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.

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.

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.

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.

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.

Creation and Use of an Electronic Health Record Reporting Database to Improve a Laboratory Test Utilization Program.

Objectives: Laboratory-based utilization management programs typically rely primarily on data derived from the laboratory information system to analyze testing volumes for trends and utilization concerns. We wished to examine the ability of an electronic health record (EHR) laboratory orders database to improve a laboratory utilization program.

Methods: We obtained a daily file from our EHR containing data related to laboratory test ordering.

3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data.

Objective: A key challenge in clinical data mining is that most clinical datasets contain missing data. Since many commonly used machine learning algorithms require complete datasets (no missing data), clinical analytic approaches often entail an imputation procedure to "fill in" missing data. However, although most clinical datasets contain a temporal component, most commonly used imputation methods do not adequately accommodate longitudinal time-based data.

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.

Utilization Management of High-Cost Imaging in an Outpatient Setting in a Large Stable Patient and Provider Cohort over 7 Years.

Purpose To quantify the effect of a comprehensive, long-term, provider-led utilization management (UM) program on high-cost imaging (computed tomography, magnetic resonance imaging, nuclear imaging, and positron emission tomography) performed on an outpatient basis. Materials and Methods This retrospective, 7-year cohort study included all patients regularly seen by primary care physicians (PCPs) at an urban academic medical center. The main outcome was the number of outpatient high-cost imaging examinations per patient per year ordered by the patient's PCP or by any specialist.

Health Care Infrastructure for Financially Sustainable Clinical Genomics.

Next-generation sequencing has evolved technically and economically into the method of choice for interrogating the genome in cancer and inherited disorders. The introduction of procedural code sets for whole-exome and genome sequencing is a milestone toward financially sustainable clinical implementation; however, achieving reimbursement is currently a major challenge. As part of a prospective quality-improvement initiative to implement the new code sets, we adopted Agile, a development methodology originally devised in software development.

Using Machine Learning to Predict Laboratory Test Results.

Objectives: While clinical laboratories report most test results as individual numbers, findings, or observations, clinical diagnosis usually relies on the results of multiple tests. Clinical decision support that integrates multiple elements of laboratory data could be highly useful in enhancing laboratory diagnosis.

Methods: Using the analyte ferritin in a proof of concept, we extracted clinical laboratory data from patient testing and applied a variety of machine-learning algorithms to predict ferritin test results using the results from other tests.

Perceptions of pathology informatics by non-informaticist pathologists and trainees.

Background: Although pathology informatics (PI) is essential to modern pathology practice, the field is often poorly understood. Pathologists who have received little to no exposure to informatics, either in training or in practice, may not recognize the roles that informatics serves in pathology. The purpose of this study was to characterize perceptions of PI by noninformatics-oriented pathologists and to do so at two large centers with differing informatics environments.

ST2 Predicts Mortality and Length of Stay in a Critically Ill Noncardiac Intensive Care Unit Population.

Objectives: The biomarker suppression of tumorigenicity 2 (ST2) is a well-established clinical biomarker of cardiac strain and is frequently elevated in a variety of cardiac conditions. Here, we sought to evaluate the prognostic value of ST2 in critically ill medical intensive care unit (MICU) patients without primary cardiac illness.

Methods: We measured ST2 and high-sensitivity troponin T (hsTnT) on plasma specimens collected on 441 patients following admission to a noncardiac MICU and evaluated the prognostic power of ST2 both alone and in multivariate models.