1Youssef A, et al. BMJ Open 2020;10:e034970. doi:10.1136/bmjopen-2019-034970

Open access

“Caring About Me”: a pilot framework
to understand patient- centered care
experience in integrated care – a
qualitative study

Alaa Youssef ,1,2 David Wiljer,2,3 Maria Mylopoulos,4 Robert Maunder,2,5
Sanjeev Sockalingam2,6

To cite: Youssef A, Wiljer D,
Mylopoulos M, et al. “Caring
About Me”: a pilot framework
to understand patient-
centered care experience
in integrated care – a
qualitative study. BMJ Open
2020;10:e034970. doi:10.1136/
bmjopen-2019-034970

► Prepublication history and
additional material for this
paper are available online. To
view these files, please visit
the journal online (http:// dx. doi.
org/ 10. 1136/ bmjopen- 2019-
034970).

Received 15 October 2019
Revised 21 April 2020
Accepted 18 June 2020

For numbered affiliations see
end of article.

Correspondence to
Dr Sanjeev Sockalingam;
sanjeev. [email protected] camh. ca

Original research

© Author(s) (or their
employer(s)) 2020. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published by
BMJ.

ABSTRACT
Objective The aim of this study is to examine patients’
experiences in integrated care (IC) settings.
Design Qualitative study using semistructured interviews.
Settings Two IC sites in Toronto, Canada: (1) a
community- based primary healthcare centre, supporting
patients with hepatitis C and comorbid mental health
and substance use issues; and (2) an integrated bariatric
surgery programme, an academic tertiary care centre.
Participants The study included patients (n=12) with
co- occurring mental and physical health conditions. Seven
participants (58%) were female and five (42%) were male.
Methods Twelve indepth semistructured interviews were
conducted with a purposeful sample of patients (n=12)
with comorbid mental and physical conditions at two
IC sites in Toronto between 2017 and 2018. Data were
collected and analysed using grounded theory approach.
Results Four themes emerged in our analysis reflecting
patients’ perspectives on patient- centred care experience
in IC: (1) caring about me; (2) collaborating with me; (3)
helping me understand and self- manage my care; and
(4) personalising care to address my needs. Patients’
experiences of care were primarily shaped by quality of
relational interactions with IC team members. Positive
interactions with IC team members led to enhanced
patient access to care and fostered personalising care
plans to address unique needs.
Conclusion This study adds to the literature on creating
patient- centredness in IC settings by highlighting the
importance of recognising patients’ unique needs and the
context of care for the specific patient population.

INTRODUCTION
Despite the significant attention and quality
improvement efforts that followed the
‘Crossing the Quality Chasm’ report by the
Institute of Medicine, notable gaps in care
delivery persist for patients with complex
care needs.1–4Although individuals with
complex care needs, defined as comorbid
existing physical and mental health condi-
tions, comprise a significant proportion of
health service users, they tend to have worse
health outcomes, poor care experiences and

increased healthcare utilisation.4 5 Delivering
high- quality care that improves individuals’
experiences of care and the health of popu-
lations requires healthcare systems capable of
adapting to a diverse range of patient needs,
emerging multimorbidity and person- specific
factors.4 6 7

Integrated care (IC) is a system- based care
delivery model that evolved to bridge frag-
mentation in care delivery in primary care
settings.8–12 Despite variation in IC imple-
mentation in care settings,13 14 the broader
health system aims9 10—improve population
health outcomes, support cost- effectiveness
and promote patient- centredness—are
similar.1 Notwithstanding the extensive
research supporting the effectiveness of IC
to improve population health outcomes, it
remains unclear how IC promotes patient-
centred care experience from the patient’s
perspective.

While patient- centred care is a hallmark
feature of high- quality care in IC, the construct
is still in its infancy, with limited empirical and
clinical evidence to indicate how this construct
is conceptualised and operationalised in
practice. For example, a robust conceptual
framework that demarcates the principal care
values that define patient- centred care expe-
rience is not well established.15 Moreover,

Strengths and limitations of this study

► This study addresses an important gap in the lit-
erature on patient experience and presents a the-
oretical framework to systematically understand
patients’ experiences in integrated care.

► This study identifies four key care domains integral
to patients perceiving patient- centredness.

► Generalisability of this framework to other care set-
tings and context warrants further investigation giv-
en the small sample size of this study’s population.

2 Youssef A, et al. BMJ Open 2020;10:e034970. doi:10.1136/bmjopen-2019-034970

Open access

the lack of consensus in defining related key concepts,
such as ‘patient- centred care’, ‘patient experience’ and
‘patient satisfaction’, has affected how these concepts are
operationalised and assessed in practice.16–18 As a result,
the absence of this empirical knowledge has limited our
ability to reliably evaluate important care domains from
the patient’s perspective with respect to patient–clinician
communication and relationship construction.19–24

This study sets out to examine patient- centred care
experience from the perspective of patients with coex-
isting health conditions in IC settings. The aim is to eluci-
date essential care elements for a patient- centred care
experience in IC to inform evaluation of patients’ care
experiences in IC.

METHODS
To examine how patients perceive patient- centred care
experience in IC, this qualitative study used a construc-
tivist grounded theory (GT) methodology.25 Construc-
tivist GT is used to gain an indepth understanding of
phenomena while recognising how social contexts, inter-
actions, sharing viewpoints and interpretative analysis of
patient and the researcher influence understanding.26 27
Semistructured interviews were used to examine the care
experiences of patients with comorbid mental and phys-
ical conditions receiving care at two distinct IC sites in
Toronto, Canada between 2017 and 2018 (table 1).

In this study, the two IC settings were identified as sites
that would enable us to conduct cross- case analysis. The
rationale for a cross- case analysis was to examine varia-
tions in patient- centred care experiences given differ-
ences in population care needs, contextual factors and
the level of clinical setting integration. IC settings were

informed by the Center for Integrated Health Solutions
(CIHS) integration framework, where IC is defined as a
continuum of care encompassing a range of care models
that vary in structure primarily based on the degree of
mental and physical health services integration, ranging
from coordinated, co- located (collaborative care), to
fully integrated care models (behavioural health inte-
gration).6 14 To examine the value of physical and
behavioural health integration on patients’ experiences,
the Toronto Community Hepatitis C Program (TCHCP)
at South Riverdale Community Health Centre (SRCHC)
was identified as a community healthcare centre adopting
an integrated behavioural health primary care model as
described on the CIHS continuum of integration frame-
work. The TCHCP supports patients managing hepatitis
C, substance use and housing insecurity.28 29 The other
IC setting was an academic- based medical centre, the
Toronto Western Hospital Bariatric Surgery Program
(TWH- BSP), a collaborative care bariatric surgery
programme supporting patients with severe obesity and
is ranked level 5 as per the CIHS continuum of integra-
tion.30 31 Therefore, collecting participant data from both
of these two IC sites allowed us to explore nuances in
patients’ experiences among diverse patient groups with
distinct care needs.

Participants
Our purposeful sample included patients with coexisting
mental and physical illnesses so as to gain an insight into
the complexity of self- management of chronic health
conditions and the value of physical and behavioural
health integration from the patient’s perspective.32 We
focused on patients with two or more comorbid condi-
tions as a common source of complexity according to the

Table 1 Demographic and clinical characteristics of participants in this study

ID Gender Setting Time in programme Comorbidities*

001 F BSP 3 years Obesity- associated comorbidities, osteoarthritis, personality disorder.

002 M BSP 5 years Obesity- associated comorbidities, depression.

003 F BSP 8 years Obesity- associated comorbidities, MDD.

004 F BSP 8 years Obesity- associated comorbidities, MDD, GAD.

005 F SRCHC 1 year Hepatitis C, GAD, depression, alcohol abuse.

006 M SRCHC 1 year Hepatitis C, osteoporosis, chronic pain, diabetes, GAD, MDD, PTSD,
ADHD, SA.

007 F SRCHC 1 year Hepatitis C, depression, SA.

008 M SRCHC 1 year Hepatitis C, depression, SA.

009 F BSP 5 years Congenital hip dysplasia, MDD, alcohol abuse.

010 F BSP 5 years Obesity- related comorbidities, alcohol abuse, bipolar disorder, BED.

011 M SRCHC 6 months Hepatitis C, depression.

012 M SRCHC 1 year Hepatitis C, HIV, depression.

*Obesity- associated comorbidities (including diabetes, sleep apnoea, hypertension), MDD, GAD, hepatitis C, PTSD, ADHD, addiction,
SA, BED and HIV.
ADHD, attention deficit hyperactivity disorder; BED, binge eating disorder; BSP, Bariatric Surgery Program; F, female; GAD, generalised
anxiety disorder; M, male; MDD, major depressive disorder; PTSD, post- traumatic stress disorder; SA, substance abuse; SRCHC, South
Riverdale Community Health Centre- Hepatitis- C programme.

