Making sense of artificial intelligence and large language models-including ChatGPT-in pediatric hematology/oncology.

ChatGPT and other artificial intelligence (AI) systems have captivated the attention of healthcare providers and researchers for their potential to improve care processes and outcomes. While these technologies hold promise to automate processes, increase efficiency, and reduce cognitive burden, their use also carries risks. In this commentary, we review basic concepts of AI, outline some of the capabilities and limitations of currently available tools, discuss current and future applications in pediatric hematology/oncology, and provide an evaluation and implementation framework that can be used by pediatric hematologist/oncologists considering the use of AI in clinical practice.

Extracting Electronic Health Record Neuroblastoma Treatment Data With High Fidelity Using the REDCap Clinical Data Interoperability Services Module.

PURPOSE: Although the International Neuroblastoma Risk Group Data Commons (INRGdc) has enabled seminal large cohort studies, the research is limited by the lack of real-world, electronic health record (EHR) treatment data. To address this limitation, we evaluated the feasibility of extracting treatment data directly from EHRs using the REDCap Clinical Data Interoperability Services (CDIS) module for future submission to the INRGdc. METHODS: Patients enrolled on the Children's Oncology Group neuroblastoma biology study ANBL00B1 (ClinicalTrials.gov identifier: NCT00904241) who received care at the University of Chicago (UChicago) or the Vanderbilt University Medical Center (VUMC) after the go-live dates for the Fast Healthcare Interoperability Resources (FHIR)-compliant EHRs were identified. Antineoplastic drug orders were extracted using the CDIS module. To validate the CDIS output, antineoplastic agents extracted through FHIR were compared with those queried through EHR relational databases (UChicago's Clinical Research Data Warehouse and VUMC's Epic Clarity database) and manual chart review. RESULTS: The analytic cohort consisted of 41 patients at UChicago and 32 VUMC patients. Antineoplastic drug orders were identified in the extracted EHR records of 39 (95.1%) UChicago patients and 26 (81.3%) VUMC patients. Manual chart review confirmed that patients with missing (n = 8) or discontinued (n = 1) orders in the CDIS output did not receive antineoplastic agents during the timeframe of the study. More than 99% of the antineoplastic drug orders in the EHR relational databases were identified in the corresponding CDIS output. CONCLUSION: Our results demonstrate the feasibility of extracting EHR treatment data with high fidelity using HL7-FHIR via REDCap CDIS for future submission to the INRGdc.

An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good.

Objective: The Pediatric Cancer Data Commons (PCDC)-a project of Data for the Common Good-houses clinical pediatric oncology data and utilizes the open-source Gen3 platform. To meet the needs of end users, the PCDC development team expanded the out-of-box functionality and developed additional custom features that should be useful to any group developing similar data commons. Materials and Methods: Modifications of the PCDC data portal software were implemented to facilitate desired functionality. Results: Newly developed functionality includes updates to authorization methods, expansion of filtering capabilities, and addition of data analysis functions. Discussion: We describe the process by which custom functionalities were developed. Features are open source and available to be implemented and adapted to suit needs of data portals that utilize the Gen3 platform. Conclusion: Data portals are indispensable tools for facilitating data sharing. Open-source infrastructure facilitates a modular and collaborative approach for meeting needs of end users and stakeholders.

Targeted Enrollment in Pediatric Oncology Trials: A Vision for Just-in-Time Matching.

@PedsDataCommons shares vision for automated clinical trials matching in pediatric oncology.

Accelerating pediatric Hodgkin lymphoma research: the Hodgkin Lymphoma Data Collaboration (NODAL).

