An investigation of whether meaningful surgical outcomes for urological malignancies can be obtained using health informatics

Dosanjh, Amandeep Raj Singh ORCID: 0000-0002-4522-7722 (2025). An investigation of whether meaningful surgical outcomes for urological malignancies can be obtained using health informatics. University of Birmingham. M.D.

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Abstract

Background
Urological malignancies represent more than 1 in 10 cancer diagnoses in the United Kingdom annually. This incidence is rising and examination of treatment outcomes is increasingly important. The aim of this thesis is to consider whether health informatics can provide a viable solution to analysing high volumes of routinely collected data, in order to examine outcomes for urological cancers.

Methods Retrospective cohort studies were utilised to examine outcomes for multiple urological malignancies. National level studies were performed utilising the Hospital Episode Statistics (HES) dataset of routine administrative data. Studies performed using local data at University Hospitals Birmingham NHS Foundation Trust allowed multiple data sets to be linked. Cancers examined included prostate, bladder and testis.

Results This collection of studies provides insight into the advantages and challenges of health informatics at this time. Routine administrative data provides an accurate record of patient events in the English National Health Service (NHS); however, the lack of clinical detail can hamper interpretation of results. Linkage to clinical data systems can provide clinical information suitable for risk stratification. There are however, concerns regarding data accuracy and missingness. Informatics data works well to support clinical research, however the ability of informatics alone to perform complete studies depends on the research question and completeness of datasets.

Conclusions Development within the field of health informatics represents a potential future direction for research into urological malignancies. Improved data sharing and linkage will allow for robust and powerful studies without need for dedicated data collection. Data privacy must be maintained throughout.

Type of Work:

Thesis (Doctorates > M.D.)

Award Type:

Doctorates > M.D.

Supervisor(s):