3Youssef A, et al. BMJ Open 2020;10:e034970. doi:10.1136/bmjopen-2019-034970

Open access

literature on the chronic care model and IC (table 1).11
Patients at both sampling sites were eligible for partici-
pating if they had two or more physical and mental health
comorbidities and have been receiving care at their
respective IC setting for at least 3–6 months. We used
semistructured individual interviews to facilitate candid
disclosure of personal experiences. We conducted a total
of 12 indepth semistructured interviews and had 6 inter-
views per site. Following the GT logic, sample size was not
determined a prior but rather informed by the iterative
process of data collection and analysis. For example, in
this study initial sampling was exploratory and provided
the interviewer (AY) with a point of departure that gradu-
ally developed to concrete categories with iterative coding
and memo writing.33 Sampling continued until theoret-
ical saturation was achieved, defined as the point where
further interviews did not advance the conceptual depth
of the developed categories or reveal new dimensions
of the relationship among categories.27 33 34 Participants
were recruited by phone or email by a study researcher
(AY) and received a compensation of $20 as a token of
appreciation for participating in the study.

Data collection
Primary interview questions were informed by collabora-
tive care core principles (ie, patient- centred care, team-
based care, measurement guided and population- based
care) and focused on patients’ experiences accessing and
interacting with care team members in IC settings.12 35 36
Initial interview questions were open- ended and devel-
oped iteratively with the research team (online supple-
mentary appendix 1). Subsequent revisions of the
interview guide were informed by emerging themes and
sensitising concepts generated through data collection
and analysis. In this study, sensitising concepts referred
to relevant concepts that facilitated exploration of new
ideas and critical analysis of the data.27 We revised the
interview guide questions informed by results from data
analysis as to iteratively challenge, refine and elaborate
on the emerging themes.

Interviews lasted approximately 90 min and were facili-
tated by a trained researcher (AY), a PhD candidate, who
received formal training in qualitative research method-
ology. The length of each interview was determined by
the patient’s level of comfort disclosing their perceptions
and sharing their experiences. We completed a total of 12
interviews resulting in 1080 min of recordings that were
used for data analysis. All participants provided informed
consent for the interviews to be audiotaped and profes-
sionally transcribed.

Patient and public involvement
Patients from the examined settings informed interview
guide development and purposeful participant selection
to explore emerging themes. Members from the IC teams
at both sites verified study findings and finalised the
manuscript. We communicated the research findings to

patients and the public through poster and oral presenta-
tion at relevant events.

Data analysis
We used a constant comparative approach to simul-
taneously collect and analyse data. Analysis of inter-
view transcripts was iterative and inductively driven,
using line- by- line coding, open coding, focused coding
and axial coding, to abstract emerging concepts that
informed framework construction (online supplemen-
tary appendix 2). This analytical approach enabled
exploration of emerging themes, contrast experiences
within and across sites, impose new questions, and refine
developing theory. Through the data collection and anal-
ysis process, the researcher (AY) independently coded
the data from an exploratory lens and generated a code
book. By comparing experiences, views, situations and
contexts from the same and different individuals, the
researcher (AY) started identifying emerging themes
and gradually refined the coding schema. Furthermore,
iterative and biweekly discussions with the research team
(MM and SS) allowed for triangulation of the data from
multiple perspectives. Research team (DW, RM, MM
and SS) discussions inspired questions to help evaluate
emerging hypotheses, develop theoretical categories and
identify constructs that formulated the thematic frame-
work of how patients conceptualised a patient- centred
care experience.

Throughout the study, the researcher (AY) incorpo-
rated memo writing to reflect on individual cases, inter-
view settings, participants’ responses, emerging concepts
and assess preconceived notions (online supplementary
appendix 3). The researcher maintained an audit trail of
the analysed interviews, memo writings and team discus-
sions. The final stages of the analysis used the NVivo soft-
ware to conduct cross- case analysis to identify patterns
and variations in codes across cases. It also served as a tool
to visualise and examine the development of a thematic
framework.

RESULTS
Analysis of patient interviews revealed that patient- centred
care experience in IC settings is dynamic and evolving
(figure 1). Four interconnected themes explained this
dynamic process from the patient’s perspective. In our
analysis, ‘Caring About Me’ emerged as the overarching
theme describing core care values linked to patients’
interactions with the IC teams. The three additional
themes, ‘Collaborating with Me’, ‘Sharing Knowledge
and Developing a Monitoring Self’ and ‘Personalising
Care to Address My Needs’, worked in service of this
central theme. The following sections describe these four
themes in further detail.

Theme 1: ‘Caring About Me’
Patients reflected on their personal interactions with the
care team and perceived the care team to be genuinely

4 Youssef A, et al. BMJ Open 2020;10:e034970. doi:10.1136/bmjopen-2019-034970

Open access

caring about them despite variations in contexts, condi-
tions and demographics. Participants across sites shared
similar experiences where they described being at the
centre of care of their provider/care team. Attributes
linked to the ‘Caring About Me’ theme described the
constructive nature of patient–care team interactions
in IC that helped patients express their care needs,
normalise failure and develop entrusted longitudinal
relationship with their care team members.

A defining component of patient- centred conversa-
tions was helping patients recognise their care needs and
express their preferences. Participants across sites valued
clinicians’ capabilities in recognising patients’ needs and
helping them address their care preferences during both
illness and wellness. Participants highlighted the shift
in care needs at these transitions between illness and
wellness moments. For example, participants identified
lacking the capacity to articulate their needs and prefer-
ences at times of illness. Participants also reported greater

confidence in their care team’s knowledge and ability to
address their care needs when their team framed their
discussions in a way that empowered them to understand
and manage their physical and emotional needs at vulner-
able times.

One participant described feeling vulnerable recov-
ering from bariatric surgery complications. Reflecting
on how her physical weakness affected her capacity to
recognise her care needs, the participant praised her care
team’s determination in helping her overcome feelings of
disappointment and her lack of motivation in completing
the recommended rehabilitation exercise.

Then there were some the physio nurses that were
helping. And then there was another nurse who was
kind of like a, get out of bed, you’re going to get out
of bed, you’re going to sit in this chair, you’re going
to…And I didn’t like it, but I would praise her now to
say thank you. (BSP, case 004)

Figure 1 ‘Caring About Me’: a framework to understand patients’ experiences in integrated care settings.

5Youssef A, et al. BMJ Open 2020;10:e034970. doi:10.1136/bmjopen-2019-034970

Open access

Conversely, at times of wellness, the patient–physician
dialogue focused on patients’ concerns and co- con-
structing care plans. For example, some participants
reported discussing research regarding new treatment
options with their primary care provider (PCP). This
process enabled patients to gain autonomy while sharing
with their care providers the responsibility for their care.

My doctor obviously does her research. She follows
up. She actively listens, and again I have to say she
follows up. If not right away, she’ll follow up via an
alternate appointment or via email. So, if she doesn’t
have an answer for me right away, she gets that answer
for me once she does her research or figures it out.
(BSP, case 002)

Feeling respected and accepted was a defining feature
of patients’ experiences in IC. The care team’s non-
judgemental approach and respect of each patient’s
journey enabled patients to perceive care settings as open
spaces, where they could share their personal values,
preferences and express their needs without feeling
judged. Participants reported that their own negative
perceptions about themselves secondary to their illness
sometimes served as a potential barrier to seeking help.
Through the IC team’s non- judgemental and accepting
approach, patients felt this helped correct their negative
self- perception and increased their trust of their care
providers.

And then the psychiatrist kind of says, okay, so this is
how I want you to kind of look at things, or this is a
perspective that I want you to think about as I journey
for the next week or two. You know, I came in today,
and I said, you know, I failed over the last two weeks,
I stopped taking my medication. And he immediately
said, I wouldn’t use the term failure. You’ve had a set-
back, you know. And he’s like, you know, we all have
setbacks in our journey of recovery, it’s very common.
So, you didn’t fail, you’re not a failure at all. Like,
that’s his response. He’s an amazing clinician, he’s a
great doctor. (BSP, case 010)

Theme 2: collaborating with me
Patients reported a stronger sense of alliance with the
patient–care team within the IC settings. Patient alliance
with the care team was fostered by supporting patient access
to timely care, advocating for patients’ concerns within
the care team (‘being my voice’), connecting patients to
support resources or promoting patient engagement in
a safe and open environment. Patients sought care team
collaborations during periods of setbacks and complica-
tions by mobilising their care team to provide immediate
attention or prompt access to specialty care.

