About

Kevin Phillip Weinfurt is a faculty member in Population Health Sciences at Duke University. His research involves the study of patient experiences and outcomes in clinical settings, with a focus on understanding pain and symptom patterns following medical procedures. In a recent multicenter prospective observational cohort study, he contributed to expanding knowledge about pain location and intensity after ureteroscopy with stent placement for ureteric and renal stones. This study utilized body maps and patient questionnaires to assess pain distribution over time, revealing significant differences based on sex and stone location. The findings from this research provide valuable insights for patient-specific counseling and tailored management decisions in urology, highlighting Dr. Weinfurt's role in advancing patient-centered outcomes research.

Research topics

• Medicine
• Intensive care medicine
• Internal medicine
• World Wide Web
• Nursing
• Physical therapy
• Family medicine
• Oncology

Selected publications

• Changing paradigms of studies in kidney diseases

  Kidney International · 2025-09-30 · 1 citations

  articleOpen access
  PublisherOA PDFDOI

• Lack of group-to-individual generalizability in people with lower urinary tract symptoms emphasizes the need for deep phenotyping and personalized treatments

  Journal of Clinical and Translational Science · 2025-01-01

  articleOpen accessCorresponding

  Introduction: Understanding how different symptoms co-occur and are correlated may provide insights into the pathophysiology of disease. The lack of group-to-individual generalizability of co-occurrence of symptoms was recently demonstrated by comparing intra-individual and inter-individual correlations in several psychological studies. Here, we investigate this phenomenon for lower urinary tract symptoms (LUTS). Methods: We analyzed data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network Recall Study. Participants responded to questions about their urinary symptoms for 25 consecutive days. These questions queried urologic symptoms including storage (urinary urgency, frequency, nocturia, and urinary incontinence), voiding (slow/weak stream), and post-micturition (incomplete emptying and post-micturition dribble) symptoms. We calculated Pearson correlation coefficients and cosine similarity measures and compared distributions of intra-individual and inter-individual (cohort) metrics. Results: Among 234 participants, distributions of intra-individual measures were 10-fold wider than those of inter-individual correlations. There are pairs of questions with distributions of correlations and cosine similarities containing individuals with extreme positive (>0.8) and extreme negative values (<-0.8). There are groups of participants with strong positive and negative correlations of urinary frequency and nocturia, urinary incontinence and weak flow, as well as strong negative and positive correlations of urinary frequency and dribbling. Information on these extreme groups is averaged out and lost in the inter-individual correlations. Conclusions: Lack of group-to-individual generalizability previously shown for psychological symptoms is confirmed for LUTS. Wealth of information on the co-occurrence and co-evolution of LUTS in the intra-individual correlations and cosine similarities corroborates heterogeneity of LUTS and can be useful for deep phenotyping and for identifying personalized treatments of LUTS.

  PublisherOA PDFDOI

• Impact of Disclosing to Patients the Use of Antiretroviral Resistance Testing Results for Molecular HIV Surveillance: A Randomized Experiment in 2 National Surveys

  JMIR Public Health and Surveillance · 2025-04-11 · 5 citations

  articleOpen access

  Background: Molecular HIV surveillance (MHS) can be used to help identify and respond to emerging clusters of rapidly spreading HIV transmissions, a practice known as cluster detection and response (CDR). In the United States, MHS relies on HIV gene sequences obtained from routine clinical antiretroviral resistance testing (ARVRT). By law, ARVRT results are reported to public health agencies for MHS and individuals are not asked for their specific consent to do so. This practice has raised ethical concerns, including the lack of consent for, and transparency surrounding, public health uses of these clinical data. Such concerns have spurred debate and could have a chilling effect on the willingness of people living with HIV to agree to ARVRT when recommended clinically and jeopardize the utility of MHS-informed HIV prevention efforts. In response to the lack of routine disclosure of use of ARVRT results for MHS, in 2022, the Presidential Advisory Council on HIV/AIDS (PACHA) issued a resolution calling on the US Centers for Disease Control to "require that providers explain MHS/CDR and the laboratory test results that are collected and used in these surveillance activities to their patients." Objective: This study aimed to examine the effect of clinician disclosure of the public health uses of ARVRT results for MHS versus clinician nondisclosure on patient willingness to undergo recommended ARVRT. Methods: We conducted a randomized survey experiment examining the effect of clinician disclosure of the public health uses of ARVRT results for MHS versus clinician nondisclosure (the current standard of care) and subsequent discovery of such uses through a "trusted media source" on patient willingness to undergo recommended ARVRT. Study participants were respondents to 1 of 2 national web-based surveys conducted annually in the United States: the American Men's Internet Survey (AMIS) and the Transgender Women's Internet Survey and Testing (TWIST). Results: Overall, 4348 AMIS participants (n=2151 disclosure; n=2197 nondisclosure) and 3314 TWIST participants (n=1670 disclosure; n=1644 nondisclosure) completed survey items regarding the randomly assigned vignettes. The majority were willing to undergo ARVRT regardless of which vignette they saw (1670/2151, 82.7% [AMIS] and 1326/1670, 80.8% [TWIST] in the disclosure group; and 1399/2197, 68% [AMIS] and 1101/1674, 68.45% [TWIST] in the nondisclosure group) after later learning about public health uses of ARVRT results. Conclusions: The majority of respondents expressed willingness to undergo ARVRT even with disclosure of public health uses of these data, but willingness markedly decreased when learning about these uses after the fact, highlighting the importance of transparency in MHS programs. Accordingly, in line with the ethical principle of respect for autonomy and the likelihood that the potential public health benefits of MHS programs will not be compromised, consideration should be given to encouraging clinicians to disclose public health uses of ARVRT at the time ARVRT is recommended.

