Causes of Patient Nonattendance at Medical Appointments: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Approximately one-third of patient appointments in Danish health care result in failures, leading to patient risk and sizable resource waste. Existing interventions to alleviate no-shows often target the patients. The underlying reason behind these interventions is a view that attendance or nonattendance is solely the patient's problem. However, these interventions often prove to be ineffective and can perpetuate social biases and health inequalities, leaving behind patients who are more vulnerable or disadvantaged (in terms of social, economical, and linguistic factors, etc). A more holistic understanding of no-shows is needed to optimize processes, reduce waste, and support patients who are vulnerable. OBJECTIVE: This study aims to gain a deep and more comprehensive understanding of the causes, mechanisms, and recurring patterns and elements contributing to nonattendance at Danish hospitals in the Region of Southern Denmark. It emphasizes the patient perspective and analyzes the relational and organizational processes surrounding no-shows in health care. In addition, the study aims to identify effective communicative strategies and organizational processes that can support the development and implementation of successful interventions. METHODS: The study uses mixed quantitative-qualitative methods, encompassing 4 analytical projects focusing on nonattendance patterns, patient knowledge and behavior, the management of hospital appointments, and in situ communication. To address the complexity of no-shows in health care, the study incorporates various data sources. The quantitative data sources include the electronic patient records, Danish central registries, Danish National Patient Registry, and Register of Medicinal Product Statistics. Baseline characteristics of patients at different levels are compared using chi-square tests and Kruskal-Wallis tests. The qualitative studies involve observational data, individual semistructured interviews with patients and practitioners, and video recordings of patient consultations. RESULTS: This paper presents the protocol of the study, which was funded by the Novo Nordisk Foundation in July 2022. Recruitment started in February 2023. It is anticipated that the quantitative data analysis will be completed by the end of September 2023, with the qualitative investigation starting in October 2023. The first study findings are anticipated to be available by the end of 2024. CONCLUSIONS: The existing studies of nonattendance in Danish health care are inadequate in addressing relational and organizational factors leading to hospital no-shows. Interventions have had limited effect, highlighting the Danish health care system's failure to accommodate patients who are vulnerable. Effective interventions require a qualitative approach and robust ethnographic data to supplement the description and categorization of no-shows at hospitals. Obtaining comprehensive knowledge about the causes of missed patient appointments will yield practical benefits, enhancing the safety, coherence, and quality of treatment in health care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46227.
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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 | 0.100 | 0.070 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.082 | 0.016 |
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".