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411 results on '"Parikh, Ravi B"'

1. ACR: A Benchmark for Automatic Cohort Retrieval

Author

Thai, Dung Ngoc, Ardulov, Victor, Mena, Jose Ulises, Tiwari, Simran, Erofeev, Gleb, Eskander, Ramy, Tarabishy, Karim, Parikh, Ravi B, and Salloum, Wael

Subjects
Computer Science - Artificial Intelligence
Abstract

Identifying patient cohorts is fundamental to numerous healthcare tasks, including clinical trial recruitment and retrospective studies. Current cohort retrieval methods in healthcare organizations rely on automated queries of structured data combined with manual curation, which are time-consuming, labor-intensive, and often yield low-quality results. Recent advancements in large language models (LLMs) and information retrieval (IR) offer promising avenues to revolutionize these systems. Major challenges include managing extensive eligibility criteria and handling the longitudinal nature of unstructured Electronic Medical Records (EMRs) while ensuring that the solution remains cost-effective for real-world application. This paper introduces a new task, Automatic Cohort Retrieval (ACR), and evaluates the performance of LLMs and commercial, domain-specific neuro-symbolic approaches. We provide a benchmark task, a query dataset, an EMR dataset, and an evaluation framework. Our findings underscore the necessity for efficient, high-quality ACR systems capable of longitudinal reasoning across extensive patient databases.

Published
2024

2. DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation

Author

Wu, Yinjun, Keoliya, Mayank, Chen, Kan, Velingker, Neelay, Li, Ziyang, Getzen, Emily J, Long, Qi, Naik, Mayur, Parikh, Ravi B, and Wong, Eric

Subjects
Computer Science - Machine Learning, Statistics - Methodology
Abstract

Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as healthcare should ideally be (i) accurate, and (ii) provide faithful explanations. However, current solutions are inadequate: state-of-the-art black-box models do not supply explanations, post-hoc explainers for black-box models lack faithfulness guarantees, and self-interpretable models greatly compromise accuracy. To address these issues, we propose DISCRET, a self-interpretable ITE framework that synthesizes faithful, rule-based explanations for each sample. A key insight behind DISCRET is that explanations can serve dually as database queries to identify similar subgroups of samples. We provide a novel RL algorithm to efficiently synthesize these explanations from a large search space. We evaluate DISCRET on diverse tasks involving tabular, image, and text data. DISCRET outperforms the best self-interpretable models and has accuracy comparable to the best black-box models while providing faithful explanations. DISCRET is available at https://github.com/wuyinjun-1993/DISCRET-ICML2024., Comment: Accepted at ICML 2024. 22 pages, 5 figures

Published
2024

5. Validation of Biomechanical Computed Tomography for Fracture Risk Classification in Metastatic Hormone-sensitive Prostate Cancer

Author

Lin, John K., Hearn, Caleb M., Getzen, Emily, Long, Qi, Lee, David C., Keaveny, Tony M., Jayadevappa, Ravishankar, Robinson, Kyle W., Wong, Yu-Ning, Maxwell, Kara N., Narayan, Vivek, Haas, Naomi B., Takvorian, Samuel U., Bikle, Daniel D., Chiang, Janet M., Khan, Amna N., Rajapakse, Chamith S., Morgans, Alicia K., and Parikh, Ravi B.

Published
2024
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21. Clinician Perspectives on Virtual Specialty Palliative Care for Patients With Advanced Illnesses.

Author

Klaiman, Tamar, Steckel, Jenna, Hearn, Caleb, Diana, Amaya, Ferrell, William J., Emanuel, Ezekiel J., Navathe, Amol S., and Parikh, Ravi B.

