About

Guilherme Del Fiol, MD, PhD, FACMI, is a Professor and Vice-Chair for Research in the Department of Biomedical Informatics at the University of Utah and Co-Director of the Digital Health Initiative. He earned his MD from the University of Sao Paulo, Brazil, his MS in Information Systems from the Catholic University of Parana, Brazil, and his PhD in Biomedical Informatics from the University of Utah. His professional background includes positions in Clinical Knowledge Management at Intermountain Healthcare and as an Assistant Professor at Duke University’s Department of Community and Family Medicine. Dr. Del Fiol has served as an elected co-chair of the Clinical Decision Support Work Group at HL7 for 12 years and is an elected Fellow of the American College of Medical Informatics. His research interests focus on standards-based clinical decision support and digital health interventions, particularly in large pragmatic clinical trials and population-based digital health interventions for cancer prevention. He is the lead author of the HL7 Infobutton Standard and project lead for OpenInfobutton, an open-source suite of infobutton tools used across healthcare organizations in the United States. His work aims to improve healthcare quality, safety, and value through the design, development, evaluation, and dissemination of clinical informatics and decision support interventions, with a special emphasis on cancer prevention and screening.

Research topics

• Computer Science
• Medicine
• Artificial Intelligence
• Knowledge management
• Family medicine
• Data Mining
• Political Science
• Sociology
• Business
• Nursing
• Process management
• Psychology
• Engineering
• Medical education
• Data science
• Pathology
• Machine Learning
• World Wide Web
• Internal medicine
• Database
• Applied psychology
• Public relations
• Management science
• Oncology

Selected publications

• A Chatbot to Meet Parents’ Information Needs for Sickle Cell Trait Newborn Screening Results: Multiple Methods Formative Study

  Journal of Medical Internet Research · 2026-03-10

  articleOpen access

  Background: Newborn screening (NBS), a mandated public health intervention, allows the identification of babies with potentially life-threatening disorders and facilitates disease diagnosis and management before the onset of symptoms. While NBS saves lives, the process can be fraught with anxiety and unanswered questions from parents or guardians of newborns, especially as they wait for an appointment with a clinician. Objective: This study aimed to describe the development and testing of an educational chatbot (NBSchat) to address the emotional support and information needs of parents of newborns identified with sickle cell trait via NBS. Methods: NBSchat, a fully scripted (ie, rule-based) chatbot, was developed by a multidisciplinary team and evaluated through a sequential multiple methods study, including interviews and a survey. To inform chatbot design, we conducted semistructured interviews with 11 adults-5 clinicians who work with parents of infants identified with sickle cell trait through NBS and 6 parents of infants aged 12 months or less-using the critical incident technique and think-aloud tasks while using a prototype of NBSchat. Transcripts underwent thematic analysis. In a survey, 250 parents of infants aged 12 months or less without abnormal NBS results were shown a mock newborn screening result letter and then interacted with NBSchat, after which they self-reported emotional and attitudinal outcomes before and after the simulated exposure. Results: Feedback from interviews confirmed that parents are distressed by trait results and actively seek information and reassurance. Thematic analysis indicated that NBSchat provided reliable, accurate information that parents wanted and had the potential to reduce negative emotions (eg, provide relief and reduce stress). Key strengths included addressing an immediate health concern and offering reassurance. The results of the postintervention survey indicated that, compared to pre-exposure scores, participants reported significantly lower negative emotions (mean 7.0 [SD 3.2] vs 5.8 [SD 3.2] out of 12; mean difference -1.2, 95% CI -1.57 to -0.83; P<.001), improved positive emotions (reflected by a decrease in the reverse-coded positive emotion score; mean 8.6 [SD 4.2] vs 7.8 [SD 4] out of 16; mean difference -0.8, 95% CI -1.27 to -0.37; P<.001), and reduced uncertainty (mean 6.5 [SD 3] vs 5.5 [SD 3.4] out of 12; mean difference -1, 95% CI -1.42 to -0.58; P<.001). Parents noted that NBSchat provided immediate reassurance and was convenient to access. They further reported that the predefined, structured questions in the script helped guide their learning and understanding. Conclusions: Overall, participants who interacted with NBSchat found it to be acceptable, with improved emotional measures after its use. Future research will investigate the outcomes of using the chatbot and its implementation in a pragmatic randomized controlled trial.

