An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.

BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it. METHODS: Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks. RESULTS: Our ML-based CT analysis (TB-Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI's CLIP Vi4). TB-Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB-lesion volumes, and disease-signs during disease pathogenesis. CONCLUSION: The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making.

Using Large Language Models to Support Content Analysis: A Case Study of ChatGPT for Adverse Event Detection.

This study explores the potential of using large language models to assist content analysis by conducting a case study to identify adverse events (AEs) in social media posts. The case study compares ChatGPT's performance with human annotators' in detecting AEs associated with delta-8-tetrahydrocannabinol, a cannabis-derived product. Using the identical instructions given to human annotators, ChatGPT closely approximated human results, with a high degree of agreement noted: 94.4% (9436/10,000) for any AE detection (Fleiss κ=0.95) and 99.3% (9931/10,000) for serious AEs (κ=0.96). These findings suggest that ChatGPT has the potential to replicate human annotation accurately and efficiently. The study recognizes possible limitations, including concerns about the generalizability due to ChatGPT's training data, and prompts further research with different models, data sources, and content analysis tasks. The study highlights the promise of large language models for enhancing the efficiency of biomedical research.

AI in Pathology: What could possibly go wrong?

The field of medicine is undergoing rapid digital transformation. Pathologists are now striving to digitize their data, workflows, and interpretations, assisted by the enabling development of whole-slide imaging. Going digital means that the analog process of human diagnosis can be augmented or even replaced by rapidly evolving AI approaches, which are just now entering into clinical practice. But with such progress comes challenges that reflect a variety of stressors, including the impact of unrepresentative training data with accompanying implicit bias, data privacy concerns, and fragility of algorithm performance. Beyond such core digital aspects, considerations arise related to difficulties presented by changing disease presentations, diagnostic approaches, and therapeutic options. While some tools such as data federation can help with broadening data diversity while preserving expertise and local control, they may not be the full answer to some of these issues. The impact of AI in pathology on the field's human practitioners is still very much unknown: installation of unconscious bias and deference to AI guidance need to be understood and addressed. If AI is widely adopted, it may remove many inefficiencies in daily practice and compensate for staff shortages. It may also cause practitioner deskilling, dethrilling, and burnout. We discuss the technological, clinical, legal, and sociological factors that will influence the adoption of AI in pathology, and its eventual impact for good or ill.

Latent Tuberculosis Screening Using Electronic Health Record Data.

Screening for latent tuberculosis infection is recommended for foreign-born persons in the United States. We used proxy data from electronic health records to determine that 17.5% of foreign-born outpatients attending the UC San Diego Health clinic (San Diego, CA, USA) underwent screening. Ending the global tuberculosis epidemic requires improved screening.

Genomics and electronic health record systems.

Several reviews and case reports have described how information derived from the analysis of genomes are currently included in electronic health records (EHRs) for the purposes of supporting clinical decisions. Since the introduction of this new type of information in EHRs is relatively new (for instance, the widespread adoption of EHRs in the United States is just about a decade old), it is not surprising that a myriad of approaches has been attempted, with various degrees of success. EHR systems undergo much customization to fit the needs of health systems; these approaches have been varied and not always generalizable. The intent of this article is to present a high-level view of these approaches, emphasizing the functionality that they are trying to achieve, and not to advocate for specific solutions, which may become obsolete soon after this review is published. We start by broadly defining the end goal of including genomics in EHRs for healthcare and then explaining the various sources of information that need to be linked to arrive at a clinically actionable genomics analysis using a pharmacogenomics example. In addition, we include discussions on open issues and a vision for the next generation systems that integrate whole genome sequencing and EHRs in a seamless fashion.

Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study.

