Abstract
The trajectory of cancer patients is marked by a multifaceted journey, including diagnosis, medical consultations, in-hospital and home-based treatments, and the post-treatment phase. Ensuring a positive experience throughout this complex journey is imperative for delivering high-quality care to cancer patients. Despite numerous studies recognizing diverse factors influencing Cancer Patient Experience (CPE), there has been a lack of research systematically exploring the interrelatedness among these factors and their relative importance in cancer patient experience outcomes. This study reviews the current approaches used for assessing CPE and evaluates the potential use of Bayesian Belief Network (BBN) models in visually mapping the CPE network, highlighting the probabilistic interactions among influencing factors. This study can assist healthcare providers and managers in better understanding the complex nature of the patient journey and leveraging data-driven BBN models with sensitivity and scenario analysis to analyze CPE within dynamic health systems in different circumstances.
| Original language | British English |
|---|---|
| Pages (from-to) | 582-591 |
| Number of pages | 10 |
| Journal | Proceedings of International Conference on Computers and Industrial Engineering, CIE |
| Volume | 2024-December |
| State | Published - 2024 |
| Event | 51st International Conference on Computers and Industrial Engineering, CIE 2024 - Sydney, Australia Duration: 9 Dec 2024 → 11 Dec 2024 |
Keywords
- Bayesian Belief Network
- Cancer patient experience
- healthcare quality
- machine learning
- patient safety
- patient satisfaction
