디지털 헬스케어의 잠재적 규제 이슈

디지털 헬스케어 분야의 잠재적 규제 이슈
: 개인 정보 보호를 중심으로
Director, Digital Healthcare Institute
Partner, Digital Healthcare Partners
Yoon Sup Choi, Ph.D.

“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”

Three Steps to Implement Digital Medicine
• Step 1. Measure the Data
• Step 2. Collect the Data
• Step 3. Insight from the Data

Digital Healthcare Industry Landscape
Data Measurement Data Integration Data Interpretation Treatment
Smartphone Gaget/Apps
DNA
Artiﬁcial Intelligence
Telemedicine
2nd Opinion
Device
On Demand (O2O)
Wearables / IoT
3D Printer
Counseling
(ver. 1)
Digital Healthcare Institute
Diretor, Yoon Sup Choi, Ph.D.
yoonsup.choi@gmail.com
EMR/EHR
Data Platform
Accelerator/early-VC

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

새로운 데이터가
새로운 방식으로
새로운 주체에 의해
측정, 저장, 통합, 분석된다.

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

의료기기와 웰니스 기기를 어떻게 구분할 것인가
어떤 기준으로 어디에 선을 그어야 하나

Heart Rate Sensor in Smartphone

CellScope’s iPhone-enabled otoscope

https://www.indiegogo.com/projects/uchek-universal-the-medical-lab-in-your-phone#/

https://www.indiegogo.com/projects/uchek-universal-the-medical-lab-in-your-phone#/
H) Substantial equivalence information
Comparison with predicate
Item uChek urine analyser Predicate device
Similarities
Intended Use A semi-automated analyser which is
designed to read test strips for
urinalysis for the measurement of
Same

AliveCor Heart Monitor (Kardia)

모바일 헬스에 대한 FDA의 가이드라인
• 2011년 7월: 모바일 의료 어플리케이션 가이드라인 초안
• 2013년 10월: 업데이트 된 최종 가이드라인 제시
• 2015년 1월: 웰니스 목적의 위험도가 낮은 기기에 대한 가이드라인

모바일 헬스에 대한 FDA의 가이드라인
모든 앱과 기기들이 FDA 규제를 적용 받아야 하는 것은 아니나,  
그 기능이 제대로 작동하지 않을 경우 소비자들의 건강을 위협할 수도 있는 앱과 기기는  
기존의 의료용 기기가 받았던 것과 같은 엄격한 수준의 규제를 적용한다.
의료 기기/앱의 경우에도 리스크가 높지 않으면
규제 받지 않을 수 있다.

http://www.nature.com/news/mental-health-there-s-an-app-for-that-1.19694
• 정신 건강 관리 앱은 잠재적인 리스크 정도를 쉽게 판단하기 어려운 경우가 많다.
• 의료기기 / 웰니스의 불명확한 경계
• 질병을 예방, 치료, 진단해주는 앱
• ‘기분을 좋게 해준다’ 거나,‘코칭’ 을 해주는 앱?

의료 기기 및 웰니스 기기의 구분 명확화
• 디지털 헬스케어 기기/앱에 대해서 의료 기기/일반 기기 구분 및 규제 여부 불명확
• FDA는 가이드라인이 지속적으로 업데이트 되고 있으나, 국내 규제는 두세발 늦게 대응 
• 모바일 앱 관련
– FDA 가이드라인 (2013. 9. 25 초안 - 2015. 2. 9 업데이트)
• Mobile Medical Applications: Guidance for Industry and Food and Drug Administration Staff
• http://www.fda.gov/downloads/MedicalDevices/.../UCM263366.pdf 
– 식약처 지침 (2013. 12. 26.)
• 모바일 의료용 앱 안전관리 지침
– “모바일 의료용 앱의 의료기기 해당여부는 사용목적이 의료기기법 제2조(정의)에 부합하는 지 여부에 따라 판단한다”
– 의료기기법 제 2조 
1. 질병을 진단·치료·경감·처치 또는 예방할 목적으로 사용되는 제품 
2. 상해(傷害) 또는 장애를 진단·치료·경감 또는 보정할 목적으로 사용되는 제품 
3. 구조 또는 기능을 검사·대체 또는 변형할 목적으로 사용되는 제품 
4. 임신을 조절할 목적으로 사용되는 제품

