5th International Meeting of Public Health (IMOPH) 2019

09 – 10 September 2019Universitas Indonesia - Depok

Smart Telehealth Implementation to Solve Disparities in Indonesian Healthcare Service

Prof. Dr. Eng. Wisnu Jatmiko Faculty of Computer Science

Universitas Indonesia

Prof. Dr. Eng. Wisnu Jatmiko, S.T., M.Kom.

Outline1. Current Innovations

Healthcare in Indonesia

2. Telemedicine IntroductionTelehealth Systems – Recent Developments in Indonesia

3. Challenges and OpportunitiesTelemedicine Implementation

4. Health Supporting SystemDriver Drowsiness Detection

5. Previous InnovationsIntelligent Transportation System & Gas Leak Detection

Current InnovationHealthcare in Indonesia

PopulationOver 250 million people with more than 1.000 tribes.

Geographical Information33 Provinces with 497 Districts and 92 Small Islands that neighborsother countries.

IndonesiaA Nation in South East Asia covering 2 million square kilometers with over 17.000 islands.

Cardiologists in Indonesiaare based in Jakarta

44% 319 24% 3100

Number of Cardiologist inIndonesia

Number of Obgyn Specialists in Indonesia

Obgyn in Indonesia are based in Jakarta

Cardiologist Obstetrics and Gynecology

Telemedicine - IntroductionTelehealth Systems – Recent Developments in Indonesia

Telemedicine is a remote medical practice, which utilizes advanced telecommunications and information technologies for the delivery of healthcare and the exchange of health information across distances.

Foguem et al., 2015

Telemedicinechanged the medical

collaborative decision making

and doctor–patient relationships and has an

impact on the responsibilities of

physicians to patients andhow to treat them

Common Telemedicine Concept

Two-way interactive CommunicationStore and forward

Video-conferencing, Live messaging, or face to face ‘real time’ consultation

Usually used for non-emergencysituations

e.g. Tele-cardiography andTele-ultrasound

Evolution of Telemedicine

TelemedicineWays of Communication

Multipoint to MultipointSeveral patient ends connected to several different specialist doctorsAt different hospitals, in different geographicaldistances

03Point to MultipointOne patient end at a time connected to many specialist doctors Within the same hospital

Point to PointOne patient connected to one doctor within same hospital

02

01

TOPOLOGY OF TELEMEDICINE ACTIVITIES

Tele-expertiseA medical professional can seek remotely an opinion of other medical professionals who have the relevant training or skills.

Tele-monitoringThe ability to monitor and supervise patients remotely.

Tele-assistanceA procedure, which enables a medical professional to assist remotely another healthcare professional during the realization of a medical act.

Patient

RequestingPhysician

Required Physician

Tele-consultation

Nurse / PhysicianTele-Monitoring

Tele-expertise

Tele-assistance

Tele-consultationA procedure whereby medical professionals can consult a patient remotely and interpret the necessary data remotely for medical follow-up.

Simon and Pellitteri, 2012

Telemedicine Expert Process Model

CompetencyRepresents the skill

set of medical participants involved

in an activity

ActivityA medical activity is rooted in a telemedicine process. An activity has objects as input and output and it needs medical competencies for performing treatments.

PatientA telemedicine

process schedules apatient to be treated

ObjectsAs defined in

telemedicine includeinformation,

documents, samples, or organs exchanged

among medical participants and/or

among a patient and medical participants

ToolsUsed by telemedicine experts, which include

computers, knowledge-based systems, to be

used by participants for accessing, forwarding, receiving, and sharing

medical objects

DirectionsProcedures to be

followed when performing an activity,

including guidelines that govern the

behavior of medical participants involved in

an activity

Stamm et al., 1998

Tele-USG

USG SystemIn our Tele-USG, a complete system have been developed. The main featureof our system is detecting body parts of the fetus to reduce the risk of death in pregnancy by monitoring the growth rate of the fetus.

