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Kiser D, Elhanan G, Bolze A, Neveux I, Schlauch KA, Metcalf WJ, Cirulli ET, McCarthy C, Greenberg LA, Grime S, Blitstein JMS, Plauth W, Grzymski JJ. Screening Familial Risk for Hereditary Breast and Ovarian Cancer. JAMA Netw Open 2024; 7:e2435901. [PMID: 39320887 PMCID: PMC11425146 DOI: 10.1001/jamanetworkopen.2024.35901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/26/2024] Open
Abstract
Importance Most patients with pathogenic or likely pathogenic (P/LP) variants for breast cancer have not undergone genetic testing. Objective To identify patients meeting family history criteria for genetic testing in the electronic health record (EHR). Design, Setting, and Participants This study included both cross-sectional (observation date, February 1, 2024) and retrospective cohort (observation period, January 1, 2018, to February 1, 2024) analyses. Participants included patients aged 18 to 79 years enrolled in Renown Health, a large health system in Northern Nevada. Genotype was known for 38 003 patients enrolled in Healthy Nevada Project (HNP), a population genomics study. Exposure An EHR indicating that a patient is positive for criteria according to the Seven-Question Family History Questionnaire (hereafter, FHS7 positive) assessing familial risk for hereditary breast and ovarian cancer (HBOC). Main Outcomes and Measures The primary outcomes were the presence of P/LP variants in the ATM, BRCA1, BRCA2, CHEK2, or PALB2 genes (cross-sectional analysis) or a diagnosis of cancer (cohort analysis). Age-adjusted cancer incidence rates per 100 000 patients per year were calculated using the 2020 US population as the standard. Hazard ratios (HRs) for cancer attributable to FHS7-positive status were estimated using cause-specific hazard models. Results Among 835 727 patients, 423 393 (50.7%) were female and 29 913 (3.6%) were FHS7 positive. Among those who were FHS7 positive, 24 535 (82.0%) had no evidence of prior genetic testing for HBOC in their EHR. Being FHS7 positive was associated with increased prevalence of P/LP variants in BRCA1/BRCA2 (odds ratio [OR], 3.34; 95% CI, 2.48-4.47), CHEK2 (OR, 1.62; 95% CI, 1.05-2.43), and PALB2 (OR, 2.84; 95% CI, 1.23-6.16) among HNP female individuals, and in BRCA1/BRCA2 (OR, 3.35; 95% CI, 1.93-5.56) among HNP male individuals. Being FHS7 positive was also associated with significantly increased risk of cancer among 131 622 non-HNP female individuals (HR, 1.44; 95% CI, 1.22-1.70) but not among 114 982 non-HNP male individuals (HR, 1.11; 95% CI, 0.87-1.42). Among 1527 HNP survey respondents, 352 of 383 EHR-FHS7 positive patients (91.9%) were survey-FHS7 positive, but only 352 of 883 survey-FHS7 positive patients (39.9%) were EHR-FHS7 positive. Of the 29 913 FHS7-positive patients, 19 764 (66.1%) were identified only after parsing free-text family history comments. Socioeconomic differences were also observed between EHR-FHS7-negative and EHR-FHS7-positive patients, suggesting disparities in recording family history. Conclusions and Relevance In this cross-sectional study, EHR-derived FHS7 identified thousands of patients with familial risk for breast cancer, indicating a substantial gap in genetic testing. However, limitations in EHR family history data suggested that other identification methods, such as direct-to-patient questionnaires, are required to fully address this gap.
