Table Of ContentCognitive Informatics in Biomedicine and Healthcare
Pei-Yun Sabrina Hsueh
Thomas Wetter
Xinxin Zhu Editors
Personal
Health
Informatics
Patient Participation
in Precision Health
Cognitive Informatics in Biomedicine
and Healthcare
Series Editor
Vimla L. Patel, Ctr Cognitive Studies in Med & PH
New York Academy of Med, Suite 454, New York, NY, USA
Enormous advances in information technology have permeated essentially all facets
of life. Although these technologies are transforming the workplace as well as
leisure time, formidable challenges remain in fostering tools that enhance
productivity, are sensitive to work practices, and are intuitive to learn and to use
effectively. Informatics is a discipline concerned with applied and basic science of
information, the practices involved in information processing, and the engineering
of information systems.
Cognitive Informatics (CI), a term that has been adopted and applied particularly
in the fields of biomedicine and health care, is the multidisciplinary study of cogni-
tion, information, and computational sciences. It investigates all facets of computer
applications in biomedicine and health care, including system design and computer-
mediated intelligent action. The basic scientific discipline of CI is strongly grounded
in methods and theories derived from cognitive science. The discipline provides a
framework for the analysis and modeling of complex human performance in tech-
nology-mediated settings and contributes to the design and development of better
information systems for biomedicine and health care.
Despite the significant growth of this discipline, there have been few systematic
published volumes for reference or instruction, intended for working professionals,
scientists, or graduate students in cognitive science and biomedical informatics,
beyond those published in this series. Although information technologies are now in
widespread use globally for promoting increased self-reliance in patients, there is
often a disparity between the scientific and technological knowledge underlying
healthcare practices and the lay beliefs, mental models, and cognitive representa-
tions of illness and disease. The topics covered in this book series address the key
research gaps in biomedical informatics related to the applicability of theories,
models, and evaluation frameworks of HCI and human factors as they apply to clini-
cians as well as to the lay public.
Pei-Yun Sabrina Hsueh • Thomas Wetter
Xinxin Zhu
Editors
Personal Health Informatics
Patient Participation in Precision Health
Editors
Pei-Yun Sabrina Hsueh Thomas Wetter
Bayesian Health Heidelberg University Hospital
New York, NY, USA Institute of Medical Informatics
Heidelberg, Germany
Xinxin Zhu
Biomedical Informatics and Medical
Center for Biomedical Data Science
Education
Yale University
University of Washington
New Haven, CT, USA
Seattle, WA, USA
ISSN 2662-7280 ISSN 2662-7299 (electronic)
Cognitive Informatics in Biomedicine and Healthcare
ISBN 978-3-031-07695-4 ISBN 978-3-031-07696-1 (eBook)
https://doi.org/10.1007/978-3-031-07696-1
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
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Foreword
We live in turbulent times. The pace of technological innovation around us is relent-
less. Its impacts on society, some expected some not, are reshaping the way we
engage with each other and with once trusted monoliths like the healthcare system.
Washing over all this tumult are the far reaching and still unrevealed consequences
of climate change and what may well be an age of pandemics. Society will not be
the same, no matter how we struggle to engineer that it will.
It is no surprise then that as individuals we all want more control, and more
understanding of the direction of our health care. Many expect to be equal partners
with clinicians in healthcare decisions. We want to know how vaccines work, and
the relative risk of one vaccine strategy over another, before we decide to follow
public health recommendations. We want to know that our disease treatments are
safe, and for major illness many want to leave no option unexplored. Some of us go
one step further and want to be the decision-maker, relegating trained professionals
to advisers.
The challenges we face as engaged patients however are formidable. If it is truly
not possible for a clinician to be up to date with medical science because the pace
of innovation is so frenetic, what chance is there for a patient? When in doubt, our
human response is to speak to others and understand their own journey and deci-
sions—but that strategy can be risky if we use social media to ask the questions. The
promised bounty of the internet, connection to all people and all things, is increas-
ingly tarnished by community polarization, lack of trust, and skepticism in science.
Truth itself is often victim to disinformation campaigns created for unclear motive.
It is with this challenging context that we must try not just to understand the
sweeping changes that are happening in health care, but to get ahead of them. We
are tasked to imagine ways of rebalancing the new dynamic of the world by giving
patients and clinicians new options to succeed. What skills and tools must we invent
so that patients can both find the information they need and understand the underly-
ing science behind it—itself ever mutating because of technological innovation?
