Digital health in diabetes: a global perspective

Professor, SAHIST, Sungkyunkwan University
Director, Digital Healthcare Institute
Yoon Sup Choi, Ph.D.
Digital Healthcare in Diabetes : Global Perspectives

“It's in Apple's DNA that technology alone is not enough.  
It's technology married with liberal arts.”

The Convergence of IT, BT and Medicine

https://rockhealth.com/reports/digital-health-funding-2015-year-in-review/

헬스케어넓은 의미의 건강 관리에는 해당되지만,
디지털 기술이 적용되지 않고, 전문 의료 영역도 아닌 것
예) 운동, 영양, 수면
디지털 헬스케어
건강 관리 중에 디지털 기술이 사용되는 것
예) 사물인터넷, 인공지능, 3D 프린터
모바일 헬스케어
디지털 헬스케어 중
모바일 기술이 사용되는 것
예) 스마트폰, 사물인터넷, SNS
개인 유전정보분석
예) 암유전체, 질병위험도,
보인자, 약물 민감도
예) 웰니스, 조상 분석
헬스케어 관련 분야 구성도(ver 0.3)
의료
질병 예방, 치료, 처방, 관리
등 전문 의료 영역
원격의료
원격진료

What is most important factor in digital medicine?

“Data! Data! Data!” he cried.“I can’t
make bricks without clay!”
- Sherlock Holmes,“The Adventure of the Copper Beeches”

새로운 데이터가
새로운 방식으로
새로운 주체에 의해
측정, 통합, 분석되어 건강관리에 활용된다.
데이터의 종류
데이터의 질적/양적 측면
웨어러블 기기
스마트폰
유전 정보 분석
인공지능
SNS
사용자/환자
대중

Digital Healthcare Industry Landscape
Data Measurement Data Integration Data Interpretation Treatment
Smartphone Gadget/Apps
DNA
Artiﬁcial Intelligence
2nd Opinion
Wearables / IoT
(ver. 3)
EMR/EHR 3D Printer
Counseling
Data Platform
Accelerator/early-VC
Telemedicine
Device
On Demand (O2O)
VR
Digital Healthcare Institute
Diretor, Yoon Sup Choi, Ph.D.
yoonsup.choi@gmail.com

Data Measurement Data Integration Data Interpretation Treatment
Smartphone Gadget/Apps
DNA
Artiﬁcial Intelligence
2nd Opinion
Device
On Demand (O2O)
Wearables / IoT
Digital Healthcare Institute
Diretor, Yoon Sup Choi, Ph.D.
yoonsup.choi@gmail.com
EMR/EHR 3D Printer
Counseling
Data Platform
Accelerator/early-VC
VR
Telemedicine
Digital Healthcare Industry Landscape (ver. 3)

Smartphone: the origin of healthcare innovation

AliveCor Heart Monitor (Kardia)

GluCase:World's First Smartphone
Case Glucometer

http://www.rolls-royce.com/about/our-technology/enabling-technologies/engine-health-management.aspx#sense
250 sensors to monitor the “health” of the GE turbines

Fig 1. What can consumer wearables do? Heart rate can be measured with an oximeter built into a ring [3], muscle activity with an electromyographi
sensor embedded into clothing [4], stress with an electodermal sensor incorporated into a wristband [5], and physical activity or sleep patterns via an
accelerometer in a watch [6,7]. In addition, a female’s most fertile period can be identified with detailed body temperature tracking [8], while levels of me
attention can be monitored with a small number of non-gelled electroencephalogram (EEG) electrodes [9]. Levels of social interaction (also known to a
PLOS Medicine 2016

PwC Health Research Institute Health wearables: Early days2
insurers—offering incentives for
use may gain traction. HRI’s survey
Source: HRI/CIS Wearables consumer survey 2014
21%
of US
consumers
currently
own a
wearable
technology
product
2%
wear it a few
times a month
2%
no longer
use it
7%
wear it a few
times a week
10%
wear it
everyday
Figure 2: Wearables are not mainstream – yet
Just one in five US consumers say they own a wearable device.
Intelligence Series sought to better
understand American consumers’
attitudes toward wearables through
done with the data.
PwC, Health wearables: early days, 2014

