[365mc] 디지털 헬스케어: 의료의 미래

디지털 헬스케어: 의료의 미래

최윤섭, PhD
출간 기념 저자 강연회

“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

최윤섭 지음
의료인공지능
표지디자인•최승협
컴퓨터공학, 생명과학, 의학의 융합을 통해 디지
털 헬스케어 분야의 혁신을 창출하고 사회적 가
치를 만드는 것을 화두로 삼고 있는 융합생명과학자, 미래의료학자,
기업가, 엔젤투자가, 에반젤리스트이다. 국내 디지털 헬스케어 분야
의 대표적인 전문가로, 활발한 연구, 저술 및 강연 등을 통해 국내에
이 분야를 처음 소개한 장본인이다.
포항공과대학교에서 컴퓨터공학과 생명과학을 복수전공하였으며
동 대학원 시스템생명공학부에서 전산생물학으로 이학박사 학위를
취득하였다. 스탠퍼드대학교 방문연구원, 서울의대 암연구소 연구
조교수, KT 종합기술원 컨버전스연구소 팀장, 서울대병원 의생명연
구원 연구조교수 등을 거쳤다. 『사이언스』를 비롯한 세계적인 과학
저널에 10여 편의 논문을 발표했다.
국내 최초로 디지털 헬스케어를 본격적으로 연구하는 연구소인 ‘최
윤섭 디지털 헬스케어 연구소’를 설립하여 소장을 맡고 있다. 또한
국내 유일의 헬스케어 스타트업 전문 엑셀러레이터 ‘디지털 헬스케
어 파트너스’의 공동 창업자 및 대표 파트너로 혁신적인 헬스케어
스타트업을 의료 전문가들과 함께 발굴, 투자, 육성하고 있다. 성균
관대학교 디지털헬스학과 초빙교수로도 재직 중이다.
뷰노, 직토, 3billion, 서지컬마인드, 닥터다이어리, VRAD, 메디히어,
소울링, 메디히어, 모바일닥터 등의 헬스케어 스타트업에 투자하고
자문을 맡아 한국에서도 헬스케어 혁신을 만들어내기 위해 노력하
고 있다. 국내 최초의 디지털 헬스케어 전문 블로그 『최윤섭의 헬스
케어 이노베이션』에 활발하게 집필하고 있으며, 『매일경제』에 칼럼
을 연재하고 있다. 저서로 『헬스케어 이노베이션: 이미 시작된 미래』
와 『그렇게 나는 스스로 기업이 되었다』가 있다.
•블로그_ http://www.yoonsupchoi.com/
•페이스북_ https://www.facebook.com/yoonsup.choi
•이메일_ yoonsup.choi@gmail.com
최윤섭
의료 인공지능은 보수적인 의료 시스템을 재편할 혁신을 일으키고 있다. 의료 인공지능의 빠른 발전과
광범위한 영향은 전문화, 세분화되며 발전해 온 현대 의료 전문가들이 이해하기가 어려우며, 어디서부
터 공부해야 할지도 막연하다. 이런 상황에서 의료 인공지능의 개념과 적용, 그리고 의사와의 관계를 쉽
게 풀어내는 이 책은 좋은 길라잡이가 될 것이다. 특히 미래의 주역이 될 의학도와 젊은 의료인에게 유용
한 소개서이다.
━ 서준범, 서울아산병원 영상의학과 교수, 의료영상인공지능사업단장
인공지능이 의료의 패러다임을 크게 바꿀 것이라는 것에 동의하지 않는 사람은 거의 없다. 하지만 인공
지능이 처리해야 할 의료의 난제는 많으며 그 해결 방안도 천차만별이다. 흔히 생각하는 만병통치약 같
은 의료 인공지능은 존재하지 않는다. 이 책은 다양한 의료 인공지능의 개발, 활용 및 가능성을 균형 있
게 분석하고 있다. 인공지능을 도입하려는 의료인, 생소한 의료 영역에 도전할 인공지능 연구자 모두에
게 일독을 권한다.
━ 정지훈, 경희사이버대 미디어커뮤니케이션학과 선임강의교수, 의사
서울의대 기초의학교육을 책임지고 있는 교수의 입장에서, 산업화 이후 변하지 않은 현재의 의학 교육
으로는 격변하는 인공지능 시대에 의대생을 대비시키지 못한다는 한계를 절실히 느낀다. 저와 함께 의
대 인공지능 교육을 개척하고 있는 최윤섭 소장의 전문적 분석과 미래 지향적 안목이 담긴 책이다. 인공
지능이라는 미래를 대비할 의대생과 교수, 그리고 의대 진학을 고민하는 학생과 학부모에게 추천한다.
━ 최형진, 서울대학교 의과대학 해부학교실 교수, 내과 전문의
최근 의료 인공지능의 도입에 대해서 극단적인 시각과 태도가 공존하고 있다. 이 책은 다양한 사례와 깊
은 통찰을 통해 의료 인공지능의 현황과 미래에 대해 균형적인 시각을 제공하여, 인공지능이 의료에 본
격적으로 도입되기 위한 토론의 장을 마련한다. 의료 인공지능이 일상화된 10년 후 돌아보았을 때, 이 책
이 그런 시대를 이끄는 길라잡이 역할을 하였음을 확인할 수 있기를 기대한다.
━ 정규환, 뷰노 CTO
의료 인공지능은 다른 분야 인공지능보다 더 본질적인 이해가 필요하다. 단순히 인간의 일을 대신하는
수준을 넘어 의학의 패러다임을 데이터 기반으로 변화시키기 때문이다. 따라서 인공지능을 균형있게 이
해하고, 어떻게 의사와 환자에게 도움을 줄 수 있을지 깊은 고민이 필요하다. 세계적으로 일어나고 있는
이러한 노력의 결과물을 집대성한 이 책이 반가운 이유다.
━ 백승욱, 루닛 대표
의료 인공지능의 최신 동향뿐만 아니라, 의의와 한계, 전망, 그리고 다양한 생각거리까지 주는 책이다.
논쟁이 되는 여러 이슈에 대해서도 저자는 자신의 시각을 명확한 근거에 기반하여 설득력 있게 제시하
고 있다. 개인적으로는 이 책을 대학원 수업 교재로 활용하려 한다.
━ 신수용, 성균관대학교 디지털헬스학과 교수
최윤섭지음
의료인공지능
값 20,000원
ISBN 979-11-86269-99-2
미래의료학자 최윤섭 박사가 제시하는
의료 인공지능의 현재와 미래
의료 딥러닝과 IBM 왓슨의 현주소
인공지능은 의사를 대체하는가
값 20,000원
ISBN 979-11-86269-99-2
소울링, 메디히어, 모바일닥터 등의 헬스케어 스타트업에 투자하고
자문을 맡아 한국에서도 헬스케어 혁신을 만들어내기 위해 노력하
고 있다. 국내 최초의 디지털 헬스케어 전문 블로그 『최윤섭의 헬스
케어 이노베이션』에 활발하게 집필하고 있으며, 『매일경제』에 칼럼
을 연재하고 있다. 저서로 『헬스케어 이노베이션: 이미 시작된 미래』
와 『그렇게 나는 스스로 기업이 되었다』가 있다.
•블로그_ http://www.yoonsupchoi.com/
•페이스북_ https://www.facebook.com/yoonsup.choi
•이메일_ yoonsup.choi@gmail.com
(2014) (2018) (2020)

•1. 디지털 헬스케어가 온다.

