원격의료 시대의 디지털 치료제

원격의료 시대의 디지털 치료제 디지털 헬스케어 파트너스

최윤섭, PhD

디지털 치료제와 원격의료는 무슨 관계일까?

•디지털 치료제를 하려면 원격의료가 필요한가?

•디지털 치료제가 확장되면 원격의료가 되는가?

•원격의료 학회에서 디지털 치료제를 왜 다뤄야 하는가?

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

•세부적 기술, 서비스 유형에 따라 상당히 다양한 모델로 구현된다

•해외에서는 새로운 서비스의 상당수가 원격의료 기능 포함

•글로벌 100대 헬스케어 서비스 중 39개가 원격의료 포함

•다른 기술 및 서비스와 결합하여 갈수록 새로운 모델이 만들어지는 중

•스마트폰, 웨어러블, IoT, 인공지능, 챗봇 등과 결합

원격 의료
원격 진료
용어 정리

원격 의료
원격 진료
용어 정리
원격 환자 모니터링
원격 수술
원격 판독
e-ICU
원격 임상 시험

원격 의료
원격 진료
화상 진료
전화 진료
2차 소견
용어 정리
온디맨드 처방
원격 수술
데이터 분석
채팅 진료
원격 판독
e-ICU

원격 의료
원격 진료
화상 진료
전화 진료
2차 소견
용어 정리
온디맨드 처방
데이터 분석
채팅 진료
synchronous
asynchronous

‘store-and-forward’
원격 수술
원격 판독
e-ICU

원격 의료
원격 진료
화상 진료
전화 진료
2차 소견
온디맨드 처방
원격 수술
데이터 분석
채팅 진료
원격 판독
e-ICU
원격의료와 디지털 치료제의 관계

원격 의료
원격 진료
온디맨드 처방
디지털 치료제

원격 의료
원격 진료
온디맨드 처방
•디지털 치료제 = 원격의료: 아님.

•디지털 치료제의 임상, 처방, 사용에서 원격의료의 일부 요소를 활용할 수 있음

•현재 디지털 치료제 분야에서 원격의료가 차지하는 비중이 큰가? 그렇지 않음.
디지털 치료제

임상 처방 사용
디지털 치료제의

원격의료와 접점

원격 임상 시험 온디맨드 처방 원격 환자 모니터링


•판데믹 상황에서 임상시험에 관한 디지털 헬스케어 스타트업에 대한 투자 규모가 폭증
https://rockhealth.com/reports/next-gen-digital-health-innovation-in-clinical-trials/

•임상 시험의 각 단계에서 다양한 방식으로 디지털 기술이 접목되고 있음

임상 프로토콜에 맞는

환자가 많은 지역을

택할 수 있게 해줌

인공지능 (자연어처리) 기술로

환자의 진료기록을 분석하여

환자 리크루팅을 도와줌

•가장 많은 주목을 받았던 분야가 바로 원격 임상 시험

J
onathan Cotliar knew he was ahead of
thecurvefouryearsagowhenhejoined
Science 37, a company that supports
virtual clinical trials conducted
mostlyonline.ThefirminLosAngeles,
California, was growing slowly before
March, receiving about a dozen calls a
week from potential clients. But since
theCOVID-19pandemicbegan,Science37has
been running at fever pitch.
Cotliar,thecompany’schiefmedicalofficer,
says Science 37 now receives hundreds of
enquiries every week from potential clients,
such as pharmaceutical companies, medical
centresandevenindividualinvestigators.With
hospitalsformingtheepicentresofCOVID-19
outbreaks around the world, clinical-trial
participantshavebecomereluctanttoattend
routine check-ups and monitoring, and
health-care workers are stretched beyond
their capacity. This has caused researchers
to put many clinical trials on hold or to shift
to a virtual trial structure by performing
consultations online and collecting as much
paperwork and data as possible remotely.
The pandemic might hasten the kind of
change in clinical trials that Cotliar and
Science 37 were hoping to make anyway.
And there could be other lasting effects
on drug development: companies that are
usually competitors are now collaborating,
and many are trying to make their supply
chains more robust to deal with disruption.
Some researchers and companies in the
drug-developmentfieldsaythesystemmight
never be the same again.
The pandemic has touched nearly all
aspects of the industry, says Kenneth Kaitin,
director of the Tufts Center for the Study of
DrugDevelopmentinBoston,Massachusetts.
“Thishasreallyturnedupsidedownthewhole
drug-development process,” he says. “The
entire investigative world is focused just on
developing treatments for COVID-19.”
Some changes are likely to be temporary,
Kaitinpredicts.DrugregulatorsintheUnited
States and other countries have acted fast
to approve clinical trials of therapies and
allow new uses of existing medicines to fight
COVID-19, without demanding as much data
andpaperworkastheynormallywould.Such
changes are likely to stick only for as long as
the outbreak lasts. “The flexibilities that are
being granted for clinical-trial development
are being granted under the auspices of
a public-health declaration,” says Esther
Krofah, executive director of FasterCures, a
WashingtonDCthinktank.“That,tome,isvery
much an emergency operation.”
Trialtweaks
In other ways,the pandemic could catalyse
lasting change. What might linger, Krofah
says, is the culture of collaboration across
government, industry and academia that
has emerged during the outbreak. “We have
traditional competitors working together in
newways,”shesays.Anallianceofmorethana
dozencompanies—includingGileadinFoster
City,California,NovartisinBasel,Switzerland,
and WuXi AppTec in Shanghai, China — has
been working to discover and test antiviral
treatmentsbysharingdataaboutearlyresults
and basic science, as well as collaborating on
designsforclinicaltrials.Ifthesegroupefforts
bear fruit, they might continue, says Krofah.
Pharmaceutical companies might also
makelong-lastingadjustmentstotheirsupply
chains, says David Simchi-Levi, who studies
operationsmanagementattheMassachusetts
InstituteofTechnologyinCambridge.Overthe
past few decades, drug makers have increas-
ingly shifted their manufacturing away from
the United States and Europe to countries
such as India and China, which can produce
the drugs at lower cost. But over the past few
years,manyfirmshavebeguntolookforways
to diversify their supplies of services and raw
materials, to reduce the risk of supply inter-
ruptionsintheeventofaUS–Chinatradewar,
says Simchi-Levi. The coronavirus outbreak
could accelerate that trend. “Some shocks
were anticipated, but not at this scale,” says
Krofah. “This is going to cause a fundamental
re-examinationofthatrisk.”
Momentum for a shift towards virtual
clinical trials has been gradually building for
years.Butprogresshadbeenhinderedbyalack
ofclearguidancefromregulatorssuchasthe
USFoodandDrugAdministration(FDA)anda
reluctancetoinvestinthetechnologyneeded
torunsuchtrials—untilthepandemichit,says
Cotliar. Companies such as Science 37 are
suddenly seeing their popularity skyrocket.
“It’s exponentially accelerated the adoption
curve of what we were already doing,” Cotliar
says. “That’s been a bit surreal.”
At the University of Minnesota in
Minneapolis,forexample,infectious-disease
specialist David Boulware and his colleagues
conducted a randomized, controlled, virtual
trial of the malaria drug hydroxychloroquine
tofindoutwhetheritcanprotectpeoplewho
are at high risk of contracting COVID-19. The
trial, which included more than 800 people
and found the treatment had no benefit (D.
R. Boulware et al. N. Engl. J. Med. http://doi.
org/dxkv; 2020), sent participants medicine
byFedExdeliveryandmonitoredtheirhealth
remotely.
Patient advocates have long pushed for
morevirtualtrials,andifthetrendcatcheson,
it could speed up participant enrolment — a
time-consumingaspectofdrugdevelopment.
And now that the pandemic has driven
medical centres to set up much-needed
technology, and forced the FDA to release
guidelines for virtual trials during the
pandemic, it is hard to imagine clinical
research going back to the way it was before,
says Krofah. “We’re going to see this as a new,
normalpartofclinicalresearch,”shesays.“The
cat is out of the bag.”
Heidi Ledford is a senior reporter with Nature
in London.
ITMIGHT
BECOME
QUICKERAND
EASIERTO
TRIALDRUGS
Thecrisisispushingthe
drug-developmentindustry
intoanewnormalofvirtual
clinicalresearch.
172 | Nature | Vol 582 | 11 June 2020
FeatureScienceafterthepandemic
• 제약 업계에서 COVID-19의 가장 큰 타격: 신약 임상시험 진행이 어려워짐

