Search results for: smart healthcare

Authors: Ponshano Kaselekela, Simooya O. Oscar, Lunshano Boyd

Abstract:

The Copperbelt University Health Services (CBUHS) was designated by the Zambia Medicines Regulatory Authority (ZAMRA), formally the Pharmaceutical Regulatory Authority (PRA) as a regional pharmacovigilance centre to carryout activities of drug safety monitoring in four provinces in Zambia. CBUHS’s mandate included stimulating the reporting of adverse drug reactions (ADRs), as well as collecting and collating ADR reports from health institutions in the four provinces. This report covers the researchers’ experiences from May 2008 to September, 2016. The main objectives are 1) to monitor ADRs in the Zambian population, 2) to disseminate information to all health professionals in the region advising that the CBU health was a centre for reporting ADRs in the region, 3) to monitor polypharmacy as well as the benefit-risk profile of medicines, 4) to generate independent, evidence based recommendations on the safety of medicines, 5) to support ZAMRA in formulating safety related regulatory decisions for medicines, and 6) to communicate findings with all key stakeholders. The methodology involved monthly visits, beginning in early May 2008 to September, 2016, by the CBUHS to health institutions in the programme areas. Activities included holding discussions with health workers, distribution of ADR forms and collection of ADRs reports. These reports, once collected, were documented and assessed at the CBUHS. A report was then prepared for ZAMRA on quarterly basis. At ZAMRA, serious ADRs were noted and recommendations made to the Ministry of Health of the Republic of Zambia. The results show that 2,600 ADRs reports were received at the pharmacovigilance regional centre. Most of the ADRs reports that received were due to antiretroviral drugs, as well as a few from anti-malarial drugs like Artemether/Lumefantrine – Coartem®. Three hundred and twelve ADRs were entered in the Uppsala Monitoring Centre WHO Vigiflow for further analysis. It was concluded that in general, 2008-16 were exciting years for the pharmacovigilance group at CBUHS. From a very tentative beginning, a lot of strides were made and contacts established with healthcare facilities in the region. The researchers were encouraged by the support received from the Copperbelt University management, the motivation provided by ZAMRA and most importantly the enthusiasm of health workers in all the health care facilities visited. As a centre for drug safety in Zambia, the results show it achieves its objectives for monitoring ADRs, Pharmacovigilance (drug safety monitoring), and activities of monitoring ADRs as well as preventing them. However, the centre faces critical challenges caused by erratic funding that prevents the smooth running of the programme.

Keywords: Assessments, evaluation, monitoring, pharmacovigilance.

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Authors: Sotirios Raptis

Abstract:

Health and Social care (HSc) services planning and scheduling are facing unprecedented challenges, due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven approaches can help to improve policies, plan and design services provision schedules using algorithms that assist healthcare managers to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as Classification and Regression Trees (CART), Random Forests (RF), and Logistic Regression (LGR). The significance tests Chi-Squared and Student’s test are used on data over a 39 years span for which data exist for services delivered in Scotland. The demands are associated using probabilities and are parts of statistical hypotheses. These hypotheses, as their NULL part, assume that the target demand is statistically dependent on other services’ demands. This linking is checked using the data. In addition, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus, groups of services. Statistical tests confirmed ML coupling and made the prediction statistically meaningful and proved that a target service can be matched reliably to other services while ML showed that such marked relationships can also be linear ones. Zero padding was used for missing years records and illustrated better such relationships both for limited years and for the entire span offering long-term data visualizations while limited years periods explained how well patients numbers can be related in short periods of time or that they can change over time as opposed to behaviours across more years. The prediction performance of the associations were measured using metrics such as Receiver Operating Characteristic (ROC), Area Under Curve (AUC) and Accuracy (ACC) as well as the statistical tests Chi-Squared and Student. Co-plots and comparison tables for the RF, CART, and LGR methods as well as the p-value from tests and Information Exchange (IE/MIE) measures are provided showing the relative performance of ML methods and of the statistical tests as well as the behaviour using different learning ratios. The impact of k-neighbours classification (k-NN), Cross-Correlation (CC) and C-Means (CM) first groupings was also studied over limited years and for the entire span. It was found that CART was generally behind RF and LGR but in some interesting cases, LGR reached an AUC = 0 falling below CART, while the ACC was as high as 0.912 showing that ML methods can be confused by zero-padding or by data’s irregularities or by the outliers. On average, 3 linear predictors were sufficient, LGR was found competing well RF and CART followed with the same performance at higher learning ratios. Services were packed only when a significance level (p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, low birth weights, alcoholism, drug abuse, and emergency admissions. The work found  that different HSc services can be well packed as plans of limited duration, across various services sectors, learning configurations, as confirmed by using statistical hypotheses.

Keywords: Class, cohorts, data frames, grouping, prediction, probabilities, services.

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