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Affiliation(s)
- Daniel Kiser
- University of Nevada Reno School of Medicine, Reno
| | - Gai Elhanan
- University of Nevada Reno School of Medicine, Reno
| | | | - Iva Neveux
- University of Nevada Reno School of Medicine, Reno
| | | | | | | | | | | | | | | | | | - Joseph J Grzymski
- University of Nevada Reno School of Medicine, Reno
- Renown Health, Reno, Nevada
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Kaphingst KA, Kohlmann WK, Lorenz Chambers R, Bather JR, Goodman MS, Bradshaw RL, Chavez-Yenter D, Colonna SV, Espinel WF, Everett JN, Flynn M, Gammon A, Harris A, Hess R, Kaiser-Jackson L, Lee S, Monahan R, Schiffman JD, Volkmar M, Wetter DW, Zhong L, Mann DM, Ginsburg O, Sigireddi M, Kawamoto K, Del Fiol G, Buys SS. Uptake of Cancer Genetic Services for Chatbot vs Standard-of-Care Delivery Models: The BRIDGE Randomized Clinical Trial. JAMA Netw Open 2024; 7:e2432143. [PMID: 39250153 PMCID: PMC11385050 DOI: 10.1001/jamanetworkopen.2024.32143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/10/2024] Open
Abstract
Importance Increasing numbers of unaffected individuals could benefit from genetic evaluation for inherited cancer susceptibility. Automated conversational agents (ie, chatbots) are being developed for cancer genetics contexts; however, randomized comparisons with standard of care (SOC) are needed. Objective To examine whether chatbot and SOC approaches are equivalent in completion of pretest cancer genetic services and genetic testing. Design, Setting, and Participants This equivalence trial (Broadening the Reach, Impact, and Delivery of Genetic Services [BRIDGE] randomized clinical trial) was conducted between August 15, 2020, and August 31, 2023, at 2 US health care systems (University of Utah Health and NYU Langone Health). Participants were aged 25 to 60 years, had had a primary care visit in the previous 3 years, were eligible for cancer genetic evaluation, were English or Spanish speaking, had no prior cancer diagnosis other than nonmelanoma skin cancer, had no prior cancer genetic counseling or testing, and had an electronic patient portal account. Intervention Participants were randomized 1:1 at the patient level to the study groups at each site. In the chatbot intervention group, patients were invited in a patient portal outreach message to complete a pretest genetics education chat. In the enhanced SOC control group, patients were invited to complete an SOC pretest appointment with a certified genetic counselor. Main Outcomes and Measures Primary outcomes were completion of pretest cancer genetic services (ie, pretest genetics education chat or pretest genetic counseling appointment) and completion of genetic testing. Equivalence hypothesis testing was used to compare the study groups. Results This study included 3073 patients (1554 in the chatbot group and 1519 in the enhanced SOC control group). Their mean (SD) age at outreach was 43.8 (9.9) years, and most (2233 of 3063 [72.9%]) were women. A total of 204 patients (7.3%) were Black, 317 (11.4%) were Latinx, and 2094 (75.0%) were White. The estimated percentage point difference for completion of pretest cancer genetic services between groups was 2.0 (95% CI, -1.1 to 5.0). The estimated percentage point difference for completion of genetic testing was -1.3 (95% CI, -3.7 to 1.1). Analyses suggested equivalence in the primary outcomes. Conclusions and Relevance The findings of the BRIDGE equivalence trial support the use of chatbot approaches to offer cancer genetic services. Chatbot tools can be a key component of sustainable and scalable population health management strategies to enhance access to cancer genetic services. Trial Registration ClinicalTrials.gov Identifier: NCT03985852.
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Affiliation(s)
- Kimberly A Kaphingst
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Communication, University of Utah, Salt Lake City
| | | | | | - Jemar R Bather
- School of Global Public Health, New York University, New York
| | | | - Richard L Bradshaw
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| | - Daniel Chavez-Yenter
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Communication, University of Utah, Salt Lake City
| | - Sarah V Colonna
- Huntsman Cancer Institute, Salt Lake City, Utah
- Veterans Administration Medical Center, Salt Lake City, Utah
| | | | | | - Michael Flynn
- Department of Internal Medicine, University of Utah, Salt Lake City
- Department of Pediatrics, University of Utah, Salt Lake City
- Community Physicians Group, University of Utah Health, Salt Lake City
| | | | - Adrian Harris
- School of Global Public Health, New York University, New York
| | - Rachel Hess
- Department of Internal Medicine, University of Utah, Salt Lake City
- Department of Population Health Sciences, University of Utah, Salt Lake City
| | | | - Sang Lee
- Perlmutter Cancer Center, NYU Langone Health, New York
| | - Rachel Monahan
- Perlmutter Cancer Center, NYU Langone Health, New York
- Department of Population Health, NYU Grossman School of Medicine, New York
| | - Joshua D Schiffman
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Pediatrics, University of Utah, Salt Lake City
| | | | - David W Wetter
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City
| | | | - Devin M Mann
- Department of Population Health, NYU Grossman School of Medicine, New York
| | - Ophira Ginsburg
- Center for Global Health, National Cancer Institute, Rockville, Maryland
| | | | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City
| | - Saundra S Buys
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Internal Medicine, University of Utah, Salt Lake City
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Gazzarata R, Almeida J, Lindsköld L, Cangioli G, Gaeta E, Fico G, Chronaki CE. HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) in digital healthcare ecosystems for chronic disease management: Scoping review. Int J Med