Family planning and multiple sclerosis: A qualitative study of patient experiences to understand information needs and promote informed decision-making.

OBJECTIVES: Ambiguity exists about the impact of multiple sclerosis (MS) on fertility and pregnancy. We explored female and male patients' experiences with MS regarding family planning to understand information needs and opportunities to improve informed decision-making. METHODS: Semi-structured interviews were conducted with Australian female (n = 19) and male (n = 3) patients of reproductive age diagnosed with MS. Transcripts were analysed thematically, adopting a phenomenological approach. RESULTS: Four main themes emerged: 'reproductive planning', revealing inconsistent experiences about pregnancy intention discussions with health care professionals (HCPs), and involvement in decisions about MS management and pregnancy; 'reproductive concerns', about the impact of the disease and its management; 'information awareness and accessibility', with participants generally reporting they had limited access to desired information and received conflicting information about family planning; and 'trust and emotional support', with continuity of care and engagement with peer-support groups about family planning needs valued. CONCLUSION: Patients with MS want consistent engagement with HCPs regarding discussion of pregnancy intent and desire improvements in quality and accessibility of available resources and support services to address reproductive concerns. PRACTICE IMPLICATIONS: Family planning conversations should be a part of routine care planning for MS patients and contemporary resources are required to support these discussions.

Improving access for the vulnerable: a mixed-methods feasibility study of a pop-up model of care in south-eastern Melbourne, Australia.

Access to appropriate health and social care is challenging for vulnerable populations. We used a 'pop-up' delivery model to bring community-based services in contact with communities with poor access to health and social care. Our aim was to examine whether pop-up events improve access to essential health and social support services for selected vulnerable communities and increase collaboration between community-based health and social services. Set in south-eastern Melbourne, two pop-up events were held, one with people at risk of homelessness attending a community lunch and the other with South Sudanese women helping at-risk youth. Providers represented 20 dental, housing, justice, employment and mental health services. We made structured observations of each event and held semi-structured interviews with consumers and providers. Pre-post surveys of managers assessed acceptability and perceived impact. We reached 100 community participants who had multiple needs, particularly for dentistry. Following the events, participants reported increased knowledge of services and access pathways, community members spoke of increased trust and partnerships between service providers were fostered. The pop-up model can increase provider collaboration and provide new options for vulnerable populations to access needed services. 'Bringing the service to the person' is a compelling alternative to asking consumers to negotiate complex access pathways.

Coding and classifying GP data: the POLAR project.

BACKGROUND: Data, particularly 'big' data are increasingly being used for research in health. Using data from electronic medical records optimally requires coded data, but not all systems produce coded data. OBJECTIVE: To design a suitable, accurate method for converting large volumes of narrative diagnoses from Australian general practice records to codify them into SNOMED-CT-AU. Such codification will make them clinically useful for aggregation for population health and research purposes. METHOD: The developed method consisted of using natural language processing to automatically code the texts, followed by a manual process to correct codes and subsequent natural language processing re-computation. These steps were repeated for four iterations until 95% of the records were coded. The coded data were then aggregated into classes considered to be useful for population health analytics. RESULTS: Coding the data effectively covered 95% of the corpus. Problems with the use of SNOMED CT-AU were identified and protocols for creating consistent coding were created. These protocols can be used to guide further development of SNOMED CT-AU (SCT). The coded values will be immensely useful for the development of population health analytics for Australia, and the lessons learnt applicable elsewhere.

POLAR Diversion: Using General Practice Data to Calculate Risk of Emergency Department Presentation at the Time of Consultation.

OBJECTIVE: This project examined and produced a general practice (GP) based decision support tool (DST), namely POLAR Diversion, to predict a patient's risk of emergency department (ED) presentation. The tool was built using both GP/family practice and ED data, but is designed to operate on GP data alone. METHODS: GP data from 50 practices during a defined time frame were linked with three local EDs. Linked data and data mapping were used to develop a machine learning DST to determine a range of variables that, in combination, led to predictive patient ED presentation risk scores. Thirteen percent of the GP data was kept as a control group and used to validate the tool. RESULTS: The algorithm performed best in predicting the risk of attending ED within the 30-day time category, and also in the no ED attendance tests, suggesting few false positives. At 0 to 30 days the positive predictive value (PPV) was 74%, with a sensitivity/recall of 68%. Non-ED attendance had a PPV of 82% and sensitivity/recall of 96%. CONCLUSION: Findings indicate that the POLAR Diversion algorithm performed better than previously developed tools, particularly in the 0 to 30 day time category. Its utility increases because of it being based on the data within the GP system alone, with the ability to create real-time "in consultation" warnings. The tool will be deployed across GPs in Australia, allowing us to assess the clinical utility, and data quality needs in further iterations.