For example, TWH- BSP patients indicated that their
PCP grounded them during periods of distress or when
they lacked information to feel confident in managing
their physical and emotional care needs. In this context,
IC systems facilitated patients’ immediate access to their

PCP, where patients felt supported during setbacks, learnt
about accessible support services and accessed specialty
services:

It feels like, [nurse], you’re not the only one, it’s
okay. We have supports, like, we have systems in place
to support you. Like, she just helped ground me to
know that, you know what, you’re going to be okay.
Like, it was amazing. And then she was just right on it,
she was so professional. Like, within a week I had an
appointment to see the addiction specialist. I think
that’s amazing, like that’s amazing care. (BSP, case
010)

Furthermore, while most patients aspired to gain
autonomy for their care, some patients required an
advocate to convey their care needs and to navigate the
healthcare system to address their needs. IC was identi-
fied as a gateway for patients to find ‘a voice’ that they
could trust to express their needs more confidently to the
care team and to leverage system resources. For example,
a participant recounted lacking the capacity to advocate
for herself and having anxiety with undergoing revisional
surgery at the same hospital where their original bari-
atric surgery was conducted. A distinguishing feature of
IC teams working with patients with complex comorbid
illness was the ability to recognise patients’ unexpressed
needs and become an additional ‘voice’ advocating for
patients and connecting them to necessary care services:

I mean, I wasn’t standing there when she did it, but
from what I understand, I was here, and she walked
out to the hall. She gathered the team together and
she said, this girl is not going back to [hospital X],
we are going to look after her, we need a doctor. And
that’s how I got my help….I think, at that time, I real-
ly just focused on the dietician. She was my connect-
er at that point…I think it was just that she was my
voice. She was a voice that people listened to. (BSP,
case 004)

Theme 3: sharing knowledge and developing a monitoring self
Participants’ experiences in IC settings revealed how
sharing their experience and knowledge with other
patients, such as in support groups, provided a space
for patients to share the ways that physical and mental
illness (obesity, surgery, hepatitis C, depression) influ-
enced their lives. Finding commonalities in their experi-
ences allowed them to question assumptions about their
thinking, feelings and habits, to care for themselves. This
process of sharing knowledge and experiences was facil-
itated by healthcare providers (ie, formally facilitating
support groups), who enabled patients to develop their
coping skills and cultivate the capacity to self- manage
their health and well- being.

In addition, patients’ discussions with care providers
encouraged them to share their challenges, seek knowl-
edge, gain confidence and develop coping skills to
manage their symptoms better and improve their health

6 Youssef A, et al. BMJ Open 2020;10:e034970. doi:10.1136/bmjopen-2019-034970

Open access

outcomes. For example, patients perceived these discus-
sions as an opportunity for them to build rapport with
their care providers and feel connected and supported.

A participant highlighted the importance of this
process of knowledge sharing as a means to strengthen
the patient–care provider alliance:

The dietician you see her most. There, the dieticians,
their level of knowledge across the board was phe-
nomenal, so that’s what I appreciated. And we de-
veloped a rapport. When you develop a rapport with
anybody, it always makes things easier. (BSP, case 009)

Similarly, patients experienced both individual and
group- based care knowledge- sharing interactions as
crucial care elements in helping them understand the
need for care services and feeling more confident in
engaging in preventative and active treatments to improve
their overall health.

So, she’s patient with me, she will explain stuff to me
so that I can do it, like on the weekend I had to do the
bandage on my own, so she showed me how to do it.
It’s an amazing place with amazing people. (SRCHC,
case 008)

Theme 4: personalising care to address patients’ unique
needs
Patients identified their varied and individual care needs
and highlighted how important it was to tailor treatments
to address these unique care needs in order to improve
their health outcomes. For instance, the complexity of
obesity- related diseases in the bariatric surgery patient
population contributes to surgical complications in some
individuals. While managing physical and emotional shifts
during this acute stage is a well- recognised challenge,
patients felt well cared for by physicians, nurses and other
team members, who listened and invested time in under-
standing their whole story to address their unique care
needs during their treatment journey.

And my surgeon, Dr. X has performed four surgeries
on me, so I know her well and I email with her and
she asks for feedback as well, so I think that…And she
cares, Dr. X, she cares, and she sits, and she listens,
and she tries to figure things out, and then when
things aren’t going great, like I’ve had…Actually, I
had one surgery where I was just getting untangled,
basically, and I said, I’m adopted, and I said I found
out that colon cancer runs in my family so when
you’re doing this is there any way you could check
things out? She did, she ran my colon and found a
tumor and it was removed last year, and benign, so
that was great. (BSP, case 003)

Patients mentioned the challenges of self- managing
their chronic conditions, seeking help and adhering to
their treatment plans as a result of psychosocial factors.
Specific psychosocial factors reported by patients
included depression and substance use issues, which

interfered with care seeking and ability to manage their
chronic health issues, specifically obesity and hepatitis C.

I do have depression and I am back on medication
and that kind of…Actually, when we’re speaking of
weight gain, I had three surgeries last year on my
bowels, and I couldn’t run for a long time, and I got
depressed, and I started eating again, and I gained
quite a bit of weight that I’m still trying to take off.
And, yeah, the weight gain and depression, for me,
do go hand- in- hand. (BSP, case 003)

Importantly, participants’ interviews highlighted the
importance of IC clinicians recognising the patient’s
whole situation, particularly during vulnerable times
when individuals might not fully understand or recognise
the impact of illness on various domains of their life. This
process of shared deliberation between the clinician and
the patient in IC was key in addressing the varied needs of
patients and helping patients realise the impact of their
illness on their social, work and functional life.

Yeah, I mean, he’s taking a vested interest in my whole
story. It’s not just about prescribing medication and
booking a follow- up appointment, checking for side-
effects, no. It’s about the whole story, like what’s go-
ing on in your life. Like, for instance, today we were
talking about me going into a treatment program.
You know, I’m not going to get teary, but it really
touched me…He asked me, what about work, what
about your work situation. Because he wants to know,
if you want to do a treatment program, you know, are
you able to take the time off work, are you going to
be supported at work, are you going to be able to af-
ford it. Like, he cares, you know. He’s recognizing my
whole situation, my whole story. Like, that means a lot
to me. (BSP, case 010)

A participant recounted his experience being helped
and receiving care from their PCP in a community- based
IC setting after suffering an acute physical trauma. The
patient had a history of care avoidance due to prior
difficult experiences with care providers in acute care
settings. As a result, he placed his trust in his PCP in the
IC programme to address these complex physical issues.

Yeah. And the car accident was last year. My ear was
dangling from the front here, it was off, and I cannot
hear on that side no more. I had five broken ribs, I
had a dislocated shoulder, I had multiple wounds on
my hands like cuts and stuff that needed injury. Yeah.
So, she stitched me up and then she gave me the stuff
I needed because usually I just do all those things my-
self. (SRCHC, case 008)

Patients reported similar examples where care providers
in IC settings used a holistic approach that was able to
adapt to patients’ unique care needs and overcome
psychosocial barriers to care, such as anxiety, stigma and
difficulty trusting healthcare providers due to past rela-
tional trauma.

7Youssef A, et al. BMJ Open 2020;10:e034970. doi:10.1136/bmjopen-2019-034970

Open access

DISCUSSION AND CONCLUSION
Discussion
The purpose of this study was to bridge the theory–
practice gap on patient- centred care experience in IC
settings. Despite the popularity of ‘patient- centred care’
as a distinguished attribute of high- quality care, limited
empirical and clinical evidence indicates how this
construct is conceptualised and operationalised in prac-
tice. Using a grounded theory approach to develop our
theoretical understanding of the patient’s perspective,
this study proposes the ‘Caring About Me’ framework,
which demarcates key features of patient- centred care
experience in IC settings (figure 1).

The quality of patients’ interactions with the care team
was a defining element of patient- centred care experi-
ences in IC settings, regardless of the complexity of patient
care needs. Based on our data, participants perceived
their care to be patient- centred if they felt supported,
listened to, respected, accepted, and their care needs
and preferences were recognised and reviewed through
collaborative discussions. Moreover, patients’ perceptions
of their IC experience developed incrementally through
their longitudinal interactions with care team members.

Patients’ perception of effective care in IC settings was
strongly influenced by perceived patient–care team inter-
actions, specifically the ability of IC teams to recognise
patients’ care needs and establish entrusting relation-
ships. The cultivation of these entrusted relationships
was distinct to the care sites. For example, patients’
experience within SRCHC involved a strong sense of
feeling accepted, mutual respect and a non- judgemental
approach, which was supported by the creation of a safe,
‘open space’. In contrast, TWH- BSP focused on recog-
nising the complexity and uniqueness of obesity- related
comorbidities and how these factors impacted TWH-
BSP individuals’ quality of life, which fostered trustable
patient–care team relationships within this setting. These
findings underscore that it is important for IC sites to
consider patients’ needs, context and values to enable
patients’ experience of care- centredness in IC.

In addition to patients’ interaction with the IC team,
patients’ complexity and variations in their care needs
influenced how this ‘Caring About Me’ model addressed
patients’ specific needs. For example, at SRCHC
programme, patients’ care needs demanded supporting
chronic disease management (primarily hepatitis C care),
addressing social disparities and promoting behavioural
change through health education. The programme met
these care needs through the creation of an open and
inclusive space that engaged patients in support groups
where they could share their experiences with one
another, gain further awareness and engage in and learn
further self- management skills for hepatitis C. Conversely,
at the TWH- BSP, patient- centredness unfolded through
patient–care team interactions at an individual level
throughout the preparation for and postsurgical
follow- up, which recognised each patient’s individual
journey and the multiple factors influencing obesity care.

In both settings, patient–care team alliance was fostered
by the IC team adapting their treatment approach within
each setting to accommodate the variability in patients’
care needs.

