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152
IMIA Yearbook of Medical Informatics 2017
© 2017 IMIA and Schattauer GmbH
Present and Future Trends in
Consumer Health Informatics and
Patient-Generated Health Data
A. M. Lai1, P.-Y. S. Hsueh2, Y. K. Choi3, R. R. Austin4
1 Institute for Informatics, Washington University in St. Louis, USA
2 Computational Health Behavior and Decision Science, Center for Computational Health,
IBM T.J. Watson Research Center, USA
3 Department of Biomedical Informatics and Medical Education, University of Washington, USA
4 School of Nursing, University of Minnesota, USA
Summary
Objectives: Consumer Health Informatics (CHI) and the use of
Patient-Generated Health Data (PGHD) are rapidly growing focus
areas in healthcare. The objective of this paper is to briefly review
the literature that has been published over the past few years
and to provide a sense of where the field is going.
Methods: We searched PubMed and the ACM Digital Library for
articles published between 2014 and 2016 on the topics of CHI
and PGHD. The results of the search were screened for relevance
and categorized into a set of common themes. We discuss the
major topics covered in these articles.
Results: We retrieved 65 articles from our PubMed query and
32 articles from our ACM Digital Library query. After a review of
titles, we were left with 47 articles to conduct our full article sur-
vey of the activities in CHI and PGHD. We have summarized these
articles and placed them into major categories of activity. Within
the domain of consumer health informatics, articles focused on
mobile health and patient-generated health data comprise the
majority of the articles published in recent years.
Conclusions: Current evidence indicates that technological
advancements and the widespread availability of affordable
consumer-grade devices are fueling research into using PGHD for
better care. As we observe a growing number of (pilot) develop-
ments using various mobile health technologies to collect PGHD,
major gaps still exist in how to use the data by both patients and
providers. Further research is needed to understand the impact of
PGHD on clinical outcomes.
Keywords
Consumer health information/methods; patient-generated health
data; mHealth; user-computer interface; consumer participation
in delivery of health care
Yearb Med Inform 2017:152-9
http://dx.doi.org/10.15265/IY-2017-016
Published online August 18, 2017
Introduction
In recent years, the widespread adoption of
personal computing technology, availability
of personal health records (PHRs), and utili-
zation of various forms of social media has
invigorated an interest in consumer health
informatics (CHI) and created an explosion
of interest in the potential of patient-gener-
ated health data (PGHD). Internet adoption
by adults in advanced economies reached a
median of 87% and, similarly, 68% of adults
reported owning a smartphone in 2015 [1].
Another factor pushing forward the po-
tential of PGHD is not only the widespread
availability of accelerometers embedded
in smartphones and wearable devices that
can collect physical activity [2], but also
affordable biometric sensors that can collect
and transmit weight, blood pressure, heart
rate, temperature, and even blood glucose
information from patients to their healthcare
providers [3]. This recent ability for consum-
ers to increase their participation in their own
healthcare by recording and sharing health
data through the use of affordable sensors
and PHRs has inextricably tied together the
topics of CHI and PGHD.
While there have been previous reviews
on the topic of CHI, the objective of this
survey paper is to generate a sense of the
recent, past, and current foci in CHI and
PGHD using a semi-structured scoping
review process.
A recent general review of the overall
trends of CHI is discussed by Demiris, in
which a majority of trends involve the use
of patient-generated health data [4]. In fact,
in the past decade, quite a large amount of
evidence has been accumulated with regards
to the use of active and passive monitoring
data from home telehealth and mobile health
technologies, as well as from PHRs [5]. The
role of PHRs has expanded in recent years,
either as parts of electronic health records
(EHRs) offered by providers, or through the
enablement of patients themselves on some
technology-based platforms for data collec-
tion and sharing, or for integration purposes.
