Recent advances in modern computing spurred by immense processing power, the unprecedented availability of memory RAM, and storage capacity have allowed computers to solve incredibly complex problems that would have been deemed unsolvable until very recently. The plethora of statistical learning algorithms, the availability of massive datasets along with the combination of mathematics, statistics, and computer science have allowed tackling unnerving decision-making challenges in sectors such as finance, economics, business and marketing, transportation and communication, to name a few. It is important to understand how the same techniques can be used to provide insights into the medical decision-making process to decrease healthcare costs while improving patient care and healthcare staff management.
The research topic will focus on research leveraging data science applications, including machine and deep learning and artificial intelligence, to help provide better care to patients while saving costs. This applies to clinical and non-clinical settings. Research published will have practical implications for healthcare providers and/or administrators.
Our goal is to understand how obstacles to administering and providing care to patients can be overcome through the applications of statistical learning approaches. For instance, providing efficient and effective care to patients has hit major hurdles in the wake of COVID-19 due to higher patient volumes and a shortage of staff. Cutting costs while providing patients with the best quality and affordable care remains on the mind of hospital administrators. We look to glean and bring forth papers that have leveraged recent advances in data mining techniques to provide solutions that would alleviate the burden on hospital administrators and healthcare providers when administering and providing quality care to patients.
This Research Topic welcomes research that includes, but is not limited to, the following topics:
• Data mining approaches that contribute to providing or enhancing patient experience in the hospital room
• Tools available to doctors or nurses to enhance the patient experience
• Tools available to hospital administrators for efficient management of staff.
• Novelty detection methods in healthcare
• Consumer analytics in healthcare
• Forecasting systems to predict patient volume for better staffing management
• Prediction of healthcare staff absenteeism, such as unscheduled time away pay (TAP)
• Prediction, determinants and impacts of staff turnover in healthcare
In this research topic, we welcome systematic reviews, reviews, technology and code and original research article types dealing with techniques to increase cost-effectiveness in public health.
Recent advances in modern computing spurred by immense processing power, the unprecedented availability of memory RAM, and storage capacity have allowed computers to solve incredibly complex problems that would have been deemed unsolvable until very recently. The plethora of statistical learning algorithms, the availability of massive datasets along with the combination of mathematics, statistics, and computer science have allowed tackling unnerving decision-making challenges in sectors such as finance, economics, business and marketing, transportation and communication, to name a few. It is important to understand how the same techniques can be used to provide insights into the medical decision-making process to decrease healthcare costs while improving patient care and healthcare staff management.
The research topic will focus on research leveraging data science applications, including machine and deep learning and artificial intelligence, to help provide better care to patients while saving costs. This applies to clinical and non-clinical settings. Research published will have practical implications for healthcare providers and/or administrators.
Our goal is to understand how obstacles to administering and providing care to patients can be overcome through the applications of statistical learning approaches. For instance, providing efficient and effective care to patients has hit major hurdles in the wake of COVID-19 due to higher patient volumes and a shortage of staff. Cutting costs while providing patients with the best quality and affordable care remains on the mind of hospital administrators. We look to glean and bring forth papers that have leveraged recent advances in data mining techniques to provide solutions that would alleviate the burden on hospital administrators and healthcare providers when administering and providing quality care to patients.
This Research Topic welcomes research that includes, but is not limited to, the following topics:
• Data mining approaches that contribute to providing or enhancing patient experience in the hospital room
• Tools available to doctors or nurses to enhance the patient experience
• Tools available to hospital administrators for efficient management of staff.
• Novelty detection methods in healthcare
• Consumer analytics in healthcare
• Forecasting systems to predict patient volume for better staffing management
• Prediction of healthcare staff absenteeism, such as unscheduled time away pay (TAP)
• Prediction, determinants and impacts of staff turnover in healthcare
In this research topic, we welcome systematic reviews, reviews, technology and code and original research article types dealing with techniques to increase cost-effectiveness in public health.
