Keywords
Community-based participatory research - participatory health - artificial intelligence
- social media - wearable electronic devices - mobile health
1 Introduction
Over the last decade, the role of people (or patients) in managing their own health
or healthcare has changed dramatically [1]
[2]. A significant paradigm shift has been observed, whereby individuals have moved
from being passengers in the health journey to more active, engaged, and empowered
partners in the shared decision-making process with their health professionals [2]
[3]
[4]. New ways of creating, developing, and consuming health care goods and services
have arisen known as participatory health or the participatory health paradigm. Participatory
health informatics involves individuals and representatives of those individuals who
would be the main beneficiaries of this technology [5]
[6]. Participatory health focuses on patient-centered initiatives to empower individuals
in making more informed health decisions where the individual/patient is at the center
of any related healthcare initiative [7]. The term was first coined in 2008 [7]. For example, Eytan provides a definition that suggests participatory health: “Enabled
by information, software, and community that we collect or create, we the patients
can be effective partners in our own health care, and we the people can participate
in reshaping the health system itself.” [8]. Furst further describes participatory health as “care characterized by the ability
to rapidly share, classify, and summarize individual health information with the goals
of improving health care systems, experiences, and outcomes via integration of patients
and stakeholders.” [9]. A number of informatics-related tools can be used in participatory health activities
such as: social media, mobile phone health apps, personal health records, health social
networks, consumer genomics, and crowdsourced health studies [4]
[8]
[9]. Each enables the generation, production, or collection of various types of data,
reflecting several facets of one’s health which includes psychological, physical,
social, physiological, or cognitive metrics.
With the reemergence of AI in healthcare in recent years, there has been growing interest
to examine the role of AI within participatory health. The quantified-self movement
is illustrative of the participatory health paradigm, where people are able to collect,
log, and track indicators to quantify their health, thereby providing insight into
their overall health status [10]. However, whilst arguably transformative, technological developments in intelligent
computing and analytical methods, namely AI, are providing the opportunity to go even
deeper into understanding one’s health [11]. This is particularly relevant as people are able to collect increased types, amounts,
and quality of data about themselves and their health.
AI techniques and methods provide individuals and professionals working in the field
of participatory health informatics with an opportunity to derive robust intelligence
from online resources [12]. AI adds another dimension to technologies such as social media, and mobile devices/apps
for the purposes of participatory health. For example, through applying AI algorithmic
modelling, multiple tasks can be performed such as: replicating or supplementing personalized
medical advice from health professionals, diagnosing rare and complex conditions,
informing how diseases or outbreaks develop and spread, predicting adverse health
events, uncovering how people engage in certain health behaviors or utilize healthcare,
and correlating experiences and outcomes from different treatments [13]
[14]
[15]
[16]
[17]
[18]. Whilst the age of AI offers a welcome and innovative way to support participatory
health, the field is still maturing, particularly from the perspective of rigorous
research evidence.
This paper investigates how AI impacts the field of participatory health and examines
which Al applications exist for participatory health from both patient and clinician
perspectives.
2 Methodology
2.1 Search Strategy
The search strategy was developed in two stages. For the stage 1 search, authors primarily
focused on social media interventions within the participatory health context. This
is because social media are one of the most widely used tools for individuals within
the participatory health context. In the second stage of the search, the focus was
related to other, less pervasive, participatory health technologies such as: mobile
phones, wearables, and chatbots.
Stage 1: The PICO framework (P – Population, I – Intervention, C – Comparison, O – Outcome)
was followed to derive the search strategy [19]. Regarding “Population”, we refered to various social media. “Intervention” comprised
the multiple AI technologies and methods, and “Outcome” included the impact or resulting
participatory health/medicine effects. A comparison intervention was not relevant
in this context. Based on this topic specification, we created a list of relevant
keywords related to machine learning (57 keywords), participatory health (12 keywords),
and digital/social media (28 keywords). The search strategy included all these agreed
terms. The complete literature search strategy is listed in the supplementay material
([Appendix 1]). The search was performed on the 10th August 2018, guided by the recommendations of PRISMA guidelines [20]. The PRISMA checklist is available in the supplementary material ([Appendix 2]). The search covered the following databases: PubMed, Embase, CINAHL, PsychInfo,
ACM Digital Library, IEEExplore, and SCOPUS.