What new conceptions of communication and relationships will help patients and
their professional carers work together with trust when the old options fall away?
v
vi Foreword
Some of the answers are social and cultural, some are to be found in the embrace
of new models of care, where one face-to-face interaction is replaced by a multitude
of small digital touches. Some technological answers bring both revolution and
their own burden of problems. Artificial intelligence will likely transform the way
many patients manage their care. Personal AIs will be our first port of call, noticing
health problems emerge before we do, suggesting pathways for care shaped by our
past preferences, and coordinating that journey for us. AIs will not just be digital
front-doors for us into the health system, they will be networked into it, creating
what is known as a cyber-social system. These AIs will be connected into our
clothes, our wearable devices and jewelry, our home, and our cars. AIs will negoti-
ate, search, recommend, optimize, and disagree—all on our behalf and not always
transparently or fairly. A new age with new opportunity, and a new bag of chal-
lenges and decisions for us to face.
In the pages of this book, you will find many of the elements of this next stage in
our journey towards a dynamic health system that better fits the challenges ahead.
New ways of interacting, new ways of behaving, and new tools for thinking are all
part of the solution. The chapters in this book also make clear how early we are in
our journey to truly embrace what is to come. The healthcare systems of different
countries are all unique, but in one way are all the same—they are monolithic in
behavior and slow to change. It is often said that revolution cannot come from
within old large organizational structures. Revolution comes from the boundaries of
the old hegemony and the wild borderlands. So maybe you need to read these chap-
ters not as stories from the old healthcare empire, but as reports from the wild bor-
der country of patient-led change.
Australian Institute of Health Innovation Enrico Coiera
Macquarie University
Sydney, NSW, Australia
[email protected]
Preface
Overview
The world of health informatics is constantly changing given the ever-increasing
variety and volume of health data, care delivery models that shift from fee-for-
service to value-based care, new entrants in the ecosystem, and the shifting regula-
tory decision landscape. In the area of cognitive informatics, the changes have
increased the importance of the role of patients in research studies for understand-
ing work processes and activities within the context of human cognition, as well as
the design and implementation of health information systems (Haldar et al. 2020;
Trevor Rohm 2010). Therefore, personal health informatics, in recent years, has
risen up to provide research tools and protocols to zoom into individual health-
related contexts when developing engineering, computing, and service solutions
that can improve clinical practice, patient engagement, and public health (Hsueh et
al. 2017a; Hsueh et al. 2017b; Lai et al. 2017; Patel and Kannanmpallil 2015;
Reading and Merrill 2018). This is particularly important to bridge the previous
gaps in patient-provider information (Tang and Lansky 2005).
The rise of personal health informatics is also in line with the emerging utiliza-
tion of real-world evidence generated from real-world data. Here, “real-world data”
(RWD) refers to data generated from the actual practice and delivery of health care
(e.g., electronic health records, insurance claims, disease registries), while “real-
world evidence” (RWE) refers to the inferences made from RWD. In some areas
such as clinical trial design, RWE has been successfully applied to bring greater
efficiency to the development of clinical programs (e.g., as its external control arm
as indicated by the FDA’s Real-World Evidence Framework (FDA, n.d.) and
received regulatory approval (Berger et al. 2016; Miksad and Abernethy 2018; Shah
et al. 2019). In many other areas, the development of personal health informatics
has opened up new opportunities to generate RWE through integrating data science
with the science of care (Bica et al. 2020; Hsueh et al. 2018).
Through this book, we intend to compile a collection of high-quality scholarly
work that seeks to provide clarity, consistency, and reproducibility, with an updated
vii
viii Preface
and shared view of the status quo of consumer and pervasive health informatics and
its relevance to precision medicine and healthcare applications. The new term
“Personal Health Informatics” is being proposed to cover a broader definition of this
emerging field. In one way or another, individuals are not just consuming health;
they are active participants, researchers, and designers in the healthcare ecosystem.
The book will offer a snapshot of this emerging field, supported by the method-
ological, practical, and ethical perspectives from researchers and practitioners. In
addition to being a research reader, this book will provide pragmatic insights for
practitioners in designing, implementing, and evaluating personal health informat-
ics in healthcare settings. The volume will also be an excellent reader for students
in all clinical disciplines as well as in biomedical and health informatics to learn
from case studies in this emerging field.
This is a starting point for us to show the direction where the whole field is going.
