Using informatics to improve cancer surveillance

Blumenthal, Wendy; Alimi, Temitope O; Jones, Sandra F; Jones, David; Rogers, Joseph D.; Benard, Vicki B.; Richardson, Lisa C.

doi:10.1093/jamia/ocaa149

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…Cancer registries and research studies will benefit tremendously from automated information extraction, where manual review and encoding of pathology reports are often necessary when these reports are in free text format [25]. Choi et al showed that LLMassisted extraction of structured information (e.g., tumor location, surgery type, histologic grade) from pathology and ultrasound reports can lead to significant time and cost savings, compared to manual methods [26].…”

Section: Information Extractionmentioning

confidence: 99%

Applications of Large Language Models in Pathology

Cheng

2024

Bioengineering

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Large language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write programs, and potentially assist in case sign-out. LLMs combined with vision models can assist in interpreting histopathology images. LLMs have immense potential in transforming pathology practice and education, but these models are not infallible, so any artificial intelligence generated content must be verified with reputable sources. Caution must be exercised on how these models are integrated into clinical practice, as these models can produce hallucinations and incorrect results, and an over-reliance on artificial intelligence may lead to de-skilling and automation bias. This review paper provides a brief history of LLMs and highlights several use cases for LLMs in the field of pathology.

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Section: Information Extractionmentioning

confidence: 99%

Applications of Large Language Models in Pathology

Cheng

2024

Bioengineering

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“…Penelitian yang mengaplikasikan pendekatan Informatika ke dalam konteks permasalaahn kanker pernah dilakukan oleh Penbherty, et al [27] dan Blumenthal, et al [28]. Penelitian Penbherty, et al [27] membahas Cancer Surveillance systems, dimana sistem tersebut digunakan sebagai dasar penelitian dan juga sumber data untuk memahami tren dalam kejadian kanker, prevalensi, dan kematian akibat kanker.…”

Section: Pendahuluanunclassified

“…Penelitian Penbherty, et al [27] membahas Cancer Surveillance systems, dimana sistem tersebut digunakan sebagai dasar penelitian dan juga sumber data untuk memahami tren dalam kejadian kanker, prevalensi, dan kematian akibat kanker. Lebih jauh lagi penelitian milik Blumenthal, et al [28] bertujuan untuk meningkatkan aktivitas penggunaan data kanker oleh dokter dan patologi, serta berguna dalam pengembangan Natural Language Processing (NLP) untuk dijadikan sebagai panduan pengawasan terhadap kanker yang berbasis Informatika.…”

Section: Pendahuluanunclassified

Pemodelan Hubungan Kausal dari Faktor-Faktor Penentu Cancer Related Fatigue Menggunakan Algoritma S3C-Latent

Endraswari

Rahmadi

Effendy

2020

RESTI

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Cancer patients experience cancer-related fatigue (CRF) that are subjective and persistent. CRF can have a negative impact on psychosocial, spiritual, and self-perceived burden. To understand more deeply about CRF, we need to answer one fundamental question: how are the causal mechanisms (cause-effect) of the factors related to CRF. The studies related so far are still limited to correlation analysis between factors and have not focused on the mechanism of a causal relationship. The purpose of this study is to model the causal relationship between CRF and psychosocial, spiritual, and self-perceived burden, using a causal method called the Stable Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). The results of this study are in the form of causal modeling between factors where self-burden has a causal relationship with CRF, spiritual need factors (religion) also have a causal relationship with CRF. Meanwhile, the social support factor (friends) with spiritual needs (religion) does not represent a causal relationship, but there is a strong association relationship. Meanwhile, the social support factor (friends) with CRF did not have a causal relationship or an association relationship between the two variables.

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“…This includes reporting on clinical quality measures for health systems, such as adherence to cancer-related preventative and screening services [ 16 ], as well as epidemiologic and clinical research, such as assessing determinants for non-adherence to cancer guidelines, developing interventions to increase screening and immunization efforts, and identifying delays in care [ 17 – 25 ]. In addition, there has been growing support and adoption of using EHRs to automate and standardize reporting to state central cancer registries [ 26 , 27 ]. However, utilization of EHR data to inform cancer prevention and control programs has been more limited.…”

Section: Introductionmentioning

confidence: 99%

The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control

Conderino

Bendik

Richards

et al. 2022

BMC Med Inform Decis Mak

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Introduction State cancer prevention and control programs rely on public health surveillance data to set objectives to improve cancer prevention and control, plan interventions, and evaluate state-level progress towards achieving those objectives. The goal of this project was to evaluate the validity of using electronic health records (EHRs) based on common data model variables to generate indicators for surveillance of cancer prevention and control for these public health programs. Methods Following the methodological guidance from the PRISMA Extension for Scoping Reviews, we conducted a literature scoping review to assess how EHRs are used to inform cancer surveillance. We then developed 26 indicators along the continuum of the cascade of care, including cancer risk factors, immunizations to prevent cancer, cancer screenings, quality of initial care after abnormal screening results, and cancer burden. Indicators were calculated within a sample of patients from the New York City (NYC) INSIGHT Clinical Research Network using common data model EHR data and were weighted to the NYC population using post-stratification. We used prevalence ratios to compare these estimates to estimates from the raw EHR of NYU Langone Health to assess quality of information within INSIGHT, and we compared estimates to results from existing surveillance sources to assess validity. Results Of the 401 identified articles, 15% had a study purpose related to surveillance. Our indicator comparisons found that INSIGHT EHR-based measures for risk factor indicators were similar to estimates from external sources. In contrast, cancer screening and vaccination indicators were substantially underestimated as compared to estimates from external sources. Cancer screenings and vaccinations were often recorded in sections of the EHR that were not captured by the common data model. INSIGHT estimates for many quality-of-care indicators were higher than those calculated using a raw EHR. Conclusion Common data model EHR data can provide rich information for certain indicators related to the cascade of care but may have substantial biases for others that limit their use in informing surveillance efforts for cancer prevention and control programs.

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Using informatics to improve cancer surveillance

Cited by 10 publications

References 17 publications

Applications of Large Language Models in Pathology

Applications of Large Language Models in Pathology

Pemodelan Hubungan Kausal dari Faktor-Faktor Penentu Cancer Related Fatigue Menggunakan Algoritma S3C-Latent

The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control

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