Stage 2: Finally, to broaden the scope and include future implementation of AI, we also included
clinical trials of AI in participatory health. More specifically, we searched for
relevant studies on the trial registration platform clinicaltrials.gov. On October
11, 2018, we searched with the keywords “AI” combined with “mobile phone”, “smartphone”,
“wearable” and the keywords “text-mining”, “conversational agent”, “chatbot”. The
complete search strategy for clinical trials is shown in the supplementary material
([Appendix 3]).
2.2 Eligibility Criteria and Screening
We included publications published from 2013 and onwards. These were: 1) primary literature
or 2) reviews (scoping reviews, systematic reviews, or meta-analyses); and 3) publications
that fulfilled 1) and 2). Publications not meeting these criteria were excluded. The
limit of 2013 was used as this captures the last five years, which is the relevant
timeframe for rapid technological advances like AI. Through this restriction, the
authors wished to represent AI in its present state, whereas the technological capabilities
have undergone a change in 2013. References were imported to EndNote X7.8, and duplicates
were removed. The first phase involved two independent reviewers (EG and OR) screening
titles and abstracts against inclusion criteria. Full-text of articles meeting inclusion
criteria were obtained and carefully examined in a second eligibility round. The agreed
articles were included in this review. The search by one reviewer (KD) on clinicaltrials.gov
identified twenty-nine trials posted since January 1, 2013; after removing duplicates
and checking for relevance, twelve trials were included for the analysis.
2.3 Review Strategy
The resulting fulltext paper and trial information were considered by all co-authors
to answer the research questions:
-
What is the current state of the art in AI techniques applied to data from social
media and wearables in the context of participatory health?
-
What outcomes and effects are reported in literature on this data using AI?
-
Which practical implications (now and future) exist from a clinical and patient perspective?
-
Do clinicians feel threatened by AI? Do patients feel more empowered?
-
What challenges and opportunities are produced by AI? What should the roadmap for
future research be?
More specifically, the authors considered several topics in this domain and analyzed
the retrieved literature with respect to these topics. These include: AI applications
based on data from wearables and social media, and trials using AI in participatory
health. From the results, we derived implications from a patient perspective, physician
perspective, and from a health systems/community perspective.
3 Results
The flowchart of the selection procedure is presented in [Figure 1], while [Figure 2] shows the trial selection procedure. It can be seen that we included 22 papers and
12 trials in the narrative synthesis.
Fig. 1 Flow chart of literature selection resources from bibliographic databases.
Fig. 2 Flow chart of trial selection from clinicaltrials.gov.
In this section, we first present AI applications for participatory health that were
described in the literature. Secondly, we present the selected clinical trials.
3.1 AI Applications Based on Data from Social Media
Of the 22 papers, 17 (77.3%) explored AI applications based on data from social media;
n=13 (76.5%) were secondary analysis of data, and n=4 (23.5%) were literature reviews.
Only one of the 22 papers reviewed technologies that involved interaction with patients
or clinicians and examined the capabilities of conversational agents for health-related
purposes [21]. The supplementary material ([Appendix 4]) provides a summary of the papers.
3.2.1 Platforms and Techniques
The most common application of AI in participatory health was secondary analysis of
data, which was reported and/or reviewed in n=16 out of 17 papers [14]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]. These articles primarily reported the use of machine learning techniques to extract
health-related data from electronic health records (EHRs) or social media. Secondary
data analyses were conducted on general social networks (e.g. Facebook, Twitter, Instagram,
Yelp, Amazon, Yahoo Q&A, Wikipedia, Reddit, LiveJournal), health-specific social networks
(e.g. WebMD, RateMD, MedlinePlus, Medlinehelp.org, Breastcancer.org, PatientsLikeMe,
DailyStrength, online health forums), and EHR and clinical text (e.g. ShARe, i2b2,
THYME).