• 의료진과 임상 참여자들의 대면이 어려워짐

• 병원의 리소스가 코로나 환자 진단/치료에 쏠림

• Virtual Clinical Trial (원격 임상 시험)이 큰 주목: Siteless, Decentralized, Patients-centric

• 이전에도 원격 임상 실험에 대한 시도가 있었으나, 판데믹으로 더욱 가속화

• 온라인으로 환자를 모집, 신약 후보 물질은 우편으로 배송

• 원격의료를 통해서 환자 증상 체크, 필요한 경우 간호사가 가정으로 방문

• 사상 최초의 원격 임상시험: 화이자의 REMOTE trial (June 2011)

• 휴대폰과 웹기반 기술로 임상시험 사이트를 방문하지 않고, 약 배송 및 데이터 수집

• 과민성 방광 치료제(OB) 데트롤 LA: 4상 결과를 그대로 재현할 수 있는지 여부 검증 목표

• 10개 주에서 600명의 환자를 등록이 목표였으나, 결국 환자 리크루팅에 실패

https://prahs.com/insights/janssen-pharmaceuticals-and-pra-health-launch-
fi
rst-fully-virtual-trial-for-heart-failure-drug-approval
• 얀센이 PRA와 파트너십을 통해, 인허가를 위한 최초의 “fully” 원격임상시험 시작 (Nov 2019)

• CHIEF trial: 당뇨병 치료제 인보카나의 심부전(HF)에 대한 효능 검증

• Primary endpoint : 증상 개선에 대한 PRO (Patient Reported Outcome)

• 모바일 플랫폼과 웨어러블에서 얻은 RWD 활용

• 판데믹 이후, 2월 초부터 5월 말까지 제약사들이 취소한 임상 시험은 340개

• 다국적 제약사 중에서 가장 빠르게 움직이는 것은 화이자

• 이미 수십개(dozens of) 임상시험 디자인을 원격으로 하도록 수정

• 향후 18개월 이내에 화이자의 ‘모든’ 임상 시험이 virtual component를 가질 것

• 최초로 fully virtual trial을 시작할 계획: 피부염 관련 임상 (피부 사진을 찍어서 전송 등)

• 노바티스도 적극적

• 지난 5년 동안 virtual trial tech 에 투자해왔음

• 최근에는 이미 1,100번 이상 약을 원격으로 보내주고, trial site 200개 이상이 원격으로 진행

• 환자의 안전이 보장되고, 적절한 수단이 있는 경우라면,
 
 
FDA의 별도 리뷰나, IRB 승인 없이도, 임상시험의 프로토콜을
 
 
화상통화, 의약품 배송 등을 통해서 원격으로 변경할 수 있도록 허용함을 적시한 가이드라인

• Science 37

• 원격 임상 시험 플랫폼을 제공하는 대표적인 스타트업

• 온라인 환자 등록부터, outcome 평가까지 end-to-end 원격 임상 시험 제공

• COVID-19 시대에 다국적 제약사 등으로부터 큰 주목을 받고 있음

• 2020년 8월 펀딩에 노바티스, 암젠, 사노피 등의 다국적 제약사가 투자자로 참여

Original Paper
An 8-Week Self-Administered At-Home Behavioral Skills-Based
Virtual Reality Program for Chronic Low Back Pain: Double-Blind,
Randomized, Placebo-Controlled Trial Conducted During
COVID-19
Laura M Garcia1
, PhD; Brandon J Birckhead1
, MD, MHDS; Parthasarathy Krishnamurthy2
, MBA, PhD; Josh Sackman1
,
MBA; Ian G Mackey1
, BA; Robert G Louis3
, MD; Vafi Salmasi4
, MD, MSc; Todd Maddox1
, PhD; Beth D Darnall4
,
PhD
1
AppliedVR, Inc, Los Angeles, CA, United States
2
CT Bauer College of Business, University of Houston, Houston, TX, United States
3
Division of Neurosurgery, Pickup Family Neurosciences Institute, Hoag Memorial Hospital, Newport Beach, CA, United States
4
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
Corresponding Author:
Beth D Darnall, PhD
Department of Anesthesiology, Perioperative and Pain Medicine
Stanford University School of Medicine
1070 Arastradero Road
Ste 200, MC5596
Palo Alto, CA, 94304
United States
Phone: 1 15035778377
Email: bdarnall@stanford.edu
Abstract
Background: Chronic low back pain is the most prevalent chronic pain condition worldwide and access to behavioral pain
treatment is limited. Virtual reality (VR) is an immersive technology that may provide effective behavioral therapeutics for chronic
pain.
Objective: We aimed to conduct a double-blind, parallel-arm, single-cohort, remote, randomized placebo-controlled trial for a
self-administered behavioral skills-based VR program in community-based individuals with self-reported chronic low back pain
during the COVID-19 pandemic.
Methods: A national online convenience sample of individuals with self-reported nonmalignant low back pain with duration
of 6 months or more and with average pain intensity of 4 or more/10 was enrolled and randomized 1:1 to 1 of 2 daily (56-day)
VR programs: (1) EaseVRx (immersive pain relief skills VR program); or (2) Sham VR (2D nature content delivered in a VR
headset). Objective device use data and self-reported data were collected. The primary outcomes were the between-group effect
of EaseVRx versus Sham VR across time points, and the between–within interaction effect representing the change in average
pain intensity and pain-related interference with activity, stress, mood, and sleep over time (baseline to end-of-treatment at day
56). Secondary outcomes were global impression of change and change in physical function, sleep disturbance, pain self-efficacy,
pain catastrophizing, pain acceptance, pain medication use, and user satisfaction. Analytic methods included intention-to-treat
and a mixed-model framework.
Results: The study sample was 179 adults (female: 76.5%, 137/179; Caucasian: 90.5%, 162/179; at least some college education:
91.1%, 163/179; mean age: 51.5 years [SD 13.1]; average pain intensity: 5/10 [SD 1.2]; back pain duration ≥5 years: 67%,
120/179). No group differences were found for any baseline variable or treatment engagement. User satisfaction ratings were
higher for EaseVRx versus Sham VR (P<.001). For the between-groups factor, EaseVRx was superior to Sham VR for all primary
outcomes (highest P value=.009), and between-groups Cohen d effect sizes ranged from 0.40 to 0.49, indicating superiority was
moderately clinically meaningful. For EaseVRx, large pre–post effect sizes ranged from 1.17 to 1.3 and met moderate to substantial
clinical importance for reduced pain intensity and pain-related interference with activity, mood, and stress. Between-group
comparisons for Physical Function and Sleep Disturbance showed superiority for the EaseVRx group versus the Sham VR group
J Med Internet Res 2021 | vol. 23 | iss. 2 | e26292 | p. 1
https://www.jmir.org/2021/2/e26292
(page number not for citation purposes)
Garcia et al
JOURNAL OF MEDICAL INTERNET RESEARCH
XSL•FO
RenderX
• 허리 통증 환자에 대한, 통증 완화 효과를 검증하기 위한 pivotal clinical trial

• 총 179명의 (최소 6개월 이상의) 허리 통증 호소 환자 대상

• 8주 동안, 환자가 집에서 스스로 활용하는 방식

• 이중맹검, 무작위배정, 플라시보 대조군 활용, 원격 임상 시험
 
• 실험군: EaseVRx (인지, 감정, 신체 반응 등의 알아차림, 교정 등을 유도한 VR)

