A novel strategy for evaluating the effects of an electronic test ordering alert message: Optimizing cardiac marker use

Clinical decision support to improve CBC and differential ordering

OBJECTIVES

Complete blood count and differential (CBC diff) is a common laboratory test that may be overused or misordered, particularly in an inpatient setting. We assessed the ability of a clinical decision support (CDS) alert to decrease unnecessary orders for CBC diff and analyzed its impact in the laboratory.

METHODS

We designed 3 CDS alerts to provide guidance to providers ordering CBC diff on inpatients at frequencies of daily, greater than once daily, or as needed.

RESULTS

The 3 alerts were highly effective in reducing orders for CBC diff at the frequencies targeted by the alert. Overall, test volume for CBC diff decreased by 32% (mean of 5257 tests per month) after implementation of the alerts, with a corresponding decrease of 22% in manual differentials performed (mean of 898 per month). Turnaround time for manual differentials decreased by a mean of 41.5 minutes, with a mean decrease of up to 90 minutes during peak morning hours.

CONCLUSIONS

The 3 CDS alerts successfully decreased inpatient orders for CBC diff and improved the quality of patient care by decreasing turnaround time for manual differentials.

Clinical Decision Support for Laboratory Testing

BACKGROUND

As technology enables new and increasingly complex laboratory tests, test utilization presents a growing challenge for healthcare systems. Clinical decision support (CDS) refers to digital tools that present providers with clinically relevant information and recommendations, which have been shown to improve test utilization. Nevertheless, individual CDS applications often fail, and implementation remains challenging.

CONTENT

We review common classes of CDS tools grounded in examples from the literature as well as our own institutional experience. In addition, we present a practical framework and specific recommendations for effective CDS implementation.

SUMMARY

CDS encompasses a rich set of tools that have the potential to drive significant improvements in laboratory testing, especially with respect to test utilization. Deploying CDS effectively requires thoughtful design and careful maintenance, and structured processes focused on quality improvement and change management play an important role in achieving these goals.

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.

Reducing redundant creatine kinase testing in cardiac injury

Background

Creatine kinase (CK) testing in the setting of suspected cardiac injury is commonly performed yet rarely provides clinical value beyond troponin testing. We sought to evaluate and reduce CK testing coupled with troponin testing by 50% or greater.

Methods

We performed root cause analysis to study prevailing processes and patterns of CK testing. We developed new institutional guidelines, removed CK from high-volume paper and electronic order bundles and conducted academic detailing for departments with highest ordering frequency. We evaluated consecutive patients at Sunnybrook Health Sciences Centre between 1 January 2018 and 31 March 2020 who had either a CK or troponin level measured. We prespecified successful implementation as a reduction of 50% in total CK orders and a decrease in the ratio of CK-to-troponin tests to one-third or less. We retained additional data beyond our study period to assess for sustained reductions in testing.

Results

Total CK tests decreased over the study period from 3963 to 2111 per month, amounting to a 46.7% reduction (95% CI 33.2 to 60.2; p<0.001) equalling 61 fewer tests per hospital day. Troponin testing did not significantly change during the intervention. Ratio of CK-to-troponin tests decreased from 0.91 to 0.49 (p<0.001). The reduction coincided with changes to order-sets, was observed across all clinical units and was sustained during additional months beyond the study period. These reductions in testing resulted in a projected annual cost savings of C$28 446.

Conclusions

We demonstrate that a low-cost and feasible quality improvement initiative may lead to significant reduction in unnecessary CK testing and substantial savings in healthcare costs for patients with suspected cardiac injury.

A systematic literature review on unnecessary diagnostic testing: The role of ICT use

BACKGROUND

The negative impact of unnecessary diagnostic tests on healthcare systems and patients has been widely recognized. Medical researchers in various countries have been devoting effort to reduce unnecessary diagnostic tests by using different types of interventions, including information and communications technology-based (ICT-based) intervention, educational intervention, audit and feedback, the introduction of guidelines or protocols, and the reward and punishment of staff. We conducted a review of ICT based interventions and a comparative analysis of their relative effectiveness in reducing unnecessary tests.