의료 기기 및 웰니스 기기의 구분 명확화
• 의료/웰니스 기기 관련
– FDA 가이드라인 (2015년 1월 20일)
• "General Wellness: Policy for Low Risk Devices"
• http://www.fda.gov/downloads/MedicalDevices/
DeviceRegulationandGuidance/GuidanceDocuments/UCM429674.pdf  
– 식약처 지침 (2015년 7월 10일)
• "의료기기와 개인용 건강관리(웰니스)제품 판단기준" 시행

"의료기기와 개인용 건강관리(웰니스)제품 판단기준" 시행
(7월 10일)
• 의료기기가 아닌 개인용 건강관리제품에는 건강 상태 또는 건강한 활동을 유지하고 향
상시킬 목적으로 사용되는 ‘일상적 건강관리용’과, 건강한 생활방식을 유도하여 만성질
환의 위험을 줄이기 위해 사용되는 ‘만성질환자 자가관리용’ 제품 2 종류가 있다.
• 일상적 건강관리용 제품
• 생체현상 측정·분석용: 체지방 측정기, 심박수 자가측정기, 스마트폰 등을 이용한 호흡량 측정기
• 신체기능 향상용: 고령자의 낙상 위험도 측정을 통해 보행교정 보조
• 운동·레저용: 운동이나 레저 활동 시 사용자의 심박수나 산소포화도 측정 제품 등
• 일상건강관리 의료정보 제공용: 응급처지방법 안내 앱, 체질량 지수 계산 앱 등
• 만성질환자 자가관리용 제품
• 만성질환 현상관리용: 고혈압(저혈압) 환자가 혈압계로부터 측정된 혈압값을 개인 스마트폰 등
으로 전송 받아 혈압값의 추이 분석 등을 하는 앱 등
• 만성질환 의료 정보 제공용: 고혈압, 비만, 당뇨 환자들의 영양섭취, 체중조절, 운동량 등 안내 앱