  PublisherDOI

• Barriers and facilitators to implementing a shared decision-making tool for anticoagulant-related drug–drug interactions: a qualitative study across three academic medical centres in the USA

  BMJ Open · 2026-03-01

  articleOpen access

  OBJECTIVES: To identify barriers and facilitators to implementing an electronic shared decision-making tool for managing anticoagulant-related drug-drug interactions that affect bleeding risk in routine clinical care. DESIGN: Preimplementation qualitative study using semistructured interviews. SETTING: Three academic medical centres in the southeastern and western USA. Interviews were conducted between 27 March and 25 September 2024. PARTICIPANTS: 36 participants, including 19 clinicians involved in prescribing or managing anticoagulants and seventeen patients prescribed anticoagulants, were recruited using purposive and convenience sampling. RESULTS: Participants identified multiple barriers and facilitators to tool implementation. Common barriers included limited visit time, challenges integrating the tool into existing workflows, role and scope-of-practice constraints, and variation in patient digital literacy. Facilitators included clear visualisation of bleeding risk, access to supporting evidence, familiar interface design and perceived potential to support patient engagement and shared decision-making. Several determinants functioned as both barriers and facilitators, depending on clinical context and user role. CONCLUSIONS: This preimplementation qualitative study identified context-specific determinants that influence the adoption of an electronic shared decision-making tool for anticoagulant-related drug-drug interactions. Findings highlight the importance of early attention to workflow integration, role alignment and usability to support uptake in routine care. Addressing these factors during design and implementation may inform strategies to support adoption and future evaluation in real-world clinical settings.

  PublisherOA PDFDOI

• Cascade Chatbot: A Scalable Approach to Family-Based Genetic Testing for Hereditary Cancer Syndromes

  JCO Clinical Cancer Informatics · 2026-01-01

  article

  PURPOSE: Cascade genetic testing enables identification of relatives at risk of hereditary cancer syndromes, creating opportunities for early detection and prevention. However, uptake of cascade testing remains low, with approximately one-third of eligible relatives completing testing, largely because of reliance on patient-mediated communication. Although clinician-mediated outreach has demonstrated improved efficacy, it is often limited by resource demands. Scalable digital health tools are a promising strategy to address this gap in testing uptake. METHODS: In this quality improvement initiative, we developed a digital cascade chatbot to deliver gene-specific education and facilitate access to genetic services among at-risk relatives. Between October 2024 and January 2025, 100 consecutive probands with a hereditary cancer pathogenic variant seen in a gynecologic oncology clinic were offered a cascade chatbot to share with their relatives. The primary outcome was proband acceptance of the cascade chatbot. Secondary outcomes included sharing of the cascade chatbot with at-risk relatives and relatives' subsequent utilization of genetic services. Outcomes were evaluated through telephone follow-up at 2 weeks and 3 months after chatbot introduction. RESULTS: Fifty-nine of 100 probands reported having relatives who had not undergone genetic testing. Among this group, 58 (98.3%) accepted the cascade chatbot. At 2-week follow-up, 44 of 58 probands (75.9%) had shared the cascade chatbot with at least one relative, and an additional eight (13.8%) reported plans to share. At 3-month follow-up with probands, 48 (82.8%) probands had shared the cascade chatbot with at least one relative. A total of 122 relatives received the cascade chatbot and 96 (78.7%) were reached for 3-month follow-up. Among the 96 relatives reached, 49 (51.0%) had scheduled or completed a genetics appointment, and of them, 36 (73.5%) had completed testing. CONCLUSION: A cascade chatbot was highly acceptable to probands and effectively engaged relatives. Scalable digital health tools may enhance cascade testing and support precision cancer prevention.

  PublisherDOI

• Multilevel Interventions for Increasing Tobacco Cessation at FQHCs

  2026-01-30

  report
  PublisherDOI

• Cascade conversations: Empowering cancer genetic testing through Cascade chatbots.