• 대조군: Sahm VR (자연 풍광이 나오는 2D 영상)
인허가를 받기 위한 목적의 임상시험

Original Paper
COVID-19
Laura M Garcia1
,
MBA; Ian G Mackey1
, MD; Vafi Salmasi4
,
PhD
1
2
3
4
Beth D Darnall, PhD
Ste 200, MC5596
United States
Phone: 1 15035778377
Abstract
pain.
Garcia et al
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a medical and scientific rationale for the VR exercises and
behavioral medicine skills for pain relief.
• Relaxation/Interoception: scenes that progressively change
from busy/active to calm in order to train users to
understand the benefits of progressive relaxation.
• Mindful escapes: high-resolution 360 videos with
therapeutic voiceovers, music, guided breathing, and sound
effects designed to maximize the relaxation response and
participant engagement.
• Pain distraction games: interactive games to train the skill
of shifting focus away from pain.
selected an active control that utilizes nonimmersive, 2D content
within a VR headset as the most rigorous VR placebo [30]. The
Sham VR headset displayed 2D nature footage (eg, wildlife in
the savannah) with neutral music that was selected to be neither
overly relaxing, aversive, nor distracting. The experience of
Sham VR is similar to viewing nature scenes on a large-screen
television and is not interactive. Twenty videos were rotated
over the 56 sessions, with average duration of sessions closely
matching those of EaseVRx (Figure 2).
Figure 2. Visual display of EaseVRx (skills-based, interactive, 3D) and Sham VR (non-interactive 2D nature scenes).
Research Standards and Compliance
In accordance with the Initiative on Methods, Measurement,
and Pain Assessment in Clinical Trials (IMMPACT)
recommendations, we included multiple measures to evaluate
the importance of change in outcomes across 4 recommended
domains: pain intensity, health-related quality of life and
functioning, and ratings of overall improvement [46-48].
Additionally, measures and individual items were included to
align directly with the National Institutes of Health (NIH) Pain
Consortium’s Report on Research Standards for Chronic Low
Back Pain [49] or assess the domains recommended in the
report. The study was performed in accordance with the
CONSORT (Consolidated Standards of Reporting Trials)
guidelines [50] (see Multimedia Appendix 2 for the completed
checklist) and the recommended extension for reporting of
psychological trials [51]. The Western Institutional Review
Board approved the study (Puyallup, WA).
Data Collection and Time Points
Data collection included electronic participant-reported measures
and objective VR device use data collected from the VR devices.
Data were collected across 3 phases of the study: pretreatment
(days –14 to 0), active treatment (days 1-55), and end of
treatment (day 56). The 14-day pretreatment phase involved
administering the pain surveys 5 times (baseline, days –10, –7,
–3, and 0); these measures were averaged within participants
to establish a single pretreatment score for each variable
assessed. During the 8-week active treatment phase, surveys
were distributed biweekly (15 total surveys during treatment)
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인허가를 받기 위한 목적의 임상시험
• 허리 통증 환자에 대한, 통증 완화 효과를 검증하기 위한 pivotal clinical trial

• 총 179명의 (최소 6개월 이상의) 허리 통증 호소 환자 대상

• 8주 동안, 환자가 집에서 스스로 활용하는 방식

• 이중맹검, 무작위배정, 플라시보 대조군 활용, 원격 임상 시험
 
• 실험군: EaseVRx (인지, 감정, 신체 반응 등의 알아차림, 교정 등을 유도한 VR)

• 대조군: Sahm VR (자연 풍광이 나오는 2D 영상)

Original Paper
COVID-19
Laura M Garcia1
,
MBA; Ian G Mackey1
, MD; Vafi Salmasi4
,
PhD
1
2
3
4
Beth D Darnall, PhD
Ste 200, MC5596
United States
Phone: 1 15035778377
Abstract
pain.
Garcia et al
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• 환자 모집, 동의서, 자격 검증, 사용, 데이터 측정: 모두 원격으로 진행

Original Paper
COVID-19
Laura M Garcia1
,
MBA; Ian G Mackey1
, MD; Vafi Salmasi4
,
PhD
1
2
3
4
Beth D Darnall, PhD
Ste 200, MC5596
United States
Phone: 1 15035778377
Abstract
pain.
Garcia et al
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• 전반적 통증 및 통증과 연계되는 활동, 수면, 감정, 수면, 스트레스 등이
 
  
모두 실험군에서, 대조군 대비 유의미하게 개선됨
Figure 6. Pain-Related Interference with Mood.
Figure 7. Pain Related Interference with Sleep.
Garcia et al
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Figure 4. Average pain intensity.
Figure 5. Pain-Related Interference with Activity.
Garcia et al
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Figure 8. Pain-Related Interference with Stress.
We observed a significant treatment effect (P=.001); on average,
the EaseVRx group had lower pain intensity compared to the
Sham VR group (Cohen d=0.49). Separately, we observed a
time effect; average pain intensity significantly decreased over
time for both treatment groups (time effect, P<.001). Most
importantly, the decrease was greater for EaseVRx versus Sham
VR (treatment × time effect, P<.001). Pain intensity reduced
by an average of 42.8% for the EaseVRx group and 25.1% for
the Sham VR group. The drm for EaseVRx was 1.31, with
combined results showing large effect size and moderate clinical
importance. The VR Sham group drm was 0.75, with combined
results showing a large effect size and minimal clinical
importance. As much as 65% (55/84) of EaseVRx and 40%
(34/84) of Sham VR participants achieved 30% or more
reduction in pain intensity. For EaseVRx, 46% (39/84) achieved
50% or more pain reduction, while for Sham VR 26% (22/84)
reached that threshold.
We observed a significant treatment effect (P=.004) on pain
interference with activity; on average, the EaseVRx group had
lower activity interference compared to the Sham VR group
(Cohen d=0.44). We also observed a time effect; pain
interference with activity decreased over time for both treatment
groups (time effect, P<.001). Most importantly, the decrease
was greater for EaseVRx versus Sham VR (treatment × time
effect, P=.013). Pain interference with activity reduced by an
average of 51.6% for the EaseVRx group and 32.4% for the
Sham VR group. The drm for the EaseVRx group was 1.27, with
combined results showing large effect size and moderate clinical
importance. As much as 71% (60/84) of EaseVRx and 57%
(48/84) of Sham VR participants achieved 30% or more
reduction in pain-interference with activity, and 56% of (47/84)
of the EaseVRx participants achieved 50% or more reduction.
The VR Sham group drm was 0.97, with combined results
showing a large effect size and moderate clinical importance.
We observed a significant treatment effect (P=.005) on pain
interference with mood; on average, the EaseVRx group had
lower mood interference compared to the Sham VR group
(Cohen d=0.42). We also observed a time effect;
pain-interference with mood decreased over time for both
treatment groups (time effect, P<.001) and the decrease was
greater for EaseVRx versus Sham VR (treatment × time effect,
P=.010). Pain interference with mood reduced by an average
of 55.7% for EaseVRx and 40.04% for the Sham VR. The drm
for the EaseVRx was 1.18, with combined results evidencing
a large effect size and substantial clinical importance. As much
as 74% (62/84) of EaseVRx participants and 60% (50/84) of
Sham VR participants achieved 30% or more reduction in
pain-related interference with mood, and 61% (51/84) of the
EaseVRx participants achieved 50% or more reduction. The
drm for the VR Sham group was 0.79, with combined results
showing a moderate effect size and moderate clinical
importance.
We observed a significant treatment effect (P=.004) on
pain-interference with sleep; on average, the EaseVRx group
had lower sleep interference compared to the Sham VR group
(Cohen d=0.44). We also observed a time effect;
pain-interference with sleep decreased over time for both
treatment groups (time effect, P<.001). However, there was no
difference between treatment groups over time (P=.755). Pain
interference with sleep reduced by an average of 54% for
EaseVRx and 39.2% for the Sham VR. The drm for the EaseVRx
was 0.95, with combined results showing large effect size and
substantial clinical importance for symptom reduction. As much
as 70% (59/84) of EaseVRx and 60% (50/84) of participants
achieved 30% or more reduction in Pain-related interference
with sleep, and 60% (50/84) of the EaseVRx participants
achieved 50% or more reduction. The VR Sham group drm was
Garcia et al
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전반적인 통증의 강도 통증과 관계된 감정 문제 통증과 관계된 스트레스
통증과 관계된 활동 문제 통증과 관계된 수면 문제

