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

Artificial intelligence in the pre-analytical phase: State-of-the art and future perspectives

The use of artificial intelligence (AI) has become widespread in many areas of science and medicine, including laboratory medicine. Although it seems obvious that the analytical and post-analytical phases could be the most important fields of application in laboratory medicine, a kaleidoscope of new opportunities has emerged to extend the benefits of AI to many manual labor-intensive activities belonging to the pre-analytical phase, which are inherently characterized by enhanced vulnerability and higher risk of errors. These potential applications involve increasing the appropriateness of test prescription (with computerized physician order entry or demand management tools), improved specimen collection (using active patient recognition, automated specimen labeling, vein recognition and blood collection assistance, along with automated blood drawing), more efficient sample transportation (facilitated by the use of pneumatic transport systems or drones, and monitored with smart blood tubes or data loggers), systematic evaluation of sample quality (by measuring serum indices, fill volume or for detecting sample clotting), as well as error detection and analysis. Therefore, this opinion paper aims to discuss the state-of-the-art and some future possibilities of AI in the preanalytical phase.

Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach

Objective

To describe a user-centered approach to develop, pilot test, and refine requirements for 3 electronic health record (EHR)-integrated interventions that target key diagnostic process failures in hospitalized patients.

Materials and Methods

Three interventions were prioritized for development: a Diagnostic Safety Column (DSC) within an EHR-integrated dashboard to identify at-risk patients; a Diagnostic Time-Out (DTO) for clinicians to reassess the working diagnosis; and a Patient Diagnosis Questionnaire (PDQ) to gather patient concerns about the diagnostic process. Initial requirements were refined from analysis of test cases with elevated risk predicted by DSC logic compared to risk perceived by a clinician working group; DTO testing sessions with clinicians; PDQ responses from patients; and focus groups with clinicians and patient advisors using storyboarding to model the integrated interventions. Mixed methods analysis of participant responses was used to identify final requirements and potential implementation barriers.

Results

Final requirements from analysis of 10 test cases predicted by the DSC, 18 clinician DTO participants, and 39 PDQ responses included the following: DSC configurable parameters (variables, weights) to adjust baseline risk estimates in real-time based on new clinical data collected during hospitalization; more concise DTO wording and flexibility for clinicians to conduct the DTO with or without the patient present; and integration of PDQ responses into the DSC to ensure closed-looped communication with clinicians. Analysis of focus groups confirmed that tight integration of the interventions with the EHR would be necessary to prompt clinicians to reconsider the working diagnosis in cases with elevated diagnostic error (DE) risk or uncertainty. Potential implementation barriers included alert fatigue and distrust of the risk algorithm (DSC); time constraints, redundancies, and concerns about disclosing uncertainty to patients (DTO); and patient disagreement with the care team's diagnosis (PDQ).

Discussion

A user-centered approach led to evolution of requirements for 3 interventions targeting key diagnostic process failures in hospitalized patients at risk for DE.

Conclusions

We identify challenges and offer lessons from our user-centered design process.

Taking a step back from testing: Preanalytical considerations in molecular infectious disease diagnostics

Recent studies evaluating the preanalytical factors that impact the outcome of nucleic-acid based methods for the confirmation of SARS-CoV-2 have illuminated the importance of identifying variables that promoted accurate testing, while using scarce resources efficiently. The majority of laboratory errors occur in the preanalytical phase. While there are many resources identifying and describing mechanisms for main laboratory testing on automated platforms, there are fewer comprehensive resources for understanding important preanalytical and environmental factors that affect accurate molecular diagnostic testing of infectious diseases. This review identifies evidence-based factors that have been documented to impact the outcome of nucleic acid-based molecular techniques for the diagnosis of infectious diseases.

Chronic disease IMPACT (chronic disease early detection and improved management in primary care project): An Australian stepped wedge cluster randomised trial

Background

Interrelated chronic vascular diseases (chronic kidney disease (CKD), type 2 diabetes (T2D) and cardiovascular disease (CVD)) are common with high morbidity and mortality. This study aimed to assess if an electronic-technology-based quality improvement intervention in primary care could improve detection and management of people with and at risk of these diseases.

Methods

Stepped-wedge trial with practices randomised to commence intervention in one of five 16-week periods. Intervention included (1) electronic-technology tool extracting data from general practice electronic medical records and generating graphs and lists for audit; (2) education regarding chronic disease and the electronic-technology tool; (3) assistance with quality improvement audit plan development, benchmarking, monitoring and support. De-identified data analysis using R 3.5.1 conducted using Bayesian generalised linear mixed model with practice and time-specific random intercepts.