PwC | The Wearable Life | 3
device (up from 21% in 2014). And 36% own more than one.
We didn’t even ask this question in our previous survey since
it wasn’t relevant at the time. That’s how far we’ve come.
millennials are far more likely to own wearables than older
adults. Adoption of wearables declines with age.
Of note in our survey findings, however: Consumers aged
35 to 49 are more likely to own smart watches.
Across the board for gender, age, and ethnicity, fitness
wearable technology is most popular.
Fitness band
Smart clothing
Smart video/
photo device
(e.g. GoPro)
Smart watch
Smart
glasses*
45%
14%
27%
15%
12%
Base: Respondents who currently own at least one device (pre-quota sample, n=700); Q10A/B/C/D/E. Please tell us your relationship with the following wearable
technology products. *Includes VR/AR glasses
Fitness runs away with it
% respondents who own type of wearable device
PwC,The Wearable Life 2.0, 2016
• 49% own at least one wearable device (up from 21% in2014)
• 36% own more than one device.

Sensor and Transmitter
Transmitter
Tiny wire inserted
Converts glucose into electrical current
Glucose range: 40-400 mg/dL
Every 5 minutes, up to 7 days
Converts sensor data into
glucose readings (Software 505)
Glucose data broadcast via
Bluetooth to display device
Sensor

CO-1
Dexcom G5 Mobile Continuous
Glucose Monitoring (CGM) System
for Non-Adjunctive Management
of Diabetes
July 21, 2016
Dexcom, Inc.
Clinical Chemistry and Clinical Toxicology
Devices Panel

Dexcom G5 Mobile Continuous Glucose
Monitoring (CGM) System for Non-Adjunctive
Management of Diabetes
• FDA의 Clinical Chemistry and Clinical Toxicology Devices Panel
• Dexcom G5가 기존의 SMBG를 대체 가능하다고 권고
• 안전 (8:2), 효과 (9:1), 위험 대비 효용 (8:2)
• Dexcom G5의 혈당 수치는 SMBG와 약 9% 차이가 날 수 있음
• 여러 회사의 SMBG 들 간에도 4-9%의 상대적 차이 존재
• 어차피 상당수(69%)의 환자들은 off-label로 CGM을 SMBG 대신 사용중
• 차라리 허용 후 환자들을 정식으로 교육/관리하는 것이 나을 것

FreeStyle Libre Flash Glucose Monitoring System
Why prick when you can scan?
http://www.freestylelibre.co.uk

Temporary Tattoo Offers Needle-Free Way  
to Monitor Glucose Levels
• A very mild electrical current applied to the skin for 10 minutes forces sodium 
 
ions in the ﬂuid between skin cells to migrate toward the tattoo’s electrodes.
• These ions carry glucose molecules that are also found in the ﬂuid.
• A sensor built into the tattoo then measures the strength of the electrical charge 
 
produced by the glucose to determine a person’s overall glucose levels.

GlucoWatch
• GlucoWatch 2 - Cygnus
• FDA approved and marketed in 2002
• Provides a glucose reading every 10 minutes
• … but the device was discontinued because it caused skin irritation

Epic MyChart App Epic EHR
Dexcom CGM
Patients/User
Devices
EHR Hospital
Whitings
+
Apple Watch
Apps
HealthKit