  Journal of Clinical Oncology · 2025-05-28

  article

  e22621 Background: Cascade genetic testing refers to the process of offering genetic testing to blood relatives of individuals with known pathogenic mutations. Identifying cancer-associated pathogenic mutations offers relatives the opportunity for targeted surveillance and preventive interventions, which can reduce cancer incidence, morbidity, and mortality. However, cascade genetic testing remains critically underutilized, and patients traditionally must shoulder the burden of facilitating relative education. We created a Cascade Chatbot to assist families with genetic testing via gene-specific educational modules and facilitated access to genetic testing services. This quality improvement initiative investigates the acceptability of Cascade Chatbot in a gynecologic oncology clinic. Methods: All patients with pathogenic mutations in the BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, RAD51C, RAD51D, PALB2, CHEK2, and BRIP genes presenting for care at a gynecologic oncology clinic were offered a Cascade Chatbot. Interested patients were provided with the Cascade Chatbot available via hyperlink and QR code to share with family members providing education and resources for testing. Patients were contacted two weeks following the receipt of the Chatbot to determine if they had shared the Chatbot with relatives. The primary outcome was the proportion of patients that accepted the Cascade Chatbot when offered at their gynecologic oncology outpatient visit. Results: One hundred consecutive patients (median age 43 years; IQR = 35-51.5 years) were offered the Cascade Chatbot; 85 (85%) identified as non-Hispanic white, 8 (8%) as Hispanic and white, 5 (5%) as Asian/Indian/Pacific Islander, 2 (2%) as black, and 1 (1%) as other/unknown ethnicity. Among patients approached, 59 (59%) had eligible relatives (blood relatives at risk for carrying the familial pathogenic mutation that had not yet completed genetic testing). Among patients with eligible relatives, 58 (98.3%) accepted the Cascade Chatbot. Two weeks following administration of the Cascade Chatbot, 44 (75.9%) patients had shared the tool with at least one relative, 8 (13.8%) had not but intended to, 3 (5.1%) patients opted not to share, and 3 (5.1%) could not be reached for follow up. Conclusions: Ninety-eight percent of patients with a hereditary cancer syndrome and relatives eligible for genetic testing accepted a Chatbot tool to facilitate cascade genetic testing. At two weeks, 76% of patients had shared the tool with relatives. Our work suggests that Chatbots may be a feasible and widely accepted tool to initiate conversations about family cascade genetic testing for patients across clinical settings, alleviating the burden currently placed on patients. Ongoing efforts are focused on evaluating the downstream impact of chatbot-driven education and facilitation on genetic testing uptake.

  PublisherDOI

• Social vulnerability and genetic service utilization among unaffected BRIDGE trial patients with inherited cancer susceptibility

  BMC Cancer · 2025-01-31 · 4 citations

  articleOpen access

  BACKGROUND: Research on social determinants of genetic testing uptake is limited, particularly among unaffected patients with inherited cancer susceptibility. METHODS: We conducted a secondary analysis of the Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE) trial at University of Utah Health and NYU Langone Health, involving 2,760 unaffected patients meeting genetic testing criteria for inherited cancer susceptibility and who were initially randomized to either an automated chatbot or an enhanced standard of care (SOC) genetic services delivery model. We used encounters from the electronic health record (EHR) to measure the uptake of genetic counseling and testing, including dichotomous measures of (1) whether participants initiated pre-test cancer genetic services, (2) completed pre-test cancer genetic services, (3) had genetic testing ordered, and (4) completed genetic testing. We merged zip codes from the EHR to construct census tract-weighted social measures of the Social Vulnerability Index. Multilevel models estimated associations between social vulnerability and genetic services utilization. We tested whether intervention condition (i.e., chatbot vs. SOC) moderated the association of social vulnerability with genetic service utilization. Covariates included study arm, study site, age, sex, race/ethnicity, language preference, rural residence, having a recorded primary care provider, and number of algorithm criteria met. RESULTS: Patients living in areas of medium socioeconomic status (SES) vulnerability had lower odds of initiating pre-test genetic services (adjusted OR [aOR] = 0.81, 95% CI: 0.67, 0.98) compared to patients living in low SES vulnerability areas. Patients in medium household vulnerability areas had a lower likelihood of completing pre-test genetic services (aOR = 0.80, 95% CI: 0.66-0.97) and having genetic testing ordered (aOR = 0.79, 95% CI: 0.63-0.99) relative to patients in low household vulnerability areas. We did not find that social vulnerability associations varied by intervention condition. CONCLUSIONS: These results underscore the importance of investigating social and structural mechanisms as potential pathways to increasing genetic testing uptake among patients with increased inherited risk of cancer. Census information is publicly available but seldom used to assess social determinants of genetic testing uptake among unaffected populations. Existing and future cohort studies can incorporate census data to derive analytic insights for clinical scientists. TRIAL REGISTRATION: BRIDGE was registered as NCT03985852 on June 6, 2019 at clinicaltrials.gov.

