Beyond empathy decline: Do the barriers to compassion change across medical training?

Role Modeling Kindness at the Bedside

Compassion and kindness are interchangeable attitudes and behaviors in society. As evidence shows the importance of compassion and kindness in healthcare, there has been a push to nurture and teach compassion through experiential learning in medical schools. However, there is not much evidence of educating learners on the importance of kindness as the complement or foundation of compassion and empathy. Kindness is the ability to act positively and appropriately and can be provided without emotion, judgment, or expecting anything in return. Kindness does not require the receiver to be in severe distress or suffering. Acts of kindness can be random acts done to anyone, anytime, with or without illness or suffering. Research shows that kindness elevates the healthcare profession for both clinicians and patients. Compassion and kindness must be taught through the integrated approach of role modeling, observation, practice, experience, and reflection in the classroom and in the clinical environment. It is vital that medical schools and healthcare institutions' faculty and staff make kindness to patients, families, and staff a key behavior, along with compassion and empathy. There is more that can be done to encourage acts of kindness through everyday actions; educators can display kindness toward colleagues and medical students in their learning. Kindness can improve conversations with patients and improve the emotional and social well-being of learners. Displaying kindness during bedside or classroom teaching would engrain its importance in the professional identity formation of future generations of physicians.

Why might medical student empathy change throughout medical school? a systematic review and thematic synthesis of qualitative studies

BACKGROUND

Several studies suggest that medical student empathy declines throughout medical school. However, no studies have synthesised the evidence regarding why empathy declines.

OBJECTIVE

To conduct a systematic review and thematic synthesis of qualitative studies investigating why student empathy may change throughout medical school.

METHODS

We included any qualitative study that investigated why empathy might change during medical school. We searched the Medline, Scopus, CINAHL, ERIC, and APA PsycInfo databases for relevant studies. All databases were searched from their inception to 18 July 2022. We also searched the reference lists of the included studies and contacted experts to identify additional studies. We used the Joanna Briggs Institute tool to evaluate the risk of bias in the included studies. Overall confidence in our results was assessed using the Confidence in the Evidence from Reviews of Qualitative research (CERQual) approach. We used thematic methods to synthesise our findings.

RESULTS

Our searches yielded 2523 records, and 16 studies involving a total of 771 students were eligible for analysis. Most studies (n = 11) were from Europe or North America. The descriptive themes and sub-themes were identified for each study. Increased complexity in patients and their diseases, together with the 'hidden curriculum' (including a stressful workload, prioritisation of biomedical knowledge, and (sometimes) poor role models), led to student adaptations, such as cynicism and desensitisation. Students' prior lives and professional experiences appeared to exacerbate the decline in empathy. However, there were bias concerns for most of the included studies.

DISCUSSION

Many of the included studies included were small, and some did not include demographic participant data. Given the likely benefits of providing empathic care for patients and practitioners, medical education interventions should focus on developing an 'empathic hidden curriculum' that mitigates the decline in medical student empathy.

TRIAL REGISTRATION

A protocol for this systematic review was submitted for registration with the International Prospective Register of Systematic Reviews (PROSPERO) on 28 July 2022 (registration number CRD42022347856).

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Background

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.

Objectives

The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?

Materials and methods

A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.

Results

Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.

Conclusion

There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.

Implications

In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.