Data commons have proven to be an indispensable avenue for advancing pediatric cancer research by serving as unified information technology platforms that, when coupled with data standards, facilitate data sharing. The Pediatric Cancer Data Commons, the flagship project of Data for the Common Good (D4CG), collaborates with disease-based consortia to facilitate development of clinical data standards, harmonization and pooling of clinical data from disparate sources, establishment of governance structure, and sharing of clinical data. In the interest of international collaboration, researchers developed the Hodgkin Lymphoma Data Collaboration and forged a relationship with the Pediatric Cancer Data Commons to establish a data commons for pediatric Hodgkin lymphoma. Herein, we describe the progress made in the formation of Hodgkin Lymphoma Data Collaboration and foundational goals to advance pediatric Hodgkin lymphoma research.

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.

PURPOSE: Matching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have used a trial-centric approach, which focuses on a single trial. In this study, we developed a patient-centric matching tool that matches patient-specific demographic and clinical information with free-text clinical trial inclusion and exclusion criteria extracted using natural language processing to return a list of relevant clinical trials ordered by the patient's likelihood of eligibility. MATERIALS AND METHODS: Records from pediatric leukemia clinical trials were downloaded from ClinicalTrials.gov. Regular expressions were used to discretize and extract individual trial criteria. A multilabel support vector machine (SVM) was trained to classify sentence embeddings of criteria into relevant clinical categories. Labeled criteria were parsed using regular expressions to extract numbers, comparators, and relationships. In the validation phase, a patient-trial match score was generated for each trial and returned in the form of a ranked list for each patient. RESULTS: In total, 5,251 discretized criteria were extracted from 216 protocols. The most frequent criterion was previous chemotherapy/biologics (17%). The multilabel SVM demonstrated a pooled accuracy of 75%. The text processing pipeline was able to automatically extract 68% of eligibility criteria rules, as compared with 80% in a manual version of the tool. Automated matching was accomplished in approximately 4 seconds, as compared with several hours using manual derivation. CONCLUSION: To our knowledge, this project represents the first open-source attempt to generate a patient-centric clinical trial matching tool. The tool demonstrated acceptable performance when compared with a manual version, and it has potential to save time and money when matching patients to trials.

Data standards in pediatric oncology: Past, present, and future.

In this commentary, we highlight the central role that data standards play in facilitating data-driven efforts to advance research in pediatric oncology. We discuss the current state of data standards for pediatric oncology and propose five steps to achieve an improved future state with benefits for clinicians, researchers, and patients.

Creating a data commons: The INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT).

In this article, we will discuss the genesis, evolution, and progress of the INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT), which aims to foster international research and collaboration focused on pediatric soft tissue sarcoma. We will begin by highlighting the current state of clinical research for pediatric soft tissue sarcomas, including rhabdomyosarcoma and non-rhabdomyosarcoma soft tissue sarcoma. We will then explore challenges and research priorities, describe the development of INSTRuCT, and discuss how the consortium aims to address key research priorities.

COVID-19-associated secondary hemophagocytic lymphohistiocytosis requiring hematopoietic cell transplant.

Coronavirus disease 2019 (COVID-19) infection causes a variety of extrapulmonary complications in pediatric patients. Multisystem inflammatory syndrome and hemophagocytic lymphohistiocytosis (HLH) are related to hypercytokinemia in COVID-19 patients. HLH is a disorder of exaggerated inflammation resulting in a cytokine storm and unrestricted hemophagocytosis. HLH can be primary (familial) or secondary (acquired). Secondary HLH (sHLH) can occur in patients with rheumatologic, oncologic, or infectious diseases. The link between COVID-19 and HLH has been reported in pediatric patients. Here we report a case of a pediatric patient who developed refractory sHLH secondary to COVID-19 infection and required a hematopoietic cell transplant for the cure.

Management Practices for Asparaginase-associated Coagulopathy: A Survey of Pediatric Oncologists.

