Enabling Digital Compassion in Digital Health Environments: Modified eDelphi Study to Identify Interprofessional Competencies and Technology Attributes (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Health care continues to advance through digital innovation, and technology-enabled processes and interventions are increasingly being introduced to deliver and expand access to care. In this evolving digital health ecosystem, health care professionals (HCPs), learners, and organizations may not be prepared or equipped with the knowledge, skills, and behaviors required to navigate these new digital tools while simultaneously sustaining and integrating compassionate care. Moreover, the tools may not be designed and implemented in a manner that facilitates digital compassion. </sec> <sec> <title>OBJECTIVE</title> This study aimed to identify (1) core digital compassion competencies for health professionals and (2) digital compassion health IT attributes. </sec> <sec> <title>METHODS</title> We conducted this study based on the Delphi method, a consensus-building technique using structured group communication that allows a group of experts to identify competencies and agree on items such as standards and attributes by achieving consensus on a given topic. To encourage enriched discussions, we used a modified eDelphi method, where the first round consisted of a group activity and focus group rather than a questionnaire. Due to COVID-19 pandemic restrictions, the first round was held online. Subsequent rounds consisted of questionnaires administered via email and a web-based survey. Using purposive sampling, participants were recruited from project partners and networks of the research team. A panel of experts across Canada in the fields of compassion, health professional or medical education, and technology was engaged to identify and prioritize professional domains and competency statements, as well as essential attributes for the development and deployment of digital technologies for compassionate care. </sec> <sec> <title>RESULTS</title> A total of 54 experts across Canada were recruited, representing diverse professions including patients or service users, HCPs, administrators, policy makers, health educators, data scientists, health technology designers, and software engineers. Overall, 9 focus groups were conducted and analyzed thematically. Seven domains of digital compassion were identified: (1) digital literacy, (2) patient preference, (3) collaboration and co-design, (4) therapeutic relationship, (5) ethical implications, (6) patient safety, and (7) technology safety. Technology attributes to facilitate digital compassion were also generated. We reached consensus after several subsequent rounds, resulting in 58 digital compassion competency statements and 15 technology attributes. </sec> <sec> <title>CONCLUSIONS</title> This study identified a digital compassion framework consisting of competencies for HCPs and attributes for digital technologies that would enhance compassion in virtual care encounters. To promote a cultural shift where technologies are perceived to be not only efficient but also compassionate, practices of co-design, training, and ongoing evaluation and iteration must be prioritized within health care organizations. Future research should explore the adaptability of the professional competencies and technology attributes to specific medical specialties or in patient populations. </sec>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".