BACKGROUND: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining the prevalence of fungal ocular involvement is important for informing clinical guidelines, such as the need for routine ophthalmologic consultations. However, manual retrospective record review to detect cases is time-consuming. OBJECTIVE: This study aimed to determine the prevalence of fungal ocular involvement in a critical care database using both structured and unstructured electronic health record (EHR) data. METHODS: We queried microbiology data from 46,467 critical care patients over 12 years (2000-2012) from the Medical Information Mart for Intensive Care III (MIMIC-III) to identify 265 patients with culture-proven fungemia. For each fungemic patient, demographic data, fungal species present in blood culture, and risk factors for fungemia (eg, presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status) were ascertained. All structured diagnosis codes and free-text narrative notes associated with each patient's hospitalization were also extracted. Screening for fungal endophthalmitis was performed using two approaches: (1) by querying a wide array of eye- and vision-related diagnosis codes, and (2) by utilizing a custom regular expression pipeline to identify and collate relevant text matches pertaining to fungal ocular involvement. Both approaches were validated using manual record review. The main outcome measure was the documentation of any fungal ocular involvement. RESULTS: In total, 265 patients had culture-proven fungemia, with Candida albicans (n=114, 43%) and Candida glabrata (n=74, 28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 121 (46%). In total, 7 patients were identified as having eye- or vision-related diagnosis codes, none of whom had fungal endophthalmitis based on record review. There were 26,830 free-text narrative notes associated with these 265 patients. A regular expression pipeline based on relevant terms yielded possible matches in 683 notes from 108 patients. Subsequent manual record review again demonstrated that no patients had fungal ocular involvement. Therefore, the prevalence of fungal ocular involvement in this cohort was 0%. CONCLUSIONS: MIMIC-III contained no cases of ocular involvement among fungemic patients, consistent with prior studies reporting low rates of ocular involvement in fungemia. This study demonstrates an application of natural language processing to expedite the review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical examination findings that are documented within clinical notes.

Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care.

We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center's acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which - dose - was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity.

Adaptive Randomization of Neratinib in Early Breast Cancer.

BACKGROUND: The heterogeneity of breast cancer makes identifying effective therapies challenging. The I-SPY 2 trial, a multicenter, adaptive phase 2 trial of neoadjuvant therapy for high-risk clinical stage II or III breast cancer, evaluated multiple new agents added to standard chemotherapy to assess the effects on rates of pathological complete response (i.e., absence of residual cancer in the breast or lymph nodes at the time of surgery). METHODS: We used adaptive randomization to compare standard neoadjuvant chemotherapy plus the tyrosine kinase inhibitor neratinib with control. Eligible women were categorized according to eight biomarker subtypes on the basis of human epidermal growth factor receptor 2 (HER2) status, hormone-receptor status, and risk according to a 70-gene profile. Neratinib was evaluated against control with regard to 10 biomarker signatures (prospectively defined combinations of subtypes). The primary end point was pathological complete response. Volume changes on serial magnetic resonance imaging were used to assess the likelihood of such a response in each patient. Adaptive assignment to experimental groups within each disease subtype was based on Bayesian probabilities of the superiority of the treatment over control. Enrollment in the experimental group was stopped when the 85% Bayesian predictive probability of success in a confirmatory phase 3 trial of neoadjuvant therapy reached a prespecified threshold for any biomarker signature ("graduation"). Enrollment was stopped for futility if the probability fell to below 10% for every biomarker signature. RESULTS: Neratinib reached the prespecified efficacy threshold with regard to the HER2-positive, hormone-receptor-negative signature. Among patients with HER2-positive, hormone-receptor-negative cancer, the mean estimated rate of pathological complete response was 56% (95% Bayesian probability interval [PI], 37 to 73%) among 115 patients in the neratinib group, as compared with 33% among 78 controls (95% PI, 11 to 54%). The final predictive probability of success in phase 3 testing was 79%. CONCLUSIONS: Neratinib added to standard therapy was highly likely to result in higher rates of pathological complete response than standard chemotherapy with trastuzumab among patients with HER2-positive, hormone-receptor-negative breast cancer. (Funded by QuantumLeap Healthcare Collaborative and others; I-SPY 2 TRIAL ClinicalTrials.gov number, NCT01042379.).