  PublisherOA PDFDOI

• Incomplete Family History and Meeting Algorithmic Criteria for Genetic Evaluation of Hereditary Cancer

  JAMA Network Open · 2025-10-28 · 1 citations

  articleOpen access

  Importance: Incomplete electronic health record (EHR) documentation may limit the effectiveness of clinical decision support (CDS) algorithms designed to identify patients eligible for hereditary cancer genetic evaluation. Objectives: To determine whether a CDS algorithm can identify patients who meet criteria for hereditary cancer genetic evaluation when family history data are incompletely documented in the EHR, and to examine whether data missingness is associated with identification patterns across patient subgroups. Design, Setting, and Participants: This cross-sectional study analyzed EHR data extracted in December 2020 from 2 large US health care systems: University of Utah Health (UHealth) and NYU Langone Health (NYULH). Eligible patients were adults aged 25 to 60 years who visited a primary care clinic within the previous 3 years and had some EHR documentation of cancer family history. Data analysis was conducted in August 2024. Exposures: Patient demographic factors (age, sex, race and ethnicity, and language preference) and cancer family history characteristics (number of cancer history records, number of affected first- and second-degree relatives, relatives with rising mortality cancers, presence of hereditary cancer-related terms in comments, and completeness of documentation). Main Outcomes and Measures: The primary outcome was meeting at least 1 CDS algorithm criterion for genetic evaluation of hereditary cancer risk based on National Comprehensive Cancer Network guidelines. Missing data patterns were assessed using the Little missing completely at random test, with analyses conducted using complete case analysis and multiple imputation. Results: This study included 157 207 patients: 55 918 from UHealth and 101 289 from NYULH. Their mean (SD) age was 43.5 (9.8) years, and most (65.7%) were female. A total of 5607 UHealth patients (10.0%) and 10 375 NYULH patients (10.2%) met CDS criteria for genetic evaluation. At UHealth, data appeared to be missing completely at random (χ239 = 39.09; P = .47), and complete case compared with multiple imputation analyses yielded similar results. At NYULH, data were not missing completely at random (χ255 = 914.89; P < .001). Compared with multiple imputation, complete case analysis produced different association magnitudes for older age and having relatives with rising mortality cancers, suggesting bias when excluding incomplete records. Conclusions and Relevance: In this cross-sectional study, the magnitude of the association between incomplete family history documentation and identification of patients eligible for hereditary cancer genetic evaluation depended on whether data were missing randomly or systematically. These findings suggest that health care organizations implementing CDS algorithms should assess their specific missing data patterns and consider tailored approaches to handling incomplete family history information to ensure equitable identification of all patients who could benefit from genetic evaluation services.

