Presently, no clinically actionable biomarker exists for any therapeutic agent used to treat psoriasis or psoriatic arthritis. The lack of validated outcome measures and conflicting results of open-label studies conducted may be attributed to a multitude of issues that confound discovery. Consequently, studies have been underpowered to identify genes or genetic variants worth translating to clinical practice. In order to achieve a pharmacogenetic/pharmacogenomic signature, improvements in study design of future investigations are required, including carefully designed prospective studies. It is imperative to combine known clinical, serological, and molecular markers with consistent outcomes and an adequate health economic evaluation before they can be adopted widely in clinical practice.

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BackgroundThe care coordination workforce includes a range of clinicians who manage care for patients with multiple chronic conditions both within and outside a hospital, in the community, or in a patient’s home. These patients require a multi-skilled approach to support complex care and social support needs as they are typically high users of health, community, and social services. In Australia, workforce structures have not kept pace with this new and emerging workforce. The aim of the study was to develop, map, and analyse workforce functions of a care coordination team.MethodsWorkflow modelling informed the development of an activity log that was used to collect workflow data in 2013 from care coordinators located within the care coordination service offered by a Local Health Network in Australia. The activity log comprised a detailed classification of care coordination functions based on two major categories – direct and indirect care. Direct care functions were grouped into eight domains. A descriptive quantitative investigation design was used for data analysis. The data was analysed using univariate descriptive statistics with results presented in tables and a figure.ResultsCare coordinators spent more time (70.9%) on direct care than indirect care (29.1%). Domains of direct care that occupied the most time relative to the 38 direct care functions were ‘Assessment’ (14.1%), ‘Documentation’ (13.9%), ‘Travel time’ (6.3%), and ‘Accepting/discussing referral’ (5.7%). ‘Administration’ formed a large component of indirect care functions (14.8%), followed by ‘Travel’ (12.4%). Sub-analyses of direct care by domains revealed that a group of designated ‘core care coordination functions’ contributed to 40.6% of direct care functions.ConclusionsThe modelling of care coordination functions and the descriptions of workflow activity support local development of care coordination capacity and workforce capability through extensive practice redesigns.

Athletes participating in high-risk sports consistently report higher scores on sensation-seeking measures than do low-risk athletes or non-athletic controls. To determine whether genetic variants commonly associated with sensation seeking were over-represented in such athletes, proficient practitioners of high-risk (n = 141) and low-risk sports (n = 132) were compared for scores on sensation seeking and then genotyped at 33 polymorphic loci in 14 candidate genes. As expected, athletes participating in high-risk sports score higher on sensation seeking than did low-risk sport athletes (P < .01). Genotypes were associated with high-risk sport participation for two genes (stathmin, (P = .004) and brain-derived neurotrophic factor (P = .03)) as well as when demographically matched subsets of the sport cohorts were compared (P < .05); however, in all cases, associations did not survive correction for multiple testing.