Hospital Bed-Capacity and Emergency-Physician Risk Management — Strategies to Design Pandemic Contingency Plans

Abstract

This article employs a discrete event simulator, CLOURAM (Cloud Risk Assessor and Manager), so to estimate risk indices in modern-day Cloud computing setting applicable to Hospital Healthcare Service Networks. This innovative approach has not been implemented earlier using a Cloud framework for digital queuing simulation. The article also innovatively examines emergency-physician management strategy through MCQS (Multi-Channel Queuing Simulation) and Hospital Scheduling. The macro-level goal is to assess and manage risk with tangible mitigation targets and to improve the operational quality of interconnected health care services for crucial needs such as improving the critical bed-count and dire physician-availability to meet growing demands towards designing pandemic contingency plans. The proposed methods are applied to five randomly selected States. The raw data originated from the national repository of States’ hospital networks. Such in-depth analyses not only assess the bed- and physician-inadequacy risk, but also foster feasibility plans by conducting cost and benefit analysis for future provisions of infrastructural needs to improve networked-healthcare services with cost-saving justifications. The results indicate that if physician-scarcities’ and bed-shortfalls’ admission and discharge input data can be traced to the States’ healthcare networks, the administrative and financial analysts can timely benefit from proactive digital simulations. JIT (Just-in-time) simulations would similarly help toward the States’ CON (Certificate of Need) laws, which require the capital expenditures’ approval by State health planning agencies to avoid unnecessary duplications of healthcare investment against wasteful practices.