In the United States, with the implementa-
tion of the federal government Meaningful
Use incentive program, the proportion of
consumers accessing their records increased
from 27% in 2014 to 45% in 2016 [6]. This
adoption has further enabled more frequent
patient-provider communication, with 64%
of United States physicians in 2015 having
an EHR with the capability to exchange
secure messages with patients, an over 50%
increase since 2013 [7]. In addition, the col-
lection of patient-reported outcomes (PROs)
and their contexts have been further enabled
by the advance of ecological momentary as-
sessment tools, which focus on the collection
of symptoms and behaviors close in time to
their experience.
Under such a healthcare landscape shift,
recent major research foci are the imple-
mentation and understanding of the value
of providing clinical notes through PHRs
and incentivizing patients to participate in
patient-provider communication [8]. In addi-
tion, with the availability of patient-generat-
ed health data from the Quantified Self (QS)
movement through a plethora of consumer
devices, Demiris called for an infrastructure
IMIA Yearbook of Medical Informatics 2017
153
Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data
that supports clinicians to become engaged
in the processes that QS facilitates and the
technologies that can help make sense of data
through massive data retrieval and trending,
as well as modeling and prediction [4].
In a review of the definitions of CHI,
Flaherty et al. have identified five quality
assessment criteria to evaluate on the defini-
tions in a set of 23 selected CHI papers [9].
Despite the fact that none of the definitions
used in these papers fulfilled all five quality
criteria identified, this review identified some
success stories, including the use of Com-
prehensive Health Enhancement Support
System (CHESS) for helping individuals
deal with health crises or medical concerns,
as well as the use of CHI devices for improv-
ing clinical and behavioral outcomes. The
varying definitions have demonstrated the
multidisciplinary nature of the field of CHI.
This nature has also reflected on the varying
efforts of PGHD evidence collection.
This article differentiates itself from
previously written reviews on the topic of
CHI and PGHD by providing an update and
summary of the recently published scientific
literature on these topics through the conduct
of a scoping review. We also examine not
only the biomedical literature, but also that
of computer science and engineering.
Methods
A scoping review of the literature was con-
ducted using a search strategy to generate
an overview survey analysis of the last three
years of the biomedical, computer science,
and engineering literature focusing on CHI
and PGHD. The authors selected PubMed
and the ACM Digital Library to understand
recent literature and up to date conference
proceedings. In this paper, the rationale for
the database selection is not intended to be
a full all-inclusive systematic review but the
aim is to propose a survey paper to highlight
and provide an overview of current trends
and the future trajectory of CHI and PGHD.
Our initial search strategy for PubMed
was to use a variety of MeSH headings to
identify the articles of interest, but an appro-
priate set of MeSH headings that yielded the
desired articles could not be identified. The
list of MeSH headings included Consumer
Health Information/methods, User-Com-
puter Interface, Health Services Needs and
Demand, Physician-Patient Relations, Con-
sumer Participation, Medical Informatics,
and Delivery of Health Care. This resulted in
the use of key search terms for any mentions
of the terms “patient-generated health data”
or “consumer health informatics” restricted
to the papers published in 2014, 2015, and
2016, and written in the English language.
As a result, the following query was used:
“patient generated health data”[All Fields]
OR “consumer health informatics”[All
Fields] AND (“2014/01/01”[PDAT] :
“2016/12/31”[PDAT])
The search strategy for the ACM Digital
library used key search terms: consumer,
patient, self-quantifier, citizen, “patient-gen-
erated data,” and “consumer health,” re-
stricted to the years 2014–2016, and limited
to English language. The team iteratively
refined the search terms in order to focus
solely on consumer health informatics and
patient-generated data and to reduce the
number of articles that were not related to
these topics and did not address the search
criteria. The key search terms allowed the
team to narrowly focus on a specified time
frame and topic for the scoping review. This
resulted in the following query:
acmdlTitle:(consumer, patient, self-quan-
tifier, citizen -mlearning, -learning, -edu-
cation, -vision, -speech, -preliminary, -3d,
-flash, -API, -search, -retrieval, -seeking,
-gaming, -simulation, -robot, -image,
-virtual, -invasive, -scratch, -middleware,
-immune, -vision, -children, -eye, -VLSI)
AND (“patient generated data”, “con-
sumer health”)
After retrieving the references from these
two queries, the authors screened the articles
based on titles for relevance to CHI and
PGHD. Articles that were selected by two or
more of the authors were placed on the list
for further review. The authors then catego-
rized the articles on the list into themes. The
articles were divided amongst the authors to
retrieve, summarize, and perform a thematic
analysis. A small subset of the articles (n=5)
could not be retrieved by any of the authors
due to limitations in publication availability
at our combined institutions. Due to time
constraints and because this is not an exhaus-
tive literature review the authors decided to
exclude this small subset of articles from
the survey. Articles that were reviewed by
the authors and determined to be general
review or survey articles were also excluded
from this analysis.
