Trial Summary
What is the purpose of this trial?In this pilot study, the investigators will show feasibility of the NLP-based feedback system in 20 consultations of men with newly diagnosed prostate cancer. The investigators will recruit from the practices of up to 10 physicians who typically see these patients. The investigators will report the top five sentences from each consultation across key content areas (cancer prognosis, life expectancy, erectile dysfunction, urinary incontinence, and irritative urinary symptoms) to both patients and physicians within 2 weeks of the consultation.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications.
What data supports the effectiveness of the treatment NLP-based Feedback for prostate cancer?
The research suggests that natural language processing (NLP) can accurately assess important patient-centered outcomes like urinary incontinence and bowel dysfunction after prostate cancer treatment, which may help improve patient care by providing better feedback on treatment effects.
12345Is NLP-based feedback generally safe for humans?
There is no specific safety data available for NLP-based feedback in humans, but NLP is used in healthcare to help identify medication-related issues and improve safety by analyzing large amounts of data.
678910How is NLP-based Feedback treatment different from other prostate cancer treatments?
NLP-based Feedback treatment is unique because it uses natural language processing (NLP) to analyze clinical notes and assess patient-centered outcomes like urinary incontinence and bowel dysfunction after prostate cancer treatment. This approach focuses on improving the understanding of patient experiences and outcomes, which is different from traditional treatments that primarily target the cancer itself.
111121314Eligibility Criteria
This trial is for men who are having their first treatment talk for localized prostate cancer or those on active surveillance with worsening cancer considering local therapy. Participants must be patients at Cedars-Sinai and able to read and write in English.Inclusion Criteria
Ability to read and write in English.
I am a man seeking initial treatment for prostate cancer that has not spread.
My prostate cancer has worsened while on active surveillance and I am considering treatment.
+1 more
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Treatment Consultation
Audio recording and transcribing treatment counseling discussions for 20 men with newly diagnosed clinically localized prostate cancers
Up to 1 year
1 visit (in-person)
NLP Feedback and Assessment
NLP-based feedback provided to patients and physicians, followed by assessment of decisional conflict and risk perception
2 weeks
1 visit (virtual)
Follow-up
Participants are monitored for changes in decisional conflict and risk perception after receiving NLP-based feedback
4 weeks
Participant Groups
The study tests an NLP-based feedback system during consultations with men newly diagnosed with prostate cancer. It aims to improve communication by reporting key consultation points back to patients and doctors within two weeks.
1Treatment groups
Experimental Treatment
Group I: NLP Intervention Experimental ArmExperimental Treatment1 Intervention
20 men with newly diagnosed clinically localized prostate cancers and utilize NLP to extract key content using the top five sentences by NLP probability for key content areas will be generated and will be provided to patients and providers within 2 weeks after each case.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Cedars-Sinai Medical CenterLos Angeles, CA
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Who Is Running the Clinical Trial?
Cedars-Sinai Medical CenterLead Sponsor
National Cancer Institute (NCI)Collaborator
References
Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment. [2020]The population-based assessment of patient-centered outcomes (PCOs) has been limited by the efficient and accurate collection of these data. Natural language processing (NLP) pipelines can determine whether a clinical note within an electronic medical record contains evidence on these data. We present and demonstrate the accuracy of an NLP pipeline that targets to assess the presence, absence, or risk discussion of two important PCOs following prostate cancer treatment: urinary incontinence (UI) and bowel dysfunction (BD).
Natural Language Processing in Oncology: A Review. [2021]Natural language processing (NLP) has the potential to accelerate translation of cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of personalized medicine. This technology can harvest important clinical variables trapped in the free-text narratives within electronic medical records.
Prediction tools in surgical oncology. [2019]Artificial neural networks, prediction tables, and clinical nomograms allow physicians to transmit an immense amount of prognostic information in a format that exhibits comprehensibility and brevity. Current models demonstrate the feasibility to accurately predict many oncologic outcomes, including pathologic stage, recurrence-free survival, and response to adjuvant therapy. Although emphasis should be placed on the independent validation of existing prediction tools, there is a paucity of models in the literature that focus on quality of life outcomes. The unification of tools that predict oncologic and quality of life outcomes into a comparative effectiveness table will furnish patients with cancer with the information they need to make a highly informed and individualized treatment decision.
Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. [2023]Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care.
Predicting clinical end points: treatment nomograms in prostate cancer. [2022]Due to the generally indolent nature of prostate cancer, patients must decide among a wide range of treatments, which will significantly affect both quality of life and survival. Thus, there is a need for instruments to aid patients and their physicians in decision analysis. Nomograms are instruments that predict outcomes for the individual patient. Using algorithms that incorporate multiple variables, nomograms calculate the predicted probability that a patient will reach a clinical end point of interest. Nomograms tend to outperform both expert clinicians and predictive instruments based on risk grouping. We outline principles for nomogram construction, including considerations for choice of clinical end points and appropriate predictive variables, and methods for model validation. Currently, nomograms are available to predict progression-free probability after several primary treatments for localized prostate cancer. There is need for additional models that predict other clinical end points, especially survival adjusted for quality of life.