•2. 디지털 헬스케어는 어떻게 구현되는가.

•3. 디지털 헬스케어의 새로운 물결과 숙제.

•4. 미래로 가는 길

의료가 맞이하는 피할 수 없는 쓰나미

기하급수적 발전
“체스판의 쌀알 한 톨로 시작해서…”

기하급수적 발전
•기술의 발전은 우리의 생각보다 훨씬 빠르다.

•그리고 그 속도는 계속 기하급수적으로 증가한다.

•현재에 기반한 아무리 과감한 예측도,

•결과적으로는 매우 보수적인 것일 수 있다.

https://rockhealth.com/reports/2018-year-end-funding-report-is-digital-health-in-a-bubble/
•2018년에는 $8.1B 가 투자되며 역대 최대 규모를 또 한 번 갱신 (전년 대비 42.% 증가)

•총 368개의 딜 (전년 359 대비 소폭 증가): 개별 딜의 규모가 커졌음

•전체 딜의 절반이 seed 혹은 series A 투자였음

•‘초기 기업들이 역대 최고로 큰 규모의 투자를’, ‘역대 가장 자주’ 받고 있음

https://rockhealth.com/reports/q3-2019-digital-health-funding-moderates-after-particularly-strong-ﬁrs
•2018년에는 $8.1B 가 투자되며 역대 최대 규모를 또 한 번 갱신 (전년 대비 42.% 증가)

•2019년은 역대 두 번째로 큰 투자가 집행될 것으로 예상 (2018년에는 못 미치지만, 2017년 보다는 큼)

•총 투자 건 수, 건당 투자 규모 역시 2018년에 조금 못 미치는 정도

2010 2011 2012 2013 2014 2015 2016 2017 2018
Q1 Q2 Q3 Q4
153
283
476
647
608
568
684
851
765
FUNDING SNAPSHOT: YEAR OVER YEAR
5
Deal Count
$1.4B
$1.7B
$1.7B
$627M
$603M$459M
$8.2B
$6.2B
$7.1B
$2.9B
$2.3B$2.0B
$1.2B
$11.7B
$2.3B
Funding surpassed 2017 numbers by almost $3B, making 2018 the fourth consecutive increase in capital investment and
largest since we began tracking digital health funding in 2010. Deal volume decreased from Q3 to Q4, but deal sizes spiked,
with $3B invested in Q4 alone. Average deal size in 2018 was $21M, a $6M increase from 2017.
$3.0B
$14.6B
DEALS & FUNDING INVESTORS SEGMENT DETAIL
Source: StartUp Health Insights | startuphealth.com/insights Note: Report based on public data through 12/31/18 on seed (incl. accelerator), venture, corporate venture, and private equity funding only. © 2019 StartUp Health LLC
•글로벌 투자 추이를 보더라도, 2018년 역대 최대 규모: $14.6B

•2015년 이후 4년 연속 증가 중
https://hq.startuphealth.com/posts/startup-healths-2018-insights-funding-report-a-record-year-for-digital-health

27
Switzerland
EUROPE
$3.2B
$1.96B $1B
$3.5B
NORTH AMERICA
$12B Valuation
$1.8B
$3.1B$3.2B
$1B
$1B
38 healthcare unicorns valued at $90.7B
Global VC-backed digital health companies with a private market valuation of $1B+ (7/26/19)
UNITED KINGDOM
$1.5B
MIDDLE EAST
$1B Valuation
ISRAEL
$7B
$1B$1.2B
$1B
$1.65B
$1.8B
$1.25B
$2.8B
$1B $1B
$2B Valuation
$1.5B
UNITED STATES
GERMANY
$1.7B
$2.5B
CHINA
ASIA
$3B
$5.5B Valuation
$5B
$2.4B
$2.4B
France
$1.1B $3.5B
$1.6B
$1B
$1B
$1B
$1B
CB Insights, Global Healthcare Reports 2019 2Q
•전 세계적으로 38개의 디지털 헬스케어 유니콘 스타트업 (=기업가치 $1B 이상) 이 있으나,

•국내에는 하나도 없음

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

5%
8%
24%
27%
36%
Life Science & Health
Mobile
Enterprise & Data
Consumer
Commerce
9%
13%
23%
24%
31%
Life Science & Health
Consumer
Enterprise
Data & AI
Others
2014 2015
Investment of GoogleVentures in 2014-2015

startuphealth.com/reports
Firm 2017 YTD Deals Stage
Early Mid Late
1 7
1 7
2 6
2 6
3 5
3 5
3 5
3 5
THE TOP INVESTORS OF 2017 YTD
We are seeing huge strides in new investors pouring money into the digital health market, however all the top 10 investors of
2017 year to date are either maintaining or increasing their investment activity.
Source: StartUp Health Insights | startuphealth.com/insights Note: Report based on public data on seed, venture, corporate venture and private equity funding only. © 2017 StartUp Health LLC
DEALS & FUNDING GEOGRAPHY INVESTORSMOONSHOTS
20
•Google Ventures와 Khosla Ventures가 각각 7개로 공동 1위,

•GE Ventures와 Accel Partners가 6건으로 공동 2위를 기록 
•GV 가 투자한 기업

•virtual fitness membership network를 만드는 뉴욕의 ClassPass

•Remote clinical trial 회사인 Science 37

•Digital specialty prescribing platform ZappRx 등에 투자. 
•Khosla Ventures 가 투자한 기업

•single-molecule 검사 장비를 만드는 TwoPoreGuys

•Mabu라는 AI-powered patient engagement robot 을 만드는 Catalia Health에 투자.