Original Paper
Self-Administered Skills-Based Virtual Reality Intervention for
Chronic Pain: Randomized Controlled Pilot Study
Beth D Darnall1
, PhD; Parthasarathy Krishnamurthy2
, MBA, PhD; Jeannette Tsuei3
, MA; Jorge D Minor4
, MD
1
2
C.T. Bauer College of Business, University of Houston, Houston, TX, United States
3
4
L.A. Pain & Wellness Institute, Los Angeles, CA, United States
Beth D Darnall, PhD
Suite 200, MC 5596
United States
Phone: 1 (650) 725 9642
Abstract
Background: Patients with chronic pain often have limited access to comprehensive care that includes behavioral pain management
strategies. Virtual reality (VR) is an immersive technology and emerging digital behavioral pain therapy with analgesic efficacy
for acute pain. We found no scientific literature on skills-based VR behavioral programs for chronic pain populations.
Objective: The primary aim of this study is to evaluate the feasibility of a self-administered VR program that included content
and skills informed by evidence-based behavioral treatment for chronic pain. The secondary aim is to determine the preliminary
efficacy of the VR program in terms of average pain intensity and pain-related interference with activity, stress, mood, and sleep,
and its impact on pain-related cognition and self-efficacy. The tertiary aim was to conduct a randomized controlled trial (RCT)
and compare the VR treatment with an audio-only treatment. This comparison isolated the immersive effects of the VR program,
thereby informing potential mechanisms of effect.
Methods: We conducted an RCT involving a web-based convenience sample of adults (N=97) aged 18-75 years with self-reported
chronic nonmalignant low back pain or fibromyalgia, with an average pain intensity >4 over the past month and chronic pain
duration >6 months. Enrolled participants were randomly assigned to 1 of 2 unblinded treatments: (1) VR: a 21-day, skills-based
VR program for chronic pain; and (2) audio: an audio-only version of the 21-day VR program. The analytic data set included
participants who completed at least 1 of 8 surveys administered during the intervention period: VR (n=39) and audio (n=35).
Results: The VR and audio groups launched a total of 1067 and 1048 sessions, respectively. The majority of VR participants
(n=19/25, 76%) reported no nausea or motion sickness. High satisfaction ratings were reported for VR (n=24/29, 83%) and audio
(n=26/33, 72%). For VR efficacy, symptom improvement over time was found for each pain variable (all P<.001), with results
strengthening after 2 weeks. Importantly, significant time×group effects were found in favor of the VR group for average pain
intensity (P=.04), pain-related inference with activity (P=.005), sleep (P<.001), mood (P<.001), and stress (P=.003). For pain
catastrophizing and pain self-efficacy, we found a significant declining trend for both treatment groups.
Conclusions: High engagement and satisfaction combined with low levels of adverse effects support the feasibility and
acceptability of at-home skills-based VR for chronic pain. A significant reduction in pain outcomes over the course of the 21-day
treatment both within the VR group and compared with an audio-only version suggests that VR has the potential to provide
enhanced treatment and greater improvement across a range of pain outcomes. These findings provide a foundation for future
research on VR behavioral interventions for chronic pain.
(JMIR Form Res 2020;4(7):e17293) doi: 10.2196/17293
JMIR Form Res 2020 | vol. 4 | iss. 7 | e17293 | p. 1
https://formative.jmir.org/2020/7/e17293
Darnall et al
JMIR FORMATIVE RESEARCH
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Effect of Digital Cognitive Behavioral Therapy for Insomnia
on Health, Psychological Well-being,
and Sleep-Related Quality of Life
A Randomized Clinical Trial
Colin A. Espie, PhD; Richard Emsley, PhD; Simon D. Kyle, PhD; Christopher Gordon, PhD; Christopher L. Drake, PhD;
A. Niroshan Siriwardena, PhD; John Cape, PhD; Jason C. Ong, PhD; Bryony Sheaves, DClinPsy; Russell Foster, PhD;
Daniel Freeman, PhD; Joan Costa-Font, PhD; Antonia Marsden, PhD; Annemarie I. Luik, PhD
IMPORTANCE Digital cognitive behavioral therapy (dCBT) is a scalable and effective
intervention for treating insomnia. Most people with insomnia, however, seek help because
of the daytime consequences of poor sleep, which adversely affects quality of life.
OBJECTIVES To investigate the effect of dCBT for insomnia on functional health,
psychological well-being, and sleep-related quality of life and to determine whether a
reduction in insomnia symptoms was a mediating factor.
DESIGN, SETTING, AND PARTICIPANTS This online, 2-arm, parallel-group randomized trial
comparing dCBT for insomnia with sleep hygiene education (SHE) evaluated 1711 participants
with self-reported symptoms of insomnia. Participants were recruited between December 1,
2015, and December 1, 2016, and dCBT was delivered using web and/or mobile channels plus
treatment as usual; SHE comprised a website and a downloadable booklet plus treatment as
usual. Online assessments took place at 0 (baseline), 4 (midtreatment), 8 (posttreatment),
and 24 (follow-up) weeks. Programs were completed within 12 weeks after inclusion.
MAIN OUTCOMES AND MEASURES Primary outcomes were scores on self-reported measures
of functional health (Patient-Reported Outcomes Measurement Information System: Global
Health Scale; range, 10-50; higher scores indicate better health); psychological well-being
(Warwick-Edinburgh Mental Well-being Scale; range, 14-70; higher scores indicate greater
well-being); and sleep-related quality of life (Glasgow Sleep Impact Index; range, 1-100;
higher scores indicate greater impairment). Secondary outcomes comprised mood, fatigue,
sleepiness, cognitive failures, work productivity, and relationship satisfaction. Insomnia was
assessed with the Sleep Condition Indicator (range: 0-32; higher scores indicate better sleep).
RESULTS Of the 1711 participants included in the intention-to-treat analysis, 1329 (77.7%)
were female, mean (SD) age was 48.0 (13.8) years, and 1558 (91.1%) were white. Use of dCBT
was associated with a small improvement in functional health compared with SHE (adjusted
difference [95% CI] at week 4, 0.90 [0.40-1.40]; week 8, 1.76 [1.24-2.28]; week 24, 1.76
[1.22-2.30]) and psychological well-being (adjusted difference [95% CI] at week 4, 1.04
[0.28-1.80]; week 8, 2.68 [1.89-3.47]; week 24, 2.95 [2.13-3.76]), and with a large
improvement in sleep-related quality of life (at week 4, −8.76 [−11.83 to −5.69]; week 8,
–17.60 [−20.81 to −14.39]; week 24, −18.72 [−22.04 to −15.41]) (all P < .01). A large
improvement in insomnia mediated these outcomes (range mediated, 45.5%-84.0%).
CONCLUSIONS AND RELEVANCE Use of dCBT is effective in improving functional health,
psychological well-being, and sleep-related quality of life in people reporting insomnia
symptoms. A reduction in insomnia symptoms mediates these improvements. These results
confirm that dCBT improves both daytime and nighttime aspects of insomnia, strengthening
existing recommendations of CBT as the treatment of choice for insomnia.
TRIAL REGISTRATION isrctn.org identifier: ISRCTN60530898
JAMA Psychiatry. doi:10.1001/jamapsychiatry.2018.2745
Published online September 25, 2018.
Supplemental content
Author Affiliations: Author
affiliations are listed at the end of this
article.
Corresponding Author: Colin A.
Espie, PhD, Sleep & Circadian
Neuroscience Institute, Nuffield
Department of Clinical
Neurosciences, University of Oxford,
Sir William Dunn School of Pathology,
South Parks Road, Oxford OX1 3RE,
United Kingdom (colin.espie@ndcn
.ox.ac.uk).
Research
JAMA Psychiatry | Original Investigation
(Reprinted) E1
© 2018 American Medical Association. All rights reserved.
Downloaded From: on 09/26/2018
“Screening, informed consent, assessments,
allocation to condition, and delivery of the
interventions were carried out entirely
online.”
“All study procedures were completed
remotely, and no in-person visits were
required.”
www.thelancet.com/digital-health Published online February 24, 2020 https://doi.org/10.1016/S2589-7500(20)30017-0 1
Articles
Lancet Digital Health 2020
Published Online
February 24, 2020
https://doi.org/10.1016/
S2589-7500(20)30017-0
See Online/Comment
https://doi.org/10.1016/
S2589-7500(20)30058-3
Psychiatry and Behavioral
Sciences, Duke University
Medical Center, Durham, NC,
USA (Prof S H Kollins PhD,
Prof R S E Keefe PhD); Duke
Clinical Research Institute,
Durham, NC, USA
(Prof S H Kollins); Akili
Interactive Labs, Boston, MA,
USA (D J DeLoss PhD,
E Cañadas PhD, J Lutz PhD);
Department of Psychiatry,
Virginia Commonwealth
University, Richmond, VA, USA
(Prof R L Findling MD); VeraSci,
Durham, NC, USA
(Prof R S E Keefe); Department
of Pediatrics, University of
Cincinnati College of Medicine,
Cincinnati, OH, USA
(Prof J N Epstein PhD); Meridien
Research & Lake Erie College of
Osteopathic Medicine,
Bradenton, FL, USA
(A J Cutler MD); and Psychiatry
and Neuroscience and
Physiology, SUNY Upstate
Medical University,
Syracuse, NY, USA
(Prof S V Faraone PhD)
Correspondence to:
Dr Scott Kollins, Psychiatry and
Behavioral Sciences, Duke
University Medical Center,
Durham, NC 27710, USA
scott.kollins@duke.edu
A novel digital intervention for actively reducing severity of
paediatricADHD (STARS-ADHD): a randomised controlledtrial
Scott H Kollins, Denton J DeLoss, Elena Cañadas, Jacqueline Lutz, Robert L Findling, Richard S E Keefe, Jeffery N Epstein, Andrew J Cutler,
StephenV Faraone
Summary
Background Attention-deficit hyperactivity disorder (ADHD) is a common paediatric neurodevelopmental disorder with
substantial effect on families and society. Alternatives to traditional care, including novel digital therapeutics, have
shown promise to remediate cognitive deficits associated with this disorder and may address barriers to standard
therapies, such as pharmacological interventions and behavioural therapy. AKL-T01 is an investigational digital
therapeutic designed to target attention and cognitive control delivered through a video game-like interface via at-home
play for 25 min per day, 5 days per week for 4 weeks. This study aimed to assess whether AKL-T01 improved attentional
performance in paediatric patients with ADHD.
Methods The Software Treatment for Actively Reducing Severity of ADHD (STARS-ADHD) was a randomised, double-
blind, parallel-group, controlled trial of paediatric patients (aged 8–12 years, without disorder-related medications) with
confirmed ADHD and Test of Variables of Attention (TOVA) Attention Performance Index (API) scores of −1·8 and
below done by 20 research institutions in the USA. Patients were randomly assigned 1:1 to AKL-T01 or a digital control
intervention. The primary outcome was mean change in TOVA API from pre-intervention to post-intervention. Safety,
tolerability, and compliance were also assessed. Analyses were done in the intention-to-treat population. This trial is
registered with ClinicalTrials.gov, NCT02674633 and is completed.
Findings Between July 15, 2016, and Nov 30, 2017, 857 patients were evaluated and 348 were randomly assigned to
receive AKL-T01 or control. Among patients who received AKL-T01 (n=180 [52%]; mean [SD] age, 9·7 [1·3] years) or
control (n=168 [48%]; mean [SD] age, 9·6 [1·3] years), the non-parametric estimate of the population median change
from baseline TOVA API was 0·88 (95% CI 0·24–1·49; p=0·0060). The mean (SD) change from baseline on the
TOVA API was 0·93 (3·15) in the AKL-T01 group and 0·03 (3·16) in the control group. There were no serious adverse
events or discontinuations. Treatment-related adverse events were mild and included frustration (5 [3%] of 180)
and headache (3 [2%] of 180). Patient compliance was a mean of 83 (83%) of 100 expected sessions played
(SD, 29·2 sessions).
Interpretation Although future research is needed for this digital intervention, this study provides evidence that
AKL-T01 might be used to improve objectively measured inattention in paediatric patients with ADHD, while
presenting minimal adverse events.
Funding Sponsored by Akili Interactive Labs.
Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
Introduction
Attention-deficit hyperactivity disorder (ADHD) is a
neurodevelopmental disorder of persistent impaired
attention, hyperactivity, and impulsivity that negatively
affects daily functioning and quality of life. ADHD is
one of the most commonly diagnosed paediatric mental
health disorders, with a prevalence estimated to be 5%
worldwide,1
and exerts a substantial burden on families
and society.2
Front-line intervention for ADHD includes pharmaco-
logical and non-pharmacological interventions, which
have shown short-term efficacy.3–5
Existing treatments
have side-effects that limit their acceptability,6
are only
effective when administered, and may not be as effective
for reducing daily impairments versus ADHD symptoms.7
Pharmacotherapy may not be suitable for some patients
due to caregiver preferences or concerns about abuse,
misuse, and diversion. Barriers to access also limit the
use of behavioural interventions, given a shortage of
properly trained paediatric mental health specialists8
and
variability in insurance coverage for such services.9,10
Indeed, studies in both the USA and the UK have found
that most children with paediatric mental health needs do
not have proper access to services.11,12
Digital therapeutics for ADHD may address these
limitations with improved access, minimal side-effects,
and low potential for abuse. Numerous studies and
meta-analyses on digital interventions targeting specific
cognitive functions have attempted to assess the
magnitude of efficacy for children and adolescents with
ADHD. In general, the quality of the studies is low, and
many do not include a control group.3
Reported effect
“Compliance was monitored electronically by
unmasked study coordinators, who notified
parents by email if the intervention was not
administered over a 48-h period.”


