Can providing real-time warnings and feedback to physicians within a hospital information system reduce inappropriate glucocorticoid prescription?

2021 ◽  
Author(s):  
Yue Chang
Keyword(s):  
Information System ◽  
Real Time ◽  
Hospital Information System

scholarly journals Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tian-Hoe Tan ◽  
Chien-Chin Hsu ◽  
Chia-Jung Chen ◽  
Shu-Lien Hsu ◽  
Tzu-Lan Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Real Time ◽  
Hospital Information System ◽  
Clinical Utility ◽  
Data Driven ◽  
Health Records ◽  
Machine Learning Approach ◽  
Testing Data ◽  
Model Training

Abstract Background Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML. Methods We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for model training and testing. Using 10 clinical variables from their electronic health records, a prediction model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes. Results The best areas under the curves of predicting outcomes were: random forest model for hospitalization (0.840), pneumonia (0.765), and sepsis or septic shock (0.857), XGBoost for intensive care unit admission (0.902), and logistic regression for in-hospital mortality (0.889) in the testing data. The predictive model was further applied in the hospital information system to assist physicians’ decisions in real time. Conclusions ML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.

scholarly journals Predicting Outcomes in Older ED Patients with Influenza in Real Time Using a Big Data-Driven and Machine Learning Approach to the Hospital Information System

2020 ◽  
Author(s):  
Chien-Chin Hsu ◽  
Tian-Hoe Tan ◽  
Chia-Jung Chen ◽  
Shu-Lien Hsu ◽  
Tzu-Lan Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Random Forest ◽  
Real Time ◽  
Hospital Information System ◽  
Random Forest Model ◽  
Forest Model ◽  
Machine Learning Approach ◽  
Testing Data ◽  
Model Training

Abstract Background: Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML.Methods: We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for ML model training and testing. Using 10 clinical variables from their electronic health records, a random forest model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes. Results: The areas under the curves of predicting outcomes using the random forest model were 0.807 for hospitalizations, 0.974 for pneumonia, 0.994 for sepsis or septic shock, 0.981 for intensive care unit admission, and 0.851 for death in the testing data. The predictive model was further applied in the hospital information system to assist physicians’ decisions in real time.Conclusions: ML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.

scholarly journals Predicting Outcomes in Older ED Patients with Influenza in Real Time Using A Big Data-Driven and Machine Learning Approach to the Hospital Information System

2020 ◽  
Author(s):  
Chien-Chin Hsu ◽  
Tian-Hoe Tan ◽  
Chia-Jung Chen ◽  
Shu-Lien Hsu ◽  
Tzu-Lan Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Random Forest ◽  
Real Time ◽  
Hospital Information System ◽  
Random Forest Model ◽  
Forest Model ◽  
Machine Learning Approach ◽  
Testing Data ◽  
Model Training

Abstract Background: Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML.Methods: We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for ML model training and testing. Using 10 clinical variables from their electronic health records, a random forest model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes. Results: The areas under the curves of predicting outcomes using the random forest model were 0.807 for hospitalizations, 0.974 for pneumonia, 0.994 for sepsis or septic shock, 0.981 for intensive care unit admission, and 0.851 for death in the testing data. The predictive model was further applied in the hospital information system to assist physicians’ decisions in real time.Conclusions: ML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.

BLE-based Real-time Location System Integration with Hospital Information System to Reduce Patient Waiting Time

2021 ◽  
Author(s):  
Mohd Shafarudin Osman ◽  
Azizul Azizan ◽  
Khairul Nizam Hassan ◽  
Hadhrami Ab. Ghani ◽  
Noor Hafizah Hassan ◽  
...  
Keyword(s):  
Information System ◽  
Real Time ◽  
Waiting Time ◽  
System Integration ◽  
Hospital Information System ◽  
Location System ◽  
Patient Waiting Time ◽  
Real Time Location System ◽  
Patient Waiting

The Karolinska Hospital Information System

1974 ◽  
Vol 13 (03) ◽  
pp. 125-140 ◽  
Author(s):  
Ch. Mellner ◽  
H. Selajstder ◽  
J. Wolodakski
Keyword(s):  
Information System ◽  
Data Processing ◽  
Data Base ◽  
Hospital Information System ◽  
Care Delivery ◽  
Traditional Approach ◽  
Processing System ◽  
Systems Design ◽  
Data Processing System ◽  
Karolinska Hospital

The paper gives a report on the Karolinska Hospital Information System in three parts.In part I, the information problems in health care delivery are discussed and the approach to systems design at the Karolinska Hospital is reported, contrasted, with the traditional approach.In part II, the data base and the data processing system, named T1—J 5, are described.In part III, the applications of the data base and the data processing system are illustrated by a broad description of the contents and rise of the patient data base at the Karolinska Hospital.

A Computer-based Clinical Information System

1987 ◽  
Vol 26 (04) ◽  
pp. 189-194
Author(s):  
S. S. El-Gamal
Keyword(s):  
Information System ◽  
Hospital Information System ◽  
Clinical Information System ◽  
Research Work ◽  
Clinical Information ◽  
Hospital Staff ◽  
Vital Role ◽  
Daily Routine ◽  
Specialized Hospital ◽  
Computer Based

SummaryModern information technology offers new opportunities for the storage and manipulation of hospital information. A computer-based hospital information system, dedicated to urology and nephrology, was designed and developed in our center. It involves in principle the employment of a program that allows the analysis of non-restricted, non-codified texts for the retrieval and processing of clinical data and its operation by non-computer-specialized hospital staff.This Hospital Information System now plays a vital role in the efficient provision of a good quality service and is used in daily routine and research work in this hospital. This paper describes this specialized Hospital Information System.

Real-Time System for Sending Data from Three Clients to One Server with MATLAB

2015 ◽  
Vol 2 (1) ◽  
pp. 35-41
Author(s):  
Rivan Risdaryanto ◽  
Houtman P. Siregar ◽  
Dedy Loebis
Keyword(s):  
Information System ◽  
Real Time ◽  
Time System ◽  
Real Time System ◽  
The Real ◽  
Performance Time ◽  
Data Process ◽  
Effectiveness And Efficiency

The real-time system is now used on many fields, such as telecommunication, military, information system, evenmedical to get information quickly, on time and accurate. Needless to say, a real-time system will always considerthe performance time. In our application, we define the time target/deadline, so that the system should execute thewhole tasks under predefined deadline. However, if the system failed to finish the tasks, it will lead to fatal failure.In other words, if the system cannot be executed on time, it will affect the subsequent tasks. In this paper, wepropose a real-time system for sending data to find effectiveness and efficiency. Sending data process will beconstructed in MATLAB and sending data process has a time target as when data will send.

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