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A bayesian approach to laboratory utilization management.

Hauser RG, Jackson BR, Shirts BH - J Pathol Inform (2015)

Bottom Line: The model identified subpopulations within the cohort with a low prevalence of disease.This suggests too many orders occurred from patients at low risk for EV.We introduce a new method for laboratory utilization management programs to audit laboratory services.

View Article: PubMed Central - PubMed

Affiliation: Department of Laboratory Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.

ABSTRACT

Background: Laboratory utilization management describes a process designed to increase healthcare value by altering requests for laboratory services. A typical approach to monitor and prioritize interventions involves audits of laboratory orders against specific criteria, defined as rule-based laboratory utilization management. This approach has inherent limitations. First, rules are inflexible. They adapt poorly to the ambiguity of medical decision-making. Second, rules judge the context of a decision instead of the patient outcome allowing an order to simultaneously save a life and break a rule. Third, rules can threaten physician autonomy when used in a performance evaluation.

Methods: We developed an alternative to rule-based laboratory utilization. The core idea comes from a formula used in epidemiology to estimate disease prevalence. The equation relates four terms: the prevalence of disease, the proportion of positive tests, test sensitivity and test specificity. When applied to a laboratory utilization audit, the formula estimates the prevalence of disease (pretest probability [PTP]) in the patients tested. The comparison of PTPs among different providers, provider groups, or patient cohorts produces an objective evaluation of laboratory requests. We demonstrate the model in a review of tests for enterovirus (EV) meningitis.

Results: The model identified subpopulations within the cohort with a low prevalence of disease. These low prevalence groups shared demographic and seasonal factors known to protect against EV meningitis. This suggests too many orders occurred from patients at low risk for EV.

Conclusion: We introduce a new method for laboratory utilization management programs to audit laboratory services.

No MeSH data available.


Related in: MedlinePlus

Deciding to test, a graph of pretest probability (PTP) versus expected utility. Utility curves for treatment, test, and nonintervention are shown as gray solid lines. The dotted line represents the maximum utility of the available choices. The cutoff PTPs were labeled PTPLow and PTPHigh. PTP: Pretest probability, EU: Expected utility
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Figure 1: Deciding to test, a graph of pretest probability (PTP) versus expected utility. Utility curves for treatment, test, and nonintervention are shown as gray solid lines. The dotted line represents the maximum utility of the available choices. The cutoff PTPs were labeled PTPLow and PTPHigh. PTP: Pretest probability, EU: Expected utility

Mentions: Our approach to test utilization management closely follows medical decision-making theory.[24] In medical decision making the optimal strategy when faced with a diagnostic dilemma is to choose the option with the highest utility: nonintervention, test, or treatment. The PTP of disease in the patient under evaluation informs the choice. Figure 1 shows a prototypical example of the utility curve for each choice across a range of PTP. At low PTP, the patient has a low probability of disease, and nonintervention maximizes utility. The patient likely has the disease at high PTP and will benefit most from treatment. The patient's disease state has the most uncertainty and the test has the greatest expected utility between the extreme values of PTP. Along the continuum of PTP the three decisions (nonintervention, test, and treat) form two points of equivalent utility (nonintervention-test and test-treat). We define these two cutoff points as PTPLow and PTPHigh [Figure 1].


A bayesian approach to laboratory utilization management.

Hauser RG, Jackson BR, Shirts BH - J Pathol Inform (2015)

Deciding to test, a graph of pretest probability (PTP) versus expected utility. Utility curves for treatment, test, and nonintervention are shown as gray solid lines. The dotted line represents the maximum utility of the available choices. The cutoff PTPs were labeled PTPLow and PTPHigh. PTP: Pretest probability, EU: Expected utility
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4355837&req=5

Figure 1: Deciding to test, a graph of pretest probability (PTP) versus expected utility. Utility curves for treatment, test, and nonintervention are shown as gray solid lines. The dotted line represents the maximum utility of the available choices. The cutoff PTPs were labeled PTPLow and PTPHigh. PTP: Pretest probability, EU: Expected utility
Mentions: Our approach to test utilization management closely follows medical decision-making theory.[24] In medical decision making the optimal strategy when faced with a diagnostic dilemma is to choose the option with the highest utility: nonintervention, test, or treatment. The PTP of disease in the patient under evaluation informs the choice. Figure 1 shows a prototypical example of the utility curve for each choice across a range of PTP. At low PTP, the patient has a low probability of disease, and nonintervention maximizes utility. The patient likely has the disease at high PTP and will benefit most from treatment. The patient's disease state has the most uncertainty and the test has the greatest expected utility between the extreme values of PTP. Along the continuum of PTP the three decisions (nonintervention, test, and treat) form two points of equivalent utility (nonintervention-test and test-treat). We define these two cutoff points as PTPLow and PTPHigh [Figure 1].

Bottom Line: The model identified subpopulations within the cohort with a low prevalence of disease.This suggests too many orders occurred from patients at low risk for EV.We introduce a new method for laboratory utilization management programs to audit laboratory services.

View Article: PubMed Central - PubMed

Affiliation: Department of Laboratory Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.

ABSTRACT

Background: Laboratory utilization management describes a process designed to increase healthcare value by altering requests for laboratory services. A typical approach to monitor and prioritize interventions involves audits of laboratory orders against specific criteria, defined as rule-based laboratory utilization management. This approach has inherent limitations. First, rules are inflexible. They adapt poorly to the ambiguity of medical decision-making. Second, rules judge the context of a decision instead of the patient outcome allowing an order to simultaneously save a life and break a rule. Third, rules can threaten physician autonomy when used in a performance evaluation.

Methods: We developed an alternative to rule-based laboratory utilization. The core idea comes from a formula used in epidemiology to estimate disease prevalence. The equation relates four terms: the prevalence of disease, the proportion of positive tests, test sensitivity and test specificity. When applied to a laboratory utilization audit, the formula estimates the prevalence of disease (pretest probability [PTP]) in the patients tested. The comparison of PTPs among different providers, provider groups, or patient cohorts produces an objective evaluation of laboratory requests. We demonstrate the model in a review of tests for enterovirus (EV) meningitis.

Results: The model identified subpopulations within the cohort with a low prevalence of disease. These low prevalence groups shared demographic and seasonal factors known to protect against EV meningitis. This suggests too many orders occurred from patients at low risk for EV.

Conclusion: We introduce a new method for laboratory utilization management programs to audit laboratory services.

No MeSH data available.


Related in: MedlinePlus