Subjects
HEALTH services accessibility, PHYSICAL therapy, PALLIATIVE treatment, QUALITATIVE research, RESEARCH funding, INTERVIEWING, PHYSICIANS' attitudes, TELEMEDICINE, MENTAL health counseling, RESEARCH, RESEARCH methodology, MEDICAL referrals, ADVANCE directives (Medical care)
Abstract

Background: Patients with serious illnesses have unmet symptom and psychosocial needs. Specialty palliative care could address many of these needs; however, access varies by geography and health system. Virtual visits and automated referrals could increase access and lead to improved quality of life, health outcomes, and patient-centered care for patients with serious illness. Objectives: We sought to understand referring clinician perspectives on barriers and facilitators to utilizing virtual tools to increase upstream access to palliative care. Design: Participants in this multisite qualitative study included practicing clinicians who commonly place palliative care referrals across multiple specialties, including hematology/oncology, family medicine, cardiology, and geriatrics. All interviews were transcribed and subsequently coded and analyzed by trained research coordinators using Atlas.ti software. Settings/Subjects: This study included 23 clinicians (21 physicians, 2 nonphysicians) across 5 specialties, 4 practice settings, and 7 states in the United States. Results: Respondents felt that community-based specialty palliative services including symptom management, advance care planning, physical therapy, and mental health counseling would benefit their patients. However, they had mixed feelings about automated referrals, with some clinicians feeling hesitant about not being alerted to such referrals. Many respondents were supportive of virtual palliative care, particularly for those who may have difficulty accessing physician offices, but most respondents felt that such care should only be provided after an initial in-person consultation where clinicians can meet face-to-face with patients. Conclusion: Clinicians believe that automated referrals and virtual palliative care could increase access to the benefits of specialty palliative care. However, virtual palliative care models should give attention to iterative communication with primary clinicians and the perceived need for an initial in-person visit. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Behavioral economic implementation strategies to improve serious illness communication between clinicians and high-risk patients with cancer: protocol for a cluster randomized pragmatic trial

Author

Takvorian, Samuel U., Bekelman, Justin, Beidas, Rinad S., Schnoll, Robert, Clifton, Alicia B. W., Salam, Tasnim, Gabriel, Peter, Wileyto, E. Paul, Scott, Callie A., Asch, David A., Buttenheim, Alison M., Rendle, Katharine A., Chaiyachati, Krisda, Shelton, Rachel C., Ware, Sue, Chivers, Corey, Schuchter, Lynn M., Kumar, Pallavi, Shulman, Lawrence N., O’Connor, Nina, Lieberman, Adina, Zentgraf, Kelly, and Parikh, Ravi B.

Published
2021
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25. The COVID-19 Pandemic Led To A Large Decline In Physician Gross Revenue Across All Specialties In 2020

Author

Parikh, Ravi B., Emanuel, Ezekiel J., Zhao, Yueming, Pagnotti, David R., Pathak, Pankti S., Hagen, Stuart, Pizza, David A., and Navathe, Amol S.

Abstract

US health care use declined during the initial phase of the COVID-19 pandemic in 2020. Although utilization is known to have recovered in 2021 and 2022, it is unknown how revenue in 2020–22 varied by physician specialty and practice setting. This study linked medical claims from a large national federation of commercial health plans to physician and practice data to estimate pandemic-associated impacts on physician revenue (defined as payments to eligible physicians) by specialty and practice characteristics. Surgical specialties, emergency medicine, and medical subspecialties each experienced a greater than 9 percent adjusted gross revenue decline in 2020 relative to prepandemic baselines. By 2022, pathology and psychiatry revenue experienced robust recovery, whereas surgical and oncology revenue remained at or below baseline. Revenue recovery in 2022 was greater for physicians practicing in hospital-owned practices and in practices participating in accountable care organizations. Pandemic-associated revenue recovery in 2021 and 2022 varied by specialty and practice type. Given that physician financial instability is associated with health care consolidation and leaving practice, policy makers should closely monitor revenue trends among physicians in specialties or practice settings with sustained gross revenue reductions during the pandemic.

Published
2024
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26. Trends in Low-Value Cancer Care During the COVID-19 Pandemic.

Author

Parikh, Ravi B., Civelek, Yasin, Ozluk, Pelin, Debono, David, Fisch, Michael J., Sylwestrzak, Gosia, Bekelman, Justin E., and Schwartz, Aaron L.