•최근 3년 동안 Merck, J&J, GSK 등의 제약사들의 디지털 헬스케어 분야 투자 급증

•2015-2016년 총 22건의 deal (=2010-2014년의 5년간 투자 건수와 동일)

•Merck 가 가장 활발: 2009년부터 Global Health Innovation Fund 를 통해 24건 투자 ($5-7M)

•GSK 의 경우 2014년부터 6건 (via VC arm, SR One): including Propeller Health

표 2
우리나라는 디지털 헬스케어 산업이 성장하기 좋은 여건을 갖추고 있다. 첫째, 높은 수준의 의료기술
력을 보유하고 있다. 2018년 OECD 통계에 따르면, 최근 5년간(2010~2014년) 국내 주요 암 환자
글로벌 디지털 헬스케어 누적투자액 TOP 100
글로벌 헬스케어 스타트업 중 대부분이 한국에서는 불법
스타트업 코리아, ‘디지털 헬스케어’, 아산나눔재단 등, 2018

헬스케어
넓은 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, VR/AR
모바일 헬스케어
디지털 헬스케어 중

모바일 기술이 사용되는 것

예) 스마트폰, 사물인터넷, SNS
개인 유전정보분석
암유전체, 질병위험도,

보인자, 약물 민감도
웰니스, 조상 분석
의료
질병 예방, 치료, 처방, 관리

등 전문 의료 영역
원격의료
원격 환자 모니터링
원격진료
전화, 화상, 판독
명상 앱
ADHD 치료 게임

PTSD 치료 VR
디지털 치료제
중독 치료 앱
헬스케어 관련 분야 구성도

EDITORIAL OPEN
Digital medicine, on its way to being just plain medicine
npj Digital Medicine (2018)1:20175 ; doi:10.1038/
s41746-017-0005-1
There are already nearly 30,000 peer-reviewed English-language
scientific journals, producing an estimated 2.5 million articles a year.1
So why another, and why one focused specifically on digital
medicine?
To answer that question, we need to begin by defining what
“digital medicine” means: using digital tools to upgrade the
practice of medicine to one that is high-definition and far more
individualized. It encompasses our ability to digitize human beings
using biosensors that track our complex physiologic systems, but
also the means to process the vast data generated via algorithms,
cloud computing, and artificial intelligence. It has the potential to
democratize medicine, with smartphones as the hub, enabling
each individual to generate their own real world data and being
far more engaged with their health. Add to this new imaging
tools, mobile device laboratory capabilities, end-to-end digital
clinical trials, telemedicine, and one can see there is a remarkable
array of transformative technology which lays the groundwork for
a new form of healthcare.
As is obvious by its definition, the far-reaching scope of digital
medicine straddles many and widely varied expertise. Computer
scientists, healthcare providers, engineers, behavioral scientists,
ethicists, clinical researchers, and epidemiologists are just some of
the backgrounds necessary to move the field forward. But to truly
accelerate the development of digital medicine solutions in health
requires the collaborative and thoughtful interaction between
individuals from several, if not most of these specialties. That is the
primary goal of npj Digital Medicine: to serve as a cross-cutting
resource for everyone interested in this area, fostering collabora-
tions and accelerating its advancement.
Current systems of healthcare face multiple insurmountable
challenges. Patients are not receiving the kind of care they want
and need, caregivers are dissatisfied with their role, and in most
countries, especially the United States, the cost of care is
unsustainable. We are confident that the development of new
systems of care that take full advantage of the many capabilities
that digital innovations bring can address all of these major issues.
Researchers too, can take advantage of these leading-edge
technologies as they enable clinical research to break free of the
confines of the academic medical center and be brought into the
real world of participants’ lives. The continuous capture of multiple
interconnected streams of data will allow for a much deeper
refinement of our understanding and definition of most pheno-
types, with the discovery of novel signals in these enormous data
sets made possible only through the use of machine learning.
Our enthusiasm for the future of digital medicine is tempered by
the recognition that presently too much of the publicized work in
this field is characterized by irrational exuberance and excessive
hype. Many technologies have yet to be formally studied in a
clinical setting, and for those that have, too many began and
ended with an under-powered pilot program. In addition, there are
more than a few examples of digital “snake oil” with substantial
uptake prior to their eventual discrediting.2
Both of these practices
are barriers to advancing the field of digital medicine.
Our vision for npj Digital Medicine is to provide a reliable,
evidence-based forum for all clinicians, researchers, and even
patients, curious about how digital technologies can transform
every aspect of health management and care. Being open source,
as all medical research should be, allows for the broadest possible
dissemination, which we will strongly encourage, including
through advocating for the publication of preprints
And finally, quite paradoxically, we hope that npj Digital
Medicine is so successful that in the coming years there will no
longer be a need for this journal, or any journal specifically
focused on digital medicine. Because if we are able to meet our
primary goal of accelerating the advancement of digital medicine,
then soon, we will just be calling it medicine. And there are
already several excellent journals for that.
ACKNOWLEDGEMENTS
Supported by the National Institutes of Health (NIH)/National Center for Advancing
Translational Sciences grant UL1TR001114 and a grant from the Qualcomm Foundation.
ADDITIONAL INFORMATION
Competing interests:The authors declare no competing financial interests.
Publisher's note:Springer Nature remains neutral with regard to jurisdictional claims
in published maps and institutional affiliations.
Change history:The original version of this Article had an incorrect Article number
of 5 and an incorrect Publication year of 2017. These errors have now been corrected
in the PDF and HTML versions of the Article.
Steven R. Steinhubl1
and Eric J. Topol1
1
Scripps Translational Science Institute, 3344 North Torrey Pines
Court, Suite 300, La Jolla, CA 92037, USA
Correspondence: Steven R. Steinhubl (steinhub@scripps.edu) or
Eric J. Topol (etopol@scripps.edu)
REFERENCES
1. Ware, M. & Mabe, M. The STM report: an overview of scientific and scholarly journal
publishing 2015 [updated March]. http://digitalcommons.unl.edu/scholcom/92017
(2015).
2. Plante, T. B., Urrea, B. & MacFarlane, Z. T. et al. Validation of the instant blood
pressure smartphone App. JAMA Intern. Med. 176, 700–702 (2016).
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this license, visit http://creativecommons.
org/licenses/by/4.0/.
© The Author(s) 2018
Received: 19 October 2017 Accepted: 25 October 2017
www.nature.com/npjdigitalmed
Published in partnership with the Scripps Translational Science Institute
디지털 의료의 미래는?

일상적인 의료가 되는 것

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, 저장, 통합, 분석된다.
데이터의 종류

데이터의 질적/양적 측면
웨어러블 기기

스마트폰

유전 정보 분석

인공지능

SNS
사용자/환자

대중

디지털 헬스케어의 3단계
•Step 1. 데이터의 측정

•Step 2. 데이터의 통합

•Step 3. 데이터의 분석

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)

데이터 소스 (1) 스마트폰

검이경 더마토스코프 안과질환 피부암
기생충 호흡기 심전도 수면
식단 활동량 발열 생리/임신

CellScope’s iPhone-enabled otoscope

First Derm
한국에서는 불법한국에서는 불법

SpiroSmart: spirometer using iPhone

AliveCor Heart Monitor (Kardia)

30분-1시간 정도 일상적인 코골이가 있음

이걸 어떻게 믿나?

