Background

Effective management of chronic diseases such as prostate cancer is important. Research suggests a tendency to use self-care treatment options such as over-the-counter (OTC) complementary medications among prostate cancer patients. The current trend in patient-driven recording of health data in an online Personal Health Record (PHR) presents an opportunity to develop new data-driven approaches for improving prostate cancer patient care. However, the ability of current online solutions to share patients’ data for better decision support is limited. An informatics approach may improve online sharing of self-care interventions among these patients. It can also provide better evidence to support decisions made during their self-managed care.

Aims

To identify requirements for an online system and describe a new case-based reasoning (CBR) method for improving self-care of advanced prostate cancer patients in an online PHR environment.

Method

A non-identifying online survey was conducted to understand self-care patterns among prostate cancer patients and to identify requirements for an online information system. The pilot study was carried out between August 2010 and December 2010. A case-base of 52 patients was developed.

Results

The data analysis showed self-care patterns among the prostate cancer patients. Selenium (55%) was the common complementary supplement used by the patients. Paracetamol (about 45%) was the commonly used OTC by the patients.

Conclusion

The results of this study specified requirements for an online case-based reasoning information system. The outcomes of this study are being incorporated in design of the proposed Artificial Intelligence (Al) driven patient journey browser system. A basic version of the proposed system is currently being considered for implementation.

Patients presenting to Emergency Departments may be categorised into different symptom groups for the purpose of research and quality improvement. The grouping is challenging due to the variability in the way presenting complaints are recorded by clinical staff. This work proposes analysis of the presenting complaint free-text using the semantics encoded in the SNOMED CT ontology. This work demonstrates a validated prototype system that can classify unstructured free-text narratives into patient’s symptom group. A rule-based mechanism was developed using variety of keywords to identify the patient’s symptom group. The system was validated against the manual identification of the symptom groups by two expert clinical research nurses on 794 patient presentations from six participating hospitals. The comparison of system results with one clinical research nurse showed 99.3% sensitivity; 80.0% specificity and 0.9 F-score for identifying “chest pain” symptom group.