RESUMO
The wild population of the African lion Panthera leo continues to decline, requiring alternate conservation programs to be considered. One such program is ex situ reintroduction. Prior to release, long-term monitoring and assessment of behavior is required to determine whether prides and coalitions behave naturally and are sufficiently adapted to a wild environment. Social network analysis (SNA) can be used to provide insight into how the pride as a whole and individuals within it, function. Our study was conducted upon 2 captive-origin prides who are part of an ex situ reintroduction program, and 1 wild pride of African lion. Social interactions were collected at all occurrence for each pride and categorized into greet, social grooming, play, and aggression. Betweenness centrality showed that offspring in each pride were central to the play network, whereas degree indicated that adults received (indegree) the greatest number of overall social interactions, and the adult males of each pride were least likely to initiate (outdegree) any interactions. Through the assessment of individual centrality and degree values, a social keystone adult female was identified for each pride. Social network results indicated that the 2 captive-origin prides had formed cohesive social units and possessed relationships and behaviors comparable with the wild pride for the studied behaviors. This study provided the first SNA comparison between captive-bred origin and a wild pride of lions, providing valuable information on individual and pride sociality, critical for determining the success of prides within an ex situ reintroduction program.
RESUMO
INTRODUCTION: We describe how techniques traditionally used in the manufacturing industry (lean management, the theory of constraints and production planning) can be applied to planning radiology services to reduce the impact of constraints such as limited radiologist hours, and to subsequently reduce delays in accessing imaging and in report turnaround. METHODS: Targets for imaging and reporting were set aligned with clinical needs. Capacity was quantified for each modality and for radiologists and recorded in activity lists. Demand was quantified and forecasting commenced based on historical referral rates. To try and mitigate the impact of radiologists as a constraint, lean management processes were applied to radiologist workflows. A production planning process was implemented. RESULTS: Outpatient waiting times to access imaging steadily decreased. Report turnaround times improved with the percentage of overnight/on-call reports completed by a 1030 target time increased from approximately 30% to 80 to 90%. The percentage of emergency and inpatient reports completed within one hour increased from approximately 15% to approximately 50% with 80 to 90% available within 4 hours. The number of unreported cases on the radiologist work-list at the end of the working day reduced. The average weekly accuracy for demand forecasts for emergency and inpatient CT, MRI and plain film imaging was 91%, 83% and 92% respectively. For outpatient CT, MRI and plain film imaging the accuracy was 60%, 55% and 77% respectively. Reliable routine weekly and medium to longer term service planning is now possible. CONCLUSIONS: Tools from industry can be successfully applied to diagnostic imaging services to improve performance. They allow an accurate understanding of the demands on a service, capacity, and can reliably predict the impact of changes in demand or capacity on service delivery.