Understanding compassion in family medicine: a qualitative study

[The new program of the Family and Community Medicine specialty: An opportunity not to be missed]

The training program for the Family and Community Medicine specialty (MFyC), which has been in effect since 2005, is currently undergoing a review and update process. This article proposes contributions to deepen the specific contents of the specialty in order to guide towards a more significant competency development. To carry out values-oriented training, it is suggested to deploy the values function and promote the creation of care spaces where the daily experience of those values is possible. It is proposed to establish a scale of values where the two essential values of the family physician are, in this order, a commitment to the individual person, and a commitment to the group of people under their care. Additionally, it is proposed to reorganize the competency map around five competency integrators or meta-competencies: patient-centered clinical method, population-based clinical governance, primary care oriented to the community, health promotion or community health based on assets, and research in the family and community field.

Working in value-discrepant environments inhibits clinicians' ability to provide compassion and reduces well-being: A cross-sectional study

BACKGROUND

The practice of compassion in healthcare leads to better patient and clinician outcomes. However, compassion in healthcare is increasingly lacking, and the rates of professional burnout are high. Most research to date has focused on individual-level predictors of compassion and burnout. Little is known regarding how organizational factors might impact clinicians' ability to express compassion and well-being. The main study objective was to describe the association between personal and organizational value discrepancies and compassion ability, burnout, job satisfaction, absenteeism and consideration of early retirement among healthcare professionals.

METHODS

More than 1000 practising healthcare professionals (doctors, nurses and allied health professionals) were recruited in Aotearoa/New Zealand. The study was conducted via an online cross-sectional survey and was preregistered on AsPredicted (75407). The main outcome measures were compassionate ability and competence, burnout, job satisfaction and measures of absenteeism and consideration of early retirement.

RESULTS

Perceived discrepancies between personal and organizational values predicted lower compassion ability (B = -0.006, 95% CI [-0.01, -0.00], p < 0.001 and f ²  = 0.05) but not competence (p = 0.24), lower job satisfaction (B = -0.20, 95% CI [-0.23, -0.17], p < 0.001 and f ²  = 0.14), higher burnout (B = 0.02, 95% CI [0.01, 0.03], p < 0.001 and f ²  = 0.06), absenteeism (B = 0.004, 95% CI [0.00, 0.01], p = 0.01 and f ²  = 0.01) and greater consideration of early retirement (B = 0.02, 95% CI [0.00, 0.03], p = 0.04 and f ²  = 0.004).

CONCLUSIONS

Working in value-discrepant environments predicts a range of poorer outcomes among healthcare professionals, including hindering the ability to be compassionate. Scalable organizational and systems-level interventions that address operational processes and practices that lead to the experience of value discrepancies are recommended to improve clinician performance and well-being outcomes.

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.

More than a feeling? What does compassion in healthcare 'look like' to patients?

OBJECTIVE

Compassion is important to patients and their families, predicts positive patient and practitioner outcomes, and is a professional requirement of physicians around the globe. Yet, despite the value placed on compassion, the empirical study of compassion remains in its infancy and little is known regarding what compassion 'looks like' to patients. The current study addresses limitations in prior work by asking patients what physicians do that helps them feel cared for.

METHODS

Topic modelling analysis was employed to identify empirical commonalities in the text responses of 767 patients describing physician behaviours that led to their feeling cared for.

RESULTS

Descriptively, seven meaningful groupings of physician actions experienced as compassion emerged: listening and paying attention (71% of responses), following-up and running tests (11%), continuity and holistic care (8%), respecting preferences (4%), genuine understanding (2%), body language and empathy (2%) and counselling and advocacy (1%).

CONCLUSION

These findings supplement prior work by identifying concrete actions that are experienced as caring by patients. These early data may provide clinicians with useful information to enhance their ability to customize care, strengthen patient-physician relationships and, ultimately, practice medicine in a way that is experienced as compassionate by patients.