Most literature (n=9 out of 17 studies) used text processing techniques, including
natural language processing or NLP (n=4) [14]
[24]
[32]
[33], sentiment analysis (n=4) [14]
[27]
[29]
[30], and text classification (n=1) [28]. Other techniques included classification (3 out of 17 studies) [22]
[25]
[26], clustering (3 out of 17) [23]
[27], conversational agent (1 out of 17 studies) [21], deep learning (1 out of 17 studies) [31], visualization [23]
[27], and Latent Dirichlet Allocation [27].
Among the applied text processing and machine learning techniques, sentiment analysis
was used to discover safety and efficacy concerns in Amazon reviews for joint and
muscle pain relief products [30]. Vocabulary lists used in the analysis consisted of words and phrases that were
more indicative of defective products than non-defective products and included non-emotive
words that may have been missed by traditional sentiment analysis techniques. Another
paper used principal components analysis and logistic regression to identify consumer
health expressions (patient terminology) to facilitate adverse drug reactions signals
detection among posts that do not use formal medical terminology [34]. A similar study investigated the use of applying word2vec, a word embedding representation,
to estimate the rate of adverse drug reactions to psychiatric drugs from social media
[31]. Word embedding representations try to predict surrounding words using a central
word and vice-versa. This technique was used to better find instances of adverse drug
reactions in social media by including colloquial terms such as ‘can’t sleep’, in
addition to medical terms such as ‘insomnia’. The estimates derived from this approach
were found to be similar when compared with official rates from the SIDER database
of drug side effects [31].
3.2.2 Health Conditions and AI Applications
The most common health condition examined was diabetes, which was investigated in
n=5 papers [22]
[23]
[24]
[26]
[35]. Other conditions included back pain [24], hypertension [24], cancer (including breast cancer) [27]
[28]
[35], Crohn’s disease [29], joint and muscle pain [29]
[30], mental health [31], lung and respiratory disorders [32], and other chronic conditions [26].
Patients and consumers were the focus of most (n=14) papers [14]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[17]
[35]. Consumers’ self-reported data was extracted from social media and included patient
experiences at healthcare facilities [14], reports of adverse drug reactions of psychiatric medications [31], safety and efficacy concerns on over-the-counter medications [30], and perspectives on certain medications [21]
[29]. Text-processing techniques were also used to detect disclosure of personal health
status information on Twitter [25], identify how consumers express their health concerns [23]
[25]
[35], and extract emotional and informational elements in an online post [26]. Other applications included determining which online forum threads required moderator
assistance [22], identifying users who were likely to leave an online social network [28], extracting terms used in a question posted online [27], highlighting contextual information that is missing from online questions and answers
[27]
[32], and paraphrasing technical medical terms for consumers [24]. Facebook data have revealed patients’ perspectives and opinions on Crohn’s disease
and new treatments [29]. One study used conditional Latent Dirichlet Allocation, a weighted generative probabilistic
model used to discover latent topics from a collection of documents and clusters of
documents with similar distributions of medical terms, to analyze health community
discussion threads, specifically in regards to physiological and psychological sentiments
for certain conditions. Another study built a corpus called COPE to use social media
to understand patient experiences at health care facilities in order to improve the
quality of healthcare [14]
[27]. This is crucial, as patients value doctors’ personality and demeanor as much as
they value their medical care proper [36].
AI has also been used to analyze patients’ understanding of medical conditions. One
study used Pathfinder networks to analyze the differences in perception of vaccines
between physicians and patients, shedding light on patients’ level of medical understanding
and potentially helping physicians develop better ways of explaining medical issues
related to vaccination [36]
[37].
Both clinicians and consumers were the focus of n=3 papers [21]
[33]
[12]. These included one systematic review of conversational agents [21], and two narrative reviews on NLP techniques in EHR, clinical text, and social media
[12]
[33]. These reviews provided commentaries on the state of the art for their respective
technology, and highlighted the challenges and the lack of rigorous studies in the
field.
