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Grant Details

Grant Number: 5R01CA183962-05 Interpret this number
Primary Investigator: Hernandez-Boussard, Tina
Organization: Stanford University
Project Title: Utilizing Electronic Health Records to Measure and Improve Prostate Cancer Care
Fiscal Year: 2019


 DESCRIPTION (provided by applicant): Prostate cancer is the most common malignancy in men. Newly diagnosed men face complex treatment choices, each with different risks of acquired patient-centered outcomes (e.g. urinary and erectile dysfunction). Currently, patients and clinicians cannot easily compare the trade-offs among patient-centered outcomes across different treatments because the empirical evidence regarding these trade-offs does not exist, because patient centered outcomes are not routinely recorded in assessable formats. However, electronic healthcare records (EHR) free text is a rich, untapped source of patient centered outcomes. We propose to assemble a robust data-mining workflow to efficiently and accurately capture treatment and outcome quality metrics from structured data and free-text in EHRs. We will put this evidence in the hands of both clinicians and patients through a web-based risk assessment tool. Our proposal has three innovative aspects. First, we will develop an EHR prostate cancer database that will allow for clinical care data to be analyzed alongside diagnostic details. Second, we will create novel ontological representations of quality metrics that will be public and reliably calculable across EHR-systems. Third, we will assemble a robust data- mining workflow that expands on existing methods by focusing on ontology-based dictionaries to annotate free text. Combining this set of innovative components will uniquely allow us to use existing EHRs to efficiently study the trade-offs among patient-centered outcomes across different treatments. In Aim 1 we will create the building blocks needed to identify quality metric data in EHRs. We will develop an EHR-database, map quality metrics to medical vocabularies and ontologies, and create electronic quality metric phenotypes. In Aim 2, we will expand our data-mining workflow with quality metric vocabulary and use it to gather data relevant to quality metrics. In Aim 3 we will develop a web-based tool that integrates the empirical evidence assessed in our first two aims with patient and clinical characteristics to estimate patients' personalized risks of patient centered outcomes across treatments. Our web tool will display such personalized risk predictions, to help clinicians and patients choose a treatment option that offers the best predicted quality of life given the importance they assign to each patient-centered outcome. This proposal will address a critical gap in evidence for prostate cancer treatment and research by providing clinicians and patients with empirical evidence needed to compare the trade-offs among patient centered outcomes across different treatments. Our work is consistent with our nation's focus on EHR `meaningful use' and the comprehensive assessment of healthcare delivery, and with NCI's focus on improving the quality of cancer care delivery.


