Visioning a hospitality-oriented patient experience (HOPE) framework in health care
Bibliographic record
Abstract
Purpose This paper considers the question: what would happen if healthcare providers, like their counterparts in the hospitality industry, adopted the principles of customer experience management (CEM) in order to facilitate a more holistic and personalized patient experience? It proposes an alternative vision of the patient experience by adding to an emerging hospitality–healthcare literature base, this time focusing upon CEM. A hospitality-oriented patient experience (HOPE) framework is introduced, designed to enhance the patient experience across all the touchpoints of the healthcare journey. Design/methodology/approach This is a conceptual paper that draws upon three distinct literatures: hospitality literature; healthcare literature; and CEM literature. It utilizes this literature to develop a framework, the HOPE framework, designed to offer an alternative lens to understanding the patient experience. The paper utilizes descriptions of three unique patient experiences, one linked to chronic pain, a second to gastro issues and a third to orthopedic issues, to illustrate how adopting the principles of hospitality management, within a healthcare context, could promote an enhanced patient experience. Findings The main theoretical contribution is the development of the HOPE framework that brings together research on CEM with research on cocreative customer practices in health care. By selecting and connecting key ingredients of two separate research streams, this vision and paradigm provide an alternative lens into ways of addressing the key challenges in the implementation of person-centered care in healthcare services. The HOPE framework offers an actionable roadmap for healthcare organizations to realize greater understanding and to operationalize new ways of improving the patient experience. Originality/value This paper applies the principles of hospitality and CEM to the domain of health care. In so doing it adds value to a hospitality literature primarily focused upon extensive employee–customer relationships. To a healthcare literature seeking to more fully understand a person-centered care model typically delivered by a care team consisting of professionals and family/friends. And to a CEM literature in hospitality, which seeks to facilitate favorable employee–customer interactions. Connecting these separate literature streams enables an original conceptual framework, a HOPE framework, to be introduced.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".