Lastly, AI has been used to help healthcare professionals in providing care to patients,
both online and in person. One study showed how a low-cost text classification system
called Linguistic Inquiry and Word Count (LIWC) assisted moderators in online health
communities. The classification determined which posts needed moderators’ attention
and medical expertise, and which did not. This provided the basis for a system that
helps moderators prioritize a selected number of posts that are classified as needing
medical expertise, rather than making moderators search through thousands of posts
in the forum. The system also allowed moderators to change settings depending on how
many posts they wanted to see [22]. A second study showed how AI has been used to develop a real-time pneumonia-screening
tool to be used in the emergency department that provides care recommendations when
pneumonia is suspected from chest x-rays. Emergency departments that employed this
tool showed increased adherence with recommendations for treatment of pneumonia [22]
[33].
3.2.3 Summary: AI Applications in Participatory Health
In summary, there are several challenges in this line of research (e.g. working with
unlabeled data from unstructured sources), with many studies that remain at the stage
of enhancing technical performance and have not yet reached the maturity for user
evaluation. Besides the review on conversational agents [21], this paper found neither AI applications that were designed for use by consumers
or clinicians in participatory health, nor literature that elicited input from consumers
or clinicians for such applications.
The literature to date shows that social media, when analyzed with AI, offers the
opportunity to learn about patient’s experiences with treatments, to study effects
of social media interactions, and many others. None of the included studies reported
AI having an effect on clinical outcomes, or an effect of social media on health status.
Instead, the literature focused on:
-
Content analysis of social media content;
-
Term correlation and term usage analysis;
-
Opinion or sentiment analysis with respect to treatments;
-
Influence of social media interaction with health status.
3.2 Clinical Trials Studying AI in Participatory Health
In contrast to published literature aleady reviewed, the 12 identified clinical trials
dealt with interventions supported by smartphone applications, broadly grouped in
two categories:
-
Capturing new kinds of data from clinical trials’ subjects using sensors, wearables,
and smartphone apps;
-
Collecting electronic diaries and patient-reported outcomes using a smartphone app.
A summary of these trials is provided in the supplementary material ([Appendix 5]). Nine trials tested conversational agents, either embodied (lifelike) or unembodied
(unlifelike); one trial assessed the efficiency of a smartphone application, one trial
exploited a wearable sensor, and one study the exploitation of Google glasses. Most
of the trials randomly assigned people to interventions but were not blinded. The
key outcomes were efficiency, impact, and usability of the systems. For example, a
randomized control trial evaluating agent technology, i.e. comparing usual care versus
usual care plus an AI agent, assessed the impact of the usage of the agent on the
quality of life in palliative care [38]. Fitzpatrick et al. performed a trial on an automated conversational agent available
as a mobile device smartphone application that provides a cognitive behavioral therapy
(CBT). The app allowed patients to immediately process and receive empathy for significant
events and removed barriers traditionally limiting treatment access (cost, stigma,
health care system navigability, and lack of childcare or transportation) [37]. Another study assessed usability, receptivity, and preliminary cessation when the
patient is supported with a conversational agent for smoking cessation [40]. Daily adherence to an embodied conversational agent to use the mobile Kardia heart
rhythm monitor has also been evaluated [41].
Two trials evaluated the smartphone intervention IntelliCare. It uses machine learning
to tailor treatment for patients suffering from depression and anxiety. The system
delivers patient-specific treatment material and motivational messaging via a mobile
phone. The purpose of one study was to obtain preliminary information on the feasibility
and effectiveness of IntelliCare in improving symptoms of depression and anxiety.
The second study is a randomized trial with the application [42]
[43].