온디맨드 처방

Reference Brands
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• ‘온디맨드 처방’ 모델: Hims, Hers, Ro, Nurx, Lemonaid Health

• 원격으로 문진을 하고, 의약품을 처방 및 배송해주는 모델

• 특정 분야 질병에 대한 처방 여부만 결정: 피임, 발기부전, 탈모, 금연, 여드름, UTI 등

• 규모의 경제 & 낮은 오버헤드: (Hims의 경우) 오프라인 약국보다 50~80% 저렴하게 판매

임상-인허가-보험-의사 처방-환자 사용-RWE
• 기존 약 대신에 의사가 처방할까

• 기존 약 대비 어떤 강점을 가져야 하나

• 복약지도는 누가 어떻게 하나
• DTx를 처방 받으면 환자는 어떻게 받아들일까

• 지속사용성: 지속해서 사용할까

• 디지털 리터러시: 사용성에 지장은 없는가
(Real World Evidence)
• 인허가 없이 공산품으로 판매할 것인지,
 
의료기기 인허가를 받을 것인지 결정

• “인허가 받는 것이 유일한 방법은 아니다”

• 인허가를 받겠다면, 어떤 규제 방식을 택할 것인가

• 규제기관은 DTx의 효능/부작용을 어떻게 심사할까
• 의사들이 활용할 여건이 되는가

• EMR 속으로 어떻게 통합 + 원격의료?