Adaptive Randomization of Veliparib-Carboplatin Treatment in Breast Cancer.

BACKGROUND: The genetic and clinical heterogeneity of breast cancer makes the identification of effective therapies challenging. We designed I-SPY 2, a phase 2, multicenter, adaptively randomized trial to screen multiple experimental regimens in combination with standard neoadjuvant chemotherapy for breast cancer. The goal is to match experimental regimens with responding cancer subtypes. We report results for veliparib, a poly(ADP-ribose) polymerase (PARP) inhibitor, combined with carboplatin. METHODS: In this ongoing trial, women are eligible for participation if they have stage II or III breast cancer with a tumor 2.5 cm or larger in diameter; cancers are categorized into eight biomarker subtypes on the basis of status with regard to human epidermal growth factor receptor 2 (HER2), hormone receptors, and a 70-gene assay. Patients undergo adaptive randomization within each biomarker subtype to receive regimens that have better performance than the standard therapy. Regimens are evaluated within 10 biomarker signatures (i.e., prospectively defined combinations of biomarker subtypes). Veliparib-carboplatin plus standard therapy was considered for HER2-negative tumors and was therefore evaluated in 3 signatures. The primary end point is pathological complete response. Tumor volume changes measured by magnetic resonance imaging during treatment are used to predict whether a patient will have a pathological complete response. Regimens move on from phase 2 if and when they have a high Bayesian predictive probability of success in a subsequent phase 3 neoadjuvant trial within the biomarker signature in which they performed well. RESULTS: With regard to triple-negative breast cancer, veliparib-carboplatin had an 88% predicted probability of success in a phase 3 trial. A total of 72 patients were randomly assigned to receive veliparib-carboplatin, and 44 patients were concurrently assigned to receive control therapy; at the completion of chemotherapy, the estimated rates of pathological complete response in the triple-negative population were 51% (95% Bayesian probability interval [PI], 36 to 66%) in the veliparib-carboplatin group versus 26% (95% PI, 9 to 43%) in the control group. The toxicity of veliparib-carboplatin was greater than that of the control. CONCLUSIONS: The process used in our trial showed that veliparib-carboplatin added to standard therapy resulted in higher rates of pathological complete response than standard therapy alone specifically in triple-negative breast cancer. (Funded by the QuantumLeap Healthcare Collaborative and others; I-SPY 2 TRIAL ClinicalTrials.gov number, NCT01042379.).

Text Simplification Using Consumer Health Vocabulary to Generate Patient-Centered Radiology Reporting: Translation and Evaluation.

BACKGROUND: Radiology reporting is a clinically oriented form of documentation that reflects critical information for patients about their health care processes. Realizing its importance, many medical institutions have started providing radiology reports in patient portals. The gain, however, can be limited because of medical language barriers, which require a way for customizing these reports for patients. The open-access, collaborative consumer health vocabulary (CHV) is a terminology system created for such purposes and can be the basis of lexical simplification processes for clinical notes. OBJECTIVE: The aim of this study was to examine the comprehensibility and suitability of CHV in simplifying radiology reports for consumers. This was done by characterizing the content coverage and the lexical similarity between the terms in the reports and the CHV-preferred terms. METHODS: The overall procedure was divided into the following two main stages: (1) translation and (2) evaluation. The translation process involved using MetaMap to link terms in the reports to CHV concepts. This is followed by replacing the terms with CHV-preferred terms using the concept names and sources table (MRCONSO) in the Unified Medical Language System (UMLS) Metathesaurus. In the second stage, medical terms in the reports and general terms that are used to describe medical phenomena were selected and evaluated by comparing the words in the original reports with the translated ones. The evaluation includes measuring the content coverage, investigating lexical similarity, and finding trends in missing concepts. RESULTS: Of the 792 terms selected from the radiology reports, 695 of them could be mapped directly to CHV concepts, indicating a content coverage of 88.5%. A total of 51 of the concepts (53%, 51/97) that could not be mapped are names of human anatomical structures and regions, followed by 28 anatomical descriptions and pathological variations (29%, 28/97). In addition, 12 radiology techniques and projections represented 12% of the unmapped concepts, whereas the remaining six concepts (6%, 12/97) were physiological descriptions. The rate of lexical similarity between the CHV-preferred terms and the terms in the radiology reports was approximately 72.6%. CONCLUSIONS: The CHV covered a high percentage of concepts found in the radiology reports, but unmapped concepts are associated with areas that are commonly found in radiology reporting. CHV terms also showed a high percentage of lexical similarity with terms in the reports, which contain a myriad of medical jargon. This suggests that many CHV terms might not be suitable for lay consumers who would not be facile with radiology-specific vocabulary. Therefore, further patient-centered content changes are needed of the CHV to increase its usefulness and facilitate its integration into consumer-oriented applications.