An AI platform on mobile devices has also been used to measure and increase medication
adherence in post stroke people with anticoagulation therapy. The AI application visually
identified the patient, the medication, and the confirmed ingestion of drugs [44]. Adherence was measured by pill counts and plasma sampling in both groups. Wall
et al. studied the effects of an AI tool, running on Google Glass through an Android
app, that performs automatic facial expression recognition and delivers social emotion
cues to children with autism during social interactions. The system is designed to
give participants non-interruptive social cues in real-time and to record social responses
that can later be used to help aid behavioral therapy. It studies the effects of the
Autism glass intervention [45]. Ally, a mHealth intervention to promote physical activity, is offered in a trial
to the customers of a large Swiss health insurance. Interventions include the use
of different incentive strategies, a weekly planning intervention and daily message
prompts to support self-regulation [46].
In summary, registered clinical trials on AI in participatory health are still limited
and are mainly evaluations of conversational agents interacting with patients.
4 Discussion
AI in participatory health informatics can be considered as still in its infancy,
as social media and mobile apps were a decade ago. AI applications within participatory
health have been assessed in the context of a large variety of health conditions,
health promotion activities, and healthcare settings. Furthermore, most evaluations
have focused on refining AI methods and techniques, and data on the impact of AI on
health outcomes to make meaningful conclusions is still awaited. To date the scholarship
of AI in participatory health has been driven by researchers’ skills and interests,
and in prevalent or challenging healthcare contexts, for example, the high prevalence
of diabetes, pain, mental health conditions, or the demands for healthcare experts
like neurologists, or cardiologists, whereby healthcare practionners with this expertise
are geographically or numerically limited [47]
[48]. The next immediately available research data, from a clinical trial identified
in this review, will also focus on health promotion or adherence to health care interventions
and include CBT, smoking cessation, autism, physical activity, and medication adherence
[37]
[40]. The very nature of participatory health would suggest that a future research agenda
must be informed and directed by people requiring healthcare as a priority, but in
partnership with academics and health care providers who understand the context and
practicalities of providing robust and meaningful information and knowledge.
4.1 Implications from the Patient Perspective
There is potential for AI to make significant improvements to the health of individuals.
However, for the field to move forward, one needs to learn the lessons from the previous
generation of technologies [21]
[49]
[50] to avoid the pitfalls in the design, evaluation, and implementation of patient-facing
technologies. These lessons include, but are not limited to: mitigating high participant
dropout, avoiding design and implementation not considering patients’ context or perspectives,
and evaluating findings becoming obsolete by the time they are available.
Patients are often overwhelmed by what they need to do to manage their health. Many
of them have busy lives and multiple competing priorities, and are not necessarily
familiar with technological developments derived from AI. Asking patients to learn
and adopt AI technologies to improve their health and wellbeing may not be something
they wish to do, nor a priority for engagement. Patients should be treated as true
partners, with research conducted neither ‘on’ people considered as passive subjects,
nor on patient participants only providing data, but research conducted ‘with’ patients.
Patient participants should be considered from initial research development to ensure
informaticians are indeed asking the right questions, so that findings provide the
right answers to improving patients’ lives [51].
Perhaps the challenge of developing patient-facing AI applications lies in human-computer
interaction design, not the technology itself [52]. True patient benefit from AI may require the design to be embedded deeply or even
invisibly in their daily routine. Without getting a good understanding of the activities
patients perform for their health (i.e. patient work), nor how their individual context,
digital literacy, or support network affect their health and wellbeing, there is a
high risk that patient-facing technology will not be adopted, used, or sustained.
One final challenge from this perspective is that of creating, or at least enabling
and motivating behavior change using design and psychological mechanisms, as data
alone cannot be counted on to bring change and improve health or increase participation
[53].
4.2 Implications from Physician Perspective
The analysis of social media data with AI has the potential of providing new insights
into patient health beliefs and perspectives on their health, healthcare use, and
efficacy and adverse effects of drugs and treatments. AI used to detect drug safety
and efficacy concerns is attractive as doctors do not always elicit patient medication
adherence and concerns about their medication [54]. Furthermore, concerns over side effects and effectiveness are among the main causes
of non-adherence [54]
[55] and they can be revealed by analyzing drug concerns in online health communities.