Subjects
*TUMOR treatment, *RISK assessment, *PROTEINS, *PHARMACOLOGY, *HEALTH insurance reimbursement, *INSURANCE, *EXECUTIVES, *RADIOTHERAPY, *RESEARCH funding, *CANCER patient medical care, *ANTIEMETICS, *BREAST tumors, *MEDICARE, *RETROSPECTIVE studies, *COLORECTAL cancer, *DESCRIPTIVE statistics, *AGE factors in disease, *LONGITUDINAL method, *MOTIVATION (Psychology), *CANCER chemotherapy, *LUNG tumors, *QUALITY of life, *RESEARCH, *TERMINAL care, *TUMOR classification, *STAKEHOLDER analysis, *LABOR incentives, *COMPARATIVE studies, *CONFIDENCE intervals, *COVID-19 pandemic, *REGRESSION analysis, *INTEGRATED health care delivery, *PAY for performance, *COMORBIDITY, *ECONOMICS
Abstract

OBJECTIVE: To assess the association between the onset of the COVID-19 pandemic and change in low-value cancer services. STUDY DESIGN: In this retrospective cohort study, we used administrative claims from the HealthCore Integrated Research Environment, a repository of medical and pharmacy data from US health plans representing more than 80 million members, between January 1, 2016, and March 31, 2021. METHODS: We used linear probability models to investigate the relation between the onset of the COVID-19 pandemic and 4 guideline-based metrics of low-value cancer care: (1) conventional fractionation radiotherapy instead of hypofractionated radiotherapy for early-stage breast cancer; (2) non--guideline-based antiemetic use for minimal-, low-, or moderate- to high-risk chemotherapies; (3) off-pathway systemic therapy; and (4) aggressive end-of-life care. We identified patients with new diagnoses of breast, colorectal, and/or lung cancer. We excluded members who did not have at least 6 months of continuous insurance coverage and members with prevalent cancers. RESULTS: Among 117,116 members (median [IQR] age, 60 [53-69] years; 72.4% women), 59,729 (51.0%) had breast cancer, 25,751 (22.0%) had colorectal cancer, and 31,862 (27.2%) had lung cancer. The payer mix was 18.7% Medicare Advantage or Medicare supplemental and 81.2% commercial non-Medicare. Rates of low-value cancer services exhibited minimal changes during the pandemic, as adjusted percentage-point differences were 3.93 (95% CI, 1.50-6.36) for conventional radiotherapy, 0.82 (95% CI, -0.62 to 2.25) for off-pathway systemic therapy, -3.62 (95% CI, -4.97 to -2.27) for non--guideline-based antiemetics, and 2.71 (95% CI, -0.59 to 6.02) for aggressive end-of-life care. CONCLUSIONS: Low-value cancer care remained prevalent throughout the pandemic. Policy makers should consider changes to payment and incentive design to turn the tide against low-value cancer care. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Remote Patient-Reported Outcomes and Activity Monitoring to Improve Patient-Clinician Communication Regarding Symptoms and Functional Status: A Randomized Controlled Trial

Author

Parikh, Ravi B., primary, Schriver, Emily, additional, Ferrell, William J., additional, Wakim, Jonathan, additional, Williamson, Joelle, additional, Khan, Neda, additional, Kopinsky, Michael, additional, Balachandran, Mohan, additional, Gabriel, Peter E., additional, Schuchter, Lynn M., additional, Patel, Mitesh S., additional, Shulman, Lawrence N., additional, and Manz, Christopher R., additional

Published
2023
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33. On-Site Nurse-Led Cancer Genetics Program Increases Cancer Genetic Testing Completion in Black Veterans

Author

Shevach, Jeffrey W., primary, Aiello, Lisa B., additional, Lynch, Julie A., additional, Petersen, Jeffrey, additional, Hoffman-Hogg, Lori, additional, Hartzfeld, Deborah, additional, Lundquist, Margaret, additional, Kelley, Michael J., additional, Scheuner, Maren T., additional, Montgomery, Robert, additional, Damjanov, Nevena, additional, Robinson, Kyle, additional, Wong, Yu-Ning, additional, Jhala, Darshana, additional, Parikh, Ravi B., additional, and Maxwell, Kara N., additional

Published
2023
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40. Abstract 5618: Multi-institutional validation of a radiomics-based artificial intelligence method for predicting response to PD-1/PD-L1 immune checkpoint inhibitor (ICI) therapy in stage IV NSCLC

