HEALTHCARE providers, especially hospitals and clinics, are always under pressure to provide better care at affordable rates. Technologies such as robotic process automation (RPA) and artificial intelligence (AI), among others, make that possible. Given the shortage of medical staff and the rise of the grey-haired population in some parts of the world, hospitals and clinics are being forced to leap into the world of technology -- and the move seems to be serving them very well. King's Daughters Medical Center, a small rural hospital with 99-beds, for example, has recently told media that it has deployed automation to reconcile electronic health records (EHRs). As a result, it has started saving up to 34 clinician hours per month and US$11,000 in nursing productivity over 12 months.

Last month, we published Part 1 of a series of posts designed to highlight the Top 10 use cases from the IBM Machine Learning Hub. In particular, we're eager to share scenarios that can stretch our understanding about machine learning -- compliments of our collaborations with data scientists from across our client base. If you are interested to try out new IBM's Watson Machine Learning service - click here The goal of the series is to look beyond the usual set of machine learning use cases that come to mind when considering a particular sector. For example, Part 1 focused on government but looked beyond the role of ML in sentencing and parole to explore use cases driving vital improvements to regional and municipal agencies. Hospital and clinics assess their own effectiveness in many ways -- from average patient stay to ER wait times to direct surveys of service.

Last month, we published Part 1 of a series of posts designed to highlight the Top 10 use cases from the IBM Machine Learning Hub. In particular, we're eager to share scenarios that can stretch our understanding about machine learning -- compliments of our collaborations with data scientists from across our client base. The goal of the series is to look beyond the usual set of machine learning use cases that come to mind when considering a particular sector. For example, Part 1 focused on government but looked beyond the role of ML in sentencing and parole to explore use cases driving vital improvements to regional and municipal agencies. Hospital and clinics assess their own effectiveness in many ways -- from average patient stay to ER wait times to direct surveys of service.