BACKGROUND: Coagulopathy and thrombosis are well-described complications of asparaginase therapy. However, treatment practices in pediatric hematology/oncology (PHO) patients vary widely as evidence-based guidelines for clinical management of these complications in this population are lacking. OBJECTIVE: The objective of this study was to assess management practices of asparaginase-related coagulopathy by pediatric hematologist/oncologist attending physicians. DESIGN/METHOD: Email survey sent to 2327 PHO physicians primarily practicing in the United States. RESULTS: Two hundred eighty-five (12.2%) attending physicians completed the survey. Only 4.6% (n=13/285) routinely prescribe prophylactic anticoagulation during induction chemotherapy for leukemia. Slightly more than half (n=145/250, 50.9%) of all providers perform baseline coagulation studies. Most providers that were surveyed (n=185/285, 64.9%) only replete coagulant factors if the patient experiences bleeding or bruising. One hundred thirty (n=130/285, 45.6%) physicians replace low fibrinogen. The median fibrinogen replacement was 100 mg/dL (range: 40 to 200 mg/dL) with the median target of at least 100 mg/dL (range: 50 to 200 mg/dL). A minority of physicians (n=39/250, 13.7%) replace low antithrombin. The median antithrombin cutoff activity level was 60% (range: 40% to 100%) with a median target of 75% (range: 40% to 125%). CONCLUSIONS: There is a significant variation in PHO physician practices for monitoring and management of asparaginase-associated hemostatic derangements. Evidence-based guidelines have the potential to standardize practices.

Drug labeling changes and pediatric hematology/oncology prescribing: Measuring the impact of U.S. legislation.

BACKGROUND/AIMS: The Pediatric Research Equity Act and Best Pharmaceuticals for Children Act are intended to promote the conduct of clinical trials that generate pediatric-specific evidence about drug safety and efficacy. This study assesses the quality of evidence generated through Pediatric Research Equity Act-mandated and Best Pharmaceuticals for Children Act-incentivized clinical trials of hematology/oncology drugs and characterizes subsequent changes in pediatric drug utilization rates. METHODS: Trial characteristics (blinding, randomization, and comparator group) were determined for clinical trials that supported pediatric label changes. Using data from OptumLabs® Data Warehouse, a de-identified administrative claims database, we calculated pediatric utilization rates for each drug. We calculated monthly utilization rates from January 2003 (or from the first month in which data were available) to December 2018. RESULTS: We identified 11 hematology/oncology drugs that underwent pediatric label changes under the Pediatric Research Equity Act Pediatric Research Equity Act and/or Best Pharmaceuticals for Children Act, and we identified 15 trials supporting these changes. Of these trials, 36% (5/14) were randomized, 31% (4/13) were blinded, and 36% (5/14) used a comparator group. A median of 49 children (interquartile range 29.5) received the drug under investigation across these trials. Pediatric label changes were not associated with subsequent changes in pediatric drug utilization. Although some drugs saw increased pediatric use after gaining new pediatric indications, this pattern was not consistently observed. In addition, there was no evidence to suggest that drugs were utilized less frequently after they failed to receive pediatric indications. CONCLUSIONS: Clinical trials of hematology/oncology drugs conducted under the Pediatric Research Equity Act Pediatric Research Equity Act and Best Pharmaceuticals for Children Act generally have low methodological rigor, and the resulting label changes are not consistently associated with changes in pediatric utilization. Alternative regulatory strategies and study designs may be necessary to maximize the impact of newly generated knowledge on drug utilization.

Hereditary Thrombotic Thrombocytopenic Purpura in a 9-Month-old: Diagnosing and Managing an Ultra-rare Disorder.

Hereditary thrombotic thrombocytopenic purpura is an ultra-rare disorder caused by biallelic mutations in the ADAMTS13 gene. Because it can be difficult to diagnose, plasma ADAMTS13 activity assessment should be considered in patients with thrombocytopenia, anemia, and schistocytes on peripheral blood smear. We present the diagnostic evaluation of a patient with hereditary thrombotic thrombocytopenic purpura. Genetic testing revealed one known pathogenic mutation and one novel mutation of ADAMTS13 classified as likely pathogenic on the basis of parental genetic testing and in silico analyses. We further discuss off-label use of prophylactic plasma-derived Factor VIII (Koate-DVI) and the benefit of rare disease registries.

