scholarly journals Preventing Genetic Testing Order Errors With a Laboratory Utilization Management Program

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


Author(s):  
Jessie Conta ◽  
Cheryl Hess ◽  
Jacquelyn Riley

Recently, hospital laboratories have significantly improved patient care by intercepting genetic tests that have been ordered in error or inappropriately. Such tests can be flagged before they are sent out to referral laboratories for testing. This is commonly performed by genetic counselors acting in support of test utilization management. This chapter details the role of the test utilization counselor. Multiple methods are described for developing and implementing a hospital-based test utilization management program for genetic testing.


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

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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