ICT: Health’s Best Friend and Worst Enemy?

Egon L. van den Broek

Department of Information and Computing Sciences, Utrecht University, PO Box 80.089, 3508 TB Utrecht, The Netherlands

http://www.human-centeredcomputing.com/

Keywords:

ICT, Health Problems, Stress, Black Plague, Affective Computing, Emotion, Personality, Coping.

Abstract:

I propose a paradigm shift for health care, as there is an urgent need for i) continuous (semi-)automatic medical

checkups, ii) cost reduction, and iii) cure for the 21st century black plague (i.e., stress-related diseases) are

very much needed. To realize this ICT’s Paradox has to be solved. On the one hand, ICT can cause i)

musculoskeletal problems, ii) vision problems, iii) headache, iv) obesity, v) stress disorders (e.g., burn out),

vi) metabolic issues, vii) addiction (e.g., to games, social media, and Internet), viii) sleeping problems, ix)

social isolation, and x) an unrealistic world view. On the other hand, ICT claims to provide these problems’

solutions. Consequently, health informatics needs to adopt a holistic approach, improve its fragile theoretical

frameworks, and handle the incredible variance we all show. As a remedy, I propose to take up the challenge

to next-generation models of personality, as they are a crucial determinant in people’s stress coping style.

Your worst enemy

Becomes your best friend,

once he’s underground.

– Euripides –

1 INTRODUCTION

Our health care system is not neither functioning ef-

fectively nor effectively. “Many of the changes in-

tended to improve or reform health care are result-

ing in increased costs, increased work, and little (if

any) improvement in health.” (Bartol, 2016) For ex-

ample, the much discussed electronic health records

were meant to improving care; but, show to be com-

plicated and inefﬁcient. Medical doctors, nurse prac-

titioners as well as patients are forced to check more

boxes, use more technology, and produce more data,

without health care improving or costs declining (Bar-

tol, 2016; Stylianou and Talias, 2017).

Health care does not need to reform or transform,

it needs to be recreated from the bottom-up, it needs

a paradigm shift! “Attempts at reforming and trans-

forming health care have been like repairing or ﬁx-

ing up a 50-year-old car, adding newer equipment

and modern technology to try to make it better. The

problem is that we do not consider if the original ve-

hicle is really what is needed today.” (Bartol, 2016)

“Health care systems around the world are both highly

institutionalized and highly professionalized.” (Ferlie

et al., 2016). Their processes are directed to patients

and their symptoms, using procedures and medica-

tion. Hereby, the focus on on treating the ill, ignoring

the healthy. So, resources are spend on health care’s

high spenders, where we ignore the healthy.

There are, at least, three reasons to support this

call for this paradigm shift:

i) Continuous (semi-)automatic medical check-

ups (Jarvis, 2016) and support for healthy liv-

ing should become part of common health

care;

ii) Extent healthy people’s health could reduce

health care costs signiﬁcantly; and

iii) Stress-related diseases are rapidly becoming

the dominant class of illness.

Next-generation medical check-ups could beneﬁt

from the many types of health care data (Stylianou

and Talias, 2017), including (Fang et al., 2016):

i) human-generated (e.g., notes, email, and paper

documents);

ii) machine-generated monitoring;

iii) ﬁnancial transactions;

iv) biometric data (e.g., genomics, genetics, x-ray,

and electrocardiogram, ECG);

v) social media; and

van den Broek E.

ICT: Healthâ

Zs Best Friend and Worst Enemy?.

DOI: 10.5220/0006345506110616

vi) publications.

A range of apps already exist that conduct limited

next-generation medical check-ups, using such data.

However, often they are not clinically validated. Ex-

amples include e-coaches that support you with sleep-

ing (Beun, 2013), running, and eating to reduce di-

abetics (Georga et al., 2014). However, many of

these apps use no or only basic biometric sensors

(cf. (van den Broek, 2010)). So, there is a world to

win for unobtrusive and wearable technologies, when

shown to result in reliable signal acquisition and, sub-

sequent, analysis.

In October 2016, the American Medical Asso-

ciation (AMA) shared a view point on health care

cost reduction: “Chronic diseases account for 7 of

10 deaths in the United States, and treatment of

these diseases accounts for more than 85% of US

health costs, including those incurring exceptional

treatment costs. About half of health expenditures

in the United States are accounted for by 5% of pa-

tients, yet many chronic conditions are preventable.