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

Constructing higher-level
contextual/relational features:
Relationships between epithelial
nuclear neighbors
Relationships between morphologically
regular and irregular nuclei
and stromal objects
nuclei and cytoplasm
Characteristics of
stromal nuclei
and stromal matrix
Characteristics of
epithelial nuclei and
epithelial cytoplasm
Building an epithelial/stromal classifier:
Epithelial vs.stroma
classifier
classifier
B
Basic image processing and feature construction:
H&E image Image broken into superpixels Nuclei identified within
each superpixel
A
Relationships of contiguous epithelial
regions with underlying nuclear objects
Learning an image-based model to predict survival
Processed images from patients Processed images from patients
C
D
onNovember17,2011stm.sciencemag.orgwnloadedfrom
TMAs contain 0.6-mm-diameter cores (median
of two cores per case) that represent only a small
sample of the full tumor. We acquired data from
two separate and independent cohorts: Nether-
lands Cancer Institute (NKI; 248 patients) and
Vancouver General Hospital (VGH; 328 patients).
Unlike previous work in cancer morphom-
etry (18–21), our image analysis pipeline was
not limited to a predefined set of morphometric
features selected by pathologists. Rather, C-Path
measures an extensive, quantitative feature set
from the breast cancer epithelium and the stro-
ma (Fig. 1). Our image processing system first
performed an automated, hierarchical scene seg-
mentation that generated thousands of measure-
ments, including both standard morphometric
descriptors of image objects and higher-level
contextual, relational, and global image features.
The pipeline consisted of three stages (Fig. 1, A
to C, and tables S8 and S9). First, we used a set of
processing steps to separate the tissue from the
background, partition the image into small regions
of coherent appearance known as superpixels,
find nuclei within the superpixels, and construct
Constructing higher-level
contextual/relational features:
nuclear neighbors
Relationships between morphologically
regular and irregular nuclei
and stromal objects
nuclei and cytoplasm
Characteristics of
stromal nuclei
and stromal matrix
Characteristics of
epithelial nuclei and
epithelial cytoplasm
classifier
classifier
Relationships of contiguous epithelial
regions with underlying nuclear objects
Learning an image-based model to predict survival
Processed images from patients
alive at 5 years
Processed images from patients
deceased at 5 years
L1-regularized
logisticregression
modelbuilding
5YS predictive model
Unlabeled images
Time
P(survival)
C
D
Identification of novel prognostically
important morphologic features
basic cellular morphologic properties (epithelial reg-
ular nuclei = red; epithelial atypical nuclei = pale blue;
epithelial cytoplasm = purple; stromal matrix = green;
stromal round nuclei = dark green; stromal spindled
nuclei = teal blue; unclassified regions = dark gray;
spindled nuclei in unclassified regions = yellow; round
nuclei in unclassified regions = gray; background =
white). (Left panel) After the classification of each
image object, a rich feature set is constructed. (D)
Learning an image-based model to predict survival.
Processed images from patients alive at 5 years after
surgery and from patients deceased at 5 years after
surgery were used to construct an image-based prog-
nostic model. After construction of the model, it was
applied to a test set of breast cancer images (not
used in model building) to classify patients as high
or low risk of death by 5 years.
www.ScienceTranslationalMedicine.org 9 November 2011 Vol 3 Issue 108 108ra113 2
onNovember17,2011stm.sciencemag.orgDownloadedfrom
Digital Pathologist
Sci Transl Med. 2011 Nov 9;3(108):108ra113

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

환자 유래의 데이터
Patients-Generated Health Data

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.
Patient enrollment
To assess this communication bridge and optimize analytic tools in
our EHR, we conducted a quality improvement pilot limited to 10 pa-
tients (Table 1) from a single provider (R.B.K.). The lead author se-
lected the first 10 interested patients during standard pediatric
diabetes clinic visits who were already using a Dexcom CGM and used
an iOS device. Those patients whose CGM receivers did not have
Bluetooth-enabled functionality (2 patients) were loaned an equipped
receiver for the duration of the 3-month pilot.
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
app updates, to maintain communication of CGM data.
EHR visualization and analytics
Given the data volume of up to 288 glucose readings per day, the
standard flowsheet did not support visualizing a patient’s trends over
weeks to months. Therefore, we implemented modal day visualization
with a custom web-service embedded in the EHR (Figure 2). Design of
this clinical decision support tool could be a barrier for other health-
care delivery systems that might want to replicate our workflow. We
therefore have made it publically available at https://gluvue.stanford
Figure 1: Overview of the CGM data communication bridge architecture.
BRIEFCOMMUNICATION
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
• 논문에 소개된 사례
• 한 유아에게 간헐적인 야간 저혈당증(intermittent nocturnal hypoglycemia) 증상이 발견
• 야간에 부모가 야간 투여 계획(dinnertime dose regimen)에 따라 추가 인슐린을 투여가 원인이라는 것을 발견
• 밤에는 적은 양의 인슈린을 주도록 가이드하고 새로운 인슐린 투여량이 MyChart를 통해 부모에게 전달