Overall, findings from this study align with the empir-
ical literature on patient- centred care. Previous work by
Kvåle and Bondevik and Marshall et al37 38 identified the
importance of patients feeling respected, connected and
involved in care planning and decision- making in acute
care settings, similar to the ‘Caring About Me’ theme in
this study. Importantly, this study advances our under-
standing of the patient- centredness phenomenon by
providing insights into how patients perceive patient-
centred care in IC. Specifically, this study highlights that
patient- centred care experience is an evolving process
that develops through productive patient–clinician inter-
actions. In the IC context, these productive interactions
flourished as the care team amended their treatment
approach to align with the recognised patient population
care needs and context. Building a strong treatment alli-
ance was vital for patients to have a longitudinal relation-
ship with their PCPs.

Limitations
Notwithstanding the inherent limitations to generalising
conclusions from this study, the purpose of this GT study
was to advance our understanding of patient- centred
care experience in IC settings and not to produce gener-
alisable findings. Future studies should investigate the
universality and applicability of this empirical model to
other care delivery models and populations. Although
our sample size may be perceived as a limitation, we
attempted to minimise selection bias to a specific site
or population by exploring this phenomenon across
multiple sites in parallel and throughout patients’ care
journey within IC. Recognising that researchers’ position
and perspectives inevitably influence access to findings
and knowledge construction, adopting constructivist GT
methodology affords strategies that helped account for
these limitations and assert research rigour.26 27 These
strategies include contrasting participants’ account
within and across cases and situations, enabling triangu-
lating data from multiple perspectives, and establishing
researcher reflexivity through memo writing and ques-
tioning one’s preconceived notions and meta- position
while constructing the emerging theory.

Conclusion
This study generated the ‘Caring About Me’ framework
that describes patient- centred care experience from
the patient’s perspectives. This model identified the
core constructs underpinning the process of patient-
centredness in IC. Our findings indicated that the versa-
tility of the IC team to amend their care processes, to the
context and patient population care needs, was critical to
facilitating patient- centred care experience. This model
needs further testing, validation and development in
different contexts.

8 Youssef A, et al. BMJ Open 2020;10:e034970. doi:10.1136/bmjopen-2019-034970

Open access

The ‘Caring About Me” framework provides a prac-
tical means to understanding how “patient- centred care”
may be practiced in reality. Findings from this study offer
a theoretical foundation to inform the utilisation of
patient- centred quality measures that better capture valu-
able quality of care domains that align with patient expec-
tations. Developing this body of practice- based evidence
is critical to advancing the implementation of evidence-
based research to practice.39–41 Future studies could
advance this model by exploring the external facilitators
and barriers to promoting patient- centredness from the
care- team’s perspective.

Author affiliations
1Institute of Medical Science (IMS), University of Toronto, Toronto, Ontario, Canada
2Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
3Education, Technology & Innovation, UHN Digital, University Health Network,
Toronto, Ontario, Canada
4Wilson Centre, Undergraduate Medical Professions Education and Department of
Paediatrics, University of Toronto, Toronto, Ontario, Canada
5Psychiatry, Sinai Health System, Toronto, Ontario, Canada
6Education, Centre for Addiction and Mental Health, Toronto, Ontario, Canada

Acknowledgements The authors would like to thank all participants and clinical
facilitators at the Toronto Community Hepatitis C Program (TCHCP) at South
Riverdale Community Health Centre (SRCHC) and the Toronto Western Hospital
Bariatric Surgery Program (TWH- BSP) who participated and supported recruitment
for this study.

Contributors AY was responsible for data collection, transcript analysis and
manuscript drafting. AY, DW, MM, RM and SS contributed to study design, iterative
data analysis, manuscript drafting and review.

Funding This work is supported in part by the Medical Psychiatry Alliance, a
collaborative health partnership of the University of Toronto, Centre for Addiction
and Mental Health, Hospital for Sick Children, Trillium Health Partners, Ontario
Ministry of Health and Long- Term Care, and an anonymous donor.

Competing interests None declared.

Patient consent for publication Not required.

Ethics approval This study was approved by the University Health Network (UHN)
Research Ethics Board.

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement All data relevant to the study are included in the
article or uploaded as supplementary information.

Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non- commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the use
is non- commercial. See: http:// creativecommons. org/ licenses/ by- nc/ 4. 0/.

ORCID iD
Alaa Youssef http:// orcid. org/ 0000- 0001- 6505- 8236

REFERENCES
1 Institute of Medicine. Crossing the quality chasm : a new health

system for the 21st century. Washington, D.C: National Academy
Press, 2001.

2 Dentzer S. Still crossing the quality chasm–or suspended over it?
Health Aff 2011;30:554–5.

3 Fernandopulle R, Ferris T, Epstein A, et al. A research agenda for
bridging the ‘quality chasm’. Health Aff 2003;22:178–90.

4 Vogeli C, Shields AE, Lee TA, et al. Multiple chronic conditions:
prevalence, health consequences, and implications for quality, care
management, and costs. J Gen Intern Med 2007;22:391–5.

5 Ludman EJ, Katon W, Russo J, et al. Depression and diabetes
symptom burden. Gen Hosp Psychiatry
2004;26:430–6.

6 Peek CJ, Baird MA, Coleman E. Primary care for patient complexity,
not only disease. Fam Syst Health 2009;27:287–302.

7 Safford MM. The complexity of complex patients. J Gen Intern Med
2015;30:1724–5.

8 Bauer AM, Thielke SM, Katon W, et al. Aligning health information
technologies with effective service delivery models to improve
chronic disease care. Prev Med 2014;66:167–72.

9 Berwick DM, Nolan TW, Whittington J. The triple aim: care, health,
and cost. Health Aff 2008;27:759–69.

10 Sikka R, Morath JM, Leape L. The quadruple aim: care, health, cost
and meaning in work. BMJ Qual Saf 2015;24:608–10.

11 Chwastiak L, Vanderlip E, Katon W. Treating complexity:
collaborative care for multiple chronic conditions. Int Rev Psychiatry
2014;26:638–47.

12 Chwastiak LA, Jackson SL, Russo J, et al. A collaborative care team
to integrate behavioral health care and treatment of poorly- controlled
type 2 diabetes in an urban safety net primary care clinic. Gen Hosp
Psychiatry 2017;44:10–15.

13 Peek CJ. Executive Summary- Lexicon for behavioral health and
primary care integration: concepts and definitions developed by
expert consensus. Rockville, MD: AHRQ, 2013.

14 Heath B WRP, Reynolds K. A review and proposed standard
framework for levels of integrated healthcare. Washington, D.C:
SAMHSA- HRSA Center for Integrated Health Solutions,
2013.

15 Stewart M. Towards a global definition of patient centred care. BMJ
2001;322:444–5.

16 Weaver M, Patrick DL, Markson LE, et al. Issues in the
measurement of satisfaction with treatment. Am J Manag Care
1997;3:579–94.

17 Murray CJ, Kawabata K, Valentine N. People’s experience versus
people’s

18 van Campen C, Sixma H, Friele RD, et al. Quality of care and patient
satisfaction: a review of measuring instruments. Med Care Res Rev
1995;52:109–33.

19 Epstein RM, Street RL. The values and value of patient- centered
care. Ann Fam Med 2011;9:100–3.

20 Roseman D, Osborne- Stafsnes J, Amy CH, et al. Early lessons from
four ‘aligning forces for quality’ communities bolster the case for
patient- centered care. Health Aff 2013;32:232–41.

21 Campbell SM, Roland MO, Buetow SA. Defining quality of care. Soc
Sci Med 2000;51:1611–25.

22 Donabedian A. The quality of care. how can it be assessed? JAMA
1988;260:1743-8.

23 Donabedian A. Evaluating the quality of medical care. Milbank Mem
Fund Q 1966;44:166–206.

24 Sunderji N, Ion A, Ghavam- Rassoul A, et al. Evaluating the
implementation of integrated mental health care: a systematic
review to guide the development of quality measures. Psychiatr Serv
2017;68:891–8.

25 Charmaz K. Constructing grounded theory. London; Thousand Oaks,
Calif: Sage, 2014.

26 Charmaz K. Grounded theory. London: SAGE Publications Ltd,
1995.

27 Charmaz K. Constructionism and the grounded theory method. In:
Handbook of constructionist research, 2008: 397–412.

28 Mason K, Dodd Z, Sockalingam S, et al. Beyond viral response: a
prospective evaluation of a community- based, multi- disciplinary,
peer- driven model of HCV treatment and support. Int J Drug Policy
2015;26:1007–13.

29 Sockalingam S, Blank D, Banga CA, et al. A novel program for
treating patients with trimorbidity: hepatitis C, serious mental
illness, and active substance use. Eur J Gastroenterol Hepatol
2013;25:1377–84.

30 Santiago VA, Warwick K, Ratnakumarasuriyar S, et al. Evaluation
of a patient- care planning intervention to improve appointment
attendance by adults after bariatric surgery. Can J Diabetes
2019;43:59–66.

31 Sockalingam S, Hawa R, Wnuk S, et al. Psychosocial predictors
of quality of life and weight loss two years after bariatric surgery:
results from the Toronto Bari- PSYCH study. Gen Hosp Psychiatry
2017;47:7–13.