PANCYTOPENIA SECONDARY TO AUTOIMMUNE VITAMIN B12 DEFICIENCY IN GRAVES DISEASE.

OBJECTIVE: To describe a case of Graves disease (GD) and coexistent pancytopenia associated with autoimmune vitamin B12 deficiency. While thyrotoxicosis and antithyroid drugs can cause pancytopenia, other autoimmune conditions such as vitamin B12 deficiency can occur, leading to severe anemia and pancytopenia. METHODS: A 19-year-old female with GD treated with methimazole presented with thyrotoxicosis and evidence of pancytopenia. Diagnostic studies included a complete blood cell count, peripheral blood smears, thyroid function tests, and a bone marrow biopsy. RESULTS: White blood cells were 2.4 × 109 cells/L (reference range [RR] is 3.4 to 9.6 × 109 cells/L), hemoglobin was 7.9 g/dL (RR is 11.6 to 15.0 g/dL), neutrophil count was 1.2 × 109 cells/L, and platelets were 84 × 109 cells/L (RR is 157 to 371 × 109 cells/L). Thyroid-stimulating hormone was <0.01 mIU/L (RR is 0.50 to 4.30 mIU/L), free thyroxine was 3.7 ng/dL (RR is 1.0 to 1.6 ng/dL), and total triiodothyronine was 221 ng/dL (RR is 91 to 218 ng/dL). Due to suspicion for drug-induced pancytopenia, methimazole was discontinued. Three days later, she was hospitalized for a syncopal episode with a further decline in hemoglobin to 6.7 g/dL, neutrophils to 0.68 × 109 cells/L, and platelets to 69 × 109 cells/L. Bone marrow biopsy findings showing marrow hypercellularity and hypersegmented neutrophils suggested vitamin B12 deficiency. Vitamin B12 was <70 ng/L (RR is 180 to 914 ng/L). Intramuscular vitamin B12 injections were initiated, and pancytopenia resolved within 1 month. CONCLUSION: Although rarely described in the literature, autoimmune vitamin B12 deficiency can be missed as an underlying etiology for pancytopenia in patients with GD. The clinical picture can be further confounded when these patients are treated with antithyroid drugs known to cause bone marrow suppression.

Mobile Point-of-Care Medical Photography: Legal Considerations for Health Care Providers.

Medical photographs have been used for decades to document clinical findings. The ease with which medical photographs can be captured and integrated into the electronic health record (EHR) has increased as digital cameras obviated the need for the film development process. Today, cameras integrated into smartphones allow for high-resolution images to be instantly uploaded and integrated into the EHR. With major EHR vendors offering mobile smartphone applications for the conduct of point-of-care medical photography, health care providers and institutions need to be aware of legal questions that arise in the conduct of medical photography. Namely, (1) what are the requirements for consent when taking medical photographs, and how may photographs be used after consent is obtained, (2) are medical photographs admissible as evidence in court, and (3) how should a provider respond to a request by a patient or parent requesting that a photograph be deleted from the medical record? Herein, we review relevant laws and legal cases in the context of accepted standards of medical practice pertaining to point-of-care medical photography. This review is intended to aid health care providers and institutions seeking to develop or revise policies regarding using a mobile application at their clinical practice.

Computer-based simulation to reduce EHR-related chemotherapy ordering errors.