Author

Parikh, Ravi B., primary, Jordan, Petr, additional, Ciaravino, Rita J., additional, Beasley, Ryan A., additional, Patel, Arpan A., additional, Owen, Dwight H., additional, Amini, Arya, additional, Curti, Brendan D., additional, Page, Ray, additional, Swalduz, Aurelie, additional, Beregi, Jean-Paul, additional, Chrusciel, Jan, additional, Snyder, Eric, additional, Mukherjee, Pritam, additional, Selby, Heather M., additional, Lee, Soohee, additional, Weerasinghe, Roshanthi, additional, Pindikuri, Shwetha, additional, Weiss, Jakob B., additional, Wentland, Andrew L., additional, Kirpalani, Anish, additional, Liu, An, additional, Gevaert, Olivier, additional, Simon, George, additional, and Aerts, Hugo JWL, additional

Published
2023
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41. Behavioural economic interventions to embed palliative care in community oncology (BE-EPIC): study protocol for the BE-EPIC randomised controlled trial

Author

Parikh, Ravi B, primary, Sedhom, Ramy, additional, Ferrell, William J, additional, Villarin, Katherine, additional, Berwanger, Kara, additional, Scarborough, Bethann, additional, Oyer, Randall, additional, Kumar, Pallavi, additional, Ganta, Niharika, additional, Sivendran, Shanthi, additional, Chen, Jinbo, additional, Volpp, Kevin G, additional, and Bekelman, Justin E, additional

Published
2023
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42. Long-term Effect of Machine Learning–Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer

Author

Manz, Christopher R., primary, Zhang, Yichen, additional, Chen, Kan, additional, Long, Qi, additional, Small, Dylan S., additional, Evans, Chalanda N., additional, Chivers, Corey, additional, Regli, Susan H., additional, Hanson, C. William, additional, Bekelman, Justin E., additional, Braun, Jennifer, additional, Rareshide, Charles A. L., additional, O’Connor, Nina, additional, Kumar, Pallavi, additional, Schuchter, Lynn M., additional, Shulman, Lawrence N., additional, Patel, Mitesh S., additional, and Parikh, Ravi B., additional

Published
2023
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48. Spending Patterns Among Commercially Insured Individuals During the COVID-19 Pandemic.

Author

Parikh, Ravi B., Emanuel, Ezekiel J., Yueming Zhao, Pagnotti, David R., Hagen, Stuart, Pizza, David A., and Navathe, Amol S.

Subjects
*CROSS-sectional method, *MEDICAL care costs, *REGRESSION analysis, *MEDICAL care use, *HEALTH insurance, *QUESTIONNAIRES, *RESEARCH funding, *DATA analysis software, *COVID-19 pandemic, *MEDICARE, *COMORBIDITY
Abstract

OBJECTIVES: To describe trends in US health care spending in a large, national, and commercially insured population during the COVID-19 pandemic. STUDY DESIGN: Cross-sectional study of commercially insured members enrolled between May 1, 2018, and December 31, 2021. METHODS: The study utilized a population-based sample of continuously enrolled members in a geographically diverse federation of Blue Cross Blue Shield plans across the United States. Our sample excluded Medicare and Medicare Advantage beneficiaries. The COVID-19 exposure period was defined as 2020-2021; 2018-2019 were pre--COVID-19 years. We defined 4 post--COVID-19 periods: March 1 to April 30, 2020; May 1 to December 31, 2020; January 1 to March 31, 2021; and April 1 to December 31, 2021. The primary outcome was inflation-adjusted overall per-member per-month (PMPM) medical spending adjusted for age, sex, Elixhauser comorbidities, area-level racial composition, income, and education. RESULTS: Our sample included 97,319,130 individuals. Mean PMPM medical spending decreased from $370.92 in January-February 2020 to $281.00 in March-April 2020. Between May and December 2020, mean PMPM medical spending recovered to--but did not exceed--prepandemic levels. Mean PMPM medical spending stayed below prepandemic levels between January and March 2021, rose above prepandemic baselines between April and June 2021, and decreased below baseline between July and December 2021. CONCLUSIONS: The COVID-19 pandemic induced a spending shock in 2020, and health care spending did not recover to near baseline until mid-2021, with some emerging evidence of pent-up demand. The observed spending below baseline through the end of 2021 will pose challenges to setting spending benchmarks for alternative payment and shared savings models. [ABSTRACT FROM AUTHOR]

Published
2023
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