This article introduces analytical techniques and a decision support tool to support capacity assessment and case mix planning (CMP) approaches previously created for hospitals. First, an optimization model is proposed to analyse the impact of making a change to an existing case mix. This model identifies how other patient types should be altered proportionately to the changing levels of hospital resource availability. Then we propose multi-objective decision-making techniques to compare and critique competing case mix solutions obtained. The proposed techniques are embedded seamlessly within an Excel Visual Basic for Applications (VBA) personal decision support tool (PDST), for performing informative quantitative assessments of hospital capacity. The PDST reports informative metrics of difference and reports the impact of case mix modifications on the other types of patient present. The techniques developed in this article provide a bridge between theory and practice that is currently missing and provides further situational awareness around hospital capacity.

February 19, 2019 – Machine learning, which provides the ability to learn a task from data (without the need of being programmed explicitly), is a key component of any Pathology AI (Artificial Intelligence) system. There are many different approaches in machine learning, reaching from simple decision trees to complex deep learning, each with its advantages and disadvantages. Deep learning, which allows to learn highly complex visual features, has created a hype about Artificial Intelligence (AI) and Healthcare AI, as is was able to solve complex computer vision problems that we believed out-of-reach just a few years ago. As pathology is a visual task it is understandable that academia and "pure" technology companies are now working heavily on deep learning approaches for pathology.The key problem for any Pathology AI system are the variations between different patient types. In a disease state, no two patient samples look identical.

The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and longstanding problem in hospital management. Majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients throughout the treatment and recovery process. The result is that current scheduling models are optimizing based on inaccurate input data. We developed a Clustering and Scheduling Integrated (CSI) approach to capture patient flows through a network of hospital services. CSI functions by clustering patients into groups based on similarity of trajectory using a novel Semi-Markov model (SMM)-based clustering scheme proposed in this paper, as opposed to clustering by admit type or condition as in previous literature. The methodology is validated by simulation and then applied to real patient data from a partner hospital where we see it outperforms current methods. Further, we demonstrate that extant optimization methods achieve significantly better results on key hospital performance measures under CSI, compared with traditional estimation approaches, increasing elective admissions by 97% and utilization by 22% compared to 30% and 8% using traditional estimation techniques. From a theoretical standpoint, the SMM-clustering is a novel approach applicable to any temporal-spatial stochastic data that is prevalent in many industries and application areas.