32 Palinkas LA, Horwitz SM, Green CA, et al. Purposeful sampling
for qualitative data collection and analysis in mixed method
implementation research. Adm Policy Ment Health
2015;42:533–44.

33 Charmaz K. Constructing grounded theory: a practical guide through
qualitative analysis. 2 edn. London: SAGE, 2014.

9Youssef A, et al. BMJ Open 2020;10:e034970. doi:10.1136/bmjopen-2019-034970

Open access

34 Nelson J. Using conceptual depth criteria: addressing the
challenge of reaching saturation in qualitative research. Qual Res
2017;17:554–70.

35 Katon WJ, Lin EHB, Von Korff M, et al. Collaborative care for
patients with depression and chronic illnesses. N Engl J Med
2010;363:2611–20.

36 Medicine AoP, Association AP. Dissemination of integrated care
within adult primary care settings: the collaborative care model, 2016.

37 Kvåle K, Bondevik M. What is important for patient centred care?
A qualitative study about the perceptions of patients with cancer.
Scand J Caring Sci 2008;22:582–9.

38 Marshall A, Kitson A, Zeitz K. Patients’ views of patient- centred care:
a phenomenological case study in one surgical unit. J Adv Nurs
2012;68:2664–73.

39 Green LW, Glasgow RE. Evaluating the relevance, generalization, and
applicability of research: issues in external validation and translation
methodology. Eval Health Prof 2006;29:126–53.

40 Glasgow RE, Green LW, Klesges LM, et al. External validity: we need
to do more. Ann Behav Med 2006;31:105–8.

41 Cohen DJ, Crabtree BF, Etz RS, et al. Fidelity versus flexibility:
translating evidence- based research into practice. Am J Prev Med
2008;35:S381–9.

Bull World Health Organ 2020;98:245–250 | doi: http://dx.doi.org/10.2471/BLT.19.237198

Policy & practice

245

Introduction
Empathy, compassion and trust are fundamental values of
a patient-centred, relational model of health care. In recent
years, the pursuit of greater efficiency in health care, including
economic efficiency, has often resulted in these values being
side-lined, making it difficult or even impossible for health-care
professionals to incorporate them in practice. Artificial intel-
ligence is increasingly being used in health care and promises
greater efficiency, and effectiveness and a level of personalization
not possible before. Artificial intelligence could help improve di-
agnosis and treatment accuracy, streamline workflow processes,
and speed up the operation of clinics and hospital departments.
The hope is that by improving efficiency, time will be freed for
health-care professionals to focus more fully on the human side
of care, which involves fostering trust relationships and engag-
ing with patients, with empathy and compassion. However, the
transformative force of artificial intelligence has the potential
to disrupt the relationship between health-care professionals
and patients as it is currently understood, and challenge both
the role and nature of empathy, compassion and trust in this
context. In a time of increasing use of artificial intelligence in
health care, it is important to re-evaluate whether and how
these values could be incorporated and exercised, but most
importantly, society needs to re-examine what kind of health
care it ought to promote.

Empathy, compassion and trust
Over the past decades, the rise of patient-centred care has
shifted the culture of clinical medicine away from paternalism,
in which the therapeutic relationship, the relationship between
the health-care professional and the patient, is led by medical
expertise, towards a more active engagement of patients in
shared medical decision-making. This model of engagement
requires the health-care professional to understand the pa-
tient’s perspective and guide the patient in making the right
decision; a decision which reflects the patient’s needs, desires

and ideals, and also promotes health-related values.1 The
central point of the patient-centred model of doctor–patient
relationship is that medical competency should not be reduced
to technical expertise, but must include relational moral com-
petency, particularly empathy, compassion and trust.2

Empathy, compassion and trust are broadly recognized as
fundamental values of good health-care practice.3–5 Empathy
allows health-care professionals to understand and share the
patient’s feelings and perspective.6 Compassion is the desire
to help, instigated by the empathetic engagement with the
patient.7,8 Patients seek out and prefer to engage with health
professionals who are competent, but also have the right inter-
personal and emotional skills. The belief and confidence in the
professional’s competency, understanding and desire to help is
what underpins patient trust.9–13 Research has demonstrated
the benefits of patient trust and empathetic care, including
improved patient satisfaction, increased treatment adherence
and improved health outcomes.14,15

Despite their importance, empathy and compassion in
health care are often side-lined. In recent years, for example,
socioeconomic factors, including an ageing population and
austerity policies in Europe that followed the 2008 economic
collapse, have led to the marginalization of these values.16 As
health-care systems struggle with resourcing, the space for
empathy and compassion has shrunk while the need for ef-
ficiency has grown.17 In the United Kingdom of Great Britain
and Northern Ireland, high-profile cases and reports, such as the
Francis report, which followed the Mid Staffordshire scandal,18
the report by the Health Service Ombudsman entitled Dying
without dignity,19 and the Leadership Alliance for the Care of
Dying People report,20 all pointed at the lack of empathy as a
major problem in clinical care. What these cases also showed
was a conflicting relationship between the need for empathy
and the pursuit of greater economic efficiency and of meeting
operational targets. In 2017, Sir Robert Francis, who chaired
the inquiry into the Mid Staffordshire scandal, mentioned in an
interview that “at the time at Mid Staffordshire there was huge
pressure on organizations to balance their books, to make pro-

Abstract Empathy, compassion and trust are fundamental values of a patient-centred, relational model of health care. In recent years, the
quest for greater efficiency in health care, including economic efficiency, has often resulted in the side-lining of these values, making it
difficult for health-care professionals to incorporate them in practice. Artificial intelligence is increasingly being used in health care. This
technology promises greater efficiency and more free time for health-care professionals to focus on the human side of care, including fostering
trust relationships and engaging with patients with empathy and compassion. This article considers the vision of efficient, empathetic
and trustworthy health care put forward by the proponents of artificial intelligence. The paper suggests that artificial intelligence has the
potential to fundamentally alter the way in which empathy, compassion and trust are currently regarded and practised in health care. Moving
forward, it is important to re-evaluate whether and how these values could be incorporated and practised within a health-care system
where artificial intelligence is increasingly used. Most importantly, society needs to re-examine what kind of health care it ought to promote.

a The Ethox Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, England.
Correspondence to Angeliki Kerasidou (email: [email protected]).
(Submitted: 11 June 2019 – Revised version received: 16 December 2019 – Accepted: 17 December 2019 – Published online: 27 January 2020 )

Artificial intelligence and the ongoing need for empathy, compassion
and trust in healthcare
Angeliki Kerasidoua

246 Bull World Health Organ 2020;98:245–250| doi: http://dx.doi.org/10.2471/BLT.19.237198

Policy & practice
Empathy and artificial intelligence in health care Angeliki Kerasidou

ductivity improvements and matters of
that nature. It all became about figures in
the books, rather than outcomes for the
patient. And I do believe there’s a danger
of that happening again.”21 Research in
2017 in accident and emergency depart-
ments in England on the effect of auster-
ity policies on the everyday experiences
of health-care professionals found that
the pressure to meet targets negatively af-
fected the doctors’ and nurses’ ability and
opportunity to practise empathetic and
holistic care,22 which led to moral distress
and burnout among these professionals.23

Against this backdrop, artificial
intelligence has been heralded as a way
to save struggling national health-care
systems24 and transform the future of
health care by providing greater effi-
ciency, effectiveness and high levels of
personalized care.25

Artificial intelligence in
health care

Artificial intelligence is broadly de-
fined as “computing technologies that
resemble processes associated with
human intelligence, such as reasoning,
learning and adaptation, sensory under-
standing, and interaction.”26 The hope is
that these technologies will transform
health-care delivery by“ streamlining
workflow processes […] improving the
accuracy of diagnosis and personal-
izing treatment, as well as helping staff
work more efficiently and effectively.”25
Artificial intelligence could help health-
care systems achieve greater efficiency,
including economic efficiency, in two
ways: (i) by improving time to and ac-
curacy of diagnosis and treatment for
patients, and where possible assisting
with early prevention; and, (ii) by using
health-care staff more efficiently.

A report published in 2018 in the
United Kingdom suggested that the
national health system could save up to
10% of its running costs by outsourc-
ing repetitive and administrative tasks
to artificial intelligence technologies.24
The same report also envisaged bedside
robots performing social-care tasks
such helping patients to eat, wash and
dress, thus reducing the workload on
care staff by 30%. But it is not only
nursing and administrative tasks that
artificial intelligence can help with.
With regard to effectiveness, artificial
intelligence systems could be used to
deliver better clinical services both by

assisting with the diagnosis and manage-
ment of patients, and by providing the
diagnosis and prescribing treatments.
Research conducted so far has shown
that machines can perform as well as,
or even better than, humans in detect-
ing skin cancer,27 heart arrhythmia28
and Alzheimer disease.29 Furthermore,
hu man – machi ne p ar t nerships c an
provide far better results than either
humans or machines alone.30 In these
examples, the principal benefits of ar-
tificial intelligence stem from its ability
to improve efficiency and effectiveness
by guiding diagnoses, delivering more
accurate results and thus eliminating
human error. With regard to greater
efficiency through prevention, artificial
intelligence technologies that track and
analyse the movement of individuals
could be used to detect people at risk of
stroke and eliminate that risk through
early intervention.31

Health care is already using tech-
nology to improve its efficiency and
effectiveness. From scalpels and syringes
to stethoscopes and X-ray machines, the
list of technologies used in medicine
to facilitate and improve patient care
is long. However, artificial intelligence
differs from previous medical techno-
logical advances. Whereas previous
technologies were used to increase the
senses and physical capacities of health-
care professionals, consider, for example,
how the stethoscope enhanced the hear-
ing of doctors and X-rays their vision,
the main role of artificial intelligence is
to increase their reasoning and decision-
making capacities. In this way, artificial
intelligence is entering the health-care
arena as another morally relevant actor
that assists, guides or makes indepen-
dent decisions regarding the treatment
and management of patients.