• 아이폰의 센서로 측정한 자신의 의료/건강 데이터를 플랫폼에 공유 가능
• 가속도계, 마이크, 자이로스코프, GPS 센서 등을 이용
• 걸음, 운동량, 기억력, 목소리 떨림 등등
• 기존의 의학연구의 문제를 해결: 충분한 의료 데이터의 확보
• 연구 참여자 등록에 물리적, 시간적 장벽을 제거 (1번/3개월 ➞ 1번/1초)
• 대중의 의료 연구 참여 장려: 연구 참여자의 수 증가
• 발표 후 24시간 내에 수만명의 연구 참여자들이 지원
• 사용자 본인의 동의 하에 진행
Research Kit

• 초기 버전으로, 5가지 질환에 대한 앱 5개를 소개
ResearchKit

Autism and Beyond EpiWatchMole Mapper
measuring facial expressions of young
patients having autism
measuring morphological changes
of moles
measuring behavioral data
of epilepsy patients

Patients-generated data
• 관련 있는 주체들
• 본인
• 국가 (국민건강보험)
• 병원
• 기기/앱/플랫폼 기업 
• 이해관계자
• 보험사
• 고용주
• 학술연구자

PRESENTATI
17%
20%
21%
42%
65%
22%
32%
38%
38%
27%
28%
28%
28%
14%
6%
16%
10%
7%
3%
17%
10%
6%
4%
2%
Source: Rock Health consumer survey data (n = 4,017)
Note: A privacy index was created based upon answers to data sharing scenarios; consumers were then divided into quintiles based on an overall score.
While consumers vehemently agree they should be in control of health data access
majority are willing to share data for personal and public health, along with discou
More than 90% of consumers agreed or
strongly agreed with the idea that they
should be in control of access to their
health data, and were then willing to
dole out their data for improved care,
research, and discounts.
Attitudes towards privacy were found to
be correlated with adoption:
58%
50%
42%
41%
38%
31%
Percent in top two privacy quintiles by number of
digital health technologies adopted
ATTITUDES TOWARDS DATA PRIVACY
Agreement with data sharing statements I should be in control of who has access to my health data
I would share my health data to contribute to medical research
I would share my health data for a discount on my health insurance
I would share my health data so I could receive better care from my doctor
I would share my health data in exchange for money
Strongly agree
LEGEND
Strongly disagree Disagree Neither Agree Strongly agree
NON-
ADOPTER
SUPER
ADOPTER

• 2013년 연구
• 43%의 앱만이 프라이버시 정책을 가지고 있음
• 72%의 앱이 개인 프라이버시에 대한 중간 (32%), 혹은 높은 (40%) 위험도