• 진단/치료/관리 기준으로 활용 가능한가

• 새로운 인허가/수가 기준에 어떻게 활용할 것인가
• DTx에도 RCT가 필요한가

• 임상 연구 디자인은 어떻게 해야 하나

• 대조군 어떻게 설정하나

• 원격 임상 시험

• 효용/가치를 어떻게 증명할 것인가
• 보험사/심평원은 DTx를 어떻게 바라볼까

• 수가를 받을 수 있는가/받아야 하는가

• 어떤 방식으로 지불해야 하는가

• 건당/value-based

• 페어 테라퓨틱스의 2021년 6월 Investor Presentation

• reSET과 reSET-O의 처방 건수가 총 20,000 여 건에 지나지 않음

• 처방해준 의사는 700여 명, 처방해주는 기관은 15개 정도

• “비용효과성을 증명해서, 메디케어/메디케이드 수가를 받으면,
 
매출을 2023년까지 30배 성장시킬 수 있을 것이다.”
•
•
•
•
•
•
•
• ➔ ➔
•
•
•

디지털 치료제의 보험 적용
• 미국: 민간 보험 및 PBM 중심으로 보험 적용

• 병원-보험 복합체: 카이져 퍼머넌테, 스펙트럼 헬스

• 메디케어 보험 적용은 아직 안 되고 있음/ COVID-19로 업계 요구 커지는 상황

• 영국: NHS가 “NHSX”를 신설하며 전향적인 태도를 보이고 있음

• Sleepio 에 대한 지불 사례

• EGF (Evidence Generation Fund) 지불: 근거를 만들기 위한 재원

• 제품에 대한 비용 일부를 우선 지불하고, 합의된 사용/결과지표 충족 시 지불을 완료

• 독일: 2019년 디지털 헬스케어 법(Digitale-Versorgung-Gesetz)

• 승인을 받은 앱은 디지털 헬스 앱 목록에 등재: 조건부 등재/정식 등재

• 등재된 앱은 12개월 동안 제조사 수가 지정 + 12개월 이후는 협상

• 목록 등재 신청은 2020년 5월부터 시작

• 프랑스, 벨기에: 조건부, 임시수가 방식의 적용 시작

• 프랑스: 3년 간 수가 적용 의료 기기 목록에 등재 + 갱신을 위해서는 real-life study 필요

• 폐암 재발/부작용 모니터링 앱 (Moovcare Poumon) 수가 적용 (2020년 7월)

WE HAVE
THE ANSWERS.
The Fast-Track Process for Digital
Health Applications (DiGA)
according to Section 139e SGB V
A Guide for Manufacturers, Service Providers and Users
PERSPECTIVE OPEN
Germany’s digital health reforms in the COVID-19 era: lessons
and opportunities for other countries
Sara Gerke 1
, Ariel D. Stern 2
and Timo Minssen 3 ✉
Reimbursement is a key challenge for many new digital health solutions, whose importance and value have been highlighted and
expanded by the current COVID-19 pandemic. Germany’s new Digital Healthcare Act (Digitale–Versorgung–Gesetz or DVG) entitles
all individuals covered by statutory health insurance to reimbursement for certain digital health applications (i.e., insurers will pay
for their use). Since Germany, like the United States (US), is a multi-payer health care system, the new Act provides a particularly
interesting case study for US policymakers. We first provide an overview of the new German DVG and outline the landscape for
reimbursement of digital health solutions in the US, including recent changes to policies governing telehealth during the COVID-19
pandemic. We then discuss challenges and unanswered questions raised by the DVG, ranging from the limited scope of the Act to
privacy issues. Lastly, we highlight early lessons and opportunities for other countries.
npj Digital Medicine (2020)3:94 ; https://doi.org/10.1038/s41746-020-0306-7
A recent survey of 284 health care, life science, and digital health
professionals in the United States (US) revealed that 42% of
respondents felt they were “likely” or “somewhat likely” to partner
or contract with an AI company over the next year1
. However, a
significant share of respondents also believed that digital health
partnerships face unique obstacles with regard to key issues such
as pricing and reimbursement (26%) and data privacy and security
(19%)1
. Consequently, they were reluctant to collaborate with
digital health companies for a variety of reasons. Of note was the
fact that 60% of respondents believed that “strongly entrenched
business and reimbursement models make it difficult to bring
digital health products to market,” highlighting reimbursement as
a key challenge for many new digital health solutions1
. The
ongoing COVID-19 pandemic has emphasized and expanded the
importance of digital health technologies, ranging from a rapid
transition to telehealth services2
to the development of contact
tracing and warning apps3
.
Despite many differences, Germany, like the US, has a multi-
payer health care system with over 100 independent insurers,
making it an especially interesting case study for American
policymakers4
. On November 7, 2019, the German parliament
(Bundestag) adopted the Digital Healthcare Act (Digitale-Versor-
gung-Gesetz or DVG)5
, which was subsequently approved by the
Federal Council (Bundesrat) and signed into law by the German
President. In addition to promoting the use of telehealth and
ensuring better usability of health data for research purposes, the
new law entitles all individuals covered by statutory health
insurance to benefits for certain digital health applications (i.e.,
insurers will pay for their use)6
.
While many consider the DVG to be a breakthrough in
incentivizing and advancing patients’ diagnosis, management,
and treatment with digital health tools, the new law has also
attracted criticism and faces a number of challenges moving
forward. In this article, we explain the relevant changes encom-
passed in the new German DVG and provide an overview of
reimbursement of digital health solutions in the US, including
changes to telehealth delivery and reimbursement during the
COVID-19 pandemic. We then discuss challenges and unanswered
questions raised by the DVG and highlight early lessons and
opportunities for other countries.
THE NEW GERMAN DIGITAL HEALTHCARE ACT
The German statutory health insurance system is one of the
largest in the world7
. Approximately 90% of the population (i.e.,
roughly 75 million people) in Germany are covered by statutory,
state-funded health insurance, while the remaining 10% are
privately insured7,8
. By primarily making amendments to the Social
Security Code V (Sozialgesetzbuch V—SGB V)9
, the DVG entitles
those insured by one of Germany’s independent statutory health
insurance providers to coverage benefits for certain digital health
applications. This means that insurers will be required to pay for
qualifying applications, making such digital health solutions
broadly accessible.
In general, insured persons are entitled to coverage benefits for
digital health applications if such applications meet the following
criteria:
1. They are lower-risk medical devices;
2. Their main function is essentially based on digital technol-
ogies;
3. They are intended to support the monitoring, detection, relief
or treatment of illnesses or the compensation, detection,
relief or treatment of injuries or disabilities in the case of
injured persons or in care provided by service providers;
4. They have been included in a newly established official
register for digital health applications maintained by the
German Federal Institute for Drugs and Medical Devices
(Bundesinstitut für Arzneimittel und Medizinprodukte—
BfArM); and
5. They are used either with the approval of the health
insurer or with the prescription of the treating physician or
psychotherapist (SGB V, § 33a(1)) (Fig. 1).
1
Project on Precision Medicine, Artificial Intelligence, and the Law; Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School, Harvard
University, Cambridge, MA, USA. 2
Technology and Operations Management Unit, Harvard Business School, and Harvard-MIT Center for Regulatory Science, Boston, MA, USA.
3
Centre for Advanced Studies in Biomedical Innovation Law (CeBIL), Karen Blixens Plads 16, 2300 Copenhagen, DK, USA. ✉email: timo.minssen@jur.ku.dk
www.nature.com/npjdigitalmed
Scripps Research Translational Institute
1234567890():,;

독일에서는 DiGA를 활용해

17개의 앱이 수가를 이미 받고 있다
• ‘디지털 헬스 앱’ 이라고 정의되어 있지만, 내용을 보면 거의 모두가 디지털 치료제

• 당뇨, 우울증, 편두통, 불면증, 이명 등 다양

• 수가: 116~743유로 (업체에서 직접 수가를 정함)

수가만 받으면, 정말 만사형통인가?
• 독일에서 DiGA가 적용된 이후에, (2021년 2월 기준)
 
 
3달 간 디지털 치료제의 처방 건수는 단 3,660건에 불과

• 수가를 준다고 의사들이 폭발적으로 처방해주는 것은 결코 아님

“선생님, 디지털 치료제
처방해주실 수 있어요?”
“그게 뭐예요?”

“약사님, 디지털 치료제
어떻게 쓰는 건가요?”
“그게 뭐예요?”

