Dynamic Surgical Waiting List Methodology: A Networking Approach (original) (raw)

A new model to prioritize waiting lists for elective surgery under the COVID-19 pandemic pressure

2020

The COVID-19 pandemic burdens non-covid elective surgical patients by reducing service capacity, forcing extreme selection of patients most in need. Our study assesses the SWALIS- 2020 model ability to prioritize access to surgery during the highest viral outbreak peaks.A 2020 March - May feasibility-pilot study tested a software-aided, inter-hospital, multidisciplinary pathway. All specialties patients in the Genoa Surgical Departments referred for urgent elective patients were prioritized by a modified Surgical Waiting List InfoSystem (SWALIS) cumulative prioritization method (PAT-2020) based on waiting time and clinical urgency, in three subcategories: A1-15 days (certain rapid disease progression), A2-21 days (probable progression), and A3-30 days (potential progression). We have studied the model’s applicability and its ability to prioritize patients by monitoring waiting list and service performance. https://www.isrctn.com/ISRCTN11384058.Following the feasibility study (N=55 p...

CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19

Machine Learning

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)—a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating hete...

PREDICTING THE DURATION OF SURGERIES TO IMPROVE PROCESS EFFICIENCY IN HOSPITALS

Predicting the duration of surgeries is an important task because of the many dependencies between surgery processes and the hospital processes within other departments. Thus, accurate predictions allow for better coordinating patient processes throughout the hospital. Prior data-driven research provides evidence for accurate predictions of surgery durations enhancing the efficiency of surgery schedules. However, the current prediction models require large sets of features, which make their adoption more intricate. Moreover, prediction models focus on the surgery department and neglect potential effects on other departments. We use a unique dataset of about 17,000 surgeries to study how particular features and machine learning algorithms affect the prediction accuracy of major surgery steps. The prediction models that we study require few features and are easy to apply. The empirical findings can be useful for the design of surgery scheduling systems.

Application of Machine Learning in Hospital Resource Allocation -A Survey

International Journal for Research in Applied Science & Engineering Technology, 2021

One of the greatest challenges of any system is the efficient allocation of resources. During any pandemic, even well organized medical systems face many issues to facilitate patients in an appropriate way. This paper will present the survey for the usage of intelligent technologies in the allocation and management of resources in hospitals. It will conclude with the scope of technologies that can be used during COVID-19 for better hospital resource management.

“P3”: an adaptive modeling tool for post-COVID-19 restart of surgical services

JAMIA Open, 2021

Objective To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic. Materials and Methods Using data from 27 866 cases (May 1 2018–May 1 2020) stored in the Johns Hopkins All Children’s data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs. Results The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios. Conclusions Predictive analytics, machine learning, and multiple variable inputs coupled with nimble scenario-creation and a user-friendly visualization helped us to determine the most effective deployment of operating room personnel. Operating rooms worldwide can ...

Daily surgery caseload prediction: towards improving operating theatre efficiency

BMC Medical Informatics and Decision Making

Background In many hospitals, operating theatres are not used to their full potential due to the dynamic nature of demand and the complexity of theatre scheduling. Theatre inefficiencies may lead to access block and delays in treating patients requiring critical care. This study aims to employ operating theatre data to provide decision support for improved theatre management. Method Historical observations are used to predict long-term daily surgery caseload in various levels of granularity, from emergency versus elective surgeries to clinical specialty-level demands. A statistical modelling and a machine learning-based approach are developed to estimate daily surgery demand. The statistical model predicts daily demands based on historical observations through weekly rolling windows and calendar variables. The machine learning approach, based on regression algorithms, learns from a combination of temporal and sequential features. A de-identified data extract of elective and emergenc...