녹음을 해줌.

PGS와의 analytical validity의 증명?

데이터 소스 (2) 웨어러블

n
n-
ng
n
es
h-
n
ne
ne
ct
d
n-
at
s-
or
e,
ts
n
a-
gs
d
ch
Nat Biotech 2015

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

데이터 소스 (3) 유전정보

2003 Human Genome Project 13 years (676 weeks) $2,700,000,000
2007 Dr. CraigVenter’s genome 4 years (208 weeks) $100,000,000
2008 Dr. James Watson’s genome 4 months (16 weeks) $1,000,000
2009 (Nature Biotechnology) 4 weeks $48,000
2013 1-2 weeks ~$5,000

The $1000 Genome is Already Here!

•2017년 1월 NovaSeq 5000, 6000 발표

•몇년 내로 $100로 WES 를 실현하겠다고 공언

•2일에 60명의 WES 가능 (한 명당 한 시간 이하)

1,000,000
2,000,000
2007-11
2011-06
2011-10
2012-04
2012-10
2013-04
2013-06
2013-09
2013-12
2014-10
2015-02
2015-06
2016-02
2017-04
2017-11
2018-04
3,000,000
5,000,000
2019-03
10,000,000
Customer growth of 23andMe

데이터 소스 (4) 디지털 표현형

스마트폰은 당신이 우울한지 알고 있다.
Ginger.io

Digital Phenotype:
Your smartphone knows if you are depressed
J Med Internet Res. 2015 Jul 15;17(7):e175.
The correlation analysis between the features and the PHQ-9 scores revealed that 6 of the 10
features were signiﬁcantly correlated to the scores:
• strong correlation: circadian movement, normalized entropy, location variance
• correlation: phone usage features, usage duration and usage frequency

Mindstrong Health
• 스마트폰 사용 패턴을 바탕으로

• 인지능력, 우울증, 조현병, 양극성 장애, PTSD 등을 측정

• 미국 국립정신건강연구소 소장인 Tomas Insel 이 공동 설립

• 아마존의 제프 베조스 투자

BRIEF COMMUNICATION OPEN
Digital biomarkers of cognitive function
Paul Dagum1
To identify digital biomarkers associated with cognitive function, we analyzed human–computer interaction from 7 days of
smartphone use in 27 subjects (ages 18–34) who received a gold standard neuropsychological assessment. For several
neuropsychological constructs (working memory, memory, executive function, language, and intelligence), we found a family of
digital biomarkers that predicted test scores with high correlations (p < 10−4
). These preliminary results suggest that passive
measures from smartphone use could be a continuous ecological surrogate for laboratory-based neuropsychological assessment.
npj Digital Medicine (2018)1:10 ; doi:10.1038/s41746-018-0018-4
INTRODUCTION
By comparison to the functional metrics available in other
disciplines, conventional measures of neuropsychiatric disorders
have several challenges. First, they are obtrusive, requiring a
subject to break from their normal routine, dedicating time and
often travel. Second, they are not ecological and require subjects
to perform a task outside of the context of everyday behavior.
Third, they are episodic and provide sparse snapshots of a patient
only at the time of the assessment. Lastly, they are poorly scalable,
taxing limited resources including space and trained staff.
In seeking objective and ecological measures of cognition, we
attempted to develop a method to measure memory and
executive function not in the laboratory but in the moment,
day-to-day. We used human–computer interaction on smart-
phones to identify digital biomarkers that were correlated with
neuropsychological performance.
RESULTS
In 2014, 27 participants (ages 27.1 ± 4.4 years, education
14.1 ± 2.3 years, M:F 8:19) volunteered for neuropsychological
assessment and a test of the smartphone app. Smartphone
human–computer interaction data from the 7 days following
the neuropsychological assessment showed a range of correla-
tions with the cognitive scores. Table 1 shows the correlation
between each neurocognitive test and the cross-validated
predictions of the supervised kernel PCA constructed from
the biomarkers for that test. Figure 1 shows each participant
test score and the digital biomarker prediction for (a) digits
backward, (b) symbol digit modality, (c) animal fluency,
(d) Wechsler Memory Scale-3rd Edition (WMS-III) logical
memory (delayed free recall), (e) brief visuospatial memory test
(delayed free recall), and (f) Wechsler Adult Intelligence Scale-
4th Edition (WAIS-IV) block design. Construct validity of the
predictions was determined using pattern matching that
computed a correlation of 0.87 with p < 10−59
between the
covariance matrix of the predictions and the covariance matrix
of the tests.
Table 1. Fourteen neurocognitive assessments covering five cognitive
domains and dexterity were performed by a neuropsychologist.
Shown are the group mean and standard deviation, range of score,
and the correlation between each test and the cross-validated
prediction constructed from the digital biomarkers for that test
Cognitive predictions
Mean (SD) Range R (predicted),
p-value
Working memory
Digits forward 10.9 (2.7) 7–15 0.71 ± 0.10, 10−4
Digits backward 8.3 (2.7) 4–14 0.75 ± 0.08, 10−5
Executive function
Trail A 23.0 (7.6) 12–39 0.70 ± 0.10, 10−4
Trail B 53.3 (13.1) 37–88 0.82 ± 0.06, 10−6
Symbol digit modality 55.8 (7.7) 43–67 0.70 ± 0.10, 10−4
Language
Animal fluency 22.5 (3.8) 15–30 0.67 ± 0.11, 10−4
FAS phonemic fluency 42 (7.1) 27–52 0.63 ± 0.12, 10−3
Dexterity
Grooved pegboard test
(dominant hand)
62.7 (6.7) 51–75 0.73 ± 0.09, 10−4
Memory
California verbal learning test
(delayed free recall)
14.1 (1.9) 9–16 0.62 ± 0.12, 10−3
WMS-III logical memory
29.4 (6.2) 18–42 0.81 ± 0.07, 10−6
Brief visuospatial memory test
10.2 (1.8) 5–12 0.77 ± 0.08, 10−5
Intelligence scale
WAIS-IV block design 46.1(12.8) 12–61 0.83 ± 0.06, 10−6
WAIS-IV matrix reasoning 22.1(3.3) 12–26 0.80 ± 0.07, 10−6
WAIS-IV vocabulary 40.6(4.0) 31–50 0.67 ± 0.11, 10−4
Received: 5 October 2017 Revised: 3 February 2018 Accepted: 7 February 2018
1
Mindstrong Health, 248 Homer Street, Palo Alto, CA 94301, USA
Correspondence: Paul Dagum (paul@mindstronghealth.com)
• 총 45가지 스마트폰 사용 패턴: 타이핑, 스크롤, 화면 터치

