Understanding Inequalities in the Uptake of Supportive Care to Improve Practices in the Cancer Care Continuum.

(1) Background: While inequalities in the prevalence of cancer, access to care, and survival have been well documented, less research has focused on inequalities in the uptake of supportive oncology care. Given its contribution to improving the quality of life of people affected by cancer, access to such care is a major public health issue. The present study focuses on the access and uptake of those supportive oncology care services. (2) Methods: This study is based on qualitative research methodology, using a thematic analysis tree on NVivo© analysis software. First, an exploratory survey was conducted with users of oncology services, and professionals from these services and supportive oncology care. Then, individual interviews were conducted in June 2022 among people who are currently being treated or have been treated for cancer. (3) Results: The experiences of the 33 respondents revealed that significant variations in the uptake of supportive oncology care are underpinned by identifiable disparities in their healthcare pathways: in their assimilation of information, difficulties in accessing oncology care, personal reluctance and motivations, perceived needs and benefits, and use of other medicines. (4) Conclusion: This study aims to gain some insight into disparities in the uptake of supportive care in the Centre-Val de Loire region (France). Thus, it provides a better understanding of the complex ways in which these inequalities in supportive oncology care uptake are constructed.

Which patients may benefit from the use of a decision support system to improve compliance of physician decisions with clinical practice guidelines: a case study with breast cancer involving data mining.

OncoDoc2 is a guideline-based clinical decision support system (CDSS) for breast cancer management. It has been used as an intervention in a randomized controlled trial carried out to evaluate the impact of using a CDSS upon the compliance with clinical practice guidelines (CPGs) of multidisciplinary staff meeting decisions. Data mining was used to discover multi-criteria regularities as "emerging patterns" (EPs) associated with compliance and non-compliance with CPGs when using and not using OncoDoc2 and to assess which patients may benefit from the use of the CDSS. Decision data was collected from all participating centers. The number of EPs associated with non-compliance is smaller in the intervention arm, which suggests a practice harmonization effect of OncoDoc2. EPs associated with compliant decisions in both arms of the trial correspond to situations well identified in CPGs. EPs associated with non-compliant decisions when the system is not used are associated with compliance when the system is used except in clinical situations where evidence is lacking.

Elicitating patient patterns of physician non-compliance with breast cancer guidelines using formal concept analysis.

Because they provide patient-specific guideline-based recommendations, clinical decision support systems (CDSSs) are expected to promote the implementation of clinical practice guidelines (CPGs). OncoDoc2 is a CDSS applied to the management of breast cancer. However, despite it was routinely used during weekly multidisciplinary staff meetings (MSMs) at the Tenon Hospital (Paris, France), the compliance rate of MSMs' decisions with CPGs did not reach 100%. Formal Concept Analysis (FCA) has been applied to elicit formal concepts related to non-compliance. A statistical pre-treatment of attributes has been proposed to leverage FCA and discriminate between compliant and non-compliant decisions. Among the 1,889 decisions made over a 3 year-period, 199 decisions of recommended re-excisions have been considered for analysis. In this sample, non-compliance was explained by uncommon clinical profiles and specific patient-centred clinical criteria.

Patient clinical profiles associated with physician non-compliance despite the use of a guideline-based decision support system: a case study with OncoDoc2 using data mining techniques.

OncoDoc2 is a guideline-based clinical decision support system (CDSS) applied to the management of breast cancer patients. OncoDoc2 has been routinely used during multidisciplinary staff meetings at the Tenon Hospital (Paris, France) for nearly 3 years. Despite the use of the CDSS that reminds physicians of the recommended treatments, the compliance rate of decisions is not 100%. We have used pattern mining techniques in order to elicit patient clinical profiles associated with non-compliance. We quantified each extracted pattern by three measures (support, growth rate, and unexpected rate) and we introduced a score to prune relevant emerging patterns. Non-compliance has concerned elderly patients in pre-surgery decisions, patients with micro invasive tumor in re-excision decisions, and patients HR+ and Her2+ in adjuvant decisions. In all cases, physician non-compliance with guidelines occurs when scientific evidence is lacking.