Sentiment analysis applied to social media data can also be used to discover consumers’
feedback on certain medical treatments. This would be helpful to physicians who can
gain insight into the prevalence of drug-related events, and how specific drugs work
and should be repositionned. Additionally, consumer opinions about new drugs or treatments
may be initially found on these sites. By analyzing online health social media data,
discovery of drug-drug interactions could change prescription practice, identify contraindications
to combination use, or even lead to timely drug withdrawal from the market [56]. This research can be used by the healthcare and pharmaceutical industry to reduce
risks for consumers [30]
[56]. Furthermore, it could potentially identify unexpected or infrequent drug reactions
that would otherwise have been missed. The systematic mining of social media data
can provide valuable information for physicians about their patients. For example,
one tweet reads “my mom makes having cancer look easy”, while another reads such as
“I have seasonal allergy”. These tweets can help physicians learn about their patients
and gain insight into patients’ family medical history that may otherwise be missing
from electronic medical records [25]
[33].
Beyond this, data collected by wearables and through smartphone applications can provide
insights into the health of patients. In particular, changes in health parameters
become more obvious. However, what needs to be avoided is that physicians are overwhelmed
with additional data to be assessed during patient encounters. AI can help in summarizing
data and in discovering patterns in data being presented and assessed by physicians.
In summary, these findings show the promise for utilising AI at the service of physicians,
who can use it to gain insight into medications and conditions. All this, often without
the patient’s knowledge, or active participation.
4.3 Implications from a Health Systems/Community Perspective
Information communication technology (ICT) has long been heralded for its potential
to improve health systems. The power of larger and more readily available data sets
to improve healthcare efficiency and effectiveness, as well as decision-making, has
long been debated. However, when combined with advances in cognitive processing and
analytics, such as those afforded through AI, the potential for health ICT to generate
health system level benefits increases exponentially. According to Accenture, growth
in the AI health market is tipped to surpass well-over US$ 6 billion by 2021 [57].
The application of AI to participatory health informatics has started and focused
on the secondary analysis of data produced within participatory health activity (as
illustrated by the findings of this review with n=16 out of the 17 selected papers).
This may be due in part because permissions to obtain access to social media and networking
data is easier than obtaining data from custom databases related to mobile devices,
or obtaining access to national health system electronic records. To date AI in participatory
health has focused on text-related data. That should not create a stereotype that
participatory health informatics focuses on text data only, since there are examples
that use EHR data. In the future it can encompass the use of physiological data used
from wearable devices [58]
[59] and clinical trials (as also identified in this review) are employing devices (sensors,
wearables, and smartphone apps) to capture data. To increase knowledge about impact
of AI applications on participatory health and health outcomes, research frameworks
and communities will need to work through the ethical and practical privacy issues
of research using healthcare data, such as medical images [60], or biological data [61]. This is since transformative insights are most likely to be achieved when conventional
health data (EHR, imaging, prescribing records) are combined with patient-generated
data, e.g. both experiential reporting and physiological data. These issues need to
be urgently addressed by health systems, regulators, and society [62].
AI can also influence positive changes in participatory health informatics by providing
insights to improve health systems functioning for individuals and communities. AI
applications offer organizations in the health system insights and solutions to offsest
rising financial pressures, increasing costs of healthcare, ageing populations, increasing
incidences of complex chronic diseases, shortages of skilled professionals, and rising
consumerism and digital patient empowerment [18]
[57]
[63]. If these improve the care experience, either directly or indirectly by freeing
up resources, this will have a positive impact on participatory health.
Consequently, as the field matures, AI has the potential to assist health systems
globally through: improved quality of healthcare, improved workflows, error reduction,
better accuracy of information, enhanced safety, better access, greater user (patient)-centeredness,
more personalized and preventative healthcare, and ultimately cost savings [18]
[57]. Furthermore, and highly relevant to the domain of participatory health informatics,
is that among the top 10 application areas for AI proposed by Accenture, at least
four arguably have participatory health connotations, including: robotics, virtual
health professionals/assistants, connected devices, and recruitment of participants
into clinical trials [64]. Some of them have already started to be explored, with large areas for future research.
