Patient-Centered Care And Health Information Technology

Read the following attached:

Artificial Intelligence and the Ongoing Need for Empathy, Compassion, and Trust in Health.

“Caring about Me”: A Pilot Framework to Understand Patient-Centered Care Experience in Integrated Care – A Qualitative Study.

Watch the following videos:

Delivering Patient-Centered Care (

An Overview of the Patient-Centered Approach (

Patient-centered care is a critical aspect of high-quality patient care, and health information plays a key role in achieving patient-centered care. Health information technology (HIT) provides patients’ health information, assists health care providers in delivering better patient-centered care, and promotes care that is based on patients’ values and preferences.

In 250 to 350 words, address the following:

Discuss the concept of patent-centered care and the roles health care professionals have that advance patient safety, engagement, and satisfaction.

Utilize one tool on the Institute for Healthcare Improvement’s Tools ( webpage to develop a healthcare organization’s culture of safety that is patient-centered.

Examine the role HIT has on measuring and improving the quality of care being delivered.

Describe how HIT can enable patient-centered care.

Support your response with at least two scholarly sources published within the last 5 years in APA Style.


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. sockalingam@ 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.


  • “Caring About Me”: a pilot framework to understand patient-­centered care experience in integrated care – a qualitative study
    • Abstract
    • Introduction
    • Methods
      • Participants
      • Data collection
      • Patient and public involvement
      • Data analysis
    • Results
      • Theme 1: ‘Caring About Me’
      • Theme 2: collaborating with me
      • Theme 3: sharing knowledge and developing a monitoring self
      • Theme 4: personalising care to address patients’ unique needs
    • Discussion and conclusion
      • Discussion
      • Limitations
      • Conclusion
    • References
  • Youssef(2020)

Bull World Health Organ 2020;98:245–250 | doi:

Policy & practice


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: (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:

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:

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:

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。


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.


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:

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.


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: 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: ecns.2016.04.003

3. Serrant L. Compassion NHS: evidencing the impact. London: NHS England; 2016. Available from: 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: 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:

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:// 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: 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:

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

13. Halpern J. What is clinical empathy? J Gen Intern Med. 2003 Aug;18(8):670– 4. doi: 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: 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: 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:// 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: 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: 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: 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: 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: 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: 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:// 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:

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: 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: [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:

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: 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: 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:

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: 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.