Proponents of artificial intelligence
technolog y in health care maintain
that outsourcing tasks and decisions to
rational machines will free up time for
health-care professionals to engage in
empathetic care and foster trust rela-
tionships with patients.4,25,32,33 A review,
outlining recommendations for National
Health Service to be the world leader
in using technology to benefit patients,
notes that while artificial intelligence
cannot deliver indispensable human
skills, such as compassion and empathy,
“the gift of time delivered by the intro-
duction of these technologies […] will
bring a new emphasis on the nurturing
of the precious inter-human bond, based

on trust, clinical presence, empathy and
communication.”25

The hope is that more free time
for health-care professionals would
not only lead to more trustworthy and
empathetic care for patients, but also
to less stress for and burnout of doc-
tors and nurses.34 In addition, despite
concerns that artificial intelligence will
lead to job losses in health care, a report
by the British Academy on the impact of
artificial intelligence on work pointed
out that professions that require the
application of expertise and interaction
with people will be less affected by auto-
mation through artificial intelligence.35
According to these aforementioned
publications, the introduction of artifi-
cial intelligence technologies in health
care offers the possibility of a win–win
situation: patients benefit from more
accurate diagnosis, better treatment
outcomes, and increased empathy and
compassion from medical staff, who in
turn experience greater job satisfaction
and less burnout.

The reimagination of health care,
where artificial intelligence takes over
specific, and even specialist, tasks while
freeing time for health-care profession-
als to communicate and empathize with
patients, assumes that the value attached
to empathy, compassion and trust will
remain high. However, patients and the
health-care system might value accuracy
and efficiency more than empathy and
judgement, which could shift the focus
in medicine away from human-specific
skills.36 In which direction health-care
delivery will evolve is an important
theoretical and practical question that
requires examination. Currently, it is
still unclear whether and how health-
care practice will be transformed by
artificial intelligence, and what effect
it may have, particularly on the role of
health-care professionals and on the
therapeutic relationship.

Potential implications of
artificial intelligence

Clinical competency is a fundamental
aspect of the identity of health-care
professionals and underpins the trust
relationship between doctors and pa-
tients. Patient trust is based on the belief
that doctors and nurses have the right
skills and expertise required to help the
patient and also the right motivation to
do so. This combination of clinical skill

247Bull World Health Organ 2020;98:245–250| doi: http://dx.doi.org/10.2471/BLT.19.237198

Policy & practice
Empathy and artificial intelligence in health careAngeliki Kerasidou

with empathy and compassion is what
justifies patients assuming a position
of vulnerability towards the health-
care professionals. Vulnerability is a
fundamental characteristic of a trust
relationship.37 The person placing trust
in another knows and accepts that this
trusted person can decisively influence
the outcome of the entrusted action.
Trust relationships involve a degree of
uncertainty that cannot be mitigated; it
is only the belief in the trusted person’s
abilities and good will that justifies tak-
ing on the risk of this uncertainty. In
the clinical context, the patient knows
that things can go wrong, but believes
and hopes that this wrong would not be
intentional, but rather because of bad
luck or unforeseeable circumstances.
Rules and regulations are put in place
to protect patients from negligence
and preventable mistakes. The constant
quest to improve care highlights the
fundamental moral obligations of non-
maleficence and of acting in the best
interests of patients. However, the fact
remains that, in some cases, preventable
harm could be the outcome of a medi-
cal action.

The use of artificial intelligence
to optimize accuracy of diagnosis and
treatment could raise issues of account-
ability when things go wrong, not only
in cases where doctors follow the recom-
mendations of artificial intelligence, but
also when they decide to override these
recommendations.38 In such situations,
it is unclear who should be held account-
able, whether responsibility should lie
with the algorithm developer, the data
provider, the health system that adopted
the artificial intelligence tool, or the
health-care professional who used it. In
addition, even in situations where the
role of artificial intelligence is assistive,
health-care professionals might not
feel confident to override its recom-
mendation. If machines are brought
into health care because they are better
than humans at making certain rational
decisions, how could humans rationally
argue against them? Yet, the question
of accountability is not the only issue
raised here. The role and nature of trust
in the therapeutic relationship is also at
stake. Would and should patients still
trust health-care professionals? If the
introduction of artificial intelligence
tools results in outsourcing clinical and
technical skills to machines, would a
belief in the good will of the doctor be
enough to sustain a therapeutic trust

relationship as currently understood?
One of the great promises of artificial
intelligence is that by increasing effec-
tiveness, accuracy and levels of person-
alization in clinical care, it will succeed
in replacing trust with certainty.39 In this
case, patients might stop considering
health-care professionals as experts in
whose skills and knowledge they need
to trust. This change might lead to a
different relationship between health-
care professionals and patients, one not
characterized by vulnerability, but one
of an assistive partnership.2 However,
even in this more positive scenario, the
transformation of society’s expecta-
tions of care provision and the role of
health-care professionals are unclear. It
is important therefore to consider how
the introduction of artificial intelligence
will alter the public’s perception and
understanding of trust in the clinical
encounter as well as the way in which
trust relationships will be formed in
this context.

Similarly, artificial intelligence
calls into question the role and value
of empathy and compassion in health
care. As mentioned earlier, in patient-
centred care, empathy allows health-care
professionals to understand the patients’
perspective, and thus helps health pro-
fessionals tailor care to promote the
patients’ values and address their indi-
vidual needs. Empathy and compassion
therefore play a very important role in an
interpersonal model of care that rejects
medical paternalism and brings the doc-
tor and the patient together to discuss
options and find appropriate solutions.40
To preserve this ideal of patient-centred
care, ar tificial intelligence systems
should be built in a way that allows for
value-plurality, meaning the possibility
that different patients might hold differ-
ent values and have different priorities
related to their care.41 In this way, the
ethical ideal of shared decision-making
can be maintained and not be replaced
by another form of paternalism, one
practised not by doctors, but by artificial
intelligence algorithms.

Even if artificial intelligence tools
are able to operate in a care context
characterized by value-plurality, the
role of empathy remains unclear. If what
patient-centred care needs to survive in
a future of artificial intelligence health
care is machines programmed to incor-
porate more than one value, what does
this mean about the nature and role of
empathy in care provision? Is empathy

still a professional value, or should it be
now understood as another technology
to be written into code and optimized?
Indeed, research in the field of artificial
intelligence suggests that it is possible to
create empathetic machines42,43 as a way
of relieving doctors and nurses from the
substantial emotional work their profes-
sions require.44 The likely effects of such
complete optimization and operational-
ization of health care are unclear. This
optimization could improve health-care
outcomes and personalized care; alterna-
tively, it could lead to the reinstitution of
a reductionist approach to medicine.45,46
Beyond these practical concerns, one
should also consider whether something
intangible, yet morally important will
be lost if the therapeutic relationship is
reduced to a set of functions performed
by a machine, however intelligent. On the
other hand, will our current understand-
ing of empathy, compassion and trust
change to fit the new context where some
parts of care are provided by intelligent
machines?

Conclusion
The potential impact of artificial intel-
ligence on health care, in general, and
on the therapeutic relationship between
health-care providers and patients, in
particular, is widely acknowledged,38,47,48
as is the fact that society needs to learn
how to deal “with new forms of agents,
patients and environments.”49 Artifi-
cial intelligence has great potential to
improve efficiency and effectiveness in
health care. However, whether artificial
intelligence can support other values
central to the delivery of a patient-cen-
tred care, such as empathy, compassion
and trust, requires careful examination.
Moving forward, and as artificial intel-
ligence is increasingly entering health
care, it is important to consider whether
these values should be incorporated
and promoted within the new type of
health care that is emerging and, if yes,
how. More importantly, it is crucial
to reflect on what kind of health care
society should promote and how new
technologies, including artificial intel-
ligence, could help achieve it. ■

Acknowledgements
AK is also affiliated with the Wellcome
Centre for Ethics and Humanities, Uni-
versity of Oxford.

Competing interests: None declared.