Diverse patient trajectories during cytotoxic chemotherapy: Capturing longitudinal patient-reported outcomes.
Authors: Azad A.D. , Yilmaz M. , Bozkurt S. , Brooks J.D. , Blayney D.W. , Hernandez-Boussard T. .
Source: Cancer medicine, 2021 Sep; 10(17), p. 5783-5793.
EPub date: 2021-07-13.
PMID: 34254459
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Clinical Trial Outcomes in Urology: Assessing Early Discontinuation, Results Reporting and Publication in ClinicalTrials.Gov Registrations 2007-2019.
Authors: Magnani C.J. , Steinberg J.R. , Harmange C.I. , Zhang X. , Driscoll C. , Bell A. , Larson J. , You J.G. , Weeks B.T. , Hernandez-Boussard T. , et al. .
Source: The Journal of urology, 2021 04; 205(4), p. 1159-1168.
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PMID: 33079618
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Assessment of a Clinical Trial-Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer.
Authors: Coquet J. , Bievre N. , Billaut V. , Seneviratne M. , Magnani C.J. , Bozkurt S. , Brooks J.D. , Hernandez-Boussard T. .
Source: JAMA network open, 2021-01-04; 4(1), p. e2031730.
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Real-world Evidence to Estimate Prostate Cancer Costs for First-line Treatment or Active Surveillance.
Authors: Magnani C.J. , Bievre N. , Baker L.C. , Brooks J.D. , Blayney D.W. , Hernandez-Boussard T. .
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Association between patient-initiated emails and overall 2-year survival in cancer patients undergoing chemotherapy: Evidence from the real-world setting.
Authors: Coquet J. , Blayney D.W. , Brooks J.D. , Hernandez-Boussard T. .
Source: Cancer medicine, 2020 11; 9(22), p. 8552-8561.
EPub date: 2020-09-28.
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Phenotyping severity of patient-centered outcomes using clinical notes: A prostate cancer use case.
Authors: Bozkurt S. , Paul R. , Coquet J. , Sun R. , Banerjee I. , Brooks J.D. , Hernandez-Boussard T. .
Source: Learning health systems, 2020 Oct; 4(4), p. e10237.
EPub date: 2020-07-17.
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Leveraging Digital Data to Inform and Improve Quality Cancer Care.
Authors: Hernandez-Boussard T. , Blayney D.W. , Brooks J.D. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2020 04; 29(4), p. 816-822.
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Real world evidence in cardiovascular medicine: ensuring data validity in electronic health record-based studies.
Authors: Hernandez-Boussard T. , Monda K.L. , Crespo B.C. , Riskin D. .
Source: Journal of the American Medical Informatics Association : JAMIA, 2019-11-01; 26(11), p. 1189-1194.
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Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.
Authors: Lenain R. , Seneviratne M.G. , Bozkurt S. , Blayney D.W. , Brooks J.D. , Hernandez-Boussard T. .
Source: Studies in health technology and informatics, 2019-08-21; 264, p. 1522-1523.
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Extracting Patient-Centered Outcomes from Clinical Notes in Electronic Health Records: Assessment of Urinary Incontinence After Radical Prostatectomy.
Authors: Gori D. , Banerjee I. , Chung B.I. , Ferrari M. , Rucci P. , Blayney D.W. , Brooks J.D. , Hernandez-Boussard T. .
Source: EGEMS (Washington, DC), 2019-08-20; 7(1), p. 43.
EPub date: 2019-08-20.
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Is it possible to automatically assess pretreatment digital rectal examination documentation using natural language processing? A single-centre retrospective study.
Authors: Bozkurt S. , Kan K.M. , Ferrari M.K. , Rubin D.L. , Blayney D.W. , Hernandez-Boussard T. , Brooks J.D. .
Source: BMJ open, 2019-07-18; 9(7), p. e027182.
EPub date: 2019-07-18.
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PSA Testing Use and Prostate Cancer Diagnostic Stage After the 2012 U.S. Preventive Services Task Force Guideline Changes.
Authors: Magnani C.J. , Li K. , Seto T. , McDonald K.M. , Blayney D.W. , Brooks J.D. , Hernandez-Boussard T. .
Source: Journal of the National Comprehensive Cancer Network : JNCCN, 2019-07-01; 17(7), p. 795-803.
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Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients.
Authors: Coquet J. , Bozkurt S. , Kan K.M. , Ferrari M.K. , Blayney D.W. , Brooks J.D. , Hernandez-Boussard T. .
Source: Journal of biomedical informatics, 2019 06; 94, p. 103184.
EPub date: 2019-04-20.
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Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment.
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Source: JAMIA open, 2019 04; 2(1), p. 150-159.
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Distribution of global health measures from routinely collected PROMIS surveys in patients with breast cancer or prostate cancer.
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Source: Cancer, 2019-03-15; 125(6), p. 943-951.
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Utilization of Prostate Cancer Quality Metrics for Research and Quality Improvement: A Structured Review.
Authors: Gori D. , Dulal R. , Blayney D.W. , Brooks J.D. , Fantini M.P. , McDonald K.M. , Hernandez-Boussard T. .
Source: Joint Commission journal on quality and patient safety, 2019 03; 45(3), p. 217-226.
EPub date: 2018-09-18.
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Merging heterogeneous clinical data to enable knowledge discovery.
Authors: Seneviratne M.G. , Kahn M.G. , Hernandez-Boussard T. .
Source: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2019; 24, p. 439-443.
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Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.
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Source: Annual review of biomedical data science, 2018 Jul; 1, p. 53-68.
EPub date: 2018-05-23.
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Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer.
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Source: EGEMS (Washington, DC), 2018-06-01; 6(1), p. 13.
EPub date: 2018-06-01.
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Secondary use of electronic medical records for clinical research: Challenges and Opportunities.
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Source: Convergent science physical oncology, 2018 Mar; 4(1), .
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Identifying Cases of Metastatic Prostate Cancer Using Machine Learning on Electronic Health Records.
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Source: AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018; 2018, p. 1498-1504.
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Source: AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017; 2017, p. 876-882.
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New Paradigms for Patient-Centered Outcomes Research in Electronic Medical Records: An Example of Detecting Urinary Incontinence Following Prostatectomy.
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