  PublisherDOI

• Artificial intelligence and the future of patient-centered outcomes

  Journal of Patient-Reported Outcomes · 2025-09-26

  articleOpen access1st authorCorresponding

  BACKGROUND: Terheyden et al. recently described a compelling vision for large language model-enabled patient-reported outcome measures (LLM-PROMs). MAIN TEXT: We support Terheyden et al.'s vision and offer complementary observations about the potential for generative artificial intelligence (GenAI) in assessing patient-centered outcomes. GenAI has the potential to improve the quality and efficiency of developing traditional PROMs and collecting patient experience data. Traditional PROMs rely on standardized questions and responses, which may introduce ambiguity about the health concept being assessed. Yet, interviewers who are trained in the meaning of the concepts can tailor questions to the respondent's experience and conversation style and have a back-and-forth clarification of meaning to ensure that both the interviewer's and respondent's meanings are aligned. The shortcoming of this approach is that it cannot be done at scale with human interviewers. However, trained GenAI interviewers could make such an assessment a reality for large samples of patients. The technology is already available to train GenAI interviewers in interview technique, the intent of each item, and a consistent approach toward coding the respondent's answer based on the conversation. CONCLUSION: The health outcomes research field should actively inquire into what patient experience data can be collected via GenAI and rigorously evaluate the quality of the assessments obtained.

  PublisherOA PDFDOI

• Interpreting the meaningfulness of treatment effects estimated in parallel groups designs: comment on Trigg et al.

  Quality of Life Research · 2025-03-28 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Meaningful Score Differences and Meaningful Score Regions of the Patient-Reported Outcomes Measurement Information System® Pediatric Asthma Impact Scale

  Value in Health · 2025-06-05 · 1 citations

  articleOpen access
  PublisherOA PDFDOI

• Correction to: Inflammatory Bowel Diseases Can Adversely Impact Domains of Sexual Function Such as Satisfaction with Sex Life

  UNC Libraries · 2025-08-16

  articleOpen accessSenior author
  PublisherDOI

• The Argument-Based Approach to Validity Applied to Clinical Outcome Assessments: Some History and Notable Features

  Value in Health · 2025-04-11 · 5 citations

  review1st authorCorresponding
  PublisherDOI

• Integrating large language models in biostatistical workflows for clinical and translational research

  Journal of Clinical and Translational Science · 2025-01-01 · 2 citations

  articleOpen access

  Introduction: Biostatisticians increasingly use large language models (LLMs) to enhance efficiency, yet practical guidance on responsible integration is limited. This study explores current LLM usage, challenges, and training needs to support biostatisticians. Methods: A cross-sectional survey was conducted across three biostatistics units at two academic medical centers. The survey assessed LLM usage across three key professional activities: communication and leadership, clinical and domain knowledge, and quantitative expertise. Responses were analyzed using descriptive statistics, while free-text responses underwent thematic analysis. Results: Of 208 eligible biostatisticians (162 staff and 46 faculty), 69 (33.2%) responded. Among them, 44 (63.8%) reported using LLMs; of the 43 who answered the frequency question, 20 (46.5%) used them daily and 16 (37.2%) weekly. LLMs improved productivity in coding, writing, and literature review; however, 29 of 41 respondents (70.7%) reported significant errors, including incorrect code, statistical misinterpretations, and hallucinated functions. Key verification strategies included expertise, external validation, debugging, and manual inspection. Among 58 respondents providing training feedback, 44 (75.9%) requested case studies, 40 (69.0%) sought interactive tutorials, and 37 (63.8%) desired structured training. Conclusions: LLM usage is notable among respondents at two academic medical centers, though response patterns likely reflect early adopters. While LLMs enhance productivity, challenges like errors and reliability concerns highlight the need for verification strategies and systematic validation. The strong interest in training underscores the need for structured guidance. As an initial step, we propose eight core principles for responsible LLM integration, offering a preliminary framework for structured usage, validation, and ethical considerations.

  PublisherOA PDFDOI

• Some Clarifications Regarding the PROMIS© SexFS: Commentary on Clements et al. (2023)

  Archives of Sexual Behavior · 2024-01-04

  article1st authorCorresponding
  PublisherDOI

Recent grants

• NIH Grant R01CA100771

  NIH · $1.9M · 2011

• NIH Grant R21MH092253

  NIH · $448k · 2014

• NIH Grant R01HG006621

  NIH · $1.6M · 2017

• HEAL Collaboratory Resource Coordinating Center (PRISM) (U24): Bioethics Supplement

  NIH · $6.4M · 2019–2024

• Advancing the Measurement and Classification of Lower Urinary Tract Dysfunction

  NIH · $5.3M · 2025–2026

Frequent coauthors

• Kevin A. Schulman

  Stanford University

  265 shared

• Jeremy Sugarman

  154 shared

• Kathryn E. Flynn

  Medical College of Wisconsin

  127 shared

• Li Lin

  Duke University

  111 shared

• Neal J. Meropol

  Case Western Reserve University

  103 shared

• Daniel P. Sulmasy

  Georgetown University

  96 shared

• Joëlle Y. Friedman

  87 shared

• Liana D. Castel

  Cigna (United States)

  80 shared