Selecting a PRO-CTCAE-based subset for patient-reported symptom monitoring in prostate cancer patients: a modified Delphi procedure. [2023]Clinician-based reporting of adverse events leads to underreporting and underestimation of the impact of adverse events on prostate cancer patients. Therefore, interest has grown in capturing adverse events directly from patients using the Patient-Reported Outcomes (PROs) version of the Common Terminology Criteria for Adverse Events (CTCAE). We aimed to develop a standardized PRO-CTCAE subset tailored to adverse event monitoring in prostate cancer patients.
Developing a cancer-specific trigger tool to identify treatment-related adverse events using administrative data. [2021]As there are few validated tools to identify treatment-related adverse events across cancer care settings, we sought to develop oncology-specific "triggers" to flag potential adverse events among cancer patients using claims data.
Months and Severity Score (MOSES) in a Phase III trial (PARCER): A new comprehensive method for reporting adverse events in oncology clinical trials. [2022]Adverse event reporting in oncology trials lacks temporal description. We propose a toxicity summarizing method that incorporates time.
Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges. [2019]The safety of medication use has been a priority in the United States since the late 1930s. Recently, it has gained prominence due to the increasing amount of data suggesting that a large amount of patient harm is preventable and can be mitigated with effective risk strategies that have not been sufficiently adopted. Adverse events from medications are part of clinical practice, but the ability to identify a patient's risk and to minimize that risk must be a priority. The ability to identify adverse events has been a challenge due to limitations of available data sources, which are often free text. The use of natural language processing (NLP) may help to address these limitations. NLP is the artificial intelligence domain of computer science that uses computers to manipulate unstructured data (i.e., narrative text or speech data) in the context of a specific task. In this narrative review, we illustrate the fundamentals of NLP and discuss NLP's application to medication safety in four data sources: electronic health records, Internet-based data, published literature, and reporting systems. Given the magnitude of available data from these sources, a growing area is the use of computer algorithms to help automatically detect associations between medications and adverse effects. The main benefit of NLP is in the time savings associated with automation of various medication safety tasks such as the medication reconciliation process facilitated by computers, as well as the potential for near-real-time identification of adverse events for postmarketing surveillance such as those posted on social media that would otherwise go unanalyzed. NLP is limited by a lack of data sharing between health care organizations due to insufficient interoperability capabilities, inhibiting large-scale adverse event monitoring across populations. We anticipate that future work in this area will focus on the integration of data sources from different domains to improve the ability to identify potential adverse events more quickly and to improve clinical decision support with regard to a patient's estimated risk for specific adverse events at the time of medication prescription or review.
Performance of a Trigger Tool for Identifying Adverse Events in Oncology. [2021]Although patient safety is a priority in oncology, few tools measure adverse events (AEs) beyond treatment-related toxicities. The study objective was to assemble a set of clinical triggers in the medical record and assess the extent to which triggered events identified AEs.
Ascertainment of Veterans With Metastatic Prostate Cancer in Electronic Health Records: Demonstrating the Case for Natural Language Processing. [2021]Prostate cancer (PCa) is among the leading causes of cancer deaths. While localized PCa has a 5-year survival rate approaching 100%, this rate drops to 31% for metastatic prostate cancer (mPCa). Thus, timely identification of mPCa is a crucial step toward measuring and improving access to innovations that reduce PCa mortality. Yet, methods to identify patients diagnosed with mPCa remain elusive. Cancer registries provide detailed data at diagnosis but are not updated throughout treatment. This study reports on the development and validation of a natural language processing (NLP) algorithm deployed on oncology, urology, and radiology clinical notes to identify patients with a diagnosis or history of mPCa in the Department of Veterans Affairs.
Deep Learning-based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports. [2022]To train a deep natural language processing (NLP) model, using data mined structured oncology reports (SOR), for rapid tumor response category (TRC) classification from free-text oncology reports (FTOR) and to compare its performance with human readers and conventional NLP algorithms.
Natural language processing and its future in medicine. [2019]If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be structured or coded. Many online patient reports are not coded, but are recorded in natural-language text that cannot be reliably accessed. Natural language processing (NLP) can solve this problem by extracting and structuring text-based clinical information, making clinical data available for use. NLP systems are quite difficult to develop, as they require substantial amounts of knowledge, but progress has definitely been made. Some NLP systems have been developed and tested and have demonstrated promising performance in practical clinical applications; some of these systems have already been deployed. The authors provide background information about NLP, briefly describe some of the systems that have been recently developed, and discuss the future of NLP in medicine.
Can ChatGPT, an Artificial Intelligence Language Model, Provide Accurate and High-quality Patient Information on Prostate Cancer? [2023]To evaluate the performance of ChatGPT, an artificial intelligence (AI) language model, in providing patient information on prostate cancer, and to compare the accuracy, similarity, and quality of the information to a reference source.