  PublisherOA PDFDOI

• Advancing Digital Access to Physical Therapy via Virtual and Extended Reality Technology: Prototype Development and Usability Evaluation

  JMIR Formative Research · 2025-12-05

  articleOpen access

  Background: The United States faces significant challenges in physical therapy (PT) access due to high demand, a shortage of professionals, and patient-related obstacles, which can adversely affect recovery and function. Limited access to PT may lead to increased dependence on medications for pain management, highlighting the need for nonpharmacologic options to reduce opioid overprescribing. Low back pain, a leading cause of disability and high medical costs, is a common reason for requiring PT following surgery. Studies have shown that virtual reality (VR)-guided movements can improve motor function and reduce pain intensity. Objective: The objective of this study was to design, develop, and evaluate a VR-based prototype for individualized postoperative PT for patients recovering from back surgery to investigate its potential to improve convenience, access, and health outcomes in future research. Methods: Study methods involved participatory design and development of VR software for PT back exercises using the design box method, an inductive, problem-oriented collaborative design approach. A usability evaluation of the resulting prototype was conducted with patients recovering from back surgery using a think-aloud protocol and usability survey. Results: Six participants evaluated the VR prototype and reported usability challenges that included mismatched VR boundaries, limited familiarity with VR, and difficulties with the headset and hand controls. The System Usability Scale resulted in a total usability score of 58.3 out of 100, indicating a below-average score (68 being average). Conclusions: In the design and evaluation of a VR-based PT prototype, we found that while participants were enthusiastic, they faced usability challenges due to insufficient instructions and difficulties operating the VR device, highlighting the need for effective onboarding and extensive prototype testing to improve accessibility and engagement in health care. Future evaluations will investigate disparities among different groups to ensure accessibility and effectiveness for all users.

  PublisherDOI

• Advancing the science of genomic learning healthcare systems

  Learning Health Systems · 2025-07-23 · 1 citations

  articleOpen access

  Introduction: Identifying key characteristics of exemplar genomic learning healthcare systems (gLHS) and knowledge gaps that can be explored by collaboration among them is likely to accelerate the sharing of best practices and generation of evidence that informs the use of genomics in clinical care. Methods: Deliberations of an expert group convened by the National Human Genome Research Institute (NHGRI) supplemented by relevant literature. Results: Recent advances in genomic data standardization, automated clinical decision support, increased interoperability, and improved genomic technologies have enabled the development of several robust gLHS. They remain concentrated in major academic centers, however, and operate largely independently. Sharing their methods and tools would increase access to these innovations and advance the field. Several gLHS have expressed willingness to collaborate in a coalition designed to gather, evaluate, and disseminate best practices and development needs. Such a coalition has recently been formed under the leadership of NHGRI. Conclusion: Increased collaboration, interoperability, and sharing of genomic information and strategies across gLHS can help define, refine, and disseminate best practices. Such cooperation can improve genomic variant curation and interpretation, diagnostic accuracy, evidence generation, and ultimately patient care through seamless integration of research as an integral component of good clinical care.

  PublisherOA PDFDOI

• Conceptual framework for prediction models of patient deterioration based on nursing documentation patterns: reproducibility and generalizability with a large number of hospitals across the United States

  Journal of Biomedical Informatics · 2025-07-27 · 2 citations

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

Recent grants

• NIH Grant K01HS018352

  NIH · $568k · 2013

• SCALE UP Utah II: Community-Academic Partnership to Address COVID-19 Testing and Vaccination Among Utah Community Health Centers

  NIH · $2.3M · 2022–2024

• Scalable Clinical Decision Support for Individualized Cancer Risk Management

  NIH · $3.7M · 2017–2022

• Utah Center for Clinical and Translational Science

  NIH · $26.0M · 2018–2023

• NIH Grant R01LM011416

  NIH · $1.3M · 2018

Frequent coauthors

• Kensaku Kawamoto

  University of Utah

  90 shared

• Charlene Weir

  University of Utah

  78 shared

• Richard L. Bradshaw

  University of Utah

  53 shared

• Kimberly A. Kaphingst

  39 shared

• Thomas S. Reese

  Drexel University

  33 shared

• Roberto A. Rocha

  32 shared

• Damián Borbolla

  University of Utah

  27 shared

• Chelsey R. Schlechter

  Huntsman Cancer Institute

  26 shared

Education

• M.D.

  University of Sao Paulo

• M.S., Information Systems

  Catholic University of Parana

• Ph.D., Biomedical Informatics

  University of Utah

Awards & honors

• Fellow of the American College of Medical Informatics (FACMI…