불면증 디지털 치료제, Somryst
• 먼저, 수면 문제에 대한 질문지에 답변을 해야 함

• 답변을 마치면 전문의와 온라인 진료가 잡힘

• 전문의에게 원격진료를 받음 ($45)

• 3rd party 원격진료 회사인 Upscript 를 통해서 원격진료를 받음

• Pear Connect 와 Truepill pharmacy 를 통해서 안내 (복약지도?)를 받고,

• Somryst 앱을 다운로드 받음

• Alkili Interactive의 EndeavorRx 를 의사가 처방하면,
 
 
처방전이 Phil Pharmacy 라는 온라인 약국으로 전송 되고,
 
 
Phil Pharmacy는 환자에게 링크와 활성화 코드 전송

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.
BRIEF
COMMUNICATION
Kumar R B, et al. J Am Med Inform Assoc 2016;0:1–6. doi:10.1093/jamia/ocv206, Brief Communication
by
guest
on
April
7,
2016
http://jamia.oxfordjournals.org/
Downloaded
from
•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

REVIEW ARTICLE OPEN
Impact of remote patient monitoring on clinical outcomes: an
updated meta-analysis of randomized controlled trials
Benjamin Noah1,2
, Michelle S. Keller1,2,3
, Sasan Mosadeghi4
, Libby Stein1,2
, Sunny Johl1,2
, Sean Delshad1,2
, Vartan C. Tashjian1,2,5
,
Daniel Lew1,2,5
, James T. Kwan1,2
, Alma Jusufagic1,2,3
and Brennan M. R. Spiegel1,2,3,5,6
Despite growing interest in remote patient monitoring, limited evidence exists to substantiate claims of its ability to improve
outcomes. Our aim was to evaluate randomized controlled trials (RCTs) that assess the effects of using wearable biosensors (e.g.
activity trackers) for remote patient monitoring on clinical outcomes. We expanded upon prior reviews by assessing effectiveness
across indications and presenting quantitative summary data. We searched for articles from January 2000 to October 2016 in
PubMed, reviewed 4,348 titles, selected 777 for abstract review, and 64 for full text review. A total of 27 RCTs from 13 different
countries focused on a range of clinical outcomes and were retained for final analysis; of these, we identified 16 high-quality
studies. We estimated a difference-in-differences random effects meta-analysis on select outcomes. We weighted the studies by
sample size and used 95% confidence intervals (CI) around point estimates. Difference-in-difference point estimation revealed no
statistically significant impact of remote patient monitoring on any of six reported clinical outcomes, including body mass index
(−0.73; 95% CI: −1.84, 0.38), weight (−1.29; −3.06, 0.48), waist circumference (−2.41; −5.16, 0.34), body fat percentage (0.11; −1.56,
1.34), systolic blood pressure (−2.62; −5.31, 0.06), and diastolic blood pressure (−0.99; −2.73, 0.74). Studies were highly
heterogeneous in their design, device type, and outcomes. Interventions based on health behavior models and personalized
coaching were most successful. We found substantial gaps in the evidence base that should be considered before implementation
of remote patient monitoring in the clinical setting.
npj Digital Medicine (2018)1:20172 ; doi:10.1038/s41746-017-0002-4
INTRODUCTION
Wearable biosensors are non-invasive devices used to acquire,
transmit, process, store, and retrieve health-related data.1
Biosensors have been integrated into a variety of platforms,
including watches, wristbands, skin patches, shoes, belts, textiles,
and smartphones.2,3
Patients have the option to share data
obtained by biosensors with their providers or social networks to
support clinical treatment decisions and disease self-
management.4
The ability of wearable biosensors to passively capture and track
continuous health data gives promise to the field of health
informatics, which has recently become an area of interest for its
potential to advance precision medicine.1
The concept of
leveraging technological innovations to enhance care delivery
has many names in the healthcare lexicon. The terms digital
health, mobile health, mHealth, wireless health, Health 2.0,
eHealth, quantified self, self-tracking, telehealth, telemedicine,
precision medicine, personalized medicine, and connected health
are among those that are often used synonymously.5
A
2005 systematic review uncovered over 50 unique and disparate
definitions for the term e-health in the literature.6
A similar
2007 study found 104 individual definitions for the term
telemedicine.7
For the purpose of this study, we employ the term
remote patient monitoring (RPM) and define it as the use of a non-
invasive, wearable device that automatically transmits data to a
web portal or mobile app for patient self-monitoring and/or health
provider assessment and clinical decision-making.
The literature on RPM reveals enthusiasm over its promises to
improve patient outcomes, reduce healthcare utilization, decrease
costs, provide abundant data for research, and increase physician
satisfaction.2,3,8
Non-invasive biosensors that allow for RPM offer
patients and clinicians real-time data that has the potential to
improve the timeliness of care, boost treatment adherence, and
drive improved health outcomes.4,9
The passive gathering of data
may also permit clinicians to focus their efforts on diagnosing,
educating, and treating patients, theoretically improving produc-
tivity and efficiency of the care provided.8
However, despite
anecdotal reports of RPM efficacy and growing interest in these
new health technologies by researchers, providers, and patients
alike, little empirical evidence exists to substantiate claims of its
ability to improve clinical outcomes, and our research indicates
many patients are not yet interested in or willing to share RPM
data with their physicians.4
A recently published systematic review
by Vegesna et al. summarized the state of RPM but provided only
a qualitative overview of the literature.10
In this review, we provide
a quantitative analysis of RPM studies to provide clinicians,
patients, and health system leaders with a clear view of the
effectiveness of RPM on clinical outcomes. Specifically, our study
questions were as follows: How effective are RPM devices and
associated interventions in changing important clinical outcomes
https://doi.org/10.1038/s41746-018-0027-3
Corrected: Author correction
Received: 11 May 2017 Revised: 28 August 2017 Accepted: 31 August 2017
1
Division of Health Services Research, Cedars-Sinai Medical Center, Los Angeles, CA, USA; 2
Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, CA,
USA; 3
Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, CA, USA; 4
Department of Medicine, University of Arizona, College of
Medicine Tucson, Tucson, AZ, USA; 5
Cedars-Sinai Medical Center, Los Angeles, CA, USA and 6
American Journal of Gastroenterology, Bethesda, USA
Correspondence: Brennan M. R. Spiegel (Brennan.Spiegel@cshs.org)
Published in partnership with the Scripps Translational Science Institute
• 원격 환자 모니터링 연구를 메타 분석한 논문 (Nat Digi Med 2018)