Results
The team executed the queries described
above on November 4th, 2016. From the
searches, a total of 97 articles (65 PubMed
and 32 ACM Digital Library) were includ-
ed for screening (Figure 1.). Three of the
authors (AL, PH, and RA) screened the
articles for relevance to CHI or PGHD. After
the review and screening of titles, 47 articles
were included in the survey of the activities
in CHI and PGHD. The authors (AL, PH,
RA, and YC) categorized the remaining 47
titles into three themes: 1) Patient-Generat-
ed Health Data; 2) Mobile Technology; 3)
Human-Computer Interaction. The articles
have been summarized below.
Patient-Generated Health Data
A large fraction of the papers on the topic
of PGHD and its application to various as-
pects of healthcare that we encountered in
our literature search could be categorized as
reviews or opinion papers [10-15]. In these
articles, the definitions of PGHD that were
used were slightly different, but many were
based on a broad definition of PGHD, which
included any observation, result, device
finding, confirmation, change correction, or
addition of data to a patient’s record that was
created, recorded, gathered, or inferred by or
from patients or their designees [10, 12]. In
order to cast a broad net with regards to the
activity in this emerging focus area, we have
used this broadest definition.
The most prominent difference separating
PGHD from data generated during clinical
settings is that with PGHD, it’s the patient
and not the provider that takes the ownership
of generating, capturing, and sharing the
data. The concept of PGHD is not new in
clinical care as patients have always been
154
IMIA Yearbook of Medical Informatics 2017
Lai et al.
recording and sharing information on their
health in a variety of ways [12]. However, it
is becoming increasingly common with the
proliferation of more affordable consumer
devices (e.g., smart phones, wearables, and
sensors) and the advent of technologies
such as patient portals and personal health
records. We discuss the use of PGHD with
mobile devices in the following section.
The recent literature points to the many
potential benefits of integrating PGHD into
health care delivery. Cohen et al. conducted
semi-structured interviews to examine the
experiences of health care professionals who
use PGHD in an outpatient clinical setting
[16]. The identified benefits included a deep-
er understanding of a patient’s conditions, the
availability of more accurate patient infor-
mation, and the ability to monitor patient’s
health between clinic visits. PGHD through
monitoring devices could be used to collect
a more comprehensive view of a patient’s
physiology [14].
A number of articles discussed different
ways PGHD can be incorporated into clin-
ical trials and discussed some of the issues
[10, 3, 17]. Wood et al. also described how
PGHD could be used to monitor medication
adherence and to remind patients to take
study drugs or follow study protocols [10].
The authors acknowledged the concern
that PGHD streams have been primarily
from consumer-grade devices rather than
calibrated research-grade devices, and that
further studies are needed to understand the
data quality from these devices.
Furthermore, ubiquitous monitoring of
a patient allowed through PGHD could not
only advance our knowledge of a patient’s
cancer experience but also enhance the ef-
ficiency and productivity of cancer clinical
trials [3, 17]. In addition, PGHD empowers
patients by engaging them as key players in
their care [3, 18, 19]. Petersen discussed how
PGHD could improve cancer survivorship
through “support of survivor autonomy, im-
provement of survivor health, and promotion
of survivor population health” [11].
Another area of interest for the research
community is how to extract and process
meaningful information from unstructured
Fig. 1 Summary of Search Strategy
IMIA Yearbook of Medical Informatics 2017
155
Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data
PGHD from online health communities.
