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

JAMIA Open ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Divya Joshi ◽  
Ali Jalali ◽  
Todd Whipple ◽  
Mohamed Rehman ◽  
Luis M Ahumada
Keyword(s):  
Data Warehouse ◽  
Predictive Analytics ◽  
Surgical Patient ◽  
Protective Equipment ◽  
Operating Room Personnel ◽  
Surgical Services ◽  
Multiple Variable ◽  
Multiple Variables ◽  
Using Data ◽  
User Friendly

Abstract 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 use this tool to overcome patient backlog safely.

scholarly journals Income, wealth and use of personal protection equipment in the Mekong Delta

2021 ◽  
Author(s):  
Matteo Migheli
Keyword(s):  
Low Income ◽  
Mekong Delta ◽  
Developed Countries ◽  
Household Wealth ◽  
Protective Equipment ◽  
Personal Protection ◽  
Personal Protection Equipment ◽  
Management Techniques ◽  
Using Data ◽  
Developing And Developed Countries

AbstractBoth in developing and developed countries, farmers often do not protect themselves adequately, especially when applying agrochemicals that are dangerous for their health. The issue is relevant because insufficient protection is between the causes leading to intoxication of farmers and workers who handle these products. The literature suggests that both lack of training and information and low income may explain why, especially in developing countries, protective equipment is under-used. Using data from the Mekong Delta, this study addresses the issue of whether income and household wealth may help explaining the use of incomplete protections against pesticides. The results suggest that income, more than wealth, is a reason why Vietnamese farmers operating in the Mekong Delta fail in using adequate protections. In particular, the data suggest that they may prefer to divert resources to increasing the production of their fields or to buying goods that may be used both as protection and as everyday garments. This behaviour leads to underinvestment in some important protective goods. Possible public interventions to mitigate the problem are suggested; in particular, the promotion of integrated pest management techniques could be useful.

Using Data Warehouse to Discover the Relation between TW-DRG and Pharmacology

2011 ◽  
Vol 474-476 ◽  
pp. 938-942
Author(s):  
Chih Sheng Chen ◽  
Guan Yu Chen ◽  
Jing Wun Hong ◽  
Ji Rou Jhang ◽  
Jia Yi Liou ◽  
...  
Keyword(s):  
Policy Analysis ◽  
Data Warehouse ◽  
Medication Cost ◽  
Pharmacodynamic Effects ◽  
Patients Rights ◽  
Medical Institutions ◽  
Pharmaceutical Cost ◽  
Using Data

This research explores the relation between TW-DRG and pharmacological information by using the concept of data warehouse as a basis. It is hoped to assist doctors, under the condition that patients’ rights will not be affected, to replace the high-priced pharmaceuticals with the pharmaceuticals which are low-priced yet with the same pharmacological and pharmacodynamic effects, in order to reduce the medication cost in medical institutions and hospitals. From this result, we learn that the differences among doctors’ medication habits can be offered to hospitals and doctors for policy analysis on medication. Also, doctors can make appropriate adjustments in medication acts and find out the replaceable pharmaceuticals so that the pharmaceutical cost can be lowered.

scholarly journals University Accreditation using Data Warehouse

2017 ◽  
Vol 801 ◽  
pp. 012030 ◽  
Author(s):  
A S Sinaga ◽  
A S Girsang
Keyword(s):  
Data Warehouse ◽  
Using Data

Abstract 20172: Identifying Hospital Cardiac Services for Acute Myocardial Infarction Using Individual Patient Claims Data

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Tiffany E Chang ◽  
Shu-Xia Li ◽  
Isuru Ranasinghe ◽  
Harlan Krumholz
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Claims Data ◽  
Administrative Claims ◽  
Hospital Data ◽  
American Hospital ◽  
American Hospital Association ◽  
Surgical Services ◽  
Chest X Ray ◽  
Using Data

