Real-World Biomarker Test Ordering Practices in Non-Small Cell Lung Cancer: Interphysician Variation and Association With Clinical Outcomes.

Purpose: Patients with metastatic or advanced non-small cell lung cancer (NSCLC) need biomarker testing, including, in most cases, anaplastic lymphoma kinase (ALK), epidermal growth factor receptor (EGFR), and PD-L1, to identify options for targeted therapies and to optimally incorporate immune checkpoint inhibitors into therapeutic regimens. We sought to examine real-world patterns of biomarker testing, quantify interphysician practice variation, and correlate testing with clinical outcomes.

Methods: We extracted real-world data from a nationwide electronic health record-derived deidentified database from 17,165 patients diagnosed with advanced NSCLC between 2018 and 2021 and receiving care in the community setting.

High Rates of Missed HIV Testing Among Oral PrEP Users in the United States From 2018-2021: A National Assessment on Compliance With HIV Testing Recommendations of the CDC PrEP Guidelines.

Background: The US Centers for Disease Control and Prevention recommends HIV testing every 3 months in oral PrEP users. We performed a national assessment of HIV testing compliance among oral PrEP users.

Methods: We analyzed 408 910 PrEP prescriptions issued to 39 809 PrEP users using a national insurance claims database that contained commercial and Medicaid claims.

Using machine learning to develop smart reflex testing protocols.

Objective: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and diagnosis. In current clinical practice, reflex testing typically relies on simple "if-then" rules; however, this limits the opportunities for reflex testing since most test ordering decisions involve more complexity than traditional rule-based approaches would allow.

Using Machine Learning to Develop Smart Reflex Testing Protocols.

Objective: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and diagnosis. In current clinical practice, reflex testing typically relies on simple "if-then" rules; however, this limits their scope since most test ordering decisions involve more complexity than a simple rule will allow.

Artificial Intelligence in the Clinical Laboratory: An Overview with Frequently Asked Questions.

This article provides an overview of machine learning fundamentals and some applications of machine learning to clinical laboratory diagnostics and patient management. A key goal of this article is to provide a basic foundation in clinical machine learning for readers with clinical laboratory experience that will set them up for more in-depth study of the topic and/or to become a better collaborator with computational colleagues in the development and deployment of machine learning-based solutions.

Real evidence to assess clinical testing interference risk (REACTIR): A strategy using real world data to assess the prevalence of interfering substances in patients undergoing clinical laboratory testing.

Introduction: Laboratory test interferences can cause spurious test results and patient harm. Knowing the frequency of various interfering substances in patient populations likely to be tested with a particular laboratory assay may inform test development, test utilization and strategies to mitigate interference risk.

Methods: We developed REACTIR (Real Evidence to Assess Clinical Testing Interference Risk), an approach using real world data to assess the prevalence of various interfering substances in patients tested with a particular type of assay.

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.

Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support.

Objective: Like most real-world data, electronic health record (EHR)-derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision support for patient-specific oncology care. Here, we sought to develop a novel ensemble approach to addressing missing data that we term the "meta-model" and apply the meta-model to patient-specific cancer prognosis.

Suffering in silence: How COVID-19 school closures inhibit the reporting of child maltreatment.

To combat the spread of COVID-19, many primary and secondary schools in the United States canceled classes and moved instruction online. This study examines an unexplored consequence of COVID-19 school closures: the broken link between child maltreatment victims and the number one source of reported maltreatment allegations-school personnel. Using current, county-level data from Florida, we estimate a counterfactual distribution of child maltreatment allegations for March and April 2020, the first two months in which Florida schools closed.

Iron studies and transferrin, a source of test ordering confusion highly amenable to clinical decision support.

Introduction: An important cause of laboratory test misordering and overutilization is clinician confusion between tests with similar sounding names or similar indications. We identified an area of test ordering confusion with iron studies that involves total iron binding capacity (TIBC), transferrin, and transferrin saturation. We observed concurrent ordering of direct transferrin along with TIBC at many hospitals within our health system and suspected this was unnecessary.