Hartzler et al. generated health interest
profiles by extracting health-related terms
from users’ past posts from an online health
community and conducted a user study to ex-
amine the validity of such profiles [20]. Their
findings demonstrated that the PGHD-gen-
erated health profiles were matched with
the users’ health interests. Furthermore,
online health community members showed
favorable views on using the profiles to find
a match and connect with other members
for peer support.
Additional challenges of widespread
adoption of PGHD reported in the literature
include: lack of randomized controlled trials
(RCTs) to examine the associations between
PGHD and longitudinal health outcomes [3,
17], PGHD data standardization and device
interoperability [3, 17, 21], integration of
PGHD with EHRs [3, 17], concerns for se-
curity and privacy [3, 17, 21], and building
data analysis and visualization tools that
facilitate informed health decision making
[3, 21-23]. Future research is needed to
address the barriers and gaps identified in
the literature to harness the true potential of
PGHD, bringing value to both patients and
health care providers.
Mobile Technology
Mobile technology or mHealth is transform-
ing the way patients, providers, and research-
ers interact with data. For the purposes of this
review, mHealth includes mobile technolo-
gies such as smartphones, tablets, wearable
devices, and sensor technologies. M-Health
is fundamental to healthcare transformation
since it shifts the ability to collect data
anywhere, at any time, and it is becoming
seamlessly integrated into our lives. This
allows for a gradual shift of healthcare closer
to the patients’ daily living and away from
the traditional clinical environment [24].
One specific application of mHealth
includes monitoring chronic disease and the
potential for enhancing self-management of
chronic disease. Chronic diseases are in the
center of mHealth developments as they re-
quire the continuous and active involvement
of not only healthcare professionals but also
patients, both of whom can be empowered
through the use of specialized mobile
applications and the analysis of data from
modern miniaturized and wearable sensing
devices [24]. Important considerations such
as system design of these technologies can
increase long-term sustained use of consum-
ers but also reduce the burden for healthcare
providers.
An example of the use of mobile health
technologies to help patients with chronic
conditions to collect and monitor their phys-
iological signs is WearCOPD, a mobile app
developed by Liaqat et al. that uses a smart-
watch and a smartphone to collect chronic
obstructive pulmonary disease (COPD)
related symptom data to detect acute exac-
erbations of COPD [25]. Patients complete
a daily health questionnaire prompted by
the app to monitor their status. The app also
collects patients’ peak expiratory flow by
having them blow into the microphone.
Another example is a mobile app that uses
wearable sensors and biochips to facilitate
self-management of Inflammatory Bowel
Disease (IBD) [26]. Pernencar et al. argue
that automatic data collection with the use
of wearables and biochips could reduce
the burden of self-report and should allow
objective physiological data to be captured
and analyzed for self-management of IBD
[21]. The previous two studies [20, 21] were
ongoing at the time of the publication and
therefore relevant evaluation results could
not be assessed.
Another study presented a privacy-pre-
serving video surveillance system using
a network of multiple IP cameras and
motion sensors to monitor patients in their
real-life settings. The system stores and
sends a “ghost” form of an image, which
only contains the information about the
object’s movement in blue color on a black
background to preserve the privacy of the
monitored individual [27]. The proposed
surveillance system demonstrated effective
patient monitoring detection algorithm while
preserving the privacy of monitored patients.
Kumar et al. described their approach to
the integration of continuous glucose mon-
itor data and the transmission of the data to
a Personal Health Record [28]. The glucose
monitor data was collected onto an Apple
mobile device via Bluetooth, stored locally
on the iOS device using the Apple HealthKit
interface, and subsequently transmitted from
HealthKit to an Epic EHR through the Epic
MyChart app running on the mobile device.
