Electronic Laboratory Medicine ordering with evidence-based Order sets in primary care (ELMO study): protocol for a cluster randomised trial

Clinical Decision Support System in laboratory medicine

Clinical Decision Support Systems (CDSS) have been implemented in almost all healthcare settings. Laboratory medicine (LM), is one of the most important structured health data stores, but efforts are still needed to clarify the use and scope of these tools, especially in the laboratory setting. The aim is to clarify CDSS concept in LM, in the last decade. There is no consensus on the definition of CDSS in LM. A theoretical definition of CDSS in LM should capture the aim of driving significant improvements in LM mission, prevention, diagnosis, monitoring, and disease treatment. We identified the types, workflow and data sources of CDSS. The main applications of CDSS in LM were diagnostic support and clinical management, patient safety, workflow improvements, and cost containment. Laboratory professionals, with their expertise in quality improvement and quality assurance, have a chance to be leaders in CDSS.

Trends and Components of Thyroid Status Evaluation in Commercially Insured Adults in the United States, 2006-2020

CONTEXT

Thyroid-stimulating hormone (TSH) is one of the most ordered laboratory tests.

OBJECTIVE

Determine trends of TSH testing rates and components of thyroid function testing.

METHODS

This was a retrospective analysis of adults 18-64 years old without evidence of thyroid disease with at least 365 days of continuous enrollment between 2006 and 2020 in the IBM MarketScan Claims Database. The main outcome measures were trends of TSH tests/1000 eligible patient-months stratified by age, sex, and region and composition of thyroid function testing.

RESULTS

Among 67 353 280 patients meeting eligibility criteria, we identified 25 606 518 TSH tests and 15 138 211 patients with ≥1 TSH test. Patients contributing an episode of TSH testing were most commonly 45-54 years old (29.8%) and female (63.6%). TSH testing rates remained consistent throughout the study period with 11.4 and 11.7 TSH tests/1000 person-months in the first and last study months, respectively (mean 12.2 TSH tests/1000 person-months). TSH testing rates dropped sharply in the spring of 2020 (4.2 TSH tests/1000 person-months). Females showed a nearly 2-fold higher rate of TSH testing than males (16.1 TSH tests/1000 person-months vs 8.6 TSH tests/1000 person-months). TSH testing rates increased with age (8.2 TSH tests/1000 person-months among individuals 18-34 years old vs 15.4 TSH tests/1000 person-months among individuals 55-64 years old). No difference in TSH testing rates was noted between regions. Thyroid function testing episodes included only TSH in most cases (70.8%).

CONCLUSION

TSH testing rates among commercially insured individuals without known thyroid disease appears stable over time, with higher frequency in females and with increasing age.

Downstream activities after laboratory testing in primary care: an exploratory outcome of the ELMO cluster randomised trial (Electronic Laboratory Medicine Ordering with evidence-based order sets in primary care)

OBJECTIVE

To estimate the rate and type of downstream activities (DAs) after laboratory testing in primary care, with a specific focus on check-up laboratory panels, and to explore the effect of a clinical decision support system (CDSS) for laboratory ordering on these DAs.

DESIGN

Cluster randomised clinical trial.

SETTING

72 primary care practices in Belgium, with 272 general practitioners (GPs), randomly assigned to the intervention arm or the control arm.

PARTICIPANTS

The study included 10 270 lab panels from 9683 primary care patients (women 55.1%, mean age 56.5). All adult patients who consulted one of the participating GPs during the trial period and needed a laboratory exam were eligible for participation.

INTERVENTIONS

GPs in the intervention group used a CDSS integrated into their online laboratory ordering system, while GPs in the control arm used their lab ordering system as usual. The trial duration was 6 months, with another 6 months follow-up.

MAIN OUTCOME MEASURES

This publication reports on the exploratory outcome of DAs after an initial laboratory exam and the effect of the CDSS on these DAs.

RESULTS

19.7% of all laboratory panels resulted in further diagnostic procedures (95% CI 18.9% to 20.5%) and 19% (95% CI 18.2% to 19.7%) in treatment changes. Check-up laboratory exams showed similar rates of DAs, with 17.5% (95% CI 13.8% to 21.2%) diagnostic DAs and 18.9% (95% CI 13.9% to 23.9%) treatment changes. Using the CDSS resulted in a significant reduction in downstream referrals (-2.4%; 95% CI -4.2% to -0.6%; p=0008), imaging and endoscopies (-0.9%; 95% CI -1.6% to -0.1%; p=0026) and treatment changes (-5.4%; 95% CI -9.5% to -1.2%; p=0.01).

CONCLUSION

This is the largest study so far to examine DAs after laboratory testing. It shows that almost one in three laboratory exams leads to further DAs, even in check-up panels. Using a CDSS for laboratory orders may reduce the rate of some DAs.

TRIAL REGISTRATION NUMBER

NCT02950142.

Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial

Background

Inappropriate laboratory test ordering poses an important burden for healthcare. Clinical decision support systems (CDSS) have been cited as promising tools to improve laboratory test ordering behavior. The objectives of this study were to evaluate the effects of an intervention that integrated a clinical decision support service into a computerized physician order entry (CPOE) on the appropriateness and volume of laboratory test ordering, and on diagnostic error in primary care.