Background: Hospital data on cardiac services provided is restricted to a limited number of services collected by the American Hospital Association (AHA) Survey. We developed an alternative method to identify hospital services using individual patient administrative claims data for acute myocardial infarction (AMI) in the Premier Database. Methods: We first determined inpatient cardiac services relevant for AMI care from guidelines. Then, we identified these services from patient claims using ICD-9, CPT, Medicare Revenue and provider specialty codes. Additionally, Premier Chargemaster and Physician Specialty Codes were used. A hospital was classified as providing a service if they had >5 AMI patient claims for the service in the Premier database from 2009-2011. To measure the accuracy of the claims based method, we compared the percentage of hospitals that were shown to provide a service identified through the AHA survey for a subset of services identifiable from both sources. Results: We identified 32 services relevant for AMI care that could be defined using data with inpatient claims among 476 hospitals in the Premier database (Figure). The availability of these services ranged from 100% (for services such as chest x-ray) to 1% for heart transplant service. When compared to the subset of 12 services also collected in the AHA survey, a high percentage of agreement (≥80%) was noted for 10/16 (63%) services (such as a dedicated ED, general CT, coronary angiography, PCI, ICU, pharmacist and physio/OT services). Moderate agreement was seen for one service (coronary care unit), and 5/16 (31%) services showed low agreement (≤50%) (EP testing, inpatient cardiac surgical services, inpatient cardiac rehabilitation, transplant unit, and social worker). Conclusion: It is feasible to use claims data to determine in-hospital AMI services, but the accuracy of the method needs to be investigated further for certain services that have a low degree of agreement in our analysis.

scholarly journals Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

2020 ◽  
Vol 32 (1) ◽  
pp. 39-53
Author(s):  
Dalia Shanshal ◽  
Ceni Babaoglu ◽  
Ayşe Başar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Injury Severity ◽  
Predictive Analytics ◽  
Machine Learning Techniques ◽  
Lasso Regression ◽  
Severe Injuries ◽  
Factors Affecting ◽  
Spatio Temporal ◽  
Using Data

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

Physical Data Warehousing Design

2011 ◽  
pp. 906-911 ◽  
Author(s):  
Ladjel Bellatreche ◽  
Mukesh Mohania
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Historical Data ◽  
Data Warehousing ◽  
Management Decision ◽  
Materialized Views ◽  
Performance Goal ◽  
Interactive Nature ◽  
Management Decision Making ◽  
Using Data

Recently, organizations have increasingly emphasized applications in which current and historical data are analyzed and explored comprehensively, identifying useful trends and creating summaries of the data in order to support high-level decision making. Every organization keeps accumulating data from different functional units, so that they can be analyzed (after integration), and important decisions can be made from the analytical results. Conceptually, a data warehouse is extremely simple. As popularized by Inmon (1992), it is a “subject-oriented, integrated, time-invariant, non-updatable collection of data used to support management decision-making processes and business intelligence”. A data warehouse is a repository into which are placed all data relevant to the management of an organization and from which emerge the information and knowledge needed to effectively manage the organization. This management can be done using data-mining techniques, comparisons of historical data, and trend analysis. For such analysis, it is vital that (1) data should be accurate, complete, consistent, well defined, and time-stamped for informational purposes; and (2) data should follow business rules and satisfy integrity constraints. Designing a data warehouse is a lengthy, time-consuming, and iterative process. Due to the interactive nature of a data warehouse application, having fast query response time is a critical performance goal. Therefore, the physical design of a warehouse gets the lion’s part of research done in the data warehousing area. Several techniques have been developed to meet the performance requirement of such an application, including materialized views, indexing techniques, partitioning and parallel processing, and so forth. Next, we briefly outline the architecture of a data warehousing system.

Cluster-Based Smartphone Predictive Analytics for Application Usage and Next Location Prediction

2018 ◽  
Vol 9 (2) ◽  
pp. 64-80
Author(s):  
Xiaoling Lu ◽  
Bharatendra Rai ◽  
Yan Zhong ◽  
Yuzhu Li
Keyword(s):  
Prediction Models ◽  
Predictive Analytics ◽  
User Behavior ◽  
Training Dataset ◽  
Location Prediction ◽  
Behavior Prediction ◽  
Test Dataset ◽  
Smartphone Sensors ◽  
Using Data ◽  
Prediction Approach

Prediction of app usage and location of smartphone users is an interesting problem and active area of research. Several smartphone sensors such as GPS, accelerometer, gyroscope, microphone, camera and Bluetooth make it easier to capture user behavior data and use it for appropriate analysis. However, differences in user behavior and increasing number of apps have made such prediction a challenging problem. In this article, a prediction approach that takes smartphone user behavior into consideration is proposed. The proposed approach is illustrated using data from over 30000 users from a leading IT company in China by first converting data in to recency, frequency, and monetary variables and then performing cluster analysis to capture user behavior. Prediction models are then developed for each cluster using a training dataset and their performance is assessed using a test dataset. The study involves ten different categories of apps and four different regions in Beijing. The proposed app usage prediction and next location prediction approach has provided interesting results.

Sign in / Sign up

Export Citation Format

Share Document