Conceptual Model for the Integration of Marketing Strategies and Biomedical Innovation in Patient-Centered Care: Mixed Methods Study
Bibliographic record
Abstract
Background: The increasing integration of biomedical technology and digital marketing is quickly transforming how patients engage with health care. The patient as an organization (PAO) model is explored in this study. The PAO model encourages patients to be active participants in health care decisions by leveraging wearables, mobile health (mHealth) apps, artificial intelligence (AI) platforms, and health care marketing strategies. Objective: This study aims to examine how the PAO model is evolving in practice and gain insight into both the opportunities and challenges created by the intersection of biomedical innovation and marketing practices in patient care. Methods: The scoping review was conducted across Scopus, Web of Science, PubMed, and Google Scholar. Selection criteria included articles published from 2014 to 2024. Studies were included if they examined connections among biomedical technologies, marketing strategies, and models of behavior and organizations. Studies lacking interdisciplinary focus or methodological rigor were excluded. Since this work was exploratory, it did not require a strict bias assessment. Additionally, findings derived from qualitative analysis of 18 semistructured interviews with patients, health care professionals, and technologists regarding their experiences with digital technologies and perceptions of trust, autonomy, and engagement were analyzed. Thematic analysis was applied to these interviews using open, axial, and selective coding. Results: From an initial pool of 22,740 records, 45 studies met the inclusion criteria and were analyzed. The review revealed that the integration of AI-based personalization, biosensors, and remote monitoring with marketing strategies, such as segmentation, customer relationship management systems, and behavioral nudging, offers potential to enhance patient autonomy and engagement. However, most studies were descriptive or exploratory, with limited empirical evaluation, particularly regarding ethical risks and digital inequality. Qualitative findings further illustrated how patients are adopting organizational behaviors, such as self-monitoring, real-time decision-making, and strategic management of health data. The following 5 key themes emerged: (1) patients as autonomous digital actors, (2) digital health as a behavioral ecosystem, (3) inequities in digital empowerment, (4) negotiating trust and ethical transparency, and (5) blended care as the preferred future. Although many participants embraced digital tools, concerns about data transparency, algorithmic bias, and loss of human connection highlighted important barriers to equitable adoption. Conclusions: The PAO model shows strong potential for personalizing care and engaging patients in health care. However, it is important to note that, so far, conceptual models have dominated the PAO literature, with little empirical evidence to support them. Therefore, as health care practices increasingly integrate digital technologies, it is crucial to develop appropriate safeguards for PAO models.
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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 | 0.126 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".