineering is

how we can manage both predictable and

unpredictable incidents in social activities.

• Based on unpredictable incidents like Tsunami

we Japanese experienced,

• Japan has more paid attention to Resilient

Systems concept

• The system can be capable even unexpected

disaster incidents may occur in the future.

3

The requirement for Resilient Healthcare in

developing countries

• Resilient Healthcare is one of resilient systems• predicted and unpredicted medical incidents and processes

are the problem domain.

• In developing countries, resilient healthcare is

very important• pandemics or severe paramedic situations may occur

• these threats are not only for the original countries but also

may be big threats for rest of the world.

• we propose ITU-D, how we can manage these

threats by applying resilient healthcare systems.

4

Resilient Healthcare as Resilient Engineering

There two levels of resilient safeties:

Safety-1: (Avoid failure processes, and keep successful

processes)

• Key principle of Resilient Healthcare is “to keep the

successful processes as much as possible”, rather

“to salvage failure processes” and avoid failure

processes.

Safety-2: (Predictive safety management)

• The activities are coordinated to keep the processes

to be succeed than to be failed by the human experts’

efforts, and predictive safety management is required.

5

Three Phase approach

• First phase

• Gather several predicted and unpredicted examples in the past

healthcare facts from every ITU-D related countries

• Data will be formalized to be a resilient example database.

• How we can set up anonymity is the challenge at this stage.

• Second phase• Analyze explicit and implicit processes in these descriptions, by

manually and semi automatically as possible.

• Third phase• Introduce statistical analysis, process mining utilizing machine

learning such as temporal deep learning technologies to analyze

both explicit and hidden processes in the database.

• Find what aspects will be the key feature for resilient healthcare

• Automatic prediction and unexpected process discoveries.

Three Phase approach

• First phase

1. Gather several predicted and unpredicted

examples in the past healthcare facts from

every ITU-D related countries

2. Data will be formalized to be a resilient

example database.

3. How we can set up anonymity is the

challenge at this stage.

Fig1. Phase1: Gathering Text Information

Raw data

Raw process

descriptions

in country A

Data Factory

Raw data

Raw process

descriptions

in country i

Raw data

Raw process

descriptions

in country n

Resilient Healthcare

data base

Data Tools

Data Tools for multi-kind Big Data

IoT censors data acquisition(censor data tools)

Scraping tools(in inter / intra)

Normalization for data schema

Anonymizetools

Legal(user agreement)

Data cleansing（noise reduction）

Training data tools

Video & imageprocessing

Fig2. Phase1: data tools

Three Phase approach

• Second phase

• Analyze explicit and implicit processes

in these descriptions, by manually and

semi automatically as possible.

Site Name 凡例

AAA hospital proces description

level1 Level3 Level3paramedic ①Process Design

②process

⑤information

③element

④medicine

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start/stop process description connectorSys 外部IF

案件情報送付

情報受領 ﾞ案件情報送付

Fi le Server

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面談調整

メール

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情報受領

指示書

検討 面談設定

案件情報確認 面談参加

面談実施

Fig4. Phase2: Process Description

Video cameras

in

in fields j

Video cameras

in

field m

Video in fields (airports, hospitals, etc.)

(Existing security cameras can be used)

Data Factory

Resilient Healthcare

data base

Data Tools

(Video&image)

On line

Off line

Fig3. Phase1: Video in the fields

Three Phase approach

• Third phase

1. Introduce statistical analysis, process mining

utilizing machine learning such as temporal

deep learning technologies to analyze both

explicit and hidden processes in the

database.

2. Find what aspects will be the key feature for

resilient healthcare

3. Automatic prediction and unexpected

process discoveries.

Deep Learning

Training

(temporal neural

Network

analysis)

Detect and Prediction

• Face and Face expression

analysis

（Gaze, Paralysis, Fever)

• Gaits, Posture

Symptom of disease

Paralysis

Depression

Lack of sleep, etc.

• Analyze videos

and predict

situations in

pandemic and

paramedic cases

• Support Safty-1

and Safty-2 in

resilient healthcare

Data Factory

Data

Tools

Fig5. Phase3: AI processing for video data

Resilient

Healthcare

Videos

Learning

Factory

Medical application1 : Rehabilitation

• Take videos for patients and analyze the status• Manage past videos then propose and how to do rehabilitation• This can be applied to sport rehabilitation too

Analyze a person who is parallelized left hand side

Discussions and further issues

• How we can find hidden processes in resilient operations

• Multi-disciplinary approach is needed

Process Mining

• Process Mining is still in more research phase than the other technologies (machine learning and deep learning).

• But through this proposed activity, many resilient processes are gathered and well manually described in phase 1 and 2

• this condition will give chances to apply deep learning technology (especially temporal deep learning technology, such as RNN, LSTM etc.

• to process mining, as generally deep leaning needs more trained data for its better learning.

Fig6. Process Mining in Resilient healthcare

Resilient Data Base

(Event logs

and

Existing Process

Descriptions)

Resilient

Processes

Discovery

Conformance

Enhancement

IT systems

(Electric Medical Record

systems,

Mail systems,

Accounting systems

Etc.

Real World

(Pandemic &

Paramedic)support

Requirements

And designModeling

And Analysis

Ref) Replace Routine works into office software robotsEx) monthly review support with RPA (Robotic Process Automation) :

• Every month, people have to create KPI graph from several fragments of

excels which are downloaded by un-flexible intra-web systems

• Replace routine operations into RPA (software robots)

19

Intra

systemsCSV

files

excel

Excel macro

Files by each

Org.

Cut&Paste

into

slides

reportsMgt

Meeting

With stakeholders

Improvement process

KPI

visualization

Back logs

Discussions and further issues

How we can find hidden processes

in resilient operations

Multi-disciplinary approach is

needed

1.Domain

Knowledge• Healthcare

• Data Source

• Semantics

of Data

• Legal

3.Data Science• Data Model

• Analytics

Framework

• Statistical model

• Machine Learning

• Deep Learning

4.Systems

Architecture• Building System

Architecture

• Rapid

Dashboard

5.Consultation for AI/IoT & Data Science

Fig7: Multi-Disciplinary Business Architecture

2.Resilient

Engineering• Process

description

• Safty-1

• Safty2

Conclusion

We proposed following phases

• Phase1: Gathering process data to analyse resilient

healthcare in daily activities.

• Phase2: Describe both successful processes and failure

processes

• Phase3: Analyze described processes, discover hidden

processes, and predict critical processes

According to these phases, we will be able to support resilient

healthcare to be more systematic and semi-automatic manner.

We would like to invite ITU-D related organizations to join this

proposed activity.

22

Appendix

23

• Take videos for paralyzed elder patients and support

• Quantize the paralysis by wireframe

• Feedback them to doctors, patients and family

Wireframe model analysis to video

High level

analysis

Feed back to

Patients,

elders

High level analysis is subscribed as patent from OrangeTechLab

Application 3: support elders

Visualize paralysis by wireframe

Gaits Analytics Systems Image

deep

learning

a)Gaitsraw data /

footage

b)Traineddata

Data Factoryc) Training byFast AI machines

Trainednetwork

d) Create compacttrained network(distillation)e)Use compact inference engine smart phones

e) re-enforcement learningBy capturing from smart phones

f) Big data for application domainsEx appl. domains) Medical, sports, etc.

Inference on

Smart phones

- Subscription biz models for several gaits, posture, action analysis -