A Digital Cohort Approach for Social Media Monitoring: A Cohort Study of People Who Vape E-Cigarettes.

INTRODUCTION: The evidence hierarchy in public health emphasizes longitudinal studies, whereas social media monitoring relies on aggregate analyses. Authors propose integrating longitudinal analyses into social media monitoring by creating a digital cohort of individual account holders, as demonstrated by a case study analysis of people who vape. METHODS: All English language X posts mentioning vape or vaping were collected from January 1, 2017 through December 31, 2020. The digital cohort was composed of people who self-reported vaping and posted at least 10 times about vaping during the study period to determine the (1) prevalence, (2) success rate, and (3) timing of cessation behaviors. RESULTS: There were 25,112 instances where an account shared at least 10 posts about vaping, with 619 (95% CI=616, 622) mean person-days and 43,810,531 cumulative person-days of observation. Among a random sample of accounts, 39% (95% CI=35, 43) belonged to persons who vaped. Among this digital cohort, 27% (95% CI=21, 33) reported making a quit attempt. For all first quit attempts, 26% (95% CI=19, 33) were successful on the basis of their subsequent vaping posts. Among those with a failed first cessation attempt, 13% (95% CI=6, 19) subsequently made an additional quit attempt, of whom 36% (95% CI=11, 61) were successful. On average, a quit attempt occurred 531 days (95% CI=474, 588) after their first vaping-related post. If their quit attempt failed, any second quit attempt occurred 361 days (95% CI=250, 474) after their first quit attempt. CONCLUSIONS: By aligning with standard epidemiologic surveillance practices, this approach can greatly enhance the usefulness of social media monitoring in informing public health decision making, such as yielding insights into the timing of cessation behaviors among people who vape.

Integrating clinical research into electronic health record workflows to support a learning health system.

Objective: Integrating clinical research into routine clinical care workflows within electronic health record systems (EHRs) can be challenging, expensive, and labor-intensive. This case study presents a large-scale clinical research project conducted entirely within a commercial EHR during the COVID-19 pandemic. Case Report: The UCSD and UCSDH COVID-19 NeutraliZing Antibody Project (ZAP) aimed to evaluate antibody levels to SARS-CoV-2 virus in a large population at an academic medical center and examine the association between antibody levels and subsequent infection diagnosis. Results: The project rapidly and successfully enrolled and consented over 2000 participants, integrating the research trial with standing COVID-19 testing operations, staff, lab, and mobile applications. EHR-integration increased enrollment, ease of scheduling, survey distribution, and return of research results at a low cost by utilizing existing resources. Conclusion: The case study highlights the potential benefits of EHR-integrated clinical research, expanding their reach across multiple health systems and facilitating rapid learning during a global health crisis.

The Athena Breast Health Network: developing a rapid learning system in breast cancer prevention, screening, treatment, and care.