RECEIVED 22 December 2013
REVISED 7 July 2014
ACCEPTED 3 August 2014
PUBLISHED ONLINE FIRST 21 August 2014
Availability and quality of mobile health app
privacy policies
Ali Sunyaev1
, Tobias Dehling1
, Patrick L Taylor2
, Kenneth D Mandl3
ABSTRACT
....................................................................................................................................................
Mobile health (mHealth) customers shopping for applications (apps) should be aware of app privacy practices so they
can make informed decisions about purchase and use. We sought to assess the availability, scope, and transparency of
mHealth app privacy policies on iOS and Android. Over 35 000 mHealth apps are available for iOS and Android. Of the
600 most commonly used apps, only 183 (30.5%) had privacy policies. Average policy length was 1755 (SD 1301)
words with a reading grade level of 16 (SD 2.9). Two thirds (66.1%) of privacy policies did not specifically address the
app itself. Our findings show that currently mHealth developers often fail to provide app privacy policies. The privacy pol-
icies that are available do not make information privacy practices transparent to users, require college-level literacy, and
are often not focused on the app itself. Further research is warranted to address why privacy policies are often absent,
opaque, or irrelevant, and to find a remedy.
....................................................................................................................................................
INTRODUCTION
Apple’s iOS and Google’s Android operating systems and asso-
ciated application (app) stores, itunes.apple.com and play.goo-
gle.com, are becoming the de facto global platforms for mobile
health (mHealth).1,2
Recently, both platforms additionally
announced the roll out of their own apps fostering app interop-
erability and offering central storage for all mHealth apps and
sensors of users’ devices.3,4
mHealth apps leverage a wide
range of embedded technology in iOS and Android devices for
collecting and storing personal data, including contacts and
calendars, and patient-reported data as well as information col-
lected with cameras and sensors, including location, accelera-
tion, audio, or orientation.
5–7
Although patients value control of
their personally identifiable data8,9
and the Federal Trade
Commission10
recommends provision of privacy policies for
mobile apps, little attention has been paid to the information
security and privacy policies and practices of mHealth app ven-
dors. Although both app stores retain the right to remove apps
for infringements of privacy, neither has explicit policies
addressing the information security and privacy of medical in-
formation. Users choose among an ecosystem of substitutable
mHealth apps11
and should have transparency as to which
apps have privacy practices best aligned with their individual
preferences. We sought to assess mHealth apps for the pres-
ence and scope of privacy policies, and what information they
offer.
METHODS
We surveyed (figure 1) the most frequently rated and thus pop-
ular English language mHealth apps in the Apple iTunes Store
and the Google Play Store. App stores organize their offerings
in categories (eg, Books, Games, and News). We selected apps
from the Medical and Health and Fitness categories offered in
both stores in May 2013. The iOS app store lists all apps by
category and offers the desired information in plain hypertext
markup language (HTML), enabling us to automatically parse
app information to extract data. On the other hand, the Android
app store uses dynamically generated HTML pages so that the
HTML texts displayed in the browser do not contain much use-
ful information, which is dynamically loaded from an underlying
database. Hence, we used a third-party open-source interface,
the android-market-api (http://code.google.com/p/android-
market-api), for retrieving app information.
Upon initial review, many apps were not available in
English, did not have an English description, or were not
health-related, despite being offered in the categories Medical
or Health and Fitness (eg, apps offering wallpapers). In order to
exclude such apps from further assessment, we tagged all app
descriptions with descriptive terms. The tags characterize
health-related app functionality, access to information, and
handling of information. We manually tagged 200 apps (100
Health and Fitness, 100 Medical) establishing an initial tag cor-
pus and employed string matching12
to automatically tag the
remaining apps. Apps not matched by at least four distinct tags
were excluded from further assessment.
Discovery and evaluation of privacy policies
We used a three-step manual procedure for privacy policy dis-
covery looking at typical locations for privacy policies. Privacy
policies were abstracted from March 2013 to June 2013. First,
we checked for a privacy policy on the app store web site for
the particular app. Then we checked the web page maintained
Correspondence to Professor Ali Sunyaev, Faculty of Management, Economics and Social Sciences, University of Cologne, Albertus-Magnus-Platz, Cologne 50923,
Germany; sunyaev@wiso.uni-koeln.de
BRIEFCOMMUNICATION
Sunyaev A, et al. J Am Med Inform Assoc 2015;22:e28–e33. doi:10.1136/amiajnl-2013-002605, Brief Communication
byguestonApril17,2016http://jamia.oxfordjournals.org/Downloadedfrom
• 2014년 연구
• 600개의 앱 중에 183 개 (약 30%) 만이 프라이버시 정책을 가지고 있었음