248 Bull World Health Organ 2020;98:245–250| doi: http://dx.doi.org/10.2471/BLT.19.237198

Policy & practice
Empathy and artificial intelligence in health care Angeliki Kerasidou

摘要
医疗保健领域内的人工智能和对理解、同情和信任的持续需求
理解、同情和信任是以患者为中心的、关系型医疗保
健模式的基本价值。近年来，为了提高医疗保健的效
率，包括经济效率，往往导致对这些价值观的背离，
使医疗保健专业人员难以将其纳入实践。人工智能正
越来越多地被应用于医疗保健。这项技术为医疗保健
专业人员提供了更高的效率和更多的空闲时间，使他
们能够专注于人性化的护理，包括培养信任关系，理
解并同情患者。本文探讨了人工智能倡导者提出的高

效、理解、可信赖的医疗保健理念。本文表明，人工
智能有可能从根本上改变目前人们在医疗保健中看待
和实践理解、同情和信任的方式。展望未来，重新评
估这些价值观是否以及如何能够在越来越多地使用人
工智能的医疗保健系统中纳入和实施是一件十分重要
的事。最重要的是，社会需要重新审视什么类型的医
疗保健值得推广。

Résumé

L’intelligence artificielle et le besoin constant d’empathie, de compassion et de confiance dans le secteur de la santé
L’empathie, la compassion et la confiance sont des valeurs fondamentales
d’un modèle de soins de santé centré sur les relations avec le patient.
Mais ces dernières années, la quête d’efficacité dans le secteur, y compris
au niveau économique, a souvent relégué ces valeurs au second plan
et les professionnels de la santé ont donc eu du mal à les intégrer à leur
pratique. De son côté, l’intelligence artificielle gagne en importance.
Cette technologie devrait accroître l’efficacité tout en libérant du temps
pour les professionnels de la santé, qui pourront ainsi se concentrer sur
l’aspect humain des soins, notamment en établissant une relation de
confiance et en faisant preuve d’empathie et de compassion envers
les patients. Le présent article s’intéresse à l’idée d’un système de

soins de santé efficace, qui repose sur l’empathie et la confiance, et
à laquelle adhèrent les adeptes de l’intelligence artificielle. Il suggère
que l’intelligence artificielle a le potentiel nécessaire pour transformer
radicalement la manière dont l’empathie, la compassion et la confiance
sont considérées et appliquées aujourd’hui dans le secteur de la santé.
À l’avenir, il est essentiel de réexaminer l’importance de ces valeurs et
la façon dont elles pourraient être incorporées et mises en œuvre dans
un système de santé où l’intelligence artificielle devient peu à peu
incontournable. Et surtout, la société a besoin de se demander quel
modèle de soins de santé elle souhaite promouvoir.

Резюме

Искусственный интеллект и постоянная потребность в эмпатии, сочувствии и доверии в сфере
здравоохранения
Эмпатия, сочувствие и доверие — это основополагающие
ценности ориентированной на пациента реляционной модели
здравоохранения. В последнее время стремление повысить
эффективность систем здравоохранения, в том числе их
рентабельность, приводит к тому, что этим ценностям часто не
уделяется должного внимания, что в свою очередь значительно
осложняет их использование на практике работниками сферы
здравоохранения. Применение искусственного интеллекта
в сфере здравоохранения неуклонно растет. Эта технология
привлекательна перспективой повышенной эффективности и тем,
что она оставляет медицинским работникам больше свободного
времени для непосредственной работы с пациентами, в

том числе для налаживания доверительных отношений и
применения эмпатии и сочувствия в профессиональном
общении с пациентами. В этой с татье рассматривается
представление об эффективной системе здравоохранения,
построенной на основе эмпатии и доверия, которое предлагается
специалистами, продвигающими внедрение технологий ИИ в
сфере здравоохранения. В статье выдвигается предположение
о том, что искусственный интеллект потенциально способен
коренным образом изменить сегодняшнее представление
о применении эмпатии, сочувс твия и доверия в сфере
здравоохранения и внедрении соответствующих практик. В
дальнейшем важно заново оценить возможность включения этих

ملخص
الذكاء االصطناعي واحلاجة املستمرة للتعاطف والشفقة والثقة يف الرعاية الصحية

الرعاية لنموذج األساسية القيم هي والثقة والشفقة التعاطف
السعي أدى األخرية، السنوات يف املريض. عىل املرتكزة الصحية
لتحقيق املزيد من الفعالية يف الرعاية الصحية، بام يف ذلك الفعالية
االقتصادية، يف الغالب إىل تباعد هذه القيم، مما جعل من الصعب
الذكاء ُيستخدم املامرسة. يف دجمها الصحية الرعاية أخصائيي عىل
هذه وتقدم الصحية. الرعاية يف متزايد بشكل االصطناعي
ألخصائيي احلر والوقت الفعالية من بمزيد وعودًا التكنولوجيا
يف بام الرعاية، من اإلنساين اجلانب عىل للرتكيز الصحية الرعاية
ذلك تعزيز عالقات الثقة واالندماج مع املرىض من خالل التعاطف

والشفقة. يناقش هذا املقال رؤية تتميز بالفعالية والتعاطف لرعاية
تشري االصطناعي. الذكاء مؤيدي يطرحها بالثقة، جديرة صحية
التي الطريقة تغيري إمكانية لديه االصطناعي الذكاء أن إىل الورقة
منها، كل ممارسة وكيفية والثقة، والشفقة التعاطف إىل هبا ينظر
بشكل جذري يف جمال الرعاية الصحية. ومع امليض قدما، من اهلام
إعادة تقييم ما إذا كان يمكن دمج وممارسة هذه القيم، داخل نظام
الرعاية الصحية، حيث يستخدم الذكاء االصطناعي بشكل متزايد،
وكيفية القيام بذلك. واألهم من ذلك، حيتاج املجتمع إىل التحقق

من نوع الرعاية الصحية الذي يمكن هلذه القيم أن ترتقي به.

249Bull World Health Organ 2020;98:245–250| doi: http://dx.doi.org/10.2471/BLT.19.237198

Policy & practice
Empathy and artificial intelligence in health careAngeliki Kerasidou

ценностей в систему здравоохранения, все чаще использующую
технологию искусственного интеллекта, и их применения на
практике. Что наиболее важно, общество нуждается в пересмотре

того, развитие какого типа системы здравоохранения следует
поощрять.

Resumen

La inteligencia artificial y la continua necesidad de empatía, compasión y confianza en la atención sanitaria
La empatía, la compasión y la confianza son valores fundamentales de
un modelo relacional de atención sanitaria centrado en el paciente. En
los últimos años, la búsqueda de una mayor eficiencia en la atención
sanitaria, incluida la eficiencia económica, ha dado lugar con frecuencia
a que estos valores se vean relegados a un segundo plano, lo que
dificulta que los profesionales sanitarios los incorporen en la práctica.
La inteligencia artificial se utiliza cada vez más en la atención sanitaria.
Esta tecnología promete una mayor eficiencia y más tiempo libre para
que los profesionales sanitarios se centren en el lado humano de la
atención, lo que incluye el fomento de las relaciones de confianza
y el trato a los pacientes con empatía y compasión. En este artículo

se examina la visión de una atención sanitaria eficiente, empática y
confiable que proponen los defensores de la inteligencia artificial. El
artículo sugiere que la inteligencia artificial tiene el potencial de alterar
fundamentalmente la forma en que la empatía, la compasión y la
confianza se consideran y practican actualmente en la atención sanitaria.
Para avanzar, es importante volver a evaluar si dichos valores se podrían
incorporar y practicar en un sistema de atención sanitaria en el que se
utiliza cada vez más la inteligencia artificial, y de qué manera. Lo más
importante es que la sociedad debe reconsiderar qué tipo de atención
sanitaria debe promover.

References
1. Emanuel EJ, Emanuel LL. Four models of the physician–patient relationship.

JAMA. 1992 Apr 22–29;267(16):2221–6. doi: http://dx.doi.org/10.1001/
jama.1992.03480160079038 PMID: 1556799

2. Bauchat JR, Seropian M, Jeffries PR. Communication and empathy in
the patient-centered care model: why simulation-based training is not
optional. Clin Simul Nurs. 2016;12(8):356–9. doi: http://dx.doi.org/10.1016/j.
ecns.2016.04.003

3. Serrant L. Compassion NHS: evidencing the impact. London: NHS
England; 2016. Available from: https://www.england.nhs.uk/wp-content/
uploads/2016/05/cip-yr-3.pdf [cited 2019 Apr 17].

