Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

BackgroundIn 2010 the Gezira Family Medicine Project (GFMP) was initiated in Gezira state, Sudan, designed as an in-service training model. The project is a collaboration project between the University of Gezira, which aims to provide a 2-year master’s programme in family medicine for practicing doctors, and the Ministry of Health, which facilitates service provision and funds the training programme. This paper presents the programme, the teaching environment, and the first batch of candidates enrolled.MethodsIn this study a self-administered questionnaire was used to collect baseline data at the start of the project from doctors who joined the programme. A checklist was also used to assess the health centres where they work. A total of 188 out of 207 doctors responded (91%), while data were gathered from all 158 health centres (100%) staffed by the programme candidates.ResultsThe Gezira model of in-service family medicine training has succeeded in recruiting 207 candidates in its first batch, providing health services in 158 centres, of which 84 had never been served by a doctor before. The curriculum is community oriented. The mean age of doctors was 32.5 years, 57% were males, and 32% were graduates from the University of Gezira. Respondents stated high confidence in practicing some skills such as asthma management and post-abortion uterine evacuation. They were least confident in other skills such as managing depression or inserting an intrauterine device. The majority of health centres was poorly equipped for management of noncommunicable diseases, as only 10% had an electrocardiography machine (ECG), 5% had spirometer, and 1% had a defibrillator.ConclusionsThe Gezira model has responded to local health system needs. Use of modern information and communication technology is used to facilitate both health service provision and training. The GFMP represents an example of a large-volume scaling-up programme of family medicine in Africa.

Information and communication technology (ICT) is progressively used in the health sector (e-health), to provide health care in a distance (telemedicine), facilitate medical education (e-learning), and manage patients' information (electronic medical records, EMRs). Gezira Family Medicine Project (GFMP) in Sudan provides a 2-year master's degree in family medicine, with ICT fully integrated in the project. This cross-sectional study describes ICT implementation and utilization at the GFMP for the years 2011-2012. Administrative data was used to describe ICT implementation, while questionnaire-based data was used to assess candidates' perceptions and satisfaction. In the period from April 2011 to December 2012, 3808 telemedicine online consultations were recorded and over 165000 new patients' EMRs were established by the study subjects (125 candidates enrolled in the program). Almost all respondents confirmed the importance of telemedicine. The majority appreciated also the importance of using EMRs. Online lectures were highly rated by candidates in spite of the few challenges encountered by combining service provision with learning activity. Physicians highlighted some patients' concerns about the use of telemedicine and EMRs during clinical consultations. Results from this study confirmed the suitability of ICT use in postgraduate training in family medicine and in service provision.