Background: Tuberculosis (TB) is a major global public health problem and one of the leading causes of death among infectious diseases. Although TB can be cured with first-line antibiotics treatment of 6 months regimen, non-adherence to the treatment remains the main challenge for TB prevention and control. Interventions to promote adherence need to address multiple underlying factors linked to non-adherence, which requires a synthesis of studies to understand these factors in the local context. Our review accordingly examines these factors for TB treatment in Ethiopia. Methods: Articles were searched from PubMed and ScienceDirect databases, as well as manual searches through Google and Google Scholar search engines. Both quantitative and qualitative studies that showed factors associated with or reasons for non-adherence, default or loss to follow up from TB treatment were included. A total of 276 articles were screened, and 29 articles were ultimately included in the review. Findings: The extracted factors were synthesized thematically into seven dimensions of patient-centred, social, economic, health system, therapy, lifestyle, and geographic access factors. More than 20 distinct factors were identified under these headings. Some of these factors may also apply quite widely in other settings, with greater or lesser influence, but some are particularly applicable to the Ethiopian setting. Conclusion: Helping patients to achieve full adherence to TB medication is a complex problem as it is influenced by interplay between many factors. Healthcare managers, providers, and researchers need to consider and address multiple underlying factors when designing adherence interventions. This work provides a reference set of such factors for Ethiopian interventions.

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

The discrepancy between the number of admissions and the allocation of hospital beds means that many patients admitted to hospital can be placed in units or wards other than that which specialise in the patient’s primary health issue (home-ward). These patients are called ‘outlier’ patients. Risk factors and health system outcomes of hospital care for ‘outlier’ patients diagnosed with dementia and/or delirium are unknown. Therefore, the aim of this research was to examine patient journeys of people with dementia and/or delirium diagnoses, to identify risk factors for ‘inlier’ or ‘outlier’ status and patient or health system outcomes (consequences) of this status. A retrospective, descriptive study compared patients who had dementia and/or delirium according to the proportion of time spent on the home ward i.e. ‘inliers’ or ‘outliers’. Data from the patient journey database at Flinders Medical Centre (FMC), a public hospital in South Australia from 2007 and 2014 were extracted and analysed. The analysis was carried out on the patient journeys of people with a dementia and/or delirium diagnosis. When 6367 inpatient journeys with dementia and/or delirium within FMC were examined, the Emergency Department (ED) Length of Stay (LOS) after being admitted as inpatient was prolonged for ‘outlier’ patients compared to ‘inlier’ patients (OR: 1.068, 95% CI: 1.057–1.079, p = 0.000). However, the inpatient LOS for’outlier’ patients was only marginally shorter than that of the ‘inlier’ patients (OR: 0.998, 95% CI: 0.998–0.998, p = 0.000). The chances of dying within 48 h of admission increased for ‘outlier’ patients (OR: 1.973, 95% CI: 1.158–3.359, p = 0.012) and their Charlson co-morbidity Index was higher (OR: 1.059, 95% CI: 1.021–1.10, p = 0.002). Completion of discharge summaries within 2 days post-discharge for ‘outlier’ patients was compromised (OR: 1.754, 95% CI: 1.492–2.061, p = 0.000).Additionally, ‘outlier’ patients were more likely to be discharged to another hospital for other care types not offered at FMC (OR: 1.931, 95% CI: 1.559–2.391, p = 0.000). An examination of the patient journeys at FMC has determined that the health system outcomes for patients with dementia and/or delirium who are admitted outside of their home-ward are affected by in-hospital location despite the homogenous nature of the study population.

This paper aims to understand the student campus mental health, mental well-being and mental wellness ecosystem by systematically identifying and synthesizing research reporting initiatives using these terms. It identifies the differences and similarities in usage to identify digital health opportunities.