METHOD

A systematic Boolean search in PubMed, EMBase and EBSCOhost research databases was performed. Keyword search and citation analysis were also conducted. Empirical studies reporting ICT based interventions, and their implications on relative effectiveness in reducing unnecessary diagnostic tests (pathology tests or medical imaging) were evaluated independently by two reviewers based on a rigorously developed coding protocol.

RESULTS

92 research articles from peer-reviewed journals were identified as eligible. 47 studies involved a single-method intervention and 45 involved multi-method interventions. Regardless of the number of interventions involved in the studies, ICT-based interventions were utilized by 71 studies and 59 of them were shown to be effective in reducing unnecessary testing. A clinical decision support (CDS) tool appeared to be the most adopted ICT approach, with 46 out of 71 studies using CDS tools. The CDS tool showed effectiveness in reducing test volume in 38 studies and reducing cost in 24 studies.

CONCLUSIONS

This review investigated five frequently utilized intervention methods, ICT-based, education, introduction of guidelines or protocols, audit and feedback, and reward and punishment. It provides in-depth analysis of the efficacy of different types of interventions and sheds insights about the benefits of ICT based interventions, especially those utilising CDS tools, to reduce unnecessary diagnostic testing. The replicability of the studies is limited due to the heterogeneity of the studies in terms of context, study design, and targeted types of tests.

HISTOBREAST, a collection of brightfield microscopy images of Haematoxylin and Eosin stained breast tissue

Modern histopathology workflows rely on the digitization of histology slides. The quality of the resulting digital representations, in the form of histology slide image mosaics, depends on various specific acquisition conditions and on the image processing steps that underlie the generation of the final mosaic, e.g. registration and blending of the contained image tiles. We introduce HISTOBREAST, an extensive collection of brightfield microscopy images that we collected in a principled manner under different acquisition conditions on Haematoxylin - Eosin (H&E) stained breast tissue. HISTOBREAST is comprised of neighbour image tiles and ensemble of mosaics composed from different combinations of the available image tiles, exhibiting progressively degraded quality levels. HISTOBREAST can be used to benchmark image processing and computer vision techniques with respect to their robustness to image modifications specific to brightfield microscopy of H&E stained tissues. Furthermore, HISTOBREAST can serve in the development of new image processing methods, with the purpose of ensuring robustness to typical image artefacts that raise interpretation problems for expert histopathologists and affect the results of computerized image analysis.

Validation and Implementation of an Ordering Alert to Improve the Efficiency of Monoclonal Gammopathy Evaluation

OBJECTIVES

To evaluate the use of a provider ordering alert to improve laboratory efficiency and reduce costs.

METHODS

We conducted a retrospective study to assess the use of an institutional reflex panel for monoclonal gammopathy evaluation. We then created a clinical decision support (CDS) alert to educate and encourage providers to change their less-efficient orders to the reflex panel.

RESULTS

Our retrospective analysis demonstrated that an institutional reflex panel could be safely substituted for a less-efficient and higher-cost panel. The implemented CDS alert resulted in 79% of providers changing their high-cost order panel to an order panel based on the reflex algorithm.

CONCLUSIONS

The validated decision support alert demonstrated high levels of provider acceptance and directly led to operational and cost savings within the laboratory. Furthermore, these studies highlight the value of laboratory involvement with CDS efforts to provide agile and targeted provider ordering assistance.

Training Aspects of Laboratory-Based Decision Support

Pathology has a large role to play in the proper development, implementation, and optimization of clinical decision support (CDS). CDS training must be supported by an educational foundation in clinical and pathology informatics. Educational opportunities are currently limited, but expanding, in the pathology residency space with Pathology Informatics Essentials for Residents. The use of an educational version of electronic clinical systems is an important educational tool to support the needed outcomes-driven and exercise-based informatics and CDS training. With the multidisciplinary nature of informatics, it is advantageous to include laboratory professionals in the training exercises as appropriate.

Machine Learning and Other Emerging Decision Support Tools

Emerging applications of machine learning and artificial intelligence offer the opportunity to discover new clinical knowledge through secondary exploration of existing patient medical records. This new knowledge may in turn offer a foundation to build new types of clinical decision support (CDS) that provide patient-specific insights and guidance across a wide range of clinical questions and settings. This article will provide an overview of these emerging approaches to CDS, discussing both existing technologies as well as challenges that health systems and informaticists will need to address to allow these emerging approaches to reach their full potential.