4. Tweedie J, Hordern J, Dacre J. Advancing medical professionalism. London:
Royal College of Physicians; 2018.

5. Spiro H. Commentary: the practice of empathy. Acad Med. 2009
Sep;84(9):1177–9. doi: http://dx.doi.org/10.1097/ACM.0b013e3181b18934
PMID: 19707052

6. Halpern J. From detached concern to empathy: humanizing medical
practice. New York: Oxford University Press; 2001. doi: http://dx.doi.
org/10.1093/acprof:osobl/9780195111194.001.0001

7. Nussbaum M. Compassion: the basic social emotion. Soc Philos Policy.
1996;13(01):27–58. doi: http://dx.doi.org/10.1017/S0265052500001515

8. Goetz JL, Keltner D, Simon-Thomas E. Compassion: an evolutionary analysis
and empirical review. Psychol Bull. 2010 May;136(3):351–74. doi: http://
dx.doi.org/10.1037/a0018807 PMID: 20438142

9. Charon R. The patient–physician relationship. Narrative medicine: a
model for empathy, reflection, profession, and trust. JAMA. 2001 Oct
17;286(15):1897–902. doi: http://dx.doi.org/10.1001/jama.286.15.1897
PMID: 11597295

10. Chin JJ. Doctor–patient relationship: a covenant of trust. Singapore Med J.
2001 Dec;42(12):579–81. PMID: 11989581

11. O’Neill O. Autonomy and trust in bioethics. Cambridge: Cambridge
University Press; 2002. doi: http://dx.doi.org/10.1017/CBO9780511606250

12. Mechanic D. Changing medical organization and the erosion of trust.
Milbank Q. 1996;74(2):171–89. doi: http://dx.doi.org/10.2307/3350245
PMID: 8632733

13. Halpern J. What is clinical empathy? J Gen Intern Med. 2003 Aug;18(8):670–
4. doi: http://dx.doi.org/10.1046/j.1525-1497.2003.21017.x PMID: 12911651

14. Kelley JM, Kraft-Todd G, Schapira L, Kossowsky J, Riess H. The influence of
the patient–clinician relationship on healthcare outcomes: a systematic
review and meta-analysis of randomized controlled trials. PLoS One. 2014
Apr 9;9(4):e94207. doi: http://dx.doi.org/10.1371/journal.pone.0094207
PMID: 24718585

15. Joffe S, Manocchia M, Weeks JC, Cleary PD. What do patients value in their
hospital care? An empirical perspective on autonomy centred bioethics.
J Med Ethics. 2003 Apr;29(2):103–8. doi: http://dx.doi.org/10.1136/
jme.29.2.103 PMID: 12672891

16. Kerasidou A, Kingori P, Legido-Quigley H. “You have to keep fighting”:
maintaining healthcare services and professionalism on the frontline of
austerity in Greece. Int J Equity Health. 2016 Jul 26;15(1):118. doi: http://
dx.doi.org/10.1186/s12939-016-0407-8 PMID: 27457098

17. Kerasidou A, Horn R. Making space for empathy: supporting doctors in the
emotional labour of clinical care. BMC Med Ethics. 2016 Jan 27;17(1):8. doi:
http://dx.doi.org/10.1186/s12910-016-0091-7 PMID: 26818248

18. Francis R. Report of the Mid Staffordshire NHS Foundation Trust Public
Inquiry. London: The Stationary Office; 2013.

19. Dying without dignity: investigations by the Parliamentary and Health
Service Ombudsman into complaints about end of life care. London:
Parliamentary and Health Service Ombudsman; 2015.

20. One chance to get it right. London: Leadership Alliance for the Care of
Dying People; 2014. Available from: https://wales.pallcare.info/files/
One_chance_to_get_it_right.pdf [cited 2019 Apr 27].

21. Matthews-King A. Government says NHS hospitals can wring out another
£300m in efficiency savings. Independent. 2017 Nov 8.

22. Kerasidou A. Empathy and efficiency in healthcare at times of austerity.
Health Care Anal. 2019 Sep;27(3):171–84. doi: http://dx.doi.org/10.1007/
s10728-019-00373-x PMID: 31152291

23. Kerasidou A, Kingori P. Austerity measures and the transforming role of
A&E professionals in a weakening welfare system. PLoS One. 2019 Feb
13;14(2):e0212314. doi: http://dx.doi.org/10.1371/journal.pone.0212314
PMID: 30759147

24. Darzi L. The Lord Darzi review of health and care. London: Institute for Public
Policy Research, 2018.

25. Topol E. The Topol review: preparing the healthcare workforce to deliver the
digital future. London: National Health Service; 2019.

26. Artificial intelligence (AI) in healthcare and research. London: Nuffield
Council on Bioethics; 2018.

27. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al.
Dermatologist-level classification of skin cancer with deep neural networks.
Nature. 2017 Feb 2;542(7639):115–8. doi: http://dx.doi.org/10.1038/
nature21056 PMID: 28117445

28. Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, et
al. Cardiologist-level arrhythmia detection and classification in ambulatory
electrocardiograms using a deep neural network. Nat Med. 2019
Jan;25(1):65–9. doi: http://dx.doi.org/10.1038/s41591-018-0268-3 PMID:
30617320

29. Fraser KC, Meltzer JA, Rudzicz F. Linguistic features identify Alzheimer’s
disease in narrative speech. J Alzheimers Dis. 2015;49(2):407–22. doi: http://
dx.doi.org/10.3233/JAD-150520 PMID: 26484921

30. Patel BN, Rosenberg L, Willcox G, Baltaxe D, Lyons M, Irvin J, et al. Human–
machine partnership with artificial intelligence for chest radiograph
diagnosis. NPJ Digit Med. 2019 Nov 18;2(1):111. doi: http://dx.doi.
org/10.1038/s41746-019-0189-7 PMID: 31754637

250 Bull World Health Organ 2020;98:245–250| doi: http://dx.doi.org/10.2471/BLT.19.237198

Policy & practice
Empathy and artificial intelligence in health care Angeliki Kerasidou

31. Villar JR, González S, Sedano J, Chira C, Trejo-Gabriel-Galan JM. Improving
human activity recognition and its application in early stroke diagnosis.
Int J Neural Syst. 2015 Jun;25(4):1450036. doi: http://dx.doi.org/10.1142/
S0129065714500361 PMID: 25684369

32. Davenport TH, Glover W, Artificial J. Intelligence and the augmentation of
health care decision-making. Massachusetts: NEJM Catalyst; 2018.

33. Topol E. Deep medicine: how artificial intelligence can make healthcare
human again. New York: Basic Books; 2019.

34. Nundy S, Hodgkins ML. The application of AI to augment physicians and
reduce burnout [internet]. Health Affairs Blog. 2018 Sep 18. Available from:
https://www.healthaffairs.org/do/10.1377/hblog20180914.711688/full/
[cited 2019 Apr 27].

35. The impact of artificial intelligence on work: an evidence synthesis on
implications for individuals, communities, and societies. London: British
Academy and Royal Society; 2018.

36. Susskind R, Susskind D. The future of the professions: how technology will
transform the work of human experts. Oxford: Oxford University Press; 2015.

37. Wright S. Trust and trustworthiness. Philosophia. 2010;38(3):615–27. doi:
http://dx.doi.org/10.1007/s11406-009-9218-0

38. Ross J, Webb C, Rahman F. Artificial intelligence in healthcare. London:
Academy of Medical Royal Colleges; 2019.

39. Zuboff S. The age of surveillance capitalism: the fight for a human future at
the new frontier of power. London: Profile Books; 2019.

40. Ahmad N, Ellins J, Krelle H, Lawrie M. Person-centred care: from ideas to
action. London: The Health Foundation; 2014.

41. McDougall RJ. Computer knows best? The need for value-flexibility
in medical AI. J Med Ethics. 2019 03;45(3):156–60. doi: http://dx.doi.
org/10.1136/medethics-2018-105118 PMID: 30467198

42. el Kaliouby R. We need computers with empathy. Cambridge: MIT
Technology Review; 2017.

43. Johnson K. Google empathy lab founder: AI will upend storytelling and
human–machine interaction: Venturebeat. 2018 Mar 11. Available from:
https://venturebeat.com/2018/03/11/google-empathy-lab-founder-ai-will-
upend-storytelling-and-human-machine-interaction/ [cited 2019 Jan 20].

44. Srivastava K, Das RC. Empathy: process of adaptation and change, is it
trainable? Ind Psychiatry J. 2016 Jan-Jun;25(1):1–3. doi: http://dx.doi.
org/10.4103/0972-6748.196055 PMID: 28163400

45. Goldhahn J, Rampton V, Spinas GA. Could artificial intelligence make
doctors obsolete? BMJ. 2018 Nov 7;363:k4563. doi: http://dx.doi.
org/10.1136/bmj.k4563 PMID: 30404897

46. Beresford MJ. Medical reductionism: lessons from the great philosophers.
QJM. 2010 Sep;103(9):721–4. doi: http://dx.doi.org/10.1093/qjmed/hcq057
PMID: 20395240

47. Fenech M, Strukelj N, Buston O. Ethical, social and political challenges of
artificial intelligence in health. London: Wellcome Trust Future Advocacy;
2018.

48. Loh E. Medicine and the rise of the robots: a qualitative review of recent
advances of artificial intelligence in health. BMJ Leader. 2018;2(2):59–63.
doi: http://dx.doi.org/10.1136/leader-2018-000071

49. Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, et al.
AI4people – an ethical framework for a good AI society: opportunities, risks,
principles, and recommendations. Minds Mach (Dordr). 2018;28(4):689–
707. doi: http://dx.doi.org/10.1007/s11023-018-9482-5 PMID: 30930541

Copyright of Bulletin of the World Health Organization is the property of World Health
Organization and its content may not be copied or emailed to multiple sites or posted to a
listserv without the copyright holder’s express written permission. However, users may print,
download, or email articles for individual use.