• 스페이스바 누른 후, 다음 문자 타이핑하는 행동

• 백스페이스를 누른 후, 그 다음 백스페이스

• 주소록에서 사람을 찾는 행동 양식 
• 스마트폰 사용 패턴과 인지 능력의 상관 관계

• 20-30대 피험자 27명

• Working Memory, Language, Dexterity etc

BRIEF COMMUNICATION OPEN
Paul Dagum1
To identify digital biomarkers associated with cognitive function, we analyzed human–computer interaction from 7 days of
smartphone use in 27 subjects (ages 18–34) who received a gold standard neuropsychological assessment. For several
neuropsychological constructs (working memory, memory, executive function, language, and intelligence), we found a family of
digital biomarkers that predicted test scores with high correlations (p < 10−4
). These preliminary results suggest that passive
measures from smartphone use could be a continuous ecological surrogate for laboratory-based neuropsychological assessment.
npj Digital Medicine (2018)1:10 ; doi:10.1038/s41746-018-0018-4
INTRODUCTION
By comparison to the functional metrics available in other
disciplines, conventional measures of neuropsychiatric disorders
have several challenges. First, they are obtrusive, requiring a
subject to break from their normal routine, dedicating time and
often travel. Second, they are not ecological and require subjects
to perform a task outside of the context of everyday behavior.
Third, they are episodic and provide sparse snapshots of a patient
only at the time of the assessment. Lastly, they are poorly scalable,
taxing limited resources including space and trained staff.
In seeking objective and ecological measures of cognition, we
attempted to develop a method to measure memory and
executive function not in the laboratory but in the moment,
day-to-day. We used human–computer interaction on smart-
phones to identify digital biomarkers that were correlated with
neuropsychological performance.
RESULTS
In 2014, 27 participants (ages 27.1 ± 4.4 years, education
14.1 ± 2.3 years, M:F 8:19) volunteered for neuropsychological
assessment and a test of the smartphone app. Smartphone
human–computer interaction data from the 7 days following
the neuropsychological assessment showed a range of correla-
tions with the cognitive scores. Table 1 shows the correlation
between each neurocognitive test and the cross-validated
predictions of the supervised kernel PCA constructed from
the biomarkers for that test. Figure 1 shows each participant
test score and the digital biomarker prediction for (a) digits
backward, (b) symbol digit modality, (c) animal fluency,
(d) Wechsler Memory Scale-3rd Edition (WMS-III) logical
memory (delayed free recall), (e) brief visuospatial memory test
(delayed free recall), and (f) Wechsler Adult Intelligence Scale-
4th Edition (WAIS-IV) block design. Construct validity of the
predictions was determined using pattern matching that
computed a correlation of 0.87 with p < 10−59
between the
covariance matrix of the predictions and the covariance matrix
of the tests.
Table 1. Fourteen neurocognitive assessments covering five cognitive
domains and dexterity were performed by a neuropsychologist.
Shown are the group mean and standard deviation, range of score,
and the correlation between each test and the cross-validated
prediction constructed from the digital biomarkers for that test
Cognitive predictions
Mean (SD) Range R (predicted),
p-value
Working memory
Digits forward 10.9 (2.7) 7–15 0.71 ± 0.10, 10−4
Digits backward 8.3 (2.7) 4–14 0.75 ± 0.08, 10−5
Executive function
Trail A 23.0 (7.6) 12–39 0.70 ± 0.10, 10−4
Trail B 53.3 (13.1) 37–88 0.82 ± 0.06, 10−6
Symbol digit modality 55.8 (7.7) 43–67 0.70 ± 0.10, 10−4
Language
Animal fluency 22.5 (3.8) 15–30 0.67 ± 0.11, 10−4
FAS phonemic fluency 42 (7.1) 27–52 0.63 ± 0.12, 10−3
Dexterity
Grooved pegboard test
(dominant hand)
62.7 (6.7) 51–75 0.73 ± 0.09, 10−4
Memory
California verbal learning test
14.1 (1.9) 9–16 0.62 ± 0.12, 10−3
WMS-III logical memory
29.4 (6.2) 18–42 0.81 ± 0.07, 10−6
Brief visuospatial memory test
10.2 (1.8) 5–12 0.77 ± 0.08, 10−5
Intelligence scale
WAIS-IV block design 46.1(12.8) 12–61 0.83 ± 0.06, 10−6
WAIS-IV matrix reasoning 22.1(3.3) 12–26 0.80 ± 0.07, 10−6
WAIS-IV vocabulary 40.6(4.0) 31–50 0.67 ± 0.11, 10−4
Received: 5 October 2017 Revised: 3 February 2018 Accepted: 7 February 2018
1
Mindstrong Health, 248 Homer Street, Palo Alto, CA 94301, USA
Correspondence: Paul Dagum (paul@mindstronghealth.com)
Fig. 1 A blue square represents a participant test Z-score normed to the 27 participant scores and a red circle represents the digital biomarker
prediction Z-score normed to the 27 predictions. Test scores and predictions shown are a digits backward, b symbol digit modality, c animal
fluency, d Wechsler memory Scale-3rd Edition (WMS-III) logical memory (delayed free recall), e brief visuospatial memory test (delayed free
recall), and f Wechsler adult intelligence scale-4th Edition (WAIS-IV) block design
P Dagum
2
1234567890():,;
• 스마트폰 사용 패턴과 인지 능력의 높은 상관 관계