Order Indication Solicitation to Assess Clinical Laboratory Test Utilization: D-Dimer Order Patterns as an Illustrative Case.

Background: A common challenge in the development of laboratory clinical decision support (CDS) and laboratory utilization management (UM) initiatives stems from the fact that many laboratory tests have multiple potential indications, limiting the ability to develop context-specific alerts. As a potential solution, we designed a CDS alert that asks the ordering clinician to provide the indication for testing, using D-dimer as an exemplar. Using data collected over a nearly 3-year period, we sought to determine whether the indication capture was a useful feature within the CDS alert and whether it provided actionable intelligence to guide the development of an UM strategy.

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.

Machine Learning Models Improve the Diagnostic Yield of Peripheral Blood Flow Cytometry.

Objectives: Peripheral blood flow cytometry (PBFC) is useful for evaluating circulating hematologic malignancies (HM) but has limited diagnostic value for screening. We used machine learning to evaluate whether clinical history and CBC/differential parameters could improve PBFC utilization.

Methods: PBFC cases with concurrent/recent CBC/differential were split into training (n = 626) and test (n = 159) cohorts.

Laboratory Blood-Based Testing for Non-Lyme Disease Tick-Borne Infections at a National Reference Laboratory.

Objectives: We evaluated trends in non-Lyme disease tick-borne disease (NLTBI) testing at a national reference laboratory.

Methods: Testing data performed at Quest Diagnostics during 2010 to 2016 were analyzed nationally and at the state level.

Results: Testing and positivity for most NLTBIs increased dramatically from 2010 through 2016 based on testing from a large reference laboratory.

Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes and Structured Multivariate Physiological Measurements.

The onset of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality. Developing novel methods to identify early AKI onset is of critical importance in preventing or reducing AKI complications. We built and applied multiple machine learning models to integrate clinical notes and structured physiological measurements and estimate the risk of new AKI onset using the MIMIC-III database.

Use of Routine Complete Blood Count Results to Rule Out Anaplasmosis Without the Need for Specific Diagnostic Testing.

Background: Anaplasmosis presents with fever, headache, and laboratory abnormalities including leukopenia and thrombocytopenia. Polymerase chain reaction (PCR) is the preferred diagnostic but is overutilized. We determined if routine laboratory tests could exclude anaplasmosis, improving PCR utilization.

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.

Laboratory Blood-Based Testing for Lyme Disease at a National Reference Laboratory.

Objectives: We evaluated trends in Lyme disease (LD) testing at a national reference laboratory.

Methods: LD screening enzyme immunoassay and Western blot testing data performed at Quest Diagnostics during 2010 to 2016 were analyzed nationally and at the state level.

Results: Overall, 593,800 (11.

Using Machine Learning-Based Multianalyte Delta Checks to Detect Wrong Blood in Tube Errors.

Objectives: An unfortunate reality of laboratory medicine is that blood specimens collected from one patient occasionally get mislabeled with identifiers from a different patient, resulting in so-called "wrong blood in tube" (WBIT) errors and potential patient harm. Here, we sought to develop a machine learning-based, multianalyte delta check algorithm to detect WBIT errors and mitigate patient harm.

Methods: We simulated WBIT errors within sets of routine inpatient chemistry test results to develop, train, and evaluate five machine learning-based WBIT detection algorithms.

Laboratory Testing for Tick-Borne Infections in a Large Northeastern Academic Medical Center: An 11-Year Experience.

Objectives: We evaluated changes in the testing menu, volume, and positivity rates for tick-borne illnesses in a New England medical center over an 11-year time frame.

Methods: Testing data were obtained by a retrospective review utilizing searchable data from a laboratory information system archive.

Results: Testing for tick-borne infections (TBI) increased 5.