scholarly journals Combined real-time PCR and galactomannan surveillance improves diagnosis of invasive aspergillosis in high risk patients with haematological malignancies

2013 ◽  
Vol 161 (4) ◽  
pp. 517-524 ◽  
Author(s):  
Thomas R. Rogers ◽  
Charles Oliver Morton ◽  
Jan Springer ◽  
Eibhlin Conneally ◽  
Werner Heinz ◽  
...  
Keyword(s):  
High Risk ◽  
Invasive Aspergillosis ◽  
Real Time ◽  
Real Time Pcr ◽  
Haematological Malignancies ◽  
High Risk Patients ◽  
Risk Patients

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

2021 ◽  
Vol 12 (02) ◽  
pp. 372-382
Author(s):  
Christine Xia Wu ◽  
Ernest Suresh ◽  
Francis Wei Loong Phng ◽  
Kai Pik Tai ◽  
Janthorn Pakdeethai ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Real Time ◽  
Risk Score ◽  
Patient Specific ◽  
Learning Models ◽  
Medicine Department ◽  
High Risk Patients ◽  
Risk Patients ◽  
Machine Learning Models

Abstract Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

scholarly journals A prospective, randomised study on the use of well-fitting masks for prevention of invasive aspergillosis in high-risk patients

2009 ◽  
Vol 20 (9) ◽  
pp. 1560-1564 ◽  
Author(s):  
G. Maschmeyer ◽  
S. Neuburger ◽  
L. Fritz ◽  
A. Böhme ◽  
O. Penack ◽  
...  
Keyword(s):  
High Risk ◽  
Invasive Aspergillosis ◽  
Randomised Study ◽  
High Risk Patients ◽  
Risk Patients ◽  
Prospective Randomised Study

scholarly journals Real-Time Emergency Department Electronic Notifications Regarding High-Risk Patients: A Systematic Review

2019 ◽  
Vol 25 (7) ◽  
pp. 604-618
Author(s):  
Hannah J. Kimmel ◽  
Yanick N. Brice ◽  
Thomas A. Trikalinos ◽  
Indra Neil Sarkar ◽  
Megan L. Ranney
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
High Risk ◽  
Real Time ◽  
High Risk Patients ◽  
Risk Patients
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