The chapters include (1) case studies including reflections on implementation les-
sons learned, (2) theoretical frameworks, (3) design methodologies, and (4) evalua-
tion and critical appraisal.
These chapters will be organized under four main sections: (1) the state-of-the-
art novel care delivery models (using case study examples), (2) methods for trans-
lating biomedical research and RWE into patient-centric precision health application,
(3) methods for patient-centric design, and (4) ethics, bias, privacy, and fairness.
Chapter authors have been invited based on their reputation and fit with the topic,
and all chapters have been reviewed independently.
This book is intended to appeal to a wide range of audiences including academic
researchers, educators, professional informaticians, healthcare providers and
administrators, healthcare consumers, and policymakers. Although this book is not
considered a standard textbook, it will be of great value for graduate programs in
which courses in applied informatics are relevant, such as courses that focus on
behavioral, cognitive, and social aspects of health information technology.
Section I: The State-of-the-Art Novel Care Delivery Models
Since its inception—then by the name Consumer Health Informatics–about 25
years ago the field of Personal Health Informatics (PHI) has achieved worldwide
reach across many health problems and clinical disciplines. The book presents ser-
vices from approximately 20 countries and several methodological chapters with
international reach. The authors provide us with a different lens to observe different
target populations, learn about various methods of deployment, and how they fit
with or transgress from present models of healthcare delivery.
Kuziemsky et al. in their Chap. 1, “E-enabled Patient-Provider Communication
in Context,” present examples of enhanced patient-provider communication from
Denmark, Fiji, Columbia, and Canada. In Denmark, the emphasis is on overcom-
ing the intermittent and fragmented practice of care for patients with Chronic
Obstructive Pulmonary Disease. In the EHealth Care Model (ECM) patients
Preface ix
receive basic measurement and problem staging equipment for their home envi-
ronment that connects them continuously, 24/7, to the same care team. Adaptations
of the treatment regime can be initiated through the distance before emerging
problems exacerbate. The project from Fiji addresses mental health problems for
which specialists are extremely rare and care is mostly provided through nurses.
To lift the level and guideline adherence of treatments, an educational effort was
launched which compared the presentation of guideline knowledge through differ-
ent media. A smartphone-b ased study arm showed the best results. In Columbia,
similar to many low and middle-income countries, perinatal mortality is still high.
In addition, the involvement of stakeholders hinders timely intervention during
pregnancy. The presented project aims to technically support interventions sug-
gested to the gynecologist through AI-driven clinical decision support. The respec-
tive system needs to technically integrate with clinical workflows and is supposed
to communicate with expecting mothers through their smartphones. The Canadian
example studies collaboration through the macro, meso, and micro levels. It
emphasizes the dynamic nature of collaboration which showed intensely in the
transition of care with the onset of the COVID pandemic. New tools were sponta-
neously adopted by individual providers and patients, posing organizational and
data security challenges at the meso level. The challenges thus call for the reform
at the national level on data standards, new legislation, and billing codes, to name
just a few.
In Chap. 2, “Direct Primary Care: A New Model for Patient-Centered Care,”
Snowdon et al. lay out one specific novel model of Direct Primary Care (DPC). The
DPC model aims to improve the quality of primary care through better patient-
provider relations and communication. The payment model of DPC is based on a
per capita fee per time period to a provider or its provider organization. It can be
either paid by the patients or by their employers. Compared to the fee-for-service
payment model, the value-based model can help incentivize providers to spend
more quality time with the patient and promote prevention. For patients, it also
removes financial and organizational barriers to healthcare access, which in turn
leads to rational and sustainable utilization of primary care services. This chapter
also helps the readers learn to distinguish the implementation needed at different
scales, and how the implementation of DPC improves utilization of designated pre-
ventive measures and decreases preventable emergency room visits.
In Chap. 3, “Smart Homes for Personal Health and Safety,” Demiris et al.
address how to make the home environment safer through controls, sensors, and
algorithms that can interpret the clinical needs behind sensor signals. Smart home
applications that can benefit from surveillance include physiological and functional
indicators of health and their trajectory over time, protection against physical and
intrusion hazards, and cognitive and social functioning. The authors illustrate the
smart home- based delivery of care through the example of Mild Cognitive
Impairment (MCI) and fall management in the Sense4Safety project. Fall risks
increase slowly and are often unnoticeable, entailing tremendous cost and morbid-
ity. Sense4Safety uses personalized configurations that include nursing assessment
of the home situation and resident education and alerts to a trusted party to mitigate