The term breast cancer covers many different conditions, whose clinical course ranges from indolent to aggressive. However, current practice in breast cancer prevention and care, and in breast cancer epidemiology, does not take into account the heterogeneity of the disease. A comprehensive understanding of the etiology and progression of different breast cancer subtypes would enable a more patient-centered approach to breast health care: assessing an individual's risk of getting specific subtypes of the disease, providing risk-based screening and prevention recommendations, and, for those diagnosed with the disease, tailored treatment options based on risk and timing of progression and mortality. The Athena Breast Health Network is an initiative of the five University of California medical and cancer centers to prototype this approach and to enable the development of a rapid learning system-connecting risk and outcome information from a heterogeneous patient population in real time and using new knowledge from research to continuously improve the quality of care. The Network is based on integrating clinical and research processes to create a comprehensive approach to accelerating patient-centered breast health care. Since its inception in 2009, the Network has developed a multi-site, transdisciplinary collaboration that enables the learning system. The five-campus collaboration has implemented a shared informatics platform, standardized electronic patient intake questionnaires, and common biospecimen protocols, as well as new clinical programs and multi-center research projects. The Athena Breast Health Network can serve as a model of a rapid learning system that integrates epidemiologic, behavioral, and clinical research with clinical care improvements.

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.Clinical relevance- This platform is currently hosting a deep learning model for the early prediction of sepsis that is operational in two emergency departments.

Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11.

Purpose: Widespread electronic health record adoption has generated a large volume of data and emphasized the need for standardized terminology to describe clinical concepts. Here, we undertook a systematic concept coverage analysis to determine the representation of clinical concepts in ophthalmic infection and ophthalmic trauma among standardized medical terminologies, including the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), the International Classification of Diseases (ICD) version 10 with clinical modifications (ICD-10-CM), and ICD version 11 (ICD-11). Design: Extraction of concepts related to ophthalmic infection and ophthalmic trauma and structured search in terminology browsers. Data Sources: The American Academy of Ophthalmology Basic and Clinical Science Course (BCSC), SNOMED-CT, and ICD-10-CM terminologies from the Observational Health Data Sciences and Informatics Athena browser, and the ICD-11 terminology browser. Methods: Concepts pertaining to ophthalmic infection and ophthalmic trauma were extracted from the 2022 BCSC free text and index terms. We searched terminology browsers to identify corresponding codes and classified the extent of semantic alignment as equal, wide, narrow, or unmatched in each terminology. The overlap of equal concepts in each terminology was represented in a Venn diagram. Main Outcome Measures: Proportions of clinical concepts with corresponding codes at various levels of semantic alignment. Results: A total of 443 concepts were identified: 304 concepts related to ophthalmic infection and 139 concepts related to ophthalmic trauma. The SNOMED-CT had the highest proportion of equal coverage, with 82.0% (249 of 304) among concepts related to ophthalmic infection and 82.0% (115 of 139) among concepts related to ophthalmic trauma. Across all concepts, 28% (124 of 443) were classified as equal in ICD-10-CM and 52.8% (234 of 443) were classified as equal in ICD-11. Conclusions: The SNOMED-CT had significantly better semantic alignment than ICD-10-CM and ICD-11 for ophthalmic infections and ophthalmic trauma. This demonstrates opportunity for continuing advancement of representation of ophthalmic concepts in standardized medical terminologies.

Ascertaining Whether an Intelligent Voice Assistant Can Meet Older Adults' Health-Related Needs in the Context of a Geriatrics 5Ms Framework.