Letters
RESEARCH LETTER
Privacy Policies of Android Diabetes Apps
and Sharing of Health Information
Mobile health apps can help individuals manage chronic
health conditions.1
One-fifth of smartphone owners had
health apps in 2012,2
and 7% of primary care physicians rec-
ommended a health app.3
The US Food and Drug Adminis-
tration has approved the prescription of some apps.4
Health
apps can transmit sensitive medical data, including disease
status and medication compliance. Privacy risks and the
relationship between privacy disclosures and practices of
health apps are understudied.
Methods | On January 3, 2014, we identified all Android dia-
betes apps by searching Google Play using the term diabetes.
Android is the most popular mobile operating system
worldwide with 82.8% market share (compared with Apple
iOS’s 13.9%).5
We collected and analyzed privacy policies
and permissions (disclosures of what apps can access or
control on the device) for apps that remained 6 months after
our initial search. Because consumers may want to know
about privacy protections before choosing an app, we deter-
mined which apps had policies available predownload and
what the policies protected. Then we installed a random
subset of apps to determine whether data were transmitted
to third parties, defined as any website not directly under
the developer’s control, such as data aggregators or adver-
tising networks.
We performed χ2
tests of independence (Excel 2010,
Microsoft) to determine whether apps with privacy policies
were more likely to protect personal information than apps
without privacy policies. A 2-sided P value less than .05 was
considered significant.
Results | We identified 271 diabetes apps and chose a random
sample of 75 for the transmission analysis. Within 6
months, 60 apps became unavailable, leaving 211 apps in
the sample and 65 apps in the subset. Most of the 211 apps
(81%) did not have privacy policies. Of the 41 apps (19%)
with privacy policies, not all of the provisions actually pro-
tected privacy (eg, 80.5% collected user data and 48.8%
shared data) (Table 1). Only 4 policies said they would ask
users for permission to share data.
Permissions, which users must accept to download an
app, authorized collection and modification of sensitive
information, including tracking location (17.5%), activating
the camera (11.4%), activating the microphone (3.8%), and
modifying or deleting information (64.0%) (Table 2).
In the transmission analysis, sensitive health informa-
tion from diabetes apps (eg, insulin and blood glucose lev-
els) was routinely collected and shared with third parties,
with 56 of 65 apps (86.2%) placing tracking cookies; 31 of
the 41 apps (76%) without privacy policies, and 19 of 24
apps (79%) with privacy policies shared user information,
which was not statistically significantly different (N = 65;
Table 1. Privacy Policy Provisions for the 41 Apps With Privacy Policies
(19% of the 211 Apps)a
Type of Privacy Policy Provision
Apps,
No. (%)
Personal Information
Shared if required by law 25 (61.0)
Collected when the app is used 21 (51.2)
Collected when a user registers
through an online account
21 (51.2)
Stored in the developer’s system 18 (43.9)
Only disclosed with the user’s consent 12 (29.3)
Shared to improve service 11 (26.8)
Shared with business partners 10 (24.4)
Not sold 9 (22.0)
No personal information
from children collected
6 (14.6)
User Data
Collected 33 (80.5)
Shared with partners and/or third parties 20 (48.8)
May be used for advertisement purposes 16 (39.0)
May be transferred to various countries
around the world
11 (26.8)
Electronic safeguards for data protection
are used
22 (53.7)
Cookies will be used 20 (48.8)
Log files will be collected 7 (17.1)
Aggregated User Data
Does not contain personal information 19 (46.3)
May be used to create statistics 15 (36.6)
May be disclosed to advertisers 7 (17.1)
User Options
Can opt out of cookies 13 (31.7)
Can opt out of receiving emails 9 (22.0)
Can opt out of receiving marketing materials 6 (14.6)
a
Provisions reflect the language used in privacy policies. Less common
provisions (n Յ5) were the following: no personal information from children
under 13 years of age is collected without the consent of a parent; personal
information will not be disclosed to third parties for direct marketing
purposes; data will be shared only with permission from the user; data will not
be shared; personal information will not be shared; health information is
treated differently than other types of information; data will not be sold;
information is collected during the app download process; personal
information will be shared with advertisers; user can opt out of information
transfer to third parties for marketing purposes; no data will be stored;
aggregated user data may be disclosed to analytics and search engine
providers; personal information will be shared with research organizations.
jama.com (Reprinted) JAMA March 8, 2016 Volume 315, Number 10 1051
• 2015년 연구
• 총 211개의 앱 중에 41개 (19%)만 프라이버시 정책이 있음
• 이 41개 앱의 프라이버시 정책을 살펴보면,
• 48%는 써드 파티에게 사용자 데이터를 공유함
• 61%는 법으로 필요한 경우는 데이터가 공개될 수 있음
• 43%는 개발자 시스템에 데이터가 저장
• 즉, 프라이버시 정책을 가지고 있는 경우에도,‘보호하지 않는’ 정책인 경우가 상당수