Decision Support Tools within the Electronic Health Record

Laboratory tests are an integral part of the electronic health record (EHR). Providing clinical decision support (CDS) for the ordering, collection, reporting, viewing, and interpretation of laboratory testing is a fundamental function of the EHR. The implementation of a sustainable, effective laboratory CDS program requires a commitment to standardization and harmonization of the laboratory dictionaries that are the foundation of laboratory-based CDS. In this review, the authors provide an overview of the tools available within the EHR to improve decision making throughout the entire laboratory testing process, from test order to clinical action.

Health Informatics Tools to Improve Utilization of Laboratory Tests

Herein, we discuss improper test utilization practices and their implications on delivery of health care, as well as providing a brief explanation of the means to reduce such practices by improvement of personnel factors, particularly involving physicians. The article also elaborates on ways to mitigate improperly utilized test practices using appropriate health informatics technologies to their maximum possible capacities.

Analysis of Daily Laboratory Orders at a Large Urban Academic Center: A Multifaceted Approach to Changing Test Ordering Patterns

OBJECTIVES

We sought to address concerns regarding recurring inpatient laboratory test order practices (daily laboratory tests) through a multifaceted approach to changing ordering patterns.

METHODS

We engaged in an interdepartmental collaboration to foster mindful test ordering through clinical policy creation, electronic clinical decision support, and continuous auditing and feedback.

RESULTS

Annualized daily order volumes decreased from approximately 25,000 to 10,000 during a 33-month postintervention review. This represented a significant change from preintervention order volumes (95% confidence interval, 0.61-0.64; P < 10-16). Total inpatient test volumes were not affected.

CONCLUSIONS

Durable changes to inpatient order practices can be achieved through a collaborative approach to utilization management that includes shared responsibility for establishing clinical guidelines and electronic decision support. Our experience suggests auditing and continued feedback are additional crucial components to changing ordering behavior. Curtailing daily orders alone may not be a sufficient strategy to reduce in-laboratory costs.

Use of a Checklist and Clinical Decision Support Tool Reduces Laboratory Use and Improves Cost

OBJECTIVE

We hypothesized that a daily rounding checklist and a computerized order entry (CPOE) rule that limited the scheduling of complete blood cell counts and chemistry and coagulation panels to a 24-hour interval would reduce laboratory utilization and associated costs.

METHODS

We performed a retrospective analysis of these initiatives in a pediatric cardiovascular ICU (CVICU) that included all patients with congenital or acquired heart disease admitted to the cardiovascular ICU from September 1, 2008, until April 1, 2011. Our primary outcomes were the number of laboratory orders and cost of laboratory orders. Our secondary outcomes were mortality and CVICU and hospital length of stay.

RESULTS

We found a reduction in laboratory utilization frequency in the checklist intervention period and additional reduction in the CPOE intervention period [complete blood count: 31% and 44% (P < .0001); comprehensive chemistry panel: 48% and 72% (P < .0001); coagulation panel: 26% and 55% (P < .0001); point of care blood gas: 43% and 44% (P < .0001)] compared with the preintervention period. Projected yearly cost reduction was $717,538.8. There was no change in adjusted mortality rate (odds ratio 1.1, 95% confidence interval 0.7-1.9, P = .65). CVICU and total length of stay (days) was similar in the pre- and postintervention periods.

CONCLUSIONS

Use of a daily checklist and CPOE rule reduced laboratory resource utilization and cost without adversely affecting adjusted mortality or length of stay. CPOE has the potential to hardwire resource management interventions to augment and sustain the daily checklist.

Electronic alerts for triage protocol compliance among emergency department triage nurses: a randomized controlled trial

BACKGROUND

Alerts embedded in electronic health records (EHRs) are designed to improve processes at the point of care.

OBJECTIVE

The aim of this study was to determine if an EHR alert-at emergency department (ED) triage-increases the ED triage nurse's utilization of triage protocols.