The Geriatrics 5Ms: Medications, Mind, Mobility, what Matters most and Multicomplexity is a framework to address the complex needs of older adults. Intelligent Voice Assistants (IVAs) are increasingly popular and have potential to support health-related needs of older adults. We utilized previously collected qualitative data on older adults' views of how an IVA may address their health-related needs and ascertained their fit into the Geriatrics 5Ms framework. The codes describing health challenges and potential IVA solutions fit the framework: (1) Medications: difficulty remembering medications. SOLUTION: reminders. (2) Mind: isolation, anxiety, memory loss. SOLUTION: companionship, memory aids. (3) Mobility: barriers to exercise. SOLUTION: incentives, exercise ideas. (4) Matters most: eating healthy foods. SOLUTION: suggest and order nutritious foods, (5) Multicomplexity; managing multimorbidity. SOLUTION: symptom tracking and communicating with health care professionals. Incorporating the 5Ms framework into IVA design can aid in addressing health care priorities of older adults.

Clinical characteristics and comorbidities associated with SARS-CoV-2 breakthrough infection in the University of California Healthcare Systems.

BACKGROUND: To evaluate the degree to which clinical comorbidities or combinations of comorbidities are associated with SARS-CoV-2 breakthrough infection. MATERIALS AND METHODS: A breakthrough infection was defined as a positive test at least 14 days after a full vaccination regimen. Logistic regression was used to calculate aORs, which were adjusted for age, sex, and race information. RESULTS: A total of 110,380 patients from the UC CORDS database were included. After adjustment, stage 5 CKD due to hypertension (aOR: 7.33; 95% CI: 4.86-10.69; p<.001; power=1) displayed higher odds of infection than any other comorbidity. Lung transplantation history (aOR: 4.79; 95% CI: 3.25-6.82; p<.001; power= 1), coronary atherosclerosis (aOR: 2.12; 95% CI: 1.77-2.52; p<.001; power=1), and vitamin D deficiency (aOR: 1.87; 95% CI: 1.69-2.06; p<.001; power=1) were significantly correlated to breakthrough infection. Patients with obesity in addition to essential hypertension (aOR: 1.74; 95% CI: 1.51-2.01; p<.001; power=1) and anemia (aOR: 1.80; 95% CI: 1.47-2.19; p<.001; power=1) were at additional risk of breakthrough infection compared to those with essential hypertension and anemia alone. CONCLUSIONS: Further measures should be taken to prevent breakthrough infection for individuals with these conditions, such as acquiring additional doses of the SARS-CoV-2 vaccine to boost immunity.

Early Ascertainment of Breast Cancer Diagnoses Comparing Self-Reported Questionnaires and Electronic Health Record Data Warehouse: The WISDOM Study.

PURPOSE: The goal of this study was to use real-world data sources that may be faster and more complete than self-reported data alone, and timelier than cancer registries, to ascertain breast cancer cases in the ongoing screening trial, the WISDOM Study. METHODS: We developed a data warehouse procedural process (DWPP) to identify breast cancer cases from a subgroup of WISDOM participants (n = 11,314) who received breast-related care from a University of California Health Center in the period 2012-2021 by searching electronic health records (EHRs) in the University of California Data Warehouse (UCDW). Incident breast cancer diagnoses identified by the DWPP were compared with those identified by self-report via annual follow-up online questionnaires. RESULTS: Our study identified 172 participants with confirmed breast cancer diagnoses in the period 2016-2021 by the following sources: 129 (75%) by both self-report and DWPP, 23 (13%) by DWPP alone, and 20 (12%) by self-report only. Among those with International Classification of Diseases 10th revision cancer diagnostic codes, no diagnosis was confirmed in 18% of participants. CONCLUSION: For diagnoses that occurred ≥20 months before the January 1, 2022, UCDW data pull, WISDOM self-reported data via annual questionnaire achieved high accuracy (96%), as confirmed by the cancer registry. More rapid cancer ascertainment can be achieved by combining self-reported data with EHR data from a health system data warehouse registry, particularly to address self-reported questionnaire issues such as timing delays (ie, time lag between participant diagnoses and the submission of their self-reported questionnaire typically ranges from a month to a year) and lack of response. Although cancer registry reporting often is not as timely, it does not require verification as does the DWPP or self-report from annual questionnaires.