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1,200,000
1,000,000
900,000
850,000
650,000
500,000
400,000
300,000
250,000
180,000
100,000
2007-11
2011-06
2011-10
2012-04
2012-10
2013-04
2013-06
2013-09
2013-12
2014-10
2015-02
2015-05
2015-06
2016-02
0
Customer growth of 23andMe

Human genomes are being sequenced at an ever-increasing rate. The 1000 Genomes Project has
aggregated hundreds of genomes; The Cancer Genome Atlas (TGCA) has gathered several thousand; and
the Exome Aggregation Consortium (ExAC) has sequenced more than 60,000 exomes. Dotted lines show
three possible future growth curves.
DNA SEQUENCING SOARS
2001 2005 2010 2015 2020 2025
100
103
106
109
Human Genome Project
Cumulativenumberofhumangenomes
1000 Genomes
TCGA
ExAC
Current amount
1st personal genome
Recorded growth
Projection
Double every 7 months (historical growth rate)
Double every 12 months (Illumina estimate)
Double every 18 months (Moore's law)
Michael Einsetein, Nature, 2015

• 개인 유전 정보는 개인식별정보인가?
• Personal Genome Project 의 익명의 DNA 기부자 중 40% 를 re-identiﬁcation
• 우편번호, 생일, 성별 등등의 데이터와 조합하여 파악

• 유전 정보를 근거로 생명보험 가입을 거절당한 여성의 사례
• GINA (Genetic Information Nondiscrimination Act)
• 유전정보에 따른 차별 금지 법안 (2008)
• 건강 보험에만 해당되고, 생명보험, 장기 간병 보험, 상해 보험 등에는 해당되지 않음
• 의사들은 보험사의 차별 때문에 필요한 경우에도 환자들이 유전자 검사를 받지 않을까 우려

생명윤리 및 안전에 관한 법률
(생명윤리법 )
• 제46조 (유전정보에 의한 차별 금지 등)
• ① 누구든지 유전정보를 이유로 교육, 고용, 승진, 보험 등 사회활동에서 다른 사람을
차별하여서는 아니 된다.
• ② 다른 법률에 특별한 규정이 있는 경우를 제외하고는 누구든지 타인에게 유전자검
사를 받도록 강요하거나 유전자검사의 결과를 제출하도록 강요하여서는 아니 된다.
• ③ 의료기관은 「의료법」 제21조제2항에 따라 환자 외의 자에게 제공하는 의무기록 및
진료기록 등에 유전정보를 포함시켜서는 아니 된다. 다만, 해당 환자와 동일한 질병의
진단 및 치료를 목적으로 다른 의료기관의 요청이 있고 개인정보 보호에 관한 조치를
한 경우에는 그러하지 아니하다.

의료기기 허가승인에 관한 규제의 모호성/비효율성
“혁신을 하려는 회사는 한국에서 사업하면 안 된다”

“스마트 의료기기는 과거의 유헬스케어와 일반 의료기기 사이의
제도적 중간지대에 속해 있으며, 크라우드 펀딩과 같은 자금 유
치와 관련해서도 구시대적 제도하에서 발전을 막고 있다.”
“이렇게 해서 국내 사업자를 죽이면 결국 소비자는 해외 제품
구매해서 쓸 것이다. 새로운 혁신을 하려는 회사는 한국에서  
사업하면 안 된다.”
https://www.facebook.com/groups/koreamobilehealthcare/permalink/1711243019151465/