Nundy et al. discussed the value of
PGHD gathered using mobile technology
for diabetes self-management [29]. They
explored provider perceptions of a summary
report based on the data extracted from a text
message-based diabetes self-management
program. The patients texted back responses
to automated text messages consisting of
reminders and educational messages. From
these responses, summary reports were
generated to inform providers of clinically
relevant patient-reported health data. The
authors used Likert-like response surveys
and interviews with primary care physicians
(PCPs) and endocrinologists to assess their
responses to summary reports. Only three
out of the 12 providers felt that the report
changed the care they provided. However,
nine of the 12 providers were willing to use
the summary report.
Another growing area of mHealth is mon-
itoring and tracking mental health conditions
with mobile devices and sensing technology.
Mobile technology makes it possible to
extend mood self-assessment, from lab to
real life, by the collection of mood data, fre-
quently, over a long time, and in different life
situations [30]. Improving data collection for
conditions such as mental health increases
the opportunity for early intervention and
improves outcomes. The utilization of per-
vasive technology, including a mobile phone
and its sensors, could potentially provide a
way to make therapies more personalized
and accessible at any time [31].
Telerehab is another area that is capitaliz-
ing on the expansion of mobile technologies.
Rehabilitation sensing and tracking allow for
pattern recognition through data analysis and
visually represent the data needed to share
the patient’s story. Conventional physical
rehabilitation in stroke and other physical
disorders is provided by sparse and home-
based systems that suffer from issues of com-
pliance, low patient engagement, and lack of
personalization [32]. Another program, Back
on Bike, a cycling rehab program, showed
tremendous promise and is expanding the
possibilities of traditional rehabilitation for
cardiac patients [33]. Dynamic approaches
to rehab used in the patient’s home for closer
156
IMIA Yearbook of Medical Informatics 2017
Lai et al.
self-monitoring have the potential to increase
adherence and to enhance secondary preven-
tion of cardiac disease.
Despite the potential benefits of col-
lecting PGHD using various mobile health
technologies, challenges remain with how
to analyze and interpret large amounts of
high-dimensional PGHD to detect trends
and to make meaningful inferences [21,
23]. To address this issue, researchers have
developed a new data analysis methodology
and a new framework to analyze PGHD.
Liang et al. proposed a new method based
on association rule mining techniques and
successfully demonstrated its validity to
discover correlations between lifestyle
factors and sleep patterns [22]. Gollamudi
et al. proposed a hypothesis testing the
framework for analyzing unstructured time
series data, a common data type of PGHD,
to discover unique trends and associations
in the data [23]. Using the framework, they
have analyzed blood pressure data collected
by patients at home and they demonstrated
the efficacy of a comprehensive smart-
phone-based health monitoring intervention.
While some initial mHealth studies are
being conducted, more large-scale studies
are however needed to develop best prac-
tices and create a foundation of evidence
to support the use of mHealth across care
settings. Barriers and opportunities exist
that incorporate new technology for both
providers and consumers. Since the locus
of healthcare is shifting to the home and
community setting, there is an increasing
need to adopt a broader approach across the
traditional boundaries of health and social
care in order to operationalize a more inte-
grated and personalized healthcare service
provision [34].
Emerging Research in Human-
Computer Interaction
Historically, a cross-cutting focus in CHI
has been human computer interaction (HCI)
and this trend has continued. While many
existing efforts are focused on the collection
of evidence on existing online health tools,
more researchers are looking for ways to
improve patient-provider communication,
and patient-led, shared experiences, using
methods ranging from theory-informed
models, to surveys, interviews, and personal
constructs.
Jacobs et al. compared health informa-
tion sharing practices between patients,
providers, and healthcare navigators. They
identified gaps between the groups with
implications on design and adoption of
technology [35]. Another study revealed
limitations of existing approaches to support
patient-provider communication and identi-
fied challenges for the design of systems that
honor patient needs and preferences [36].
Chung et al. surveyed 211 patients,
interviewed 18 patients, and re-analyzed
interviews of 21 providers [37]. They found
that there exist needs of support for collab-
oration in every stage of self-tracking and
that patients and providers create boundary
negotiating artifacts to support the collabora-
tion. These are important findings to further
support the design of PGHD tools based on
stage-based models of personal informatics.
