Predictive Analytics – Examining the Effects on Decision Making in Organizations

Predictive analytics is a type of business analytics which enables predictions to be made, about occurrence of particular events in the future, based on data of the past. The predictive analytics is widely incorporated among the most successful organizations where it supports their decision-making process. The aim of our study is to examine the effects on decision making in organizations caused by predictive analytics. We perform a qualitative study to investigate the effects by using Simon’s model to break down the decision-making process and analyse how the predictive analytics affects each stage. Additionally we test the propositions from Huber’s theory of the effects of advanced information technology on organizational design, intelligence and decision making, in the context of predictive analytics as an advanced information technology. Our contribution to IS knowledge is derived from our findings which show that the predictive analytics offers strong support in the intelligence and design phase of the decision-making process, while having no effect on the choice phase. Furthermore, through the prism of Huber’s theory, we find that the predictive analytics generates effects on the organizational intelligence and decision making, while also having effects at subunit level, organizational level and the organizational memory

Eliciting policymakers' and stakeholders' opinions to help shape health system research priorities in the Middle East and North Africa region

Evidence-informed decisions can strengthen health systems. Literature suggests that engaging policymakers and other stakeholders in research priority-setting exercises increases the likelihood of the utilization of research evidence by policymakers. To our knowledge, there has been no previous priority-setting exercise in health policy and systems research in countries of the Middle East and North Africa (MENA) region. This paper presents the results of a recent research priority-setting exercise that identified regional policy concerns and research priorities related to health financing, human resources and the non-state sector, based on stakeholders in nine low and middle income countries (LMICs) of the MENA region. The countries included in this study were Algeria, Egypt, Jordan, Lebanon, Morocco, Palestine, Syria, Tunisia and Yemen. This multi-phased study used a combination of qualitative and quantitative research techniques. The overall approach was guided by the listening priority-setting approach, adapted slightly to accommodate the context of the nine countries. The study was conducted in four key phases: preparatory work, country-specific work, data analysis and synthesis, and validation and ranking. The study identified the top five policy-relevant health systems research priorities for each of the three thematic areas for the next 3-5 years. Study findings can help inform and direct future plans to generate, disseminate and use research evidence for LMICs in the MENA region. Our study process and results could help reduce the great chasm between the policy and research worlds in the MENA region. It is hoped that funding agencies and countries will support and align financial and human resources towards addressing the research priorities that have been identifie

Modeling the Impact of COVID-19 Vaccination in Lebanon: A Call to Speed-Up Vaccine Roll Out

Four months into the SARS-CoV-2 vaccination campaign, only 10.7% of the Lebanese population have received at least one dose, raising serious concerns over the speed of vaccine roll-out and its impact in the event of a future surge. Using mathematical modeling, we assessed the short-term impact of various vaccine roll-out scenarios on SARS-CoV-2 epidemic course in Lebanon. At current population immunity levels, estimated by the model at 40% on 15 April 2021, a large epidemic wave is predicted if all social distancing restrictions are gradually eased and variants of concern are introduced. Reaching 80% vaccine coverage by the end of 2021 will flatten the epidemic curve and will result in a 37% and 34% decrease in the peak daily numbers of severe/critical disease cases and deaths, respectively; while reaching intermediate coverage of 40% will result in only a 10–11% decrease in each. Reaching 80% vaccine coverage by August would prevent twice as many severe/critical disease cases and deaths than if it were reached by December. Easing restrictions over a longer duration resulted in more favorable vaccination impact. In conclusion, for vaccination to have impact in the short-term, scale-up has to be rapid and reach high coverage (at least 70%), while sustaining social distancing measures during roll-out. At current vaccination pace, this is unlikely to be achieved. Concerted efforts need to be made to overcome local challenges and substantially scale up vaccination to avoid a surge that the country, with its multiple crises and limited health-care capacity, is largely unprepared for