Patient Insights Into the Design of Technology to Support a Strengths-Based Approach to Health Care
Bibliographic record
Abstract
BACKGROUND: An increasing number of research studies in the psychological and biobehavioral sciences support incorporating patients' personal strengths into illness management as a way to empower and activate the patients, thus improving their health and well-being. However, lack of attention to patients' personal strengths is still reported in patient-provider communication. Information technology (IT) has great potential to support strengths-based patient-provider communication and collaboration, but knowledge about the users' requirements and preferences is inadequate. OBJECTIVE: This study explored the aspirations and requirements of patients with chronic conditions concerning IT tools that could help increase their awareness of their own personal strengths and resources, and support discussion of these assets in consultations with health care providers. METHODS: We included patients with different chronic conditions (chronic pain, morbid obesity, and chronic obstructive pulmonary disease) and used various participatory research methods to gain insight into the participants' needs, values, and opinions, and the contexts in which they felt strengths-based IT tools could be used. RESULTS: Participants were positive toward using technology to support them in identifying and discussing their personal strengths in clinical consultation, but also underlined the importance of fitting it to their specific requirements and the right contexts of use. Participants recommended that technology be designed for use in preconsultation settings (eg, at home) and felt that it should support them in both identifying strengths and in finding out new ways how strengths can be used to attain personal health-related goals. Participants advocated use of technology to support advance preparation for consultations and empower them to take a more active role. IT tools were suggested to be potentially useful in specific contexts, including individual or group consultations with health care providers (physician, nurse, specialist, care team) in clinical consultations but also outside health care settings (eg, as a part of a self-management program). Participants' requirements for functionality and design include, among others: providing examples of strengths reported by other patients with chronic conditions, along with an option to extend the list with personal examples; giving an option to briefly summarize health-related history; using intuitive, easy-to-use but also engaging user interface design. Additionally, the findings are exemplified with a description of a low-fidelity paper prototype of a strengths-based tool, developed with participants in this study. CONCLUSIONS: Users requirements for IT support of a strengths-based approach to health care appear feasible. The presented findings reflect patients' values and lists potential contexts where they feel that technology could facilitate meaningful patient-provider communication that focuses not just on symptoms and problems, but also takes into account patients' strengths and resources. The findings can be used to inform further development of IT tools for use in clinical consultations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".