Briggs et al. have further supported that
patients and caregivers frequently get their
health information and advice from web-
sites containing patient-led shared health
experiences [38]. This means that they often
engage in a very idiosyncratic selection
process in order to determine which websites
have personally-resonant material. In this
paper, a Repertory Grid (repgrid) technique
was applied on websites for patients with
chronic asthma and caregivers of people
with multiple sclerosis (MS), presenting
each patient/caregiver with a set of health
websites relevant to the condition for which
they were looking for information. Hyper-
personal representations of those constructs
provided new insights on the ways individual
patients can use the online health tool in their
own contexts.
Finally, Maniam et al. applied a more tra-
ditional approach to assess literature, derive
an HIT adoption model, and validated it with
surveys in the case of diabetic patients [39].
The results are used to inform the design
of Diabetes Self-Management Applications
(DSMA) as impactful patient-centered
tools, enabling diabetic patients to manage
their health conditions and thereby prevent
complications. The findings indicate that
perceived financial risk, perceived privacy
and security risk, technology anxiety, and
facilitating conditions have significantly
positive relationships with the intention to
adopt DSMA. This will help to design the
next-generation DSMA applications.
Discussion
Studies in CHI and PGHD have begun to
show positive results that impact care deliv-
ery, improve patient-provider communica-
tion, and enhance health outcomes. Despite
early positive results, significant barriers and
challenges exist related to the use of PGHD,
mobile technologies, and digital sensors
within clinical settings.
A key finding from this review is the
overall increase in acceptance of PGHD to
be used by consumers, healthcare providers,
and researchers. Polling data shows that
the adoption of mobile apps for health has
doubled in two years, reaching 33% in 2016,
and, that patient data is believed to be ben-
eficial for maintaining health [40]. Another
poll from 13,000 consumers in Europe also
reveals that 32% of the general populations
in Europe are considering they are using
wearable devices [41]. In fact, the survey
has further shown that more than 78% of
customers are willing to wear technology
for health tracking. This can be attributed
to the availability of smartphones and to the
increasing prevalence of patient-reported
outcomes and ecological momentary assess-
ment technologies that allow for data collec-
tion [42]. While this signifies an enormous
opportunity, it can also be stated as a concern
for some segments of the population that
may not have access to these technologies
[29]. This disparity was not addressed in this
current review but is something that should
be addressed in future reviews.
Despite the increased use of PGHD, there
exist gaps for how to use the data by both
patients and providers. The first step was to
collect and store data. Now, we need addi-
tional guidance regarding how to operation-
alize and interpret the data. In the following
discussion, we will discuss the gaps existing
in the two latter aspects.
First, operationalizing data can include
adjusting clinical workflow, improving in-
teroperability, and providing guidelines for
IMIA Yearbook of Medical Informatics 2017
157
Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data
responsibility of the data. Data interpretation
could include the use of a clear language,
enhanced data visualization techniques, and
built-in summaries of the data. The major
barriers commonly faced by healthcare
professionals in routine care include a lack
of proper incentives and of a supportive
infrastructure to integrate PGHD into the
clinical workflow [42, 43]. These barriers
limit the utilization of PGHD in clinical deci-
sion-making and make it hard to assess how
much extra time it would take the healthcare
professionals in a consultation, a home visit,
or a care management call. As a result so far,
the adoption by health professionals to incor-
porate PGHD into routine care is still slow.
Given that 78% of US consumers would be
willing to wear fitness and lifestyle and/or
vital sign monitoring technology [6], the
disparity between the interest of consumers
to provide PGHD and the ability of providers
to incorporate the data into their workflow
is alarming. On the consumer side, current
self-monitoring tools also lack flexibility,
standardized formats, and mechanisms to
share data with providers.
Second, for interpreting data, the major
barriers are the lack of standard data models
for integration and of a supportive infrastruc-
ture to make sense of the collected data for
patients themselves. From the accumulated
evidence in the past, self-monitoring has
been shown to be good for activation, but
not enough for sustaining behavior [44].