• 파란색: 표준 인지 능력 테스트 결과

• 붉은색: 마인드 스트롱의 스마트폰 사용 패턴

the manifestations of disease by providing a
more comprehensive and nuanced view of the
experience of illness. Through the lens of the
digital phenotype, an individual’s interaction
The digital phenotype
Sachin H Jain, Brian W Powers, Jared B Hawkins & John S Brownstein
In the coming years, patient phenotypes captured to enhance health and wellness will extend to human interactions with
digital technology.
In 1982, the evolutionary biologist Richard
Dawkins introduced the concept of the
“extended phenotype”1, the idea that pheno-
types should not be limited just to biological
processes, such as protein biosynthesis or tissue
growth, but extended to include all effects that
a gene has on its environment inside or outside
ofthebodyoftheindividualorganism.Dawkins
stressed that many delineations of phenotypes
are arbitrary. Animals and humans can modify
their environments, and these modifications
andassociatedbehaviorsareexpressionsofone’s
genome and, thus, part of their extended phe-
notype. In the animal kingdom, he cites damn
buildingbybeaversasanexampleofthebeaver’s
extended phenotype1.
Aspersonaltechnologybecomesincreasingly
embedded in human lives, we think there is an
important extension of Dawkins’s theory—the
notion of a ‘digital phenotype’. Can aspects of
ourinterfacewithtechnologybesomehowdiag-
nosticand/orprognosticforcertainconditions?
Can one’s clinical data be linked and analyzed
together with online activity and behavior data
to create a unified, nuanced view of human dis-
ease?Here,wedescribetheconceptofthedigital
phenotype. Although several disparate studies
have touched on this notion, the framework for
medicine has yet to be described. We attempt to
define digital phenotype and further describe
the opportunities and challenges in incorporat-
ing these data into healthcare.
Jan. 2013
0.000
0.002
0.004
Density
0.006
July 2013 Jan. 2014 July 2014
User 1
User 2
User 3
User 4
User 5
User 6
User 7
Date
Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions
(probability density functions) are shown for seven individual users over a two-year period. Density on
the y axis highlights periods of relative activity for each user. A representative tweet from each user is
shown as an example.
npg©2015NatureAmerica,Inc.Allrightsreserved.
http://www.nature.com/nbt/journal/v33/n5/full/nbt.3223.html

ers, Jared B Hawkins & John S Brownstein
phenotypes captured to enhance health and wellness will extend to human interactions with
st Richard
pt of the
hat pheno-
biological
sis or tissue
effects that
or outside
m.Dawkins
phenotypes
can modify
difications
onsofone’s
ended phe-
cites damn
hebeaver’s
ncreasingly
there is an
heory—the
aspects of
ehowdiag-
Jan. 2013
0.000
0.002
0.004
Density
0.006
July 2013 Jan. 2014 July 2014
User 1
User 2
User 3
User 4
User 5
User 6
User 7
Date
Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions
(probability density functions) are shown for seven individual users over a two-year period. Density on
Timeline of insomnia-related tweets from representative individuals.
Nat. Biotech. 2015
트위터는 당신이 불면증이 있는지 알고 있다.

Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016)
higher Hue (bluer)
lower Saturation (grayer)
lower Brightness (darker)

Rao (MVR) (24) .

Results
Both Alldata and Prediagnosis models were decisively superior to a null model
. Alldata predictors were significant with 99% probability.57.5;(KAll = 1 K 49.8) Pre = 1 7
Prediagnosis and Alldata confidence levels were largely identical, with two exceptions:
Prediagnosis Brightness decreased to 90% confidence, and Prediagnosis posting frequency
dropped to 30% confidence, suggesting a null predictive value in the latter case.
Increased hue, along with decreased brightness and saturation, predicted depression. This
means that photos posted by depressed individuals tended to be bluer, darker, and grayer (see
Fig. 2). The more comments Instagram posts received, the more likely they were posted by
depressed participants, but the opposite was true for likes received. In the Alldata model, higher
posting frequency was also associated with depression. Depressed participants were more likely
to post photos with faces, but had a lower average face count per photograph than healthy
participants. Finally, depressed participants were less likely to apply Instagram filters to their
posted photos.

Fig. 2. Magnitude and direction of regression coefficients in Alldata (N=24,713) and Prediagnosis (N=18,513)
models. Xaxis values represent the adjustment in odds of an observation belonging to depressed individuals, per

Fig. 1. Comparison of HSV values. Right photograph has higher Hue (bluer), lower Saturation (grayer), and lower
Brightness (darker) than left photograph. Instagram photos posted by depressed individuals had HSV values
shifted towards those in the right photograph, compared with photos posted by healthy individuals.

Units of observation
In determining the best time span for this analysis, we encountered a difficult question:
When and for how long does depression occur? A diagnosis of depression does not indicate the
persistence of a depressive state for every moment of every day, and to conduct analysis using an
individual’s entire posting history as a single unit of observation is therefore rather specious. At
the other extreme, to take each individual photograph as units of observation runs the risk of
being too granular. DeChoudhury et al. (5) looked at all of a given user’s posts in a single day,
and aggregated those data into perperson, perday units of observation. We adopted this
precedent of “userdays” as a unit of analysis .  5

Statistical framework
We used Bayesian logistic regression with uninformative priors to determine the strength
of individual predictors. Two separate models were trained. The Alldata model used all
collected data to address Hypothesis 1. The Prediagnosis model used all data collected from
higher Hue (bluer)
lower Saturation (grayer)
lower Brightness (darker)
인스타그램은 당신이 우울한지 알고 있다.

. In particular, depressedχ2 07.84, p .17e 64;( All = 9 = 9 − 1 13.80, p .87e 44)χ2Pre = 8 = 2 − 1
participants were less likely than healthy participants to use any filters at all. When depressed
participants did employ filters, they most disproportionately favored the “Inkwell” filter, which
converts color photographs to blackandwhite images. Conversely, healthy participants most
disproportionately favored the Valencia filter, which lightens the tint of photos. Examples of
filtered photographs are provided in SI Appendix VIII.

Fig. 3. Instagram filter usage among depressed and healthy participants. Bars indicate difference between observed
and expected usage frequencies, based on a Chisquared analysis of independence. Blue bars indicate
disproportionate use of a filter by depressed compared to healthy participants, orange bars indicate the reverse.
인스타그램은 당신이 우울한지 알고 있다.

Step1. 데이터의 측정
•스마트폰

•웨어러블 디바이스

•개인 유전 정보 분석

•디지털 표현형
환자 유래의 의료 데이터 (PGHD)

Epic MyChart Epic EHR
Dexcom CGM
Patients/User
Devices
EH Hospit
Whitings
+
Apple Watch
Apps
HealthKit

Hospital B
Hospital C
Hospital A

Hospital A Hospital B
Hospital C
interoperability

•2018년 1월 출시 당시, 존스홉킨스, UC샌디에고 등 12개의 병원에 연동

•2019년 2월, 출시 1년 만에 200개 이상의 병원에 연동

•VA와도 연동된다고 밝힘 (with 9 million veterans)

•2008년 구글 헬스는 3년 동안 12개 병원에 연동에 그쳤음

•2019년 6월, 모든 병원이 등록 가능하도록 확대

How to Analyze and Interpret the Big Data?

and/or
Two ways to get insights from the big data

원격의료
• ‘명시적’으로, ‘전면적’으로 ‘금지’된 곳은 한국 밖에 없는 듯

• 해외에서는 새로운 서비스의 상당수가 원격의료 기능 포함

• 글로벌 100대 헬스케어 서비스 중 39개가 원격의료 포함

• 다른 모델과 결합하여 갈수록 새로운 모델이 만들어지는 중

• 스마트폰, 웨어러블, IoT, 인공지능, 챗봇 등과 결합

원격 의료
원격 진료
원격 환자 모니터링
화상 진료
전화 진료
2차 소견
용어 정리
온디맨드 처방
원격 수술

•원격 진료: 화상 진료

•원격 진료: 2차 소견

•원격 진료: 애플리케이션

•원격 환자 모니터링
원격 의료에도 종류가 많다.