1 Fetal Organ Segmentation

2 Fetal Organ Approximation

3 Fetal BiometricMeasurement

Product Video

TELE-USG METHODSFetal organ detection uses supervised approach using boosting ensemble classifier based on stump weak classifier.For fetal organ detection, we have made training sample by cropping fetal organ from ultrasound images. Then the instances are used to training the classifier. In this study, the classifier used haar feaures generated from training samples

Fetal OrganSegmentation

TELE-USG METHODSAfter being segmented, then the curve of fetal organs is approximated using Hough Transform based approximation. Fetal head and abdomen are approximated by ellipse curve, whereas fetal femur and humerus are approximated by line curve. In this study, there are several variations of Hough transform used. There are Randomize Hough Transform (RHT), Iterative Randomize Hough Transform (IRHT), and Eliminating Particle Swarm Optimization Hough Transform (EPSO-HT)

!= "#+ $!% 1

" = − $##% %

#&'(θ + ysinθ 2

)2 +xsinθ − ycosθ 2

*2 = 1

#2 + !2 − + #2− !2 − 2,#!− -#− .!− /= 0

Ellipse parameters [a, b, x0, y0, θ] can be extracted using following set of equations.

.,+ -+ -+#0 = 2(1 − +2 − ,2)

-,+ .− .+!0 = 2 1 − +2 − ,2

)= 2/+ #0 -+ !0 .

2 1 − +2 + ,2

*= 2/+ #0-+ !0 .

2 22 1 + + + ,12

0 = arctan,+

(16)

(17)

(18)

(19)

(20)

Fetal Organ

Approximation

Fetal Biometric Measurement

The last step, from the fetal head image, thesystem computes head circumference (HC) andbiparietal diameter (BPD).From the fetal abdomen, the system computesthe abdomen circumference (AC).From the fetal femur and humerus, the systemcomputes the femur length (FL) and thehumerus length (HL).

Tele-ECG

ECardioE-Cardio is an integrated system that helps people to examine their cardiovascular health, without having to meet a doctor. This is especially useful in a situation like Indonesia.

1

2

3

SensorsThe system utilizes sensors to measure a person’s heartbeat and will visualize and store the heartbeat data in an Android smartphone

ClassificationThe system could also provide an automatic classification of the person’s cardiovascular health. In addition to that, the system also sends the person’s data to a doctor.

TransmissionDeveloped a method for ECG signal compression to be transmittedvia cellular signal

Tele-ECG Demo

TELE-ECG METHODSOur Tele-ECG systems has an automatic heart diseases prediction. For the prediction feature, it uses Adaptive Mahalonobis Generalized Learning Vector Quantization (AMGLVQ) . The Architecture of AMGLVQ is shown below

Heartbeat Classification

In AMGLVQ, input vector is denote as x. Input data in eigen space is denoted as x’ defined as written in equation below.

!′ = ""!

Therefore we need to find best value of transformation matrix T duringtraining process. Update rule for matrix T is defined as written in equationbelow.

"" # ←"" #− 1 $%&1 + &2 2

$' 4&2+ ( ""!− "") !− )1 1

)1 ←)1 $%$' 4&2

+ ( 2 ""!− "") 1

)2 ←)2

&1 + &2$' 4&1

− ( $%&1 + &2 2 ""!− "") 2

Where w1 the nearest reference vector that belongs to the same class of x.Likewise, let w2 be the nearest reference vector that belongs to a differentclass from x.

TELE-ECG METHODS

2D & 3D SPIHTCompression

Frames 3D SPIHT3D Structure

Multilead ECG signal

Large Data Analytics

Big Data DriversLarge data creation and analysis are driven by several factors. These factorshelp

push the research in the areas of Big Data.

1

2

3

Data ProliferationThe proliferation of data capture and creation technologies

Data ConsumptionIncreased “interconnectedness” drives consumption (creating more

data)

Hardware and SoftwareInexpensive storage makes it possible to keep more, longer.