More frequent feedback has been expected
as an effective counter strategy to address
barriers such as stress level, lack of social
support, and discomfort with recording that
can affect adherence to self-monitoring [45,
46]. However, as pointed out in [47], in the
area of mobile health, only a limited number
of mHealth apps integrated health behavior
theory and left room for future work.
In order to start addressing the missing
support for healthcare professionals and
the patients themselves, researchers and
policy makers in the field need to re-exam-
ine evidence to select the most important
opportunity areas and prioritize them. This
review thus serves as a status update for
the emerging evidence for the promising
opportunity areas to pursue. In particular, the
review points out the potential of secondary
use of PGHD for a variety of applications,
particularly with respect to the application
of data analytics and mining techniques for
the generation of new knowledge and to
help create effective learning health systems.
However, many open questions still exist and
we anticipate an increased focus of research
in interpreting the context of collected
PGHD, with better automated techniques
for cleaning PGHD collected from sensors,
understanding the reliability of PGHD sen-
sors, and PGHD interoperability.
One such gap relates to standard vocabu-
laries and data models. There have been quite
a few consortium-based and commercial
efforts that have attempted to address these
issues. For example, the new Fast Health-
care Interoperability Resources (FHIR)
specification from the HL7 organization
is designed to allow health consumers to
share their data with clinical systems [48].
Patient-Centered Outcomes Research In-
stitute (PCORI) is starting to collect cross-
site patient ambulatory assessment data to
enable patient-centered effectiveness study
[49]. The creation of ecologically-valid
tools when using ambulatory assessment
can then lend support to the understanding
of bio-psychosocial processes as they unfold
naturally in time and in context. Emerging
mobile health data and research platforms
such as Apple HealthKit and ResearchKit
have also added support to the interchange-
ability of PGHD for future clinical integra-
tion needs [50, 51].
Another open research question is the
sense-making of PGHD. Many have ob-
served the problems but so far no strong
evidence has emerged for the right solutions
yet. While only 10% of the population are
using health-tracking devices/apps to help
manage chronic conditions [52], the pro-
portion of US health consumers accessing
their health records has increased to 45% in
2016 (an increase of 67% in two years) [53].
The increasing access and connection be-
tween clinics and homes have indicated new
opportunities in bridging the gaps. Better
interpretations of patient data for the patients
are expected to provide better connections to
user’s internal motivators for sense-making
and persuasion [54-56]. This will then allow
the adoption of a positive psychology and
help the target users to focus on their own
reason for goal attainment.
In the future, we expect more research
to be conducted to identify the association
between the top barriers and the possible
intervention designs to address the different
opportunity areas of PGHD in clinical care.
Opportunity areas include diagnosis support
(e.g. clinical decision support systems for
better reliability and resolution), interaction
support (e.g., interactive feedback apps for
collaborative reflection and communication),
focus on education goals (e.g., automatic
synthesis of data logs for content absorbing),
and a better understanding of patient values
(e.g., care management support for better
communication of contextual information).
Only when a better assessment is done, then
we can start addressing the health consumer
demands. Research is also needed to extend
the standardized consumer health vocab-
ulary, in which more up-to-date concepts
are included and similar symptoms can be
grouped together to provide standardization.
Research has started to build open-source
Consumer Health Vocabulary (CHV) [57]
to deal with this issue.
Conclusion
Current evidence indicates that the techno-
logical advancement and widespread avail-
ability of affordable consumer-grade devices
are fueling research into using PGHD for
better care. As we see a growing number of
(pilot) developments using various mobile
health technologies to collect PGHD, major
gaps still exist in how to use the data by both
patients and providers. Further research is
needed to understand the impact of PGHD
on clinical outcomes.
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Correspondence to:
Albert M. Lai, PhD
Institute for Informatics (I2)
Washington University in St. Louis
660 S. Euclid Ave., MS 8102-13-610
St. Louis MO 63110, USA
Tel: +1 (314) 273-1391
Fax: +1 (314) 273-1390
E-mail: amlai@wustl.edu