transfer from Share2 to HealthKit as mandated by Dexcom receiver
Food and Drug Administration device classification. Once the glucose
values reach HealthKit, they are passively shared with the Epic
MyChart app (https://www.epic.com/software-phr.php). The MyChart
patient portal is a component of the Epic EHR and uses the same data-
base, and the CGM values populate a standard glucose flowsheet in
the patient’s chart. This connection is initially established when a pro-
vider places an order in a patient’s electronic chart, resulting in a re-
quest to the patient within the MyChart app. Once the patient or
patient proxy (parent) accepts this connection request on the mobile
device, a communication bridge is established between HealthKit and
MyChart enabling population of CGM data as frequently as every 5
minutes. All provider workflow is in the EHR.
Participation required confirmation of Bluetooth pairing of the CGM re-
ceiver to a mobile device, updating the mobile device with the most recent
version of the operating system, Dexcom Share2 app, Epic MyChart app,
and confirming or establishing a username and password for all accounts,
including a parent’s/adolescent’s Epic MyChart account. Setup time aver-
aged 45–60 minutes in addition to the scheduled clinic visit. During this
time, there was specific verbal and written notification to the patients/par-
ents that the diabetes healthcare team would not be actively monitoring
or have real-time access to CGM data, which was out of scope for this pi-
lot. The patients/parents were advised that they should continue to contact
the diabetes care team by established means for any urgent questions/
concerns. Additionally, patients/parents were advised to maintain updates
for their linked mobile devices, including the latest operating system and
Figure 1: Overview of the CGM data communication bridge architecture.
BRIEFCOMMUNICATION
Kumar R B, et al. J Am Med Inform Assoc 2016;0:1–6. doi:10.1093/jamia/ocv206, Brief Communication
byguestonApril7,2016http://jamia.oxfordjournals.org/Downloadedfrom
•Apple HealthKit, Dexcom CGM기기를 통해 지속적으로 혈당을 모니터링한 데이터를 EHR과 통합
•당뇨환자의 혈당관리를 향상시켰다는 연구결과
•Stanford Children’s Health와 Stanford 의대에서 10명 type 1 당뇨 소아환자 대상으로 수행 (288 readings /day)
•EHR 기반 데이터분석과 시각화는 데이터 리뷰 및 환자커뮤니케이션을 향상
•환자가 내원하여 진료하는 기존 방식에 비해 실시간 혈당변화에 환자가 대응
JAMIA 2016
Remote Patients Monitoring
via Dexcom-HealthKit-Epic-Stanford

GluVue
https://gluvue.stanfordchildrens.org/dashboard/?src=DEMO

No choice but to bring AI into the medicine

Martin Duggan,“IBM Watson Health - Integrated Care & the Evolution to Cognitive Computing”

Deep Learning
http://theanalyticsstore.ie/deep-learning/

Business Area
Medical Image Analysis
VUNOnet and our machine learning technology will help doctors and hospitals manage
medical scans and images intelligently to make diagnosis faster and more accurately.
Original Image Automatic Segmentation EmphysemaNormal ReticularOpacity
Our system finds DILDs at the highest accuracy * DILDs: Diffuse Interstitial Lung Disease
Digital Radiologist
Collaboration with Prof. Joon Beom Seo (Asan Medical Center)
Analysed 1200 patients for 3 months

Train
Test
whole slide image
sample
sample
training data
normaltumor
deep model
P(tumor)
whole slide image
overlapping image
patches tumor prob. map
1.0
0.0
0.5
Figure 2: The framework of cancer metastases detection.
extract millions of small positive and negative patches from
the set of training WSIs. If the small patch is located in
a tumor region, it is a tumor / positive patch and labeled
more than 6 million parameters.
Table 2: Evaluation of Various Deep Models
Deep Learning for Identifying Metastatic Breast Cancer
International Symposium on Biomedical Imaging 2016

Prediction ofVentricular Arrhythmia

Prediction ofVentricular Arrhythmia
Collaboration with Prof. Segyeong Joo (Asan Medical Center)
Analysed “Physionet Spontaneous Ventricular Tachyarrhythmia Database” for 2.5 months (on going project)
Joo S, Choi KJ, Huh SJ, 2012, Expert Systems with Applications (Vol 39, Issue 3)
▪ Recurrent Neural Network with Only Frequency Domain Transform
• Input : Spectrogram with 129 features obtained after ectopic beats removal
• Stack of LSTM Networks
• Binary cross-entropy loss
• Trained with RMSprop
• Prediction Accuracy : 76.6% ➞ 89.6%
Dropout
Dropout

In an early research project involving 600 patient cases, the team was able to  
predict near-term hypoglycemic events up to 3 hours in advance of the symptoms.
IBM Watson-Medtronic
Jan 7, 2016

Sugar.IQ
사용자의 음식 섭취와 그에 따른 혈당 변
화, 인슐린 주입 등의 과거 기록 기반
식후 사용자의 혈당이 어떻게 변화할지
Watson 이 예측

#WeAreNotWaiting
환자 주도의 의료 혁신

NightScout Project
•연속 혈당계 기기를 해킹해서 클라우드에 혈당 수치를 전송할 수 있게
•언제 어디서든 스마트폰, 스마트 워치 등으로 자녀의 혈당 수치를 확인 가능
•소아 당뇨병 환자의 부모들이 자발적으로 개발 + 오픈소스로 무료 배포 + 본인이 자발적으로 설치
•상용 의료기기가 아니므로 FDA의 규제 없음