. . . Perhaps the most powerful chronic disease pre-

vention approaches are those that can motivate and fa-

cilitate health-promoting choices in daily life.”(Dietz

et al., 2016). I propose to adopt Mandl and Kohane

(2016)’s patient-control, where patients themselves

can be asked to collect data. They selected eight rea-

sons for pursuing patient-controlled data:

i) complete data: A more holistic view of pa-

tients;

ii) data sharing for coordinated care: Patients as

vehicle for data sharing;

iii) as foundation for next-generation medical

check-up apps;

iv) support of diagnostic journals (e.g., speciﬁc

with genetic disorders);

v) data donation for research purposes;

vi) patients as reporters;

vii) an additional pairs of eyes; and

viii) social networking.

I would like to stress the importance of including

not-yet-patients, as also healthy people may become

patients and, consequently, their contributions are at

least as valuable.

Ten years ago, Cary L. Cooper stated: “We’re

talking now I think about the 21st century black

plague. I see stress as the main source of disease or

the trigger for disease in the 21st century developed

world.” (Newby, ABC Catalyst, 2007). In their hand-

book, Lundberg and Cooper (2011) provided a rich

source of evidence for this strong statement. A few

months ago, the European Occupational Safety and

Health Agency (EU-OSHA) suggested a possible so-

lution: “Software exists that allows the emotions of a

computer user to be monitored remotely - this could

even be promoted by the developers as a way of de-

tecting early signs of stress in employees, . . . ”. This

would aid all three reasons as it could be part of a

next-generation medical check-ups, should realize a

signiﬁcant health care cost reduction, and focusses on

stress. The development of such software is consid-

ered to be part of health informatics’ subﬁeld affective

computing. Affective computing can be deﬁned as

“the scientiﬁc understanding and computation of the

mechanisms underlying affect and their embodiment

in machines” (p. 10) (van den Broek, 2011). Despite

its unquestioned potential, affective computing’s in-

herent interdisciplinary complexity limits its progress

in performance, as I already denoted in a series of arti-

cles presented at this conference (van den Broek et al.,

2009; van den Broek et al., 2010a; van den Broek

et al., 2010b; van den Broek et al., 2010c; van den

Broek et al., 2011). More speciﬁcally, affective com-

puting’s complexity primarily lays in:

i) its need for a holistic approach (note. this

is not a new idea at all; cf. (Follmer, 2016;

Schmitz and Wolkenhauer, 2016);

ii) the fragile theoretical frameworks from

medicine (e.g., incl. physiology and neu-

roscience) and psychology it has to rely on

(e.g., (Kalisch et al., 2015; Jarvis, 2016); and

iii) the incredible, continuous variance that char-

acterizes our world (Follmer, 2016; Schmitz

and Wolkenhauer, 2016).

Moreover, ICT solutions such as affective computing

both has their positive as well as its negative sides, as

we will discuss in the next section.

2 ICT: HEALTH’S BEST FRIEND

AND WORST ENEMY?

Even before the age of smartphones and tablets, Joan

Stigliani (1995) identiﬁed six main health problems,

related to computer usage. Since then, more than

two decades elapsed in which ICT usage intensiﬁed,

nowadays using tablets, smartphones, smartwatches,

and hardly desktop PCs anymore. Consequently,

starting with Stigliani (1995)’s original list, I com-

posed a new list of the 10 main ICT-related health

problems:

i) stress disorders (e.g., burn out) (

Aborg and

Billing, 2003);

ii) musculoskeletal problems (

Aborg and Billing,

2003; Gowrisankaran and Sheedy, 2015), in-

cluding Repetitive Stress Injury (RSI)

;

iii) vision problems (Salibello and Nilsen, 1995;

Gowrisankaran and Sheedy, 2015);

iv) headache (Salibello and Nilsen, 1995;

Gowrisankaran and Sheedy, 2015);

v) obesity (de Jong et al., 2013; Schmiege et al.,

2016);

which can be complemented by:

vi) metabolic issues, such as vitamin deﬁcien-

cies (Palacios and Gonzalez, 2014) and diabet-

ics (de Jong et al., 2013);

vii) addiction (e.g., to games, social media, and In-

ternet (Zhou et al., 2017);

viii) sleeping problems (Beun, 2013; Nuutinen

et al., 2014);

ix) social isolation (Cacioppo and Hawkley, 2003;

Liu and Baumeister, 2016); and

x) an unrealistic world view (e.g., resulting in de-

pression) (Donnelly and Kuss, 2016; Wood

et al., 2016).