Results

At baseline, eight included practices had 37,946 active patients (attending practice ≥3 times within 2 years) aged ≥18 years. Intervention was associated with increased OR (95% CI) for: kidney health checks (estimated glomerular filtration rate, urine albumin:creatinine ratio (uACR) and blood pressure) in those at risk 1.34 (1.26-1.42); coded diagnosis of CKD 1.18 (1.09-1.27); T2D diagnostic testing (fasting glucose or HbA1c) in those at risk 1.15 (1.08-1.23); uACR in patients with T2D 1.78 (1.56-2.05). Documented eye checks within recommended frequency in patients with T2D decreased 0.85 (0.77-0.96). There were no significant changes in other assessed variables.

Conclusions

This electronic-technology-based intervention in primary care has potential to help translate guidelines into practice but requires further refining to achieve widespread improvements across the interrelated chronic vascular diseases.

Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis

Objectives

Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases.

Material and Methods

We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted.

Results

The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY.

Conclusion

We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies.

Registration

PROSPERO (CRD42020203716)

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 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.

Implementation of Electronic Medical Records in Mental Health Settings: Scoping Review

BACKGROUND

The success of electronic medical records (EMRs) is dependent on implementation features, such as usability and fit with clinical processes. The use of EMRs in mental health settings brings additional and specific challenges owing to the personal, detailed, narrative, and exploratory nature of the assessment, diagnosis, and treatment in this field. Understanding the determinants of successful EMR implementation is imperative to guide the future design, implementation, and investment of EMRs in the mental health field.

OBJECTIVE

We intended to explore evidence on effective EMR implementation for mental health settings and provide recommendations to support the design, adoption, usability, and outcomes.

METHODS

The scoping review combined two search strategies that focused on clinician-facing EMRs, one for primary studies in mental health settings and one for reviews of peer-reviewed literature in any health setting. Three databases (Medline, EMBASE, and PsycINFO) were searched from January 2010 to June 2020 using keywords to describe EMRs, settings, and impacts. The Proctor framework for implementation outcomes was used to guide data extraction and synthesis. Constructs in this framework include adoption, acceptability, appropriateness, feasibility, fidelity, cost, penetration, and sustainability. Quality assessment was conducted using a modified Hawker appraisal tool and the Joanna Briggs Institute Critical Appraisal Checklist for Systematic Reviews and Research Syntheses.

RESULTS

This review included 23 studies, namely 12 primary studies in mental health settings and 11 reviews. Overall, the results suggested that adoption of EMRs was impacted by financial, technical, and organizational factors, as well as clinician perceptions of appropriateness and acceptability. EMRs were perceived as acceptable and appropriate by clinicians if the system did not interrupt workflow and improved documentation completeness and accuracy. Clinicians were more likely to value EMRs if they supported quality of care, were fit for purpose, did not interfere with the clinician-patient relationship, and were operated with readily available technical support. Evidence on the feasibility of the implemented EMRs was mixed; the primary studies and reviews found mixed impacts on documentation quality and time; one primary study found downward trends in adverse events, whereas a review found improvements in care quality. Five papers provided information on implementation outcomes such as cost and fidelity, and none reported on the penetration and sustainability of EMRs.

CONCLUSIONS

The body of evidence relating to EMR implementation in mental health settings is limited. Implementation of EMRs could benefit from methods used in general health settings such as co-designing the software and tailoring EMRs to clinical needs and workflows to improve usability and acceptance. Studies in mental health and general health settings rarely focused on long-term implementation outcomes such as penetration and sustainability. Future evaluations of EMRs in all settings should consider long-term impacts to address current knowledge gaps.

Laboratory Demand Management Strategies-An Overview

Inappropriate laboratory test selection in the form of overutilization as well as underutilization frequently occurs despite available guidelines. There is broad approval among laboratory specialists as well as clinicians that demand management strategies are useful tools to avoid this issue. Most of these tools are based on automated algorithms or other types of machine learning. This review summarizes the available demand management strategies that may be adopted to local settings. We believe that artificial intelligence may help to further improve these available tools.

Deep into Laboratory: An Artificial Intelligence Approach to Recommend Laboratory Tests

Laboratory tests are performed to make effective clinical decisions. However, inappropriate laboratory test ordering hampers patient care and increases financial burden for healthcare. An automated laboratory test recommendation system can provide rapid and appropriate test selection, potentially improving the workflow to help physicians spend more time treating patients. The main objective of this study was to develop a deep learning-based automated system to recommend appropriate laboratory tests. A retrospective data collection was performed at the National Health Insurance database between 1 January 2013, and 31 December 2013. We included all prescriptions that had at least one laboratory test. A total of 1,463,837 prescriptions from 530,050 unique patients was included in our study. Of these patients, 296,541 were women (55.95%), the range of age was between 1 and 107 years. The deep learning (DL) model achieved a higher area under the receiver operating characteristics curve (AUROC micro = 0.98, and AUROC macro = 0.94). The findings of this study show that the DL model can accurately and efficiently identify laboratory tests. This model can be integrated into existing workflows to reduce under- and over-utilization problems.