IPE: Interprofessional Healthcare Education (IPE) competencies provide the criteria against which to measure the capacity and capability of fully collaborative healthcare teams to learn and work together. Significant work already exists in the determination of IPE competencies across all disciplines. Although there is still a lack of agreement on a single set of shared core competencies, successive competency iterations enhance its development. IPE competencies need to take into account local and cultural contexts as recommended by WHO, ( 2010). Here we present a collaborative process that builds on existing competency development, assessing additional academic IPE needs. Core competencies: After the development of a set of shared core IPE competencies a two-day workshop was delivered to healthcare students from four professions. The results and feedback from students showed the value of the competencies. We discuss the evolving process through two major stages: (1) development of a model determining four shared core IPE domains, (2) the development and delivery of a set of IPE workshops explicitly and intentionally based on the model. This process is an example for the future development of IPE and IPP in any local setting. Results: Testing the developed IPE in specific workshops revealed that most clinical scenarios were on a similar standard but also showed a deficit in collaborative patient centered care, an aspect suggestive of deficient interprofessional contact and prioritization.

Qatar has grown rapidly over the past 10 years particularly in the areas of healthcare needs and provisioning. The population has grown from 617,000 in 2000 to over 1.7 million in 2010. The number of hospitals both private and public has nearly doubled with the number of healthcare workers surpassing 11,000 in 2011. To help meet the demand for trained healthcare professionals there are now 4 educational institutions in Qatar addressing medicine, nursing, pharmacy, and allied healthcare (School of Health Sciences at the College of the North Atlantic – Qatar, College of Pharmacy at Qatar University, University of Calgary – Qatar, and Weill-Cornell Medical College in Qatar). The World Health Organization (WHO) has identified a need to integrate all areas of healthcare and to foster team-based collaborative models to help improve healthcare service delivery. Interprofessional Education (IPE) provides a framework to facilitate such a model. A truly comprehensive and inclusive IPE program would include agreement on IPE competencies (shared competencies) amongst and between all healthcare educational providers (pre- and post-licensure) accompanied by collaborative models that promote and facilitate working together as teams. Measures of success include meeting the shared IPE competencies. This paper describes the formation of the Qatar Interprofessional Health Council (QIHC) to help address healthcare needs in Qatar and their efforts to move IPE forward in the state and in the region. The QIHC consists of members from the 4 healthcare educational institutions in Qatar as well as members from Sidra Medical and Research Center and Hamad Medical Corporation (HMC). A discussion of barriers and solutions is included as well as the efforts of the member institutions to provide IPE support and integration into their programs. The QIHC has recently been awarded a National Priorities Research Program (NPRP) research grant to help provide a solid and contextually appropriate framework for IPE in Qatar.

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Open source software (OSS) is increasingly being integrated into educational institutions, and many countries require the use of OSS in government departments. However, not much focus is placed on integrating it into the educational sector in a strategic and productive manner. This paper examines the existing literature on the use of OSS in terms of the potential enhancements it can provide for computer science studies in high schools in general, and those in the UAE more specifically. It also details a survey conducted among 400 high school teachers after teaching them about multiple types of OSS that might enhance their teaching experience. After examining more than 69 different research papers and taking the survey findings into account, we drafted a roadmap that can be used by any educational institute—especially high schools—to strategically integrate OSS into the educational system.

Purpose Approximately 700,000 people in the USA have chronic kidney disease requiring dialysis. Protein-energy wasting (PEW), a condition of advanced catabolism, contributes to three-year survival rates of 50%. PEW occurs at all levels of Body Mass Index (BMI) but is devastating for those people at the extremes. Treatment for PEW depends on an accurate understanding of energy expenditure. Previous research established that current methods of identifying PEW and assessing adequate treatments are imprecise. This includes disease-specific equations for estimated resting energy expenditure (eREE). In this study, we applied machine learning (ML) modelling techniques to a clinical database of dialysis patients. We assessed the precision of the ML algorithms relative to the best-performing traditional equation, the MHDE. Methods This was a secondary analysis of the Rutgers Nutrition and Kidney Database. To build the ML models we divided the population into test and validation sets. Eleven ML models were run and optimized, with the best three selected by the lowest root mean squared error (RMSE) from measured REE. Values for eREE were generated for each ML model and for the MHDE. We compared precision using Bland-Altman plots. Results Individuals were 41.4% female and 82.0% African American. The mean age was 56.4 ± 11.1 years, and the median BMI was 28.8 (IQR = 24.8 − 34.0) kg/m 2 . The best ML models were SVR, Linear Regression and Elastic net with RMSE of 103.6 kcal, 119.0 kcal and 121.1 kcal respectively. The SVR demonstrated the greatest precision, with 91.2% of values falling within acceptable limits. This compared to 47.1% for the MHDE. The models using non-linear techniques were precise across extremes of BMI. Conclusion ML improves precision in calculating eREE for dialysis patients, including those most vulnerable for PEW. Further development for clinical use is a priority.