Hyojung Kang

Patient Flow in the Emergency Department: A Classification and Analysis of Admission Process Policies

STUDY OBJECTIVEnWe investigate the effect of admission process policies on patient flow in the emergency department (ED).nnnMETHODSnWe surveyed an advisory panel group to determine approaches to admission process policies and classified them as admission decision is made by the team of providers (attending physicians, residents, physician extenders) (type 1) or attending physicians (type 2) on the admitting service, team of providers (type 3), or attending physicians (type 4) in the ED. We developed discrete-event simulation models of patient flow to evaluate the potential effect of the 4 basic policy types and 2 hybrid types, referred to as triage attending physician consultation and remote collaborative consultation on key performance measures.nnnRESULTSnCompared with the current admission process policy (type 1), the alternatives were all effective in reducing the length of stay of admitted patients by 14% to 26%. In other words, patients may spend 1.4 to 2.5 hours fewer on average in the ED before being admitted to internal medicine under a new admission process policy. The improved flow of admitted patients decreased both the ED length of stay of discharged patients and the overall length of stay by up to 5% and 6.4%, respectively. These results are framed in context of teaching mission and physician experience.nnnCONCLUSIONnAn efficient admission process can reduce waiting times for both admitted and discharged ED patients. This study contributed to demonstrating the potential value of leveraging admission process policies and developing a framework for pursuing these policies.

A dynamic network analysis approach for evaluating knowledge dissemination in a multi-disciplinary collaboration network in obesity research

Effective knowledge dissemination is important to promote the adoption of new concepts and tools. This study aims to provide a framework that assesses strategies for successful knowledge dissemination in a research collaboration network. We propose a Markov-chain Monte Carlo (MCMC) approach along with Dynamic Network Analysis (DNA) to model a social network and understand how different knowledge dissemination strategies can be used in a research collaboration network. The proposed method was demonstrated through a case study that uses a multi-disciplinary collaboration network in obesity research at an academic medical center. To assess the impact of initial disseminators on knowledge dissemination, four different strategies were considered. The simulation results indicated that the best strategy to disseminate knowledge within this obesity research network may be to use central agents in clusters when considering the coverage and speed of knowledge dissemination.

Healthcare Systems Engineering: Griffin/Healthcare Systems Engineering

The Healthcare Systems Engineering program at Johns Hopkins University provides engineers and healthcare professionals with the indepth knowledge and skills necessary to apply systems engineering principles and best practices to address today’s healthcare challenges and create healthcare of the future. Students will be well prepared to re-engineer healthcare delivery on a broad scale by using a systems approach. This approach will lead to solutions that seamlessly integrate technology into the cultural and workflow dynamics prevalent in healthcare, while holistically addressing interoperability, security/ privacy, safety, cost, performance (i.e., outcomes, etc.), and other key requirements.