• 다양한 질병에 대한 원격 환자 모니터링 연구 27개 분석

• 혈압, 체중, 체지방, 허리 둘레, 체질량 지수(BMI) 등을 기준으로

• 원격 환자 모니터링을 통해 대조군 대비 개선 효과가 있었는지를 분석

REVIEW ARTICLE OPEN
Impact of remote patient monitoring on clinical outcomes: an
updated meta-analysis of randomized controlled trials
Benjamin Noah1,2
, Michelle S. Keller1,2,3
, Sasan Mosadeghi4
, Libby Stein1,2
, Sunny Johl1,2
, Sean Delshad1,2
, Vartan C. Tashjian1,2,5
,
Daniel Lew1,2,5
, James T. Kwan1,2
, Alma Jusufagic1,2,3
and Brennan M. R. Spiegel1,2,3,5,6
Despite growing interest in remote patient monitoring, limited evidence exists to substantiate claims of its ability to improve
outcomes. Our aim was to evaluate randomized controlled trials (RCTs) that assess the effects of using wearable biosensors (e.g.
activity trackers) for remote patient monitoring on clinical outcomes. We expanded upon prior reviews by assessing effectiveness
across indications and presenting quantitative summary data. We searched for articles from January 2000 to October 2016 in
PubMed, reviewed 4,348 titles, selected 777 for abstract review, and 64 for full text review. A total of 27 RCTs from 13 different
countries focused on a range of clinical outcomes and were retained for final analysis; of these, we identified 16 high-quality
studies. We estimated a difference-in-differences random effects meta-analysis on select outcomes. We weighted the studies by
sample size and used 95% confidence intervals (CI) around point estimates. Difference-in-difference point estimation revealed no
statistically significant impact of remote patient monitoring on any of six reported clinical outcomes, including body mass index
(−0.73; 95% CI: −1.84, 0.38), weight (−1.29; −3.06, 0.48), waist circumference (−2.41; −5.16, 0.34), body fat percentage (0.11; −1.56,
1.34), systolic blood pressure (−2.62; −5.31, 0.06), and diastolic blood pressure (−0.99; −2.73, 0.74). Studies were highly
heterogeneous in their design, device type, and outcomes. Interventions based on health behavior models and personalized
coaching were most successful. We found substantial gaps in the evidence base that should be considered before implementation
of remote patient monitoring in the clinical setting.
npj Digital Medicine (2018)1:20172 ; doi:10.1038/s41746-017-0002-4
INTRODUCTION
Wearable biosensors are non-invasive devices used to acquire,
transmit, process, store, and retrieve health-related data.1
Biosensors have been integrated into a variety of platforms,
including watches, wristbands, skin patches, shoes, belts, textiles,
and smartphones.2,3
Patients have the option to share data
obtained by biosensors with their providers or social networks to
support clinical treatment decisions and disease self-
management.4
The ability of wearable biosensors to passively capture and track
continuous health data gives promise to the field of health
informatics, which has recently become an area of interest for its
potential to advance precision medicine.1
The concept of
leveraging technological innovations to enhance care delivery
has many names in the healthcare lexicon. The terms digital
health, mobile health, mHealth, wireless health, Health 2.0,
eHealth, quantified self, self-tracking, telehealth, telemedicine,
precision medicine, personalized medicine, and connected health
are among those that are often used synonymously.5
A
2005 systematic review uncovered over 50 unique and disparate
definitions for the term e-health in the literature.6
A similar
2007 study found 104 individual definitions for the term
telemedicine.7
For the purpose of this study, we employ the term
remote patient monitoring (RPM) and define it as the use of a non-
invasive, wearable device that automatically transmits data to a
web portal or mobile app for patient self-monitoring and/or health
provider assessment and clinical decision-making.
The literature on RPM reveals enthusiasm over its promises to
improve patient outcomes, reduce healthcare utilization, decrease
costs, provide abundant data for research, and increase physician
satisfaction.2,3,8
Non-invasive biosensors that allow for RPM offer
patients and clinicians real-time data that has the potential to
improve the timeliness of care, boost treatment adherence, and
drive improved health outcomes.4,9
The passive gathering of data
may also permit clinicians to focus their efforts on diagnosing,
educating, and treating patients, theoretically improving produc-
tivity and efficiency of the care provided.8
However, despite
anecdotal reports of RPM efficacy and growing interest in these
new health technologies by researchers, providers, and patients
alike, little empirical evidence exists to substantiate claims of its
ability to improve clinical outcomes, and our research indicates
many patients are not yet interested in or willing to share RPM
data with their physicians.4
A recently published systematic review
by Vegesna et al. summarized the state of RPM but provided only
a qualitative overview of the literature.10
In this review, we provide
a quantitative analysis of RPM studies to provide clinicians,
patients, and health system leaders with a clear view of the
effectiveness of RPM on clinical outcomes. Specifically, our study
questions were as follows: How effective are RPM devices and
associated interventions in changing important clinical outcomes
https://doi.org/10.1038/s41746-018-0027-3
Corrected: Author correction
Received: 11 May 2017 Revised: 28 August 2017 Accepted: 31 August 2017
1
Division of Health Services Research, Cedars-Sinai Medical Center, Los Angeles, CA, USA; 2
Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, CA,
USA; 3
Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, CA, USA; 4
Department of Medicine, University of Arizona, College of
Medicine Tucson, Tucson, AZ, USA; 5
Cedars-Sinai Medical Center, Los Angeles, CA, USA and 6
American Journal of Gastroenterology, Bethesda, USA
Correspondence: Brennan M. R. Spiegel (Brennan.Spiegel@cshs.org)
Published in partnership with the Scripps Translational Science Institute
control patients and 498 intervention patients (Fig. 5). The meta-
analysis yielded a mean difference point estimate of 0.11 (95%
Confidence Interval: [−1.56, 1.34]), indicating no statistically
significant difference. The I2
statistic was 86% (95% [59%, 95%]),
illustrating a moderate to high degree of heterogeneity.
Systolic blood pressure
Five studies15,17,19,23,33,36
reported data for both intervention and
control groups for systolic blood pressure, with a total of 548
control patients and 1135 intervention patients (Fig. 6). The meta-
analysis yielded a mean difference point estimate of −0.99 (95%
Confidence Interval: [−2.73, 0.74]), indicating no statistically
significant difference. The I2
statistic was 44% (95% [0%, 81%]),
illustrating an unknown degree of heterogeneity.
Diastolic blood pressure
DISCUSSION
Based on our systematic review and examination of high-quality
studies on RPM, we found that remote patient monitoring showed
early promise in improving outcomes for patients with select
conditions, including obstructive pulmonary disease, Parkinson’s
disease, hypertension, and low back pain. Interventions aimed at
increasing physical activity and weight loss using various activity
trackers showed mixed results: cash incentives and automated
text messages were ineffective, whereas interventions based on
validated health behavior models, care pathways, and tailored
coaching were the most successful. However, even within these
interventions, certain populations appeared to benefit more from
RPM than others. For example, only adults over 55 years of age
saw benefits from RPM in one hypertension study. Future studies
should be powered to analyze sub-populations to better under-
stand when and for whom RPM is most effective.
Fig. 3 Point estimates of the mean difference for each study (green squares) and the corresponding 95% Confidence Intervals (horizontal
lines) are shown, with the size of the green square representing the relative weight of the study. The black diamond represents the overall
pooled estimate, with the tips of the diamond representing the 95% Confidence Intervals
Fig. 4 Point estimates of the mean difference for each study (green squares) and the corresponding 95% Confidence Intervals (horizontal
Fig. 5 Point estimates of the mean difference for each study (green squares) and the corresponding 95% confidence intervals (horizontal
Effects of remote patient monitoring on clinical outcomes
B Noah et al.
8
• 표면적 결론: 27개 연구 ‘모두’ 대조군 대비 유의미한 개선을 보여주지 못함

• 더 생각해볼 부분

• 원격 환자 모니터링에 대한 양질의 연구가 아직 적음 (환자수, 기간, 대조군 여부 등등)

• 센서, 통신 등 기술이 빠르게 발전하고 있으나, RCT 및 메타 분석은 이런 동향의 반영 어려움

• 무엇을 기준으로 효용을 판단할 것인가?

• 직접적인 치료 성과 vs. 삶의 질 향상 / 만족도 / 입원 기간 등등 + 비용

• reSET과 reSET-O 에는 의료진용 대시보드가 존재

• 환자의 컴플라이언스, PRO, urine drug screening test 등의 결과를 볼 수 있음

• Somryst 에도 의료진용 대시보드가 존재

• 환자의 컴플라이언스, ISI, PHQ-8, 수면 관련 수치 등을 모니터링 가능

원격 환자 모니터링의

세부적 단계
•단순 데이터 전송

•내원 안내

•의료 상담

•진단/처방
어디부터
의료 행위인가?

• Oleena는 FDA 허가 받은 디지털 치료제 중에서는 드물게
 
  
중심 기능으로 원격 모니터링 요소를 포함하고 있음

• 암 환자의 증상 관리라는 목적 자체에 의료진의 모니터링이 중심적 요소이기 때문일 수도

원격 임상 시험 온디맨드 처방 원격 환자 모니터링

•디지털 치료제 = 원격의료: 아님.

•현재 디지털 치료제 분야에서 원격의료가 차지하는 비중이 큰가? 그렇지 않음.

•디지털 치료제의 임상, 처방, 사용에서 원격의료의 일부 요소를 활용할 수 있음

•원격 임상 시험: DTx 의 특성을 활용한 임상 시험의 전체/일부를 원격으로

•온디맨드 처방: DTx 의 처방/복약지도의 약점을 보완 + 확장성의 극대화

•원격 환자 모니터링: RWD의 활용 + 증상 관리 목적의 모니터링

Feedback/Questions
• E-mail: yoonsup.choi@gmail.com

• Blog: http://www.yoonsupchoi.com

• Youtube: 최윤섭의 헬스케어 이노베이션
웰트 김주영 (Danny Jooyoung Kim) 이사님 인터뷰
에임메드 김수진 본부장님 인터뷰

원격의료 시대의 디지털 치료제

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