METHODS

ED triage nurses were randomized to receive either a passive EHR alert or no intervention for patients with the following complaints: minor trauma with extremity injuries or female patients with abdominal pain. The EHR alert notified the nurse that the patient was eligible for diagnostic testing: radiographs for patients with injured extremities or urinalysis for female patients with abdominal pain.

RESULTS

Twenty-eight nurses triaged 20,410 patients in the 6 months before the intervention and 19,157 in the 6 months after the intervention. Before the intervention, the urinalysis protocol was implemented in 101/624 (16.2%) patients triaged by the intervention group and 116/711 (16.3%) triaged by the control group. After the intervention, the urinalysis protocol was implemented in 146/530 (27.6%) patients triaged by the intervention group and 174/679 (25.6%) triaged by the control group. Before the intervention, the radiograph protocol was implemented in 58/774 (7.5%) patients triaged by the intervention group and 45/684 (6.6%) triaged by the control group. After the intervention, the radiograph protocol was implemented in 78/614 (12.7%) patients triaged by the intervention group and 79/609 (13.0%) triaged by the control group.

CONCLUSION

The use of a passive EHR alert to promote ED triage protocols showed little benefit. Before the widespread implementation of EHR alerts for patient care, rigorous studies are required to determine the best alert methods and the impacts of such interventions.

Clinical laboratory analytics: Challenges and promise for an emerging discipline

The clinical laboratory is a major source of health care data. Increasingly these data are being integrated with other data to inform health system-wide actions meant to improve diagnostic test utilization, service efficiency, and “meaningful use.” The Academy of Clinical Laboratory Physicians and Scientists hosted a satellite meeting on clinical laboratory analytics in conjunction with their annual meeting on May 29, 2014 in San Francisco. There were 80 registrants for the clinical laboratory analytics meeting. The meeting featured short presentations on current trends in clinical laboratory analytics and several panel discussions on data science in laboratory medicine, laboratory data and its role in the larger healthcare system, integrating laboratory analytics, and data sharing for collaborative analytics. One main goal of meeting was to have an open forum of leaders that work with the “big data” clinical laboratories produce. This article summarizes the proceedings of the meeting and content discussed.

Creatine kinase-MB: the journey to obsolescence

OBJECTIVES

To evaluate the clinical utility of and demand for the creatine kinase (CK)-MB assay.

METHODS

We examined the number of CK-MB tests from 2007 through 2013 while we progressively deemphasized their use. We first removed CK-MB from the acute coronary syndrome (ACS) panel and then from the main menu and observed the demand for the test. We also reviewed patient medical records to assess the appropriateness of its use.

RESULTS

After removing CK-MB from the ACS panel, the test volume dropped from around 12,000 per year to about 150 per year. In reviewing the records of 171 patients who had CK-MB determination done over a 28-month period, we discovered that CK-MB contributed to the diagnosis in only one patient, although it was not essential. Since removing CK-MB from the laboratory menu, two CK-MB tests were ordered in 4 months, and neither added value.

CONCLUSIONS

CK-MB determinations do not add value to information available from the troponin assay and can be safely removed from the laboratory menu.

Reference laboratory utilization management

This chapter describes the unique challenges of managing reference laboratory utilization. The nature of reference laboratory testing and how it differs from routine hospital clinical laboratory testing is discussed. The vast majority of reference laboratory testing is high complexity, low volume testing to support specialized care. In contrast the hospital clinical laboratory is most effective at performing rapid turn-around, routine, high volume testing. The implication of these differences with respect to identifying utilization issues and interventions to manage utilization is presented along with examples.

The role of informatics and decision support in utilization management

Information systems provide a critical link between clinical laboratories and the clinicians and patients they serve. Strategic deployment of informatics resources can enable a wide array of utilization initiatives and can substantially improve the appropriateness of test selection and results interpretation. In this article, we review information systems including computerized provider order entry (CPOE) systems, laboratory information systems (LISs), electronic health records (EHRs), laboratory middleware, knowledge management systems and systems for data extraction and analysis, and describe the role that each can play in utilization management. We also discuss specific utilization strategies that laboratories can employ within these systems, citing examples both from our own institution and from the literature. Finally, we review how emerging applications of decision support technologies may help to further refine test utilization, "personalize" laboratory diagnosis, and enhance the diagnostic value of laboratory testing.