Innovative software and analysis tools turn data into information

MorDevi

eces

More Consumpti

on

More Content

&er atio

NewBett

Informn

Cost-effectively managing the volume, velocity and variety of data

Deriving value acrossstructured and unstructured data

Adapting to context changes and integrating

new data sources and types

Big Data ChallengesIt’s not just about “big”

Enhanced Tele ECG System Using Hadoop Framework To Deal With Big Data Processing

Enhanced Tele ECG To Deal With Big Data Processing :Experiment and Result

Cluster SpecificationRequest Time Evaluation

Example of Our Reseach Data

Analyzing Healthcare DataData AnalyticsData undergoes three stages before it can be used for sustainable, meaningful analytics.

DATACAPTURE

• Acquire key data elements• Assure data quality• Integrate data capture into operational

workflow

DATAANALYSIS

• Interpret data• Discover new information in the data

(data mining)• Evaluate data quality

DATAPROVISIONING

• Move data from transactional systems Build visualization for use by clinicians

Challenges and OpportunitiesTelemedicine Implementation

1. Policy and regulation not yetestablished

2. Infrastructure: Internet connection coverage in rural Indonesia and limited 4G connection access in some sites

3. Human Resources: Lack of awareness to technology

4. Device: Product driven (dependency) “not based on what we need, but what we can do with the product”.

5. Sustainability: Integrating the telemedicine service to the national health insurance scheme (year 2014)

Challenges inImplementingTelemedicine

1. Will be so overwhelming: Need the right people and solve the right problems

2. Costs escalate too fast: Is itnecessary to capture 100% ofthe data?

3. Many sources of big data is privacy: Need to set Self-regulation and have aLegal regulation

BigData

1. Standardization : Proposing ISO Standard for telehealth adoption

2. Building Community : National center of excellence of telemedicine.

3. Cooperating with international association of telemedicine.

4. To establish a national authority of telemedicine.

Association & Standardization

Health Supporting SystemDrowsiness Detection System

Background• “Drowsy driving may be the cause of 1 out of every 10 auto crashes”

• “The percentage of accidents caused by drowsy driving is much higher than previously expected.”

AAA Foundation for Traffic Safety – CNBC 2018

Car Drivers Drowsiness Detection System

Scope of Prevention Research

Scope of Prevention Research

Research Signal on Denoising

Implementation of Car Drivers Drowsiness Detection System

OBU

OBU

OBU

OBU

OBU

RSU RSU

V2V

I2I

V2V

V2VV2I

V2IV2I

V2I

V2V = Vehicle to Vehicle V2I = Vehicle to Infrastucture I2I = Infrastucture to

Legend :OBU = On Board Unit RSU = Road side Unit Infrastucture

1

2

3

4

5

Communication fromCommunication from

1 to 3 directly is called assingle-hop.1 to 5 via 3 and 4 is called as multi-hop.

Previous InnovationsIntelligent Transportation System & Gas Leak Detection

Pollu

tion

Our Innovation

Robotics and

Artificial Intelligence

Traffic Jam

Gas Leak

Health Problems

Solution

Intelligent Transportation

System

(ITS)

Vehicle Detection and Counting inDayand Night

Adaptive TrafficLight

ITS

ITS

Traffic big data prediction and visualization using Fast Incremental Model Trees-Drift Detection (FIMT-DD)

Ari Wibisono, Wisnu Jatmiko, Hanief Arief Wisesa, Benny Hardjono, Petrus Mursanto, Knowledge-Based Systems,Volume 93, 2016, Pages 33-46, ISSN 0950-7051

Self-organizing urban traffic controlarchitecture with swarm-self organizing map in jakarta: signal control system and simulator

Jatmiko, W. Azurat, A.;, Wibowo, A., Marihot, H., Wicaksana, M., Takagawa, I., Sekiyama, K., Fukuda, T, International Journal on Smart Sensing & Intelligent Systems . Sep2010, Vol. 3 Issue 3, p443-465. 24p. 3

Traffic big data prediction and visualization

Pollu

tion

Our Innovation

Robotics and

Artificial Intelligence

Traffic Jam

Gas Leak

Health Problems

Solution

Swarm Robot & Unmanned Aerial Vehicle

Innovation on Gas / Pollutant Source Detection

Thank You!Any Questions?