Are GPTs the Answer to Small Clinics’ Digital Struggles? A Comprehensive Implementation Study
Bibliographic record
Abstract
Small clinics in North America often struggle to keep pace with the digital transformation sweeping the healthcare industry due to limited financial resources and technological expertise. This digital divide has become more pronounced with the increasing reliance on digital solutions, such as online booking systems and telehealth services, exacerbated further by the COVID-19 pandemic. This paper evaluates whether Generative Pre-trained Transformers (GPTs), introduced by OpenAI, can effectively bridge this gap by providing a cost-effective and efficient solution for small clinics. We detail the implementation of a GPT-based online booking system tai lored to the needs of small clinics. The methodology includes a flowchart of the system’s components and descriptions, supplemented by code and scripts in the appendix. Our findings show that GPTs can significantly improve booking efficiency, reduce administrative workload, and enhance patient experience. However, we also identify drawbacks such as technical issues and the need for staff adaptation. We discuss potential issues, including error handling, privacy concerns, and appointment conflicts. The paper concludes with recommendations for small clinics on leveraging GPT technology to enhance their digital capabilities, ultimately aiming to provide more efficient and accessible healthcare services.
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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 | 0.001 |
| 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".