David S. Toledano

Automated Bed Assignments in a Complex and Dynamic Hospital Environment

Bed management, an important function of any hospital, has a major impact on patient care, patient flow, patient and staff satisfaction, and ultimately on the hospitals operating margin. A key challenge in bed management is optimizing the bed-assignment process in a complex and dynamic operating environment. Efficient bed assignment requires the merging of clinical information, hospital operations information, interdependencies between units, and real-time information on patients, resources, and workflows. We have developed analytical decision support tools with embedded mathematical models to periodically recommend bed-patient assignments. Using an innovative mixed-integer goal-programming modeling approach, we are able to accommodate the multiple goals and complex operating rules of different hospitals. We implemented and hosted our prototype bed-assignment solution as a cloud-based application for Mount Sinai Medical Center in New York.

GE Asset Management, Genworth Financial, and GE Insurance Use a Sequential-Linear-Programming Algorithm to Optimize Portfolios

GE Asset Management Incorporated (GEAM), a wholly owned subsidiary of General Electric Company (GE), manages investment portfolios on behalf of various GE units and over 200 unaffiliated clients worldwide, including Genworth Financial (Genworth) and GE Insurance (GEI) portfolios worth billions of dollars. GEAM invests portfolios of assets—derived from cash flows for various insurance, reinsurance, and financial products—primarily in corporate and government bonds in accordance with risk and regulatory constraints. In asset-liability management (ALM) applications, portfolio managers try to maximize return or minimize risk and match the characteristics of asset portfolios with corresponding liabilities. While risk is widely represented by variance or volatility, it is usually a nonlinear measure; ALM portfolio managers traditionally need to use linear risk sensitivities for computational tractability. We developed a novel, sequential-linear-programming algorithm that handles the nonlinearity iteratively but efficiently. Patented and implemented on a limited basis since 2003, GE used it to optimize more than 30 portfolios valued at over

Occupancy and Activity Monitoring with Doppler Sensing and Edge Analytics: Demo Abstract

30 billion. It is now in broader use at GEAM, GEI, and Genworth. Hypothetically, based on

System and method to schedule resources in delivery of healthcare to a patient

100 billion of assets, the present value of potential benefits could approximate

System and method to manage delivery of healthcare to a patient

75 million over five years.

SYSTEMS AND METHODS FOR VIEWING BIOMETRICAL INFORMATION AND DYNAMICALLY ADAPTING SCHEDULE AND PROCESS INTERDEPENDENCIES WITH CLINICAL PROCESS DECISIONING

We demonstrate an occupancy detection and human activity monitoring system using low-cost motion sensing, edge computing and wireless networking devices. We design a dual Doppler sensor to achieve high signal sensitivity and wide sensing range. We develop signal filtering, detection and machine learning algorithms on an embedded computer to generate classification result in real-time. We also implement web services to enable users to access signal and room state via a wireless network. Compared with conventional occupancy sensors, the dual Doppler system has higher detection rate, and also has the capability of detecting activities even for multiple people.