scholarly journals Accuracy of the Orebro Musculoskeletal Pain Questionnaire and Work Assessment Triage Tool for selecting interventions in workers with spinal conditions

Author(s):  
Eloi Gergelé ◽  
Eric Parent ◽  
Douglas P. Gross

BACKGROUND: Accurate clinical decision support tools may help clinicians select appropriate interventions for patients with spinal conditions. The Orebro Musculoskeletal Pain Questionnaire (OMPQ) is a screening questionnaire extensively studied as a predictive tool. The Work Assessment Triage Tool (WATT) is a clinical decision support tool developed to help select interventions for injured workers. OBJECTIVE: To compare the classification accuracy of the OMPQ and WATT to clinician recommendations for selecting interventions leading to a successful return to work in patients with spinal conditions. METHODS: A secondary analysis was undertaken of data from injured workers with spinal conditions assessed between 2013 and 2016. We considered it a success if the workers did not receive wage replacement benefits 30 days after assessment. Analysis included positive likelihood ratio (LR+) as an indicator of predictive accuracy. RESULTS: Within the database, 2,872 patients had complete data on the OMPQ, WATT, and clinician recommendations. At 30 days, the OMPQ was most accurate for identifying treatments that lead to successful outcomes with a LR+= 1.51 (95% Confidence Interval 1.26–1.82) compared to 1.05 (95% Confidence Interval 1.02–1.09) for clinicians, and 0.85 (95% Confidence Interval 0.79–0.91) for the WATT. CONCLUSIONS: All tool recommendations had poor accuracy, however the OMPQ demonstrated significantly better results.

2020 ◽  
Vol 11 (02) ◽  
pp. 315-322
Author(s):  
Cameron Escovedo ◽  
Douglas Bell ◽  
Eric Cheng ◽  
Omai Garner ◽  
Alyssa Ziman ◽  
...  

Abstract Objective A growing body of evidence suggests that testing for influenza virus alone is more appropriate than multiplex respiratory viral panel (RVP) testing for general populations of patients with respiratory tract infections. We aimed to decrease the proportion of RVPs out of total respiratory viral testing ordered during influenza season. Methods We implemented two consecutive interventions: reflex testing for RVPs only after a negative influenza test, and noninterruptive clinical decision support (CDS) including modifications of the computerized physician order entry search behavior and cost display. We conducted an interrupted time series of RVPs and influenza polymerase chain reaction tests pre- and postintervention, and performed a mixed-effects logistic regression analysis with a primary outcome of proportion of RVPs out of total respiratory viral tests. The primary predictor was the intervention period, and covariates included the provider, clinical setting, associated diagnoses, and influenza incidence. Results From March 2013 to April 2019, there were 24,294 RVPs and 26,012 influenza tests (n = 50,306). Odds of ordering an RVP decreased during the reflex testing period (odds ratio: 0.432, 95% confidence interval: 0.397–0.469), and decreased more dramatically during the noninterruptive CDS period (odds ratio: 0.291, 95% confidence interval: 0.259–0.327). Discussion The odds of ordering an RVP were 71% less with the noninterruptive CDS intervention, which projected 4,773 fewer RVPs compared with baseline. Assuming a cost equal to Medicare reimbursement rates for RVPs and influenza tests, this would generate an estimated averted cost of $1,259,474 per year. Conclusion Noninterruptive CDS interventions are effective in reducing unnecessary and expensive testing, and avoid typical pitfalls such as alert fatigue.


2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.


2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.


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