smart telehealth implementation to solve disparities in
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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
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
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 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 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.
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 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
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
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
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.
Pollu
tion
Our Innovation
Robotics and
Artificial Intelligence
Traffic Jam
Gas Leak
Health Problems
Solution
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
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