BACKGROUND: The electronic health record (EHR) is a contributor to serious patient harm occurring within a sociotechnical system. Chemotherapy ordering is a high-risk task due to the complex nature of ordering workflows and potential detrimental effects if wrong chemotherapeutic doses are administered. Many chemotherapy ordering errors cannot be mitigated through systems-based changes due to the limited extent to which individual institutions are able to customize proprietary EHR software. We hypothesized that simulation-based training could improve providers' ability to identify and mitigate common chemotherapy ordering errors. METHODS: Pediatric hematology/oncology providers voluntarily participated in simulations using an EHR testing ("Playground") environment. The number of safety risks identified and mitigated by each provider at baseline was recorded. Risks were reviewed one-on-one after initial simulations and at a group "lunch-and-learn" session. At three-month follow-up, repeat simulations assessed for improvements in error identification and mitigation, and providers were surveyed about prevention of real-life safety events. RESULTS: The 8 participating providers identified and mitigated an average of 5.5 out of 10 safety risks during the initial simulation, compared 7.4 safety risks at the follow up simulation (p=0.030). Two of the providers (25%) reported preventing at least one real-world patient safety event in the clinical setting as a result of the initial training session. CONCLUSIONS: Simulation-based training may reduce providers' susceptibility to chemotherapy ordering safety vulnerabilities within the EHR. This approach may be used when systems-based EHR improvements are not feasible due to limited ability to customize local instances of proprietary EHR software.

Development of a Taxonomy for Medication-Related Patient Safety Events Related to Health Information Technology in Pediatrics.

BACKGROUND: Although electronic health records (EHRs) are designed to improve patient safety, they have been associated with serious patient harm. An agreed-upon and standard taxonomy for classifying health information technology (HIT) related patient safety events does not exist. OBJECTIVES: We aimed to develop and evaluate a taxonomy for medication-related patient safety events associated with HIT and validate it using a set of events involving pediatric patients. METHODS: We performed a literature search to identify existing classifications for HIT-related safety events, which were assessed using real-world pediatric medication-related patient safety events extracted from two sources: patient safety event reporting system (ERS) reports and information technology help desk (HD) tickets. A team of clinical and patient safety experts used iterative tests of change and consensus building to converge on a single taxonomy. The final devised taxonomy was applied to pediatric medication-related events assess its characteristics, including interrater reliability and agreement. RESULTS: Literature review identified four existing classifications for HIT-related patient safety events, and one was iteratively adapted to converge on a singular taxonomy. Safety events relating to usability accounted for a greater proportion of ERS reports, compared with HD tickets (37 vs. 20%, p = 0.022). Conversely, events pertaining to incorrect configuration accounted for a greater proportion of HD tickets, compared with ERS reports (63 vs. 8%, p < 0.01). Interrater agreement (%) and reliability (kappa) were 87.8% and 0.688 for ERS reports and 73.6% and 0.556 for HD tickets, respectively. DISCUSSION: A standardized taxonomy for medication-related patient safety events related to HIT is presented. The taxonomy was validated using pediatric events. Further evaluation can assess whether the taxonomy is suitable for nonmedication-related events and those occurring in other patient populations. CONCLUSION: Wider application of standardized taxonomies will allow for peer benchmarking and facilitate collaborative interinstitutional patient safety improvement efforts.

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital.

BACKGROUND: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. OBJECTIVE: The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. METHODS: A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. RESULTS: Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. CONCLUSION: We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions.

Clinical evaluation and diagnostic yield following evaluation of abnormal pulse detected using Apple Watch.

OBJECTIVE: The study sought to characterize the evaluation of patients who present following detection of an abnormal pulse using Apple Watch. MATERIALS AND METHODS: We conducted a retrospective review of patients evaluated for abnormal pulse detected using Apple Watch over a 4-month period. RESULTS: Among 264 included patients, clinical documentation for 41 (15.5%) explicitly noted an abnormal pulse alert. Preexisting atrial fibrillation was noted in 58 (22.0%). Most commonly performed testing included 12-lead echocardiography (n = 158; 59.8%), Holter monitor (n = 77; 29.2%), and chest x-ray (n = 64; 24.2%). A clinically actionable cardiovascular diagnosis of interest was established in only 30 (11.4%) patients, including 6 of 41 (15%) patients who received an explicit alert. DISCUSSION: False positive screening results may lead to overutilization of healthcare resources. CONCLUSIONS: The Food and Drug Administration and Apple should consider the unintended consequences of widespread screening for asymptomatic ("silent") atrial fibrillation and use of the Apple Watch abnormal pulse detection functionality by populations in whom the device has not been adequately studied.