Impact of Time-Motion Patient Flow Analysis on the Productivity of an Endoscopy Unit in a Large Safety-Net Health Center

Introduction: The COVID-19 pandemic disrupted health care delivery, particularly for high-volume procedural areas. To improve productivity in the Los Angeles County 1 University of Southern California Medical Center (LAC + USC) Endoscopy Unit, we initiated an iterative rapid cycle quality improvement process to identify inefficiencies and implement changes to our workflow. Method(s): A time-motion analysis of patient flow through the LAC + USC Endoscopy Unit was used to construct a time-tracked flow sheet to track individual patients as they moved through the Unit. Data were collected weekly over 3 9-10 week phases, and intervening plan-do-study-act (PDSA) cycles were conducted to direct interventions for subsequent phases. Following phase 1 (9/1/21 to 11/9/21) we implemented targeted interventions at the start of phase 2 (12/1/21 to 2/1/22) and phase 3 (3/15/22 to 5/31/22). Phase 2 was focused on our anesthesia supported endoscopy room which requires greater resource coordination. Metrics were compared to published benchmarks. Linear regression was used to compare outcome parameters for the lean process flow improvement project. Result(s): Our phase 1 analysis showed operational delays in room turnover time for all procedures and pre-operative assessment and first-case on time start percentage for procedures supported by anesthesia, when compared to published benchmarks (Table 1). In phase 2 we implemented an intervention of combining pre-anesthesia visits with endoscopy teaching visits for patients designated to have anesthesia support. This significantly improved both turnover time and throughput for the anesthesia room (Table 1). In phase 3 we initiated a policy of preparing the first patient of the day in the procedure room which dramatically increased first-case on time start percentage. We further streamlined inter-procedure processes by simultaneously consenting, placingmonitoring equipment and documenting in the time between procedures, leading to a greater than 20% increase in total procedure volume (Table 1). Procedure throughput for the anesthesia supported procedure room increased from 4.5 to 7 to 9 procedures per room per day for phases 1, 2, and 3 respectively (Table 1). EndoscopyUnit staffing remained unchanged throughout the study period. Conclusion(s): Time-motion analysis of patient flow may be used to perform targeted interventions with significant improvements in Endoscopy Unit efficiency. This may be achieved without costly interventions such as hiring additional support staff or faculty. (Table Presented).

Community Health in the Global and Asia-Pacific Context

Community health is the health status, including physical and mental wellbeing, of a group of people within a defined geographic region. Factors like population size, economy, politics, social and culture play important roles on affecting the health of a community. Health care systems vary across the globe and there is a huge performance disparity between high-income and low-income countries and regions. Developed and wealthy countries in Americas and Europe like United States, Netherlands and United Kingdom have sufficient finance to support their health care systems, while many countries in the Asia-Pacific are once being identified as being in short of resources to support health care developments. In fact, regions like Australia, Singapore and Hong Kong have well-developed health care systems and China, too, has made great achievements in improving the health status of the population. Facing continuous threats to global health, ranging from growing of noncommunicable diseases, outbreaks of emergent infectious diseases, such as Ebola, to the latest pandemic of COVID-19, health care systems in all countries are facing the challenges in tackling these worldwide health issues. There is an urgent need to strengthen the equity of access to medical care and resources so that citizens are protected from diseases and can maintain a healthy living. © 2022 selection and editorial matter, Ben Y.F. Fong and Martin C.S. Wong;individual chapters, the contributors.

Geospatial analytics for COVID-19 active case detection

Ever since the COVID-19 pandemic started in Wuhan, China, much research work has been focusing on the clinical aspect of SARS-CoV-2. Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus. Limited studies have, however, reported on COVID-19 transmission pattern analysis, and using geography features for prediction of potential outbreak sites. Predicting the next most probable outbreak site is crucial, particularly for optimizing the planning of medical personnel and supply resources. To tackle the challenge, this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude, when would the outbreak likely to happen and the duration of the outbreak. The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia. The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19. Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases, next outbreak location, and the time interval between start dates of two similar sites. Such findings provided valuable insights for policymakers to perform Active Case Detection. © 2021 Tech Science Press. All rights reserved.