• 의료기기법(시행령, 시행규칙)
• 의료기기 인허가(동등, 개량, 임상)
• 모바일 의료용 앱 안전관리 지침
• 전기, 기계적 안정성(IEC 60601-1 3.X판)
• 전자파 안정성(IEC 606011-2)
• 생물학적 안전규격(ISO 10993)
• 의료기기별 안정성 시험기준
• 의료기기 소프트웨어 벨리데이션 가이드라인
• 개인의료정보 보안의 요구사항(개인정보보호법, 의료법, 생명윤리법, 정보통신망법)
• 의료기기 데이터 국제 표준(ISO/IEEE, IHE, HL7) 상호운용성 평가기준(유헬스케어)
• KC 전파 인증
• SIG 블루투스 인증
• GMP 품질 인증
• 신의료기기 평가(보험수가)
• 의료기기 포장 공정 벨리데이션
• 의료기기 판매업 허가/신고(체온계는 면제)
• 의료기기 광고 사전심의 규정

• 인허가 의료기기 품목의 모호성
• 유헬스케어 의료기기 품목허가가 유명무실한 상태에서도 현재까지 품목허가가 있음
• 식약처가 가정용 스마트 의료기기의 경우 사용 목적을 가정용으로 기재하면 의료기기에 통
신기능을 내장하더라도 유헬스케어 기기가 아니라는 유권해석이 있기 전까지 유헬스케어
의료기기 규격에 맞춰 제품을 개발
• 전파 인증 중복 인허가 문제
• 의료기기 전자파 안정성(IEC 606011-2) 과 KC, SIG 전파 인증의 중복
• 유사한 테스트를 각각 받아야 함: 인증 비용 / 시간 낭비
• 의료데이터 표준의 상호운용성 시험
• 의료데이터 국제 표준(ISO/IEEE 11073,PHD) 준수 위해 추가 개발 비용/ 시간을 소요
• 이를 제대로 구현했는지와 허가 전 의료기관 연계 테스트가 불가
• 개인 정보 보안 관련
• 스마트 체온계에서 전달된 개인 건강 정보 (개인별 체온 관리)의 경우
• 의료법, 개인정보보호법, 생명윤리보호법, 정보통신망법 등의 개인 데이터 관리 규제 적용
• 익명화/개인정보 보호 위해, 어떤 규제를 적용해야 하는지 모호
• 방통위, 미래부, 식약처, 행안부와 같은 개인정보 관리 기관의 3-4중의 중복 규제 가능성

• 제품 디자인과 제품 사양이 노출된 것이 의료기기 사전 광고에 해당
• 법규 위반으로 고발되어 현재 제품 디자인 및 기능소개 삭제
• 식약처에 미리 문의했더라도, 크라우드 펀딩에 제품 사진을 노출 허용은 불가하다는 답변

혁신적인 기기의 Crowdfunding
• 제품 출시 전 고객의 feedback 을 받아 제품 개선 가능
• 개발비가 부족한 스타트업이 대중으로부터 아이디어만으로도 직접 펀딩 가능
• 혁신적인 제품의 마케팅 및 수요 파악 가능

• 해외의 경우 의료기기의 크라우드 펀딩에 의한 선판매는 매우 빈번함
• 사진 및 자세한 스펙도 공개

• Scanadu SCOUT의 경우 8,000 명 이상의 backer 로부터 $1m 이상을 모금

• 심박, 체온, 산소포화도, 호흡수, 혈압, 심전도, 스트레스 등을 측정 계획

• 특히 Scanadu SCOUT의 경우,  
인디고고 backer 들을 단순 투자자가 아닌  
‘공동 연구자’ 라고 주장
• Backer 들은 임상 연구에 참여하며,  
자신의 데이터를 공유한다는 서명을 하고,
FDA 허가 받지 않은 의료 기기를 수령
• 현재 이 데이터를 이용해서 SCOUT는  
FDA 심사 중

Feedback/Questions
• Email: yoonsup.choi@gmail.com
• Blog: http://www.yoonsupchoi.com
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