Average Time to Appointment (Familiy Medicine)
Boston
LA
Portland
Miami
Atlanta
Denver
Detroit
New York
Seattle
Houston
Philadelphia
Washington DC
San Diego
Dallas
Minneapolis
Total
0 30 60 90 120
20.3
10
8
24
30
9
17
8
24
14
14
9
7
8
59
63
19.5
10
5
7
14
21
19
23
26
16
16
24
12
13
20
66
29.3 days
8 days
12 days
13 days
17 days
17 days
21 days
26 days
26 days
27 days
27 days
27 days
28 days
39 days
42 days
109 days
2017
2014
2009

0
125
250
375
500
2013 2014 2015 2016 2017 2018
417.9
233.3
123
77.4
44
20
0
550
1100
1650
2200
2013 2014 2015 2016 2017 2018
2,036
1,461
952
575
299
127
0
6
12
18
24
2013 2014 2015 2016 2017 2018
22.8
19.6
17.5
11.5
8.1
6.2
Revenue ($m) Visits (k) Members (m)
Growth of Teladoc

https://ﬁnance.yahoo.com/chart/TDOC
코로나 바이러스 사태와 Teladoc의 주가

- 11 -
붙임5 전화상담․처방 및 대리처방 한시적 허용방안
1. 전화상담․처방 한시적 허용방안
2020년 2월 22일

복지부 보도자료

메디히어 앱 다운로드 : http://bit.ly/39Dbouv

의사용 가입 주소 : https://admin.medihere.com/

Epic MyChart 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
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
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

의료계 일각에서는 원격 환자 모니터링의 합법화를 요구하기도

미국에서는 원격의료의

퀄리티 컨트롤이 잘 되고 있나?

미국의 원격 진료는 얼마나 정확한가?
Variation in Quality of Urgent Health Care
Provided During Commercial Virtual Visits
Adam J. Schoenfeld, MD; Jason M. Davies, MD, PhD; Ben J. Marafino, BS; Mitzi Dean, MS, MHA;
Colette DeJong, BA; Naomi S. Bardach, MD, MAS; Dhruv S. Kazi, MD, MS; W. John Boscardin, PhD;
Grace A. Lin, MD, MAS; Reena Duseja, MD; Y. John Mei, AB; Ateev Mehrotra, MD, MPH; R. Adams Dudley, MD, MBA
IMPORTANCE Commercial virtual visits are an increasingly popular model of health care for
the management of common acute illnesses. In commercial virtual visits, patients access a
website to be connected synchronously—via videoconference, telephone, or webchat—to a
physician with whom they have no prior relationship. To date, whether the care delivered
through those websites is similar or quality varies among the sites has not been assessed.
OBJECTIVE To assess the variation in the quality of urgent health care among virtual visit
companies.
DESIGN, SETTING, AND PARTICIPANTS This audit study used 67 trained standardized patients
who presented to commercial virtual visit companies with the following 6 common acute
illnesses: ankle pain, streptococcal pharyngitis, viral pharyngitis, acute rhinosinusitis, low
back pain, and recurrent female urinary tract infection. The 8 commercial virtual visit
websites with the highest web traffic were selected for audit, for a total of 599 visits. Data
were collected from May 1, 2013, to July 30, 2014, and analyzed from July 1, 2014, to
September 1, 2015.
MAIN OUTCOMES AND MEASURES Completeness of histories and physical examinations, the
correct diagnosis (vs an incorrect or no diagnosis), and adherence to guidelines of key
management decisions.
RESULTS Sixty-seven standardized patients completed 599 commercial virtual visits during
the study period. Histories and physical examinations were complete in 417 visits (69.6%;
95% CI, 67.7%-71.6%); diagnoses were correctly named in 458 visits (76.5%; 95% CI,
72.9%-79.9%), and key management decisions were adherent to guidelines in 325 visits
(54.3%; 95% CI, 50.2%-58.3%). Rates of guideline-adherent care ranged from 206 visits
(34.4%) to 396 visits (66.1%) across the 8 websites. Variation across websites was
significantly greater for viral pharyngitis and acute rhinosinusitis (adjusted rates, 12.8% to
82.1%) than for streptococcal pharyngitis and low back pain (adjusted rates, 74.6% to 96.5%)
or ankle pain and recurrent urinary tract infection (adjusted rates, 3.4% to 40.4%). No
statistically significant variation in guideline adherence by mode of communication
(videoconference vs telephone vs webchat) was found.
Invited Commentary
page 643
Supplemental content at
jamainternalmedicine.com
Research
Original Investigation
단순히 규제/허용의 이슈에서 더 나아가,

‘어떤 방식으로 허용’하고,

‘어떻게 질 관리를 할 것인가’도 중요

미국의 원격 진료는 얼마나 정확한가?
Choice, Transparency, Coordination, and Quality Among
Direct-to-Consumer Telemedicine Websites
and Apps Treating Skin Disease
Jack S. Resneck Jr, MD; Michael Abrouk; Meredith Steuer, MMS; Andrew Tam; Adam Yen; Ivy Lee, MD;
Carrie L. Kovarik, MD; Karen E. Edison, MD
IMPORTANCE Evidence supports use of teleconsultation for improving patient access to
dermatology. However, little is known about the quality of rapidly expanding
direct-to-consumer (DTC) telemedicine websites and smartphone apps diagnosing and
treating skin disease.
OBJECTIVE To assess the performance of DTC teledermatology services.
DESIGN AND PARTICIPANTS Simulated patients submitted a series of structured dermatologic
cases with photographs, including neoplastic, inflammatory, and infectious conditions, using
regional and national DTC telemedicine websites and smartphone apps offering services to
California residents.
MAIN OUTCOMES AND MEASURES Choice of clinician, transparency of credentials, clinician
location, demographic and medical data requested, diagnoses given, treatments
recommended or prescribed, adverse effects discussed, care coordination.
RESULTS We received responses for 62 clinical encounters from 16 DTC telemedicine
websites from February 4 to March 11, 2016. None asked for identification or raised concerns
about pseudonym use or falsified photographs. During most encounters (42 [68%]), patients
were assigned a clinician without any choice. Only 16 (26%) disclosed information about
clinician licensure, and some used internationally based physicians without California
licenses. Few collected the name of an existing primary care physician (14 [23%]) or offered
to send records (6 [10%]). A diagnosis or likely diagnosis was proffered in 48 encounters
(77%). Prescription medications were ordered in 31 of 48 diagnosed cases (65%), and
relevant adverse effects or pregnancy risks were disclosed in a minority (10 of 31 [32%] and
6 of 14 [43%], respectively). Websites made several correct diagnoses in clinical scenarios
where photographs alone were adequate, but when basic additional history elements (eg,
fever, hypertrichosis, oligomenorrhea) were important, they regularly failed to ask simple
relevant questions and diagnostic performance was poor. Major diagnoses were repeatedly
missed, including secondary syphilis, eczema herpeticum, gram-negative folliculitis, and
polycystic ovarian syndrome. Regardless of the diagnoses given, treatments prescribed were
sometimes at odds with existing guidelines.
CONCLUSIONS AND RELEVANCE Telemedicine has potential to expand access to high-value
health care. Our findings, however, raise concerns about the quality of skin disease diagnosis
Editor's Note
Author Affiliations: Department of
Dermatology, and Philip R. Lee
Institute for Health Policy Studies,
University of California, San Francisco
School of Medicine, San Francisco
(Resneck); University of California,
San Francisco School of Medicine,
Research
Original Investigation
단순히 규제/허용의 이슈에서 더 나아가,