Note that, compared to Stigliani (1995)’s original list,

this list includes both more indirect ICT-related and

more mental health problems.

If any ICT branch should be health’s best friend,

it is health informatics. In solving its challenges,

health informatics relies on both clinical experience

and knowledge of public health systems and organi-

zations, while conducting experiments, interventions,

and scalable approaches. (Kulikowski et al., 2012).

Par excellence, ICT’s health informatics, should con-

tain the identiﬁed computer-related health problems.

From an ethical point of view, the ICT community

should even consider this as one of its main priorities.

When going through scientiﬁc literature, health

informatics seems to have solved all ICT-related

health problems. For example, musculoskeletal prob-

lems can be prevented using persuasive technol-

ogy (Wang et al., 2014), the problem of obesity

is approached similarly (Wang et al., 2014), as are

headache (Minen et al., 2016), diabetics (Georga

et al., 2014), sleeping problems (Beun, 2013), and so-

cial isolation (Chen and Schulz, 2016). So, it seems

to be a case of “One size ﬁts all” (Suomi, 1996).

However, many solutions are fragile, random control

Note. Stigliani (1995) mentioned RSI as separate entry.

However, essentially it is a musculoskeletal problem and,

hence, placed here under this entry.

trails are absent or conducted at a small scale, and so-

lutions are at a gadget level, instead of at the level

of aimed clinical solutions. Many roads can be fol-

lowed to remedy this practice. The problem lies is

the increasing tendency to just see what the computer

shows. (van den Broek, 2012)

In the next section, I will pose one critical con-

cept for health informatics, complementary to the

prerequisites deﬁned before (van den Broek et al.,

2009; van den Broek et al., 2010a; van den Broek

et al., 2010b; van den Broek et al., 2010c; van den

Broek et al., 2011): personality. Par excellence,

this concept illustrates affective computing’s three-

folded complexity, as depicted in Section 1. More-

over, health informatics is also the claimed road to-

wards next-generation personalized medicine (Poon

et al., 2013). How can this be, if clients personalities

are ignored?

3 PERSONALITY

The urge to completely redesign our health care sys-

tem relies on a both crucial and often ignored deter-

minant: personality. For each of the three reasons for

this call for a paradigm shift, I will explain why per-

sonality is an essential part of the equation:

i) Next-generation medical check-ups are per-

sonalized. However, solely the medical check-

up does not help; in particular, healthy peo-

ple have to be persuaded to start, improve,

or maintain a healthy living style. Tailored

health messages are needed, next-generation

customized (semi-)automatic ICT-based com-

munication (Kreuter et al., 2012).

ii) I proposed to implement Mandl and Kohane

(2016)’s patient-control paradigm and extend

it to people-health control to realize cost re-

duction; that is, ask people themselves to col-

lect their data. This requires sincere cooper-

ation from people, as they are asked to con-

tribute to their own electronic health records.

iii) The 21st century black plague is directly

linked to people’s coping style and, hence, per-

sonality (Zhou et al., 2017). “Coping is of-

ten deﬁned as efforts to prevent or diminish

threat, harm, and loss, or to reduce associ-

ated distress. Some prefer to limit the con-

cept of coping to voluntary responses; others

include automatic and involuntary responses

within the coping construct. . . . Personality

does inﬂuence coping in many ways, however,

some of which occur prior to coping. Even

prior to coping, personality inﬂuences the fre-

quency of exposure to stressors, the type of

stressors experienced, and appraisals” (Carver

and Connor-Smith, 2010).

With a foundation provided for personality’s key role

in the proposed paradigm shift, I will now sketch its

history, challenges as well as a solution approach.

