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Objectives@#Real-time relevant information helps guide the healthcare decision-making process in daily clinical practice as well as the management and optimization of healthcare processes. However, proprietary business intelligence suite solutions supporting the production of decision-making information requires investment that is out of reach of small and mediumsized healthcare facilities or those with limited resources, particularly in developing countries. This paper describes our experience in designing and implementing a real-time healthcare monitoring system solution to manage healthcare emergency units. @*Methods@#Through the use of free Business Intelligence tools and Python data science language we designed a real-time monitoring system, which was implemented to explore the Electronic Medical Records system of a university mental health emergency unit and render an electronic dashboard to support health professional daily practice. @*Results@#Three main dashboards were created to monitor patient waiting time, to access the clinical notes summary for the next waiting patient, and to obtain insights into activity during the last 24 hours. @*Conclusions@#The designed system could serve as a monitoring support model using free and user-friendly data science tools, which are good alternatives to proprietary business intelligence solutions and drastically reduce cost. Still, the key to success in decision-making systems is based on investment in human resources, business intelligence skills training, the organizational aspect of the decision-making process, and data production quality insurance.
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ObjectivesMonitoring healthcare activities is the first step for health stakeholders and health professionals to improve the quality and performance of healthcare services. However, monitoring remains a challenge for healthcare facilities, especially in developing countries. Fortunately, advances in business analytics address this need. This paper aims to describe the experience of a low-income healthcare facility in a developing country in using business analytics descriptive techniques and to discuss business analytics implementation challenges and opportunities in such an environment.MethodsBusiness analytics descriptive techniques were applied on 3 years’ electronic medical records of outpatient consultation of the University Psychiatric Centre (CPU) of Casablanca. Statistical analysis was conducted to compare results over years.ResultsOver the 3 monitored years, the monthly number of computerized physician order entries increased significantly (p < 0.001). Physicians improved their personal recording over years. Schizophrenia as well as depressive and bipolar disorders were noted at the top of outpatient mental disorders. Antipsychotics are the most prescribed drugs, and a significant annual decrease in outpatient care wait time was noted (p < 0.001).ConclusionsBusiness analytics allowed CPU to monitor mental healthcare outpatient activity and to adopt its business processes according to outcomes. However, challenges mainly in the organizational dimension of the decision-making process and the definition of strategic key metrics, data structuration, and the quality of data entry had to be considered for the optimal use of business analytics.
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PURPOSE: Aberrant glycosylation of the histo-blood group antigens (including the angina bullosa haemorrhagica [ABH]) is often observed during malignant transformation in most types of carcinomas. Data concerning their ethnic distributions are diverse which explains why their biological characteristics have to be studied in different populations. Our aim was to analyze the expression of the histo-blood group (specifically the ABH) antigens in breast carcinoma.METHODS: The expression of the histo-blood group (specifically the ABH) antigens was studied in 109 patients with breast carcinoma using immunohistochemistry. Statistical analysis was performed using χ² and Fisher analyses.RESULTS: The loss of expression of histo-blood group (ABH) antigens in breast carcinoma was observed in 81.13% of patients with blood group O, 37.93% with blood group A, and 96.30% with blood group B. One key finding of this study was that the loss of expression of the ABH antigen was also observed in normal tissues adjacent to the tumor. The loss of expression was associated with higher tumor grade (p < 0.05). Expression of H antigen was observed in 50% of cases with loss of expression of B antigen and was associated with human epidermal growth factor receptor 2 (HER2) overexpression (p < 0.05). The loss of H antigen in patients with blood group O was associated with estrogen receptor expression (p < 0.001). Incompatible A antigen in tumor was expressed in 20.75% of patients with blood group O.CONCLUSION: Loss of the ABH antigens correlated with the Scarff-Bloom-Richardson histologic grading. H antigen was associated with HER2 overexpression in breast cancer. However, further studies are needed to determine the role of incompatible A antigen in mammary carcinogenesis.