ABSTRACT
Artificial intelligence has become an important tool for governments around the world. However, it is not clear to what extent artificial intelligence can improve decision-making, and some policy domains have not been the focus of most recent studies, including the public budget process. More specifically, budget allocation is one of the areas in which AI may have greatest potential. Therefore, this study attempts to contribute to this gap in our existing knowledge by answering the following research question: To what extent can artificial intelligence techniques help distribute public spending to increase GDP, decrease inflation and reduce the Gini index? In order to respond to this question, this article proposes an algorithmic approach on how budget inputs (specific expenditures) are processed to generate certain outputs (economic, political, and social outcomes). The authors use the multilayer perceptron and a multiobjective genetic algorithm to analyze World Bank Open Data from 1960 to 2019, including 217 countries. The advantages of implementing this type of decision support system in public expenditures allocation arise from the ability to process large amounts of data and to find patterns that are not easy to detect, which include multiple non-linear relationships. Some technical aspects of the expenditure allocation process could be improved with the help of these kinds of techniques. In addition, the results of the AIbased approach are consistent with the findings of the scientific literature on public budgets, using traditional statistical techniques.
ABSTRACT
Background: The successful use of hormone therapy (HT) has contributed to improved 5-year cause-specific breast cancer survival rates, and evidence shows that long-term use produces a larger reduction in recurrence and mortality, with nearly 50% reduction in breast cancer mortality during the second decade after diagnosis. Despite the proven benefits, hormone therapy adherence is suboptimal (less than 80% of daily doses taken), and about 33% of women who are prescribed HT do not take their medication as prescribed and are at increased risk of disease recurrence and increased mortality. Smartphone ownership has increased substantially over the past decade, providing an extraordinary opportunity for innovation in the delivery of tailored interventions to improve patients' adherence to hormonal therapy. Purpose: We present preliminary results of a pilot study that involves a theory-based, culturally tailored, interactive mobile app + patient navigation to improve adherence to HT among breast cancer patients attending the breast clinic at the Mays Cancer Center (MCC). Methods: This is a 2-group parallel, randomized control trial that is currently recruiting 120 breast cancer patients and randomly assigning them to the intervention (60) or the control (60) group. The intervention group receives two components: 1) the HT Helper phone app;and 2) assistance from a patient navigator who will provide educational, psychosocial support and reinforcement, address common barriers, and facilitate the interaction with the medical team as needed. The control group receives the usual care and information provided by the MCC's breast clinic to patients undergoing HT. The app and navigation support are based in Social Cognitive Theory and principles of motivational interviewing. Results: Due to the COVID-19 pandemic, we were forced to suspend the start of the intervention until May 2021. We have recruited 27 patients and will present a general description of participants and preliminary results of the 3- month follow-up. This theory-based intervention will empower patients' self-monitoring and management. It will facilitate patient education, identification/reporting of side effects, delivery of self-care advice, and simplify communication between the patient and the oncology team. Conclusions: The anticipated outcome is a scalable, evidence-based, and easily disseminated intervention with potentially broad use to patients using HT and other oral anticancer agents. The ultimate goal of this innovative multi-communication intervention is to improve overall survival and life expectancy, enhance quality of life, reduce recurrence, and decrease healthcare costs.