British Gynaecological Cancer Society Recommendations for Evidence Based, Population Data Derived Quality Performance Indicators for Ovarian Cancer

Exploring global barriers to optimal ovarian cancer care: thematic analysis

OBJECTIVE

To explore the barriers to ovarian cancer care, as reported in the open ended responses of a global expert opinion survey, highlighting areas for improvement in global ovarian cancer care. Potential solutions to overcome these barriers are proposed.

METHODS

Data from the expert opinion survey, designed to assess the organization of ovarian cancer care worldwide, were analyzed. The survey was distributed across a global network of physicians. We examined free text, open ended responses concerning the barriers to ovarian cancer care. A qualitative thematic analysis was conducted to identify, analyze, and report meaningful patterns within the data.

RESULTS

A total of 1059 physicians from 115 countries completed the survey, with 438 physicians from 93 countries commenting on the barriers to ovarian cancer care. Thematic analysis gave five major themes, regardless of income category or location: societal factors, inadequate resources in hospital, economic barriers, organization of the specialty, and need for early detection. Suggested solutions include accessible resource stratified guidelines, multidisciplinary teamwork, public education, and development of gynecological oncology training pathways internationally.

CONCLUSIONS

This analysis provides an international perspective on the main barriers to optimal ovarian cancer care. The themes derived from our analysis highlight key target areas to focus efforts to reduce inequalities in global care. Future regional analysis involving local representatives will enable country specific recommendations to improve the quality of care and ultimately to work towards closing the care gap.

Medicolegal, infrastructural, and financial aspects in gynecologic cancer surgery and their implications in decision making processes: Quo Vadis?

Surgical decision making is complex and involves a combination of analytic, intuitive, and cognitive processes. Medicolegal, infrastructural, and financial factors may influence these processes depending on the context and setting, but to what extent can they influence surgical decision making in gynecologic oncology? This scoping review evaluates existing literature related to medicolegal, infrastructural, and financial aspects of gynecologic cancer surgery and their implications in surgical decision making. Our objective was to summarize the findings and limitations of published research, identify gaps in the literature, and make recommendations for future research to inform policy.

Quality of surgery and treatment and its association with hospital volume: A population-based study in more than 5000 Belgian ovarian cancer patients

BACKGROUND

Different sets of quality indicators are used to identify areas for improvement in ovarian cancer care. This study reports transparently on how (surgical) indicators were measured and on the association between hospital volume and indicator results in Belgium, a country setting without any centralisation of ovarian cancer care.

METHODS

From the population-based Belgian Cancer Registry, patients with a borderline malignant or invasive epithelial ovarian tumour diagnosed between 2014 and 2018 were selected and linked to health insurance and vital status data (n = 5119). Thirteen quality indicators on diagnosis and treatment were assessed and the association with hospital volume was analysed using logistic regression adjusted for case-mix.

RESULTS

The national results for most quality indicators on diagnosis and systemic therapy were around the predefined target value. Other indicators showed results below the benchmark: genetic testing, completeness of staging surgery, lymphadenectomy with at least 20 pelvic/para-aortic lymph nodes removed, and timely start of chemotherapy after surgery (within 42 days). Ovarian cancer care in Belgium is dispersed over 100 hospitals. Lower volume hospitals showed poorer indicator results compared to higher volume hospitals for lymphadenectomy, staging, timely start of chemotherapy and genetic testing. In addition, surgery for advanced stage tumours was performed less often in lower volume hospitals.

CONCLUSIONS

The indicators that showed poorer results on a national level were also those with poorer results in lower-volume hospitals compared to higher-volume hospitals, consequently supporting centralisation. International benchmarking is hampered by different (surgical) definitions between countries and studies.

Can natural language processing be effectively applied for audit data analysis in gynaecological oncology at a UK cancer centre?

BACKGROUND

The British Gynaecological Cancer Society (BGCS) has highlighted the disparity of ovarian cancer outcomes in the UK compared to other European countries. Therefore, cancer quality assurance audits and subspecialty training are important in improving the UK standard of care for these patients. The current workforce crisis afflicting the NHS creates difficulty in dedicating teams of clinicians to these audits. We present a single institution study to evaluate if NLP-generated code can improve the efficiency of ovarian cancer and subspeciality reaccreditations audits. We used the chat bot Google Bard to write Visual Basic Applications algorithms that utilise Excel files from electronic health records.

METHODS

Primary ovarian cancer data from 2019 to 2022 was retrospectively collected from the Cambridge University Hospital electronic health records. The surgical subspecialty reaccreditation audit analysed the 2022 surgical database. A modular coding approach with Google Bard was applied to generate audit algorithms. The time to complete these current audits was compared against the 2016 ovarian cancer and 2020 subspeciality reaccreditation audits.

RESULTS

The previous ovarian cancer audit conducted in 2016 required 3 clinicians for the 135 cases and data collection required 1800 min. Data analysis was completed in 300 min. The current ovarian cancer audit allocated 2 clinicians to the 600 surgical cases. Data collection was completed in 3120 min, 3360 min for code development and 720 min for testing. The 2020 subspecialty reaccreditation audit was completed in 360 min. The 2022 subspecialty reaccreditation audit was completed in 1680 min, with 960 min for code development, 240 for debugging and 480 min for testing.

CONCLUSION

We have demonstrated that NLP-generated code can significantly increase the efficiency of surgical quality assurance audits by eliminating the need for manual data analysis. With the current trajectory of NLP development, increasingly complex algorithms can be developed with minimal programming knowledge.