PUBLIC CONTRIBUTION

This study involves the analysis of data provided by a diverse sample of patients from the general community population of New Zealand.

Compassion in healthcare: an updated scoping review of the literature

Background

A previous review on compassion in healthcare (1988-2014) identified several empirical studies and their limitations. Given the large influx and the disparate nature of the topic within the healthcare literature over the past 5 years, the objective of this study was to provide an update to our original scoping review to provide a current and comprehensive map of the literature to guide future research and to identify gaps and limitations that remain unaddressed.

Methods

Eight electronic databases along with the grey literature were searched to identify empirical studies published between 2015 and 2020. Of focus were studies that aimed to explore compassion within the clinical setting, or interventions or educational programs for improving compassion, sampling clinicians and/or patient populations. Following title and abstract review, two reviewers independently screened full-text articles, and performed data extraction. Utilizing a narrative synthesis approach, data were mapped onto the categories, themes, and subthemes that were identified in the original review. Newly identified categories were discussed among the team until consensus was achieved.

Results

Of the 14,166 number of records identified, 5263 remained after removal of duplicates, and 50 articles were included in the final review. Studies were predominantly conducted in the UK and were qualitative in design. In contrast to the original review, a larger number of studies sampled solely patients (n = 12), and the remainder focused on clinicians (n = 27) or a mix of clinicians and other (e.g. patients and/or family members) (n = 11). Forty-six studies explored perspectives on the nature of compassion or compassionate behaviours, traversing six themes: nature of compassion, development of compassion, interpersonal factors related to compassion, action and practical compassion, barriers and enablers of compassion, and outcomes of compassion. Four studies reported on the category of educational or clinical interventions, a notable decrease compared to the 10 studies identified in the original review.

Conclusions

Since the original scoping review on compassion in healthcare, while a greater number of studies incorporated patient perspectives, clinical or educational interventions appeared to be limited. More efficacious and evidence-based interventions or training programs tailored towards improving compassion for patients in healthcare is required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12904-022-00942-3.

Predictors of Physician Compassion, Empathy, and Related Constructs: a Systematic Review

BACKGROUND

Compassion in healthcare provides measurable benefits to patients, physicians, and healthcare systems. However, data regarding the factors that predict care (and a lack of care) are scattered. This study systematically reviews biomedical literature within the Transactional Model of Physician Compassion and synthesizes evidence regarding the predictors of physician empathy, compassion, and related constructs (ECRC).

METHODS

A systematic literature search was conducted in CENTRAL, MEDLINE, PsycINFO, EMBASE, CINAHL, AMED, OvidJournals, ProQuest, Web of Science, and Scopus using search terms relating to ECRC and its predictors. Eligible studies included physicians as participants. Methodological quality was assessed based on the Cochrane Handbook, using ROBINS-I risk of bias tool for quantitative and CASP for qualitative studies. Confidence in findings was evaluated according to GRADE-CERQual approach.

RESULTS

One hundred fifty-two included studies (74,866 physicians) highlighted the diversity of influences on compassion in healthcare (54 unique predictors). Physician-related predictors (88%) were gender, experience, values, emotions and coping strategies, quality of life, and burnout. Environmental predictors (38%) were organizational structure, resources, culture, and clinical environment and processes. Patient-related predictors (24%) were communication ease, and physicians' perceptions of patients' motives; compassion was also less forthcoming with lower SES and minority patients. Evidence related to clinical predictors (15%) was scarce; high acuity presentations predicted greater ECRC.

DISCUSSION

The growth of evidence in the recent years reflects ECRC's ongoing importance. However, evidence remains scattered, concentrates on physicians' factors that may not be amenable to interventions, lacks designs permitting causal commentary, and is limited by self-reported outcomes. Inconsistent findings in the direction of the predictors' effects indicate the need to study the relationships among predictors to better understand the mechanisms of ECRCs. The current review can guide future research and interventions.