OpenAPS: DIY 인공췌장OpenAPS: DIY 인공췌장

Hood Thabit et. al. Home Use of an Artificial Beta Cell in Type 1 Diabetes, NEJM (2015)
Home Use of an Artificial Beta Cell in Type 1 Diabetes
The proportion of time that the glycated hemoglobin level was in the target range
(primary end point) was significantly greater during the intervention period than during
the control period — by a mean of 11.0 percentage points (95% confidence interval [CI],
8.1 to 13.8; P<0.001).

Hood Thabit et. al. Home Use of an Artificial Beta Cell in Type 1 Diabetes, NEJM (2015)
The overnight mean glucose level was significantly lower with the closed-loop system
than with the control system (P<0.001), and the proportion of time that the glucose level
was within the overnight target range was greater with the closed-loop system (P<0.001)
Home Use of an Artificial Beta Cell in Type 1 Diabetes

• Self-reported data from a small group – 18 of the first 40 users
• The positive glucose and quality of life impact this system has had
• 0.9% improvement in A1c (from 7.1% to 6.2%)
• a strong time-in-range improvement from 58% to 81%
• near-unanimous improvements in sleep quality
OpenAPS DIY Automated Insulin Delivery Users Report 81%
Time in Range, Better Sleep, and a 0.9% A1c Improvement
https://openaps.org/2016/06/11/real-world-use-of-open-source-artificial-pancreas-systems-poster-presented-at-american-diabetes-association-scientific-sessions/

#OpenAPS rigs are shrinking in size
https://diyps.org

First FDA-approved Artiﬁcial Pancreas
http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm522974.htm
• 메드트로닉의 MiniMed 670G 가 최초로 제 1형 당뇨병 환자에 대해서 FDA 승인
• 14세 이상의 제 1형 당뇨병 환자 123명을 대상으로 진행된 임상
• 3개월의 추적 관찰 결과 당화혈색소(A1c) 수치가 7.4%에서 6.9%로 유의미하게 개선
• 당뇨병성 케톤산증, 저혈당증 등의 심각한 부작용이 이 기간 동안 발생 없음
• 메드트로닉은 향후 7-13세 환자들에 대해서 효과성과 안전성을 추가적으로 검증 계혹
(2016. 9. 28)

https://myglu.org/articles/a-pathway-to-an-artiﬁcial-pancreas-an-interview-with-jdrf-s-aaron-kowalski
•Step 1: 혈당 수치가 미리 정해놓은 기준까지 낮아지면, 인슐린 주입을 멈춤
•Step 2: 사용자의 혈당이 기준치까지 낮아질 것을 ‘예측’하여, 인슐린 주입을 미리 멈추거나 줄인다.
•Step 3: 혈당이 기준치 이하로 너무 낮아지는 것뿐만 아니라, 기준치 이상으로 너무 높아지는 것도 막는다.
•Step 4: 특정 범위 이내가 아니라, 특정 혈당 수치를 유지하는 것을 목표로 한다. (Hybrid closed-loop product)
•Step 5: Step 4 에서 더 나아가, 식전의 별도 인슐린 주입까지도 자동화한다.
•Step 6: 인슐린 뿐만 아니라, 글루카곤과 같은 추가적인 호르몬도 조절
Six Steps of Artiﬁcial Pancreas (JDRF)

MiniMed 670G vs. OpenAPS
http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm522974.htm
•120 mg/dl 이외의 다른 수치는 지정하기 어려움
•13세 이하의 환자에 대해서는 활용이 불가능
•미국 이외에서는 아직 인허가 이전
•고가의 유지 비용 (800만원+ 매달 40만원)

On the courtesy of Miyeong Kim (aka 소명맘)
NightScout in Korea

On the courtesy of Miyeong Kim (aka 소명맘)

Feedback/Questions
• E-mail: yoonsup.choi@gmail.com
• Blog: http://www.yoonsupchoi.com
• Facebook: 최윤섭 디지털 헬스케어 연구소

Digital health in diabetes: a global perspective

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Digital health in diabetes: a global perspective