The Promise-and Pitfalls-of Computerized Provider Alerts for Laboratory Test Ordering

Behavioral Strategies in Diagnostic Stewardship

Diagnostic stewardship refers to the responsible and judicious use of diagnostic tests to reduce low value care and improve patient outcomes. This article provides an overview of behavioral strategies, their relevance to diagnostic stewardship and highlights behavioral determinants that drive diagnostic testing behavior, drawing on theoretic frameworks. Additionally, we provide concrete examples of evidence-based behavioral strategies for promoting appropriate diagnostic testing while acknowledging associated challenges. Finally, we highlight the significance of evaluating these strategies and provide an overview of evaluation frameworks and methods.

Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts

Objectives

While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and alert fatigue. We sought to develop machine learning models to predict whether a clinician will accept the advice provided by a CDS alert. Such models could reduce alert burden by targeting CDS alerts to specific cases where they are most likely to be effective.

Materials and Methods

We focused on a set of laboratory test ordering alerts, deployed at 8 hospitals within the Partners Healthcare System. The alerts notified clinicians of duplicate laboratory test orders and advised discontinuation. We captured key attributes surrounding 60 399 alert firings, including clinician and patient variables, and whether the clinician complied with the alert. Using these data, we developed logistic regression models to predict alert compliance.

Results

We identified key factors that predicted alert compliance; for example, clinicians were less likely to comply with duplicate test alerts triggered in patients with a prior abnormal result for the test or in the context of a nonvisit-based encounter (eg, phone call). Likewise, differences in practice patterns between clinicians appeared to impact alert compliance. Our best-performing predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.82. Incorporating this model into the alerting logic could have averted more than 1900 alerts at a cost of fewer than 200 additional duplicate tests.

Conclusions

Deploying predictive models to target CDS alerts may substantially reduce clinician alert burden while maintaining most or all the CDS benefit.

The Role of Diagnostic Stewardship in Clostridioides difficile Testing: Challenges and Opportunities

PURPOSE OF REVIEW

Accurate and timely diagnosis of Clostridioides difficile infection (CDI) is imperative to prevent C. difficile transmission and reduce morbidity and mortality due to CDI, but CDI laboratory diagnostics are complex. The purpose of this article is to review the role of laboratory tests in the diagnosis of CDI, and the role of diagnostic stewardship in optimization of C. difficile testing.

RECENT FINDINGS

Results from C. difficile diagnostic tests should be interpreted with an understanding of the strengths and limitations inherent in each testing approach. Use of highly sensitive molecular diagnostic tests without accounting for clinical signs and symptoms may lead to over-diagnosis of CDI and increased facility CDI rates. Current guidelines recommend a two-step, algorithmic approach for testing. Diagnostic stewardship interventions, such as education, order sets, order search menus, reflex orders, hard and soft stop alerts, electronic references, feedback and benchmarking, decision algorithms, and predictive analytics may help improve use of C. difficile laboratory tests and CDI diagnosis. The diagnostic stewardship approaches with the highest reported success rates include computerized clinical decision support (CCDS) interventions, face-to-face feedback, and real-time evaluations.

SUMMARY

CDI is a clinical diagnosis supported by laboratory findings. Together, clinical evaluation combined with diagnostic stewardship can optimize the accurate diagnosis of CDI.

Improving laboratory test utilisation at the multihospital Yale New Haven Health System

Background

Waste persists in healthcare and negatively impacts patients. Clinicians have direct control over test ordering and ongoing international efforts to improve test utilisation have identified multifaceted approaches as critical to the success of interventions. Prior to 2015, Yale New Haven Health lacked a coherent strategy for laboratory test utilisation management.

Methods

In 2015, a system-wide laboratory formulary committee was formed at Yale New Haven Health to manage multiple interventions designed to improve test utilisation. We report here on specific interventions conducted between 2015 and 2017 including reduction of (1) obsolete or misused testing, (2) duplicate orders, and (3) daily routine lab testing. These interventions were driven by a combination of modifications to computerised physician order entry, test utilisation dashboards and physician education. Measurements included test order volume, blood savings and cost savings.

Results

Testing for a number of obsolete/misused analytes was eliminated or significantly decreased depending on alert rule at order entry. Hard stops significantly decreased duplicate testing and educational sessions significantly decreased daily orders of routine labs and increased blood savings but the impact waned over time for select groups. In total, we realised approximately $100 000 of cost savings during the study period.

Conclusion

Through a multifaceted approach to utilisation management, we show significant reductions in low-value clinical testing that have led to modest but significant savings in both costs and patients’ blood.