Methods

This study was a pragmatic, cluster randomized, open-label, controlled clinical trial.

Setting

Two hundred eighty general practitioners (GPs) from 72 primary care practices in Belgium.

Patients

Patients aged ≥ 18 years with a laboratory test order for at least one of 17 indications: cardiovascular disease management, hypertension, check-up, chronic kidney disease (CKD), thyroid disease, type 2 diabetes mellitus, fatigue, anemia, liver disease, gout, suspicion of acute coronary syndrome (ACS), suspicion of lung embolism, rheumatoid arthritis, sexually transmitted infections (STI), acute diarrhea, chronic diarrhea, and follow-up of medication.

Interventions

The CDSS was integrated into a computerized physician order entry (CPOE) in the form of evidence-based order sets that suggested appropriate tests based on the indication provided by the general physician.

Measurements

The primary outcome of the ELMO study was the proportion of appropriate tests over the total number of ordered tests and inappropriately not-requested tests. Secondary outcomes of the ELMO study included diagnostic error, test volume, and cascade activities.

Results

CDSS increased the proportion of appropriate tests by 0.21 (95% CI 0.16–0.26, p < 0.0001) for all tests included in the study. GPs in the CDSS arm ordered 7 (7.15 (95% CI 3.37–10.93, p = 0.0002)) tests fewer per panel. CDSS did not increase diagnostic error. The absolute difference in proportions was a decrease of 0.66% (95% CI 1.4% decrease–0.05% increase) in possible diagnostic error.

Conclusions

A CDSS in the form of order sets, integrated within the CPOE improved appropriateness and decreased volume of laboratory test ordering without increasing diagnostic error.

Trial registration

ClinicalTrials.gov Identifier: NCT02950142, registered on October 25, 2016

Supplementary Information

The online version contains supplementary material available at 10.1186/s13012-020-01059-y.

Health Data for Research Through a Nationwide Privacy-Proof System in Belgium: Design and Implementation

Background

Health data collected during routine care have important potential for reuse for other purposes, especially as part of a learning health system to advance the quality of care. Many sources of bias have been identified through the lifecycle of health data that could compromise the scientific integrity of these data. New data protection legislation requires research facilities to improve safety measures and, thus, ensure privacy.

Objective

This study aims to address the question on how health data can be transferred from various sources and using multiple systems to a centralized platform, called Healthdata.be, while ensuring the accuracy, validity, safety, and privacy. In addition, the study demonstrates how these processes can be used in various research designs relevant for learning health systems.

Methods

The Healthdata.be platform urges uniformity of the data registration at the primary source through the use of detailed clinical models. Data retrieval and transfer are organized through end-to-end encrypted electronic health channels, and data are encoded using token keys. In addition, patient identifiers are pseudonymized so that health data from the same patient collected across various sources can still be linked without compromising the deidentification.

Results

The Healthdata.be platform currently collects data for >150 clinical registries in Belgium. We demonstrated how the data collection for the Belgian primary care morbidity register INTEGO is organized and how the Healthdata.be platform can be used for a cluster randomized trial.

Conclusions

Collecting health data in various sources and linking these data to a single patient is a promising feature that can potentially address important concerns on the validity and quality of health data. Safe methods of data transfer without compromising privacy are capable of transporting these data from the primary data provider or clinician to a research facility. More research is required to demonstrate that these methods improve the quality of data collection, allowing researchers to rely on electronic health records as a valid source for scientific data.

The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support

Background

Computerised decision support (CDS) based on trustworthy clinical guidelines is a key component of a learning healthcare system. Research shows that the effectiveness of CDS is mixed.

Multifaceted context, system, recommendation and implementation factors may potentially affect the success of CDS interventions. This paper describes the development of a checklist that is intended to support professionals to implement CDS successfully.

Methods

We developed the checklist through an iterative process that involved a systematic review of evidence and frameworks, a synthesis of the success factors identified in the review, feedback from an international expert panel that evaluated the checklist in relation to a list of desirable framework attributes, consultations with patients and healthcare consumers and pilot testing of the checklist.

Results

We screened 5347 papers and selected 71 papers with relevant information on success factors for guideline-based CDS. From the selected papers, we developed a 16-factor checklist that is divided in four domains, i.e. the CDS context, content, system and implementation domains. The panel of experts evaluated the checklist positively as an instrument that could support people implementing guideline-based CDS across a wide range of settings globally. Patients and healthcare consumers identified guideline-based CDS as an important quality improvement intervention and perceived the GUIDES checklist as a suitable and useful strategy.

Conclusions

The GUIDES checklist can support professionals in considering the factors that affect the success of CDS interventions. It may facilitate a deeper and more accurate understanding of the factors shaping CDS effectiveness. Relying on a structured approach may prevent that important factors are missed.

Electronic supplementary material

The online version of this article (10.1186/s13012-018-0772-3) contains supplementary material, which is available to authorized users.