‘어떤 방식으로 허용’하고,

‘어떻게 질 관리를 할 것인가’도 중요

원격의료로 오진의 가능성이 높다면?

Is It Time for a New Medical Specialty?
The Medical Virtualist
Medicinehasseenaproliferationofspecialtiesoverthe
last50years,asscientificdiscoveryandcaredeliveryad-
vanced. Diagnoses and treatments have become more
complex, so the need for formal training for specialty
competenceincognitiveandsurgicaldisciplineshasbe-
comeclear.Therearecurrently860 000physicianswith
active certifications through the American Board of
Medical Specialties and 34 000 through the American
Osteopathic Association.1
Drivers of Specialty Expansion
Specialty development has been driven by advances in
technology and expansion of knowledge in care deliv-
ery. Physician-led teams leverage technology and new
knowledgeintoastructuredapproachforamedicaldis-
cipline, which gains a momentum of its own with adop-
tion. For instance, critical care was not a unique spe-
cialty until 30 years ago. The refinement in ventilator
techniques,cardiacmonitoringandintervention,anes-
thesia, and surgical advancements drove the develop-
ment of the specialty and certification, with subse-
quentsubspecialization(eg,neurologicalintensivecare).
The development of laparoscopic and robotic surgical
equipment,withadvancedimaging,spawnednewspe-
cialty and subspecialty categories including colon and
rectal surgery, general surgical oncology, interven-
tional radiology, and electrophysiology.
Innonproceduralareas,uniquecertificationwases-
tablishedforgeriatricsandpalliativecare.Additionalnew
specialties include hospitalists, laborists, and extensiv-
ists, to name a few. These clinical areas do not yet have
formal training programs or certification but are specific
disciplineswithcorecompetenciesandmeasuresofper-
formance that might be likely recognized in the future.
Telemedicine and Medical Care
Telemedicine is the delivery of health care services
remotely by the use of various telecommunications
modalities. The expansion of web-based services, use
of videoconferencing in daily communication, and
social media coupled with the demand for convenience
by consumers of health care are all factors driving
exponential growth in telehealth.2
According to one estimate, the global telehealth
market is projected to increase at an annual com-
pounded rate of 30% between 2017 and 2022, achiev-
inganestimatedvalueof$12.1billiion.2
Somerecentmar-
ket surveys show that more than 70% of consumers
wouldconsideravirtualhealthcareservice.3
Aprepon-
deranceofhigherincomeandprivatelyinsuredconsum-
ersindicateapreferencefortelehealth,particularlywhen
reassured of the quality of the care and the appropriate
scopeofthevirtualvisit.3
Telemedicineisbeingusedto
provide health care to some traditionally underserved
and rural areas across the United States and increased
shortages of primary care and specialty physicians are
anticipated in those areas.4
A New Specialty
Digital advances within health care and patients acting
more like consumers have resulted in more physicians
and other clinicians delivering virtual care in almost ev-
ery medical discipline. Second-opinion services, emer-
gency department express care, virtual intensive care
units (ICUs), telestroke with mobile stroke units, tele-
psychiatry, and remote services for postacute care are
some examples.
In the traditional physician office, answering ser-
vicesandweb-basedportalsfocusedontelephoneand
email communication. The advent of
telehealth has resulted in incremental
growth of video face-to-face communi-
cation with patients by mobile phone,
tablet, or other computer devices.2,3,5
In larger enterprises or commercial ven-
tures, the scale is sufficient to “make
or buy” centralized telehealth command centers to
servicefunctionsacrossbroadgeographicareasinclud-
ing international.
Early telehealth focused on minor ailments such as
coughs,colds,andrashes,butnowtelehealthisbeingused
inbroaderapplications,suchascommunicatingimaging
andlaboratoryresults,changingmedication,andmostsig-
nificantly managing more complex chronic disease.
Thecoordinationofvirtualcarewithhomevisits,re-
mote monitoring, and simultaneous family engage-
ment is changing the perception and reality of virtual
health care. Commercialization is well under way with
numerous start-ups and more established companies.
These services are provided by the companies alone or
in collaboration with physician groups.
The Medical Virtualist
We propose the concept of a new specialty represent-
ingthemedicalvirtualist.Thistermcouldbeusedtode-
scribe physicians who will spend the majority or all of
their time caring for patients using a virtual medium. A
professional consensus will be needed on a set of core
competencies to be further developed over time.
VIEWPOINT
Medical virtualists could be involved in
a substantial proportion of health care
delivery for the next generation.
Michael Nochomovitz,
MD
New York Presbyterian,
New York, New York.
Rahul Sharma, MD,
MBA
New York Presbyterian,
Weill Cornell Medicine,
New York, New York.
Corresponding
Author: Michael
Nochomovitz, MD,
Physician Services
Division, New York
Presbyterian,
525 E 68th St,
PO Box 182,
New York, NY 10021
(mnochomovitz
@nyp.org).
Opinion
jama.com (Reprinted) JAMA Published online November 27, 2017 E1
© 2017 American Medical Association. All rights reserved.
Downloaded From: on 11/29/2017
JAMA: 원격의료에 맞는 전문의를 육성하면 되지 않는가?

•새로운 전공: 원격의료 전문의

•or 새로운 세부 전공: 원격 내과 전문의 / 원격 정신과 전문의

•의료 전달 체계
•수가
•의약품 배송
•신뢰, 신뢰, 신뢰
•+ 환자들은 어떻게 생각할까?
더 근본적인 이슈는?

No choice but to bring AI into the medicine

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

[365mc] 디지털 헬스케어: 의료의 미래

[365mc] 디지털 헬스케어: 의료의 미래

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