In this article, at a functional level, I will take

a methodological perspective to personality and de-

scribe it in terms of its research traditions (or ap-

proaches) (Pervin, 2003):

i) clinical (e.g., including Freud’s work);

ii) statistical (e.g., the big-ﬁve (BF) model); and

iii) experimental (e.g., including the work of

Wundt, Pavlov, and Skinner).

Clinical approaches to personality allow a holistic ap-

proach, while observing a great variety of phenom-

ena, using self-reports and behavioral observations.

As such they satisfy two of the three dimensions of

complexity (i.e., holistic approach and explain the

huge variance). However, clinical approaches to per-

sonality fail in the third dimension of complexity:

generation of solid theoretical frameworks, as reliable

observations and tests of hypotheses are complicated

if possible at all. Statistical approaches to personality

focus on individual differences, using trait question-

naires instead of self-reports. This practice enables

statistical analysis of the relation between personal-

ity traits and other variables. However, this approach

suffers from studies on non-representative samples

(e.g., students) and a lack of both generalizability and

speciﬁcity. The statistical approach can provide the-

oretical frameworks; but, fails to take a true holistic

approach and simpliﬁes reality and, hence, is unable

to explain the existing real world variance in personal-

ity traits. Experimental approaches to personality rely

on laboratory studies on cause-effect relationships,

as such they are the opposite of clinical approaches.

They violate the holistic approach and cannot explain

the huge variance of the real world. However, they

comply to the third dimension of complexity: gener-

ation of solid theoretical frameworks.

Models and theory used in affective computing

are heavily skewed towards the statistical approaches

(cf. (Vinciarelli and Mohammadi, 2014)). To some

extent this makes sense as the statistical approaches

are most straightforward to model (e.g., using ma-

chine learning or general linear models). Although

understandable from a pragmatic stance, the above

analysis of the three research traditions of personal-

ity makes it hard to justify this position. I propose to

reconsider and improve the clinical approach to per-

sonality, such that computational models can be build

based on it. This requires a well-argued combination

of lab and ﬁeld data and, most likely, a merge of quan-

titative and qualitative data (Fine and Elsbach, 2000;

McCusker and Gunaydin, 2015).

4 CONCLUSION

Health care is vibrant and yet conservative, a highly

complex ﬁeld of science, engineering, and practice.

As is argued in Section 1, its foundations should be

reconsidered. ICT, and in particular, health informat-

ics, can play a crucial role in this process. However,

the stakes are high, including potentially big losses as

well as big gains, as is discussed in Section 2, which

makes such endeavor even more challenging. With

paradigms such as personalized medicine and mobile

health, the client is put central. However, so far the

client’s personality has been ignored (see Section 3). I

pose that the clinical tradition of personality research

should be embraced by health informatics, realizing

that this would require a ﬁrm step back to enable the

so much needed steps forward.

As is illustrated via this article’s relatively

lengthly list of references, an interdisciplinary, holis-

tic approach was taken. Nevertheless, many issues

remained untouched. For example, social and cul-

tural (Kaplan, 2017) and psychosocial issues (Kun,

2016) should be considered on top of the evident

privacy and security concerns (Blobel et al., 2016).

An analysis of the current state-of-the-art of elec-

tronic health records would have been appropriate as

well (Wuyts et al., 2012; Stylianou and Talias, 2017).

Moreover, a discussion on computational techniques

for personality models at a functional level would

have been in place (cf. (Huys et al., 2016; Adams

et al., 2016)). However, this article’s list of references

provides a good starting point.

Health informatics is struggling, already since its

recent conception (Kulikowski et al., 2012; Nelson

and Staggers, 2018). Omnipresent computing power,

big data, rapidly improving sensor technology, and

our extended lives, have put it in the top list of so-

ciety’s health agenda. This is a promising trend; but,

as posed, signiﬁcantly more mass is needed to change

the ﬁeld’s paradigm and see all humans as clients, in-

stead of only those who are already patients.

ACKNOWLEDGMENTS

I thank the two anonymous reviewers, who provided

excellent comments on an early draft of this arti-

cle. Moreover, I thank Winnie Teunissen for carefully

proof reading of the pre-ﬁnal version of this article.

Last, I thank Gert-Jan de Vries for his invitation to

present this article at his special session.

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