Integrating Decision Support into a Laboratory Utilization Management Program

2019 ◽  
Vol 39 (2) ◽  
pp. 245-257
Author(s):  
Kent Lewandrowski
2018 ◽  
Vol 09 (03) ◽  
pp. 519-527 ◽  
Author(s):  
Danielle Kurant ◽  
Jason Baron ◽  
Genti Strazimiri ◽  
Kent Lewandrowski ◽  
Joseph Rudolf ◽  
...  

Objectives Laboratory-based utilization management programs typically rely primarily on data derived from the laboratory information system to analyze testing volumes for trends and utilization concerns. We wished to examine the ability of an electronic health record (EHR) laboratory orders database to improve a laboratory utilization program. Methods We obtained a daily file from our EHR containing data related to laboratory test ordering. We then used an automated process to import this file into a database to facilitate self-service queries and analysis. Results The EHR laboratory orders database has proven to be an important addition to our utilization management program. We provide three representative examples of how the EHR laboratory orders database has been used to address common utilization issues. We demonstrate that analysis of EHR laboratory orders data has been able to provide unique insights that cannot be obtained by review of laboratory information system data alone. Further, we provide recommendations on key EHR data fields of importance to laboratory utilization efforts. Conclusion We demonstrate that an EHR laboratory orders database may be a useful tool in the monitoring and optimization of laboratory testing. We recommend that health care systems develop and maintain a database of EHR laboratory orders data and integrate this data with their laboratory utilization programs.


2016 ◽  
Vol 146 (2) ◽  
pp. 221-226 ◽  
Author(s):  
Patrick C. Mathias ◽  
Jessie H. Conta ◽  
Eric Q. Konnick ◽  
Darci L. Sternen ◽  
Shannon M. Stasi ◽  
...  

2015 ◽  
Vol 6 (1) ◽  
pp. 10 ◽  
Author(s):  
RonaldG Hauser ◽  
BrianR Jackson ◽  
BrianH Shirts

Author(s):  
Brahim Jabir ◽  
Noureddine Falih

<span>In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected.</span>


1995 ◽  
Vol 8 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Karen Cardiff ◽  
Geoffrey Anderson ◽  
Samuel Sheps

The objective of this study was to evaluate the impact of a utilization management (UM) program designed to decrease inappropriate use of acute care hospital beds while maintaining quality of care. The measure used to define appropriateness was the ISD-A, a diagnosis-independent measurement tool which relies on severity of illness and intensity of service criteria. The outcome measures for the study included appropriate admission to hospital and continued days of stay in hospital, 30-day readmission rates and physician perceptions of the impact of the intervention on quality of care, access to services and patient discharge patterns. The sample frame for the study included two control and two intervention community hospitals, involving 1,800 patient charts. Readmission rates were determined by analyzing all separations from medical services (N=42,014) in the two experimental and two control hospitals. All physicians with admitting privileges (N=312) at the intervention hospitals were surveyed; obstetricians, pediatricians, and psychiatrists were excluded from the survey. The results of the study demonstrated that the proportion of inappropriate admissions did not decrease significantly in any of the hospitals, but there were significant decreases in inappropriate continued stay in the intervention hospitals (p < 0.05). Both intervention and one of the control hospitals had lower 30-day readmission rates in the “after” period than in the “before” period (p < 0.05). Eighty-six percent believed that there had been no adverse impact on access to care and, although 25% thought the program may have led to premature discharge, this was not supported by the readmission data.


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