It has been already noted that AI offers tremendous potential to support and progress
the diagnosis of key conditions on the World Health Organisation’s health burden list
[9], for example, cancer, neurological and cardiac conditions [11]. In these conditions, the major application of AI centers on diagnosis and relative
risk prediction. However, before AI can play a meaningful role in supporting models
of participatory health in the community, further work and research is needed on its
utility to support treatment, and emerging models of care [11]
[66]. Despite this, AI techniques are already being leveraged for treatment purposes,
for example to support movement of prostheses in stroke and spinal cord injury [11].
5 Conclusions and Roadmap for Future Research
5 Conclusions and Roadmap for Future Research
The authors are cognizant of the impact and growing interest of AI in the field of
participatory health, increased research funds being made available, and advances
in the analysis, classification, and prediction of participatory health related data
over the past five years. Nevertheless, many challenges remain to be addressed before
AI is able to have a measurable impact on participatory health. More coordinated efforts
between local, regional, and international research networks to develop an actionable
and realistic research agenda for AI research in participatory health is needed. Such
a roadmap for research should be able to separate the dissonance between the hype
and the reality of AI in participatory health informatics, and be actionable within
the realities of the contemporary global healthcare context.
There are numbers of research challenges as well as opportunities for AI in participatory
health that need to be considered. The introduction of AI technologies for participatory
health will provide health consumers access to a global health network including of
healthcare services and access to other like-minded health consumers. Some of the
immediate research challenges to be considered are where AI for participatory health
best fits within the global healthcare spectrum in relation to preventive health,
home care, primary care, secondary, and tertiary care. In particular, the focus should
perhaps be put on developed nations that have a larger proportion of elderly individuals,
versus developing countries with a larger proportion of young adults and adolescents.
Also, AI-related technologies empower health consumers and may reduce health consumer
reliance on medical advice which makes the impacts of AI on the patient-doctor relationship
difficult to predict. Furthermore, technological advances in AI are increasing dramatically
and evaluating AI impact on consumer engagement and general patient well-being remains
to be investigated. An Ernst and Young 2017 research report indicates that for participatory
health to become embedded within the healthcare system, three important elements must
be considered: 1) capitalization on the advances of technology; 2) empowering platforms
that combine technologies, people, and processes; and 3) cultural shifts that enable
the system transition towards an ecosystem [67].
Considering all these factors, there are a number of key and important research priorities
that should be considered for the advancement of AI research in participatory health
([Figure 3]). Since healthcare ecosystems are slow to change, AI technology will continue to
advance and participatory health informatics will need to adapt to the technological
advancements of AI, as more demands from health consumers and the healthcare industry
place pressure on the overall healthcare ecosystem to use, apply, and accept AI technology.
More work to understand the impact of AI on participatory health is needed, as explained
and illustrated below in the suggested research priority areas for AI in participatory
health:
-
Evaluation - Research evaluating the impacts of AI on participatory health is needed. In particular
research around medical errors, technology acceptance, healthcare costs, quality of
care, and impacts on decision making;
-
Patient-Clinician Relationship - Research evaluating the impacts of AI on patient-clinician relationships is needed
as the autonomy of physicians decreases due to more empowered patients, whereas empowerment
and engagement of consumers through AI technology increase;
-
Healthcare Readiness - Healthcare organizations are slow to change and research into the readiness of
healthcare organizations to accept AI is needed;
-
Technology Adoption - Research to understand how healthcare professionals and consumers, of all ages,
are ready to accept AI as a complementary or alternative type of care;
-
Psychosocial Wellbeing - Researching the psychosocial impacts of participatory health, especially, on health
consumers is needed as more interactions occur with an artificial agent vs. human
interaction;
-
Privacy and Security - Research on sharing health information with AI technology and the impacts on patient
privacy and security should be addressed;
-
Technology Advancement - As AI technology is advancing at a rapid pace, research on the types and relevance
of AI technology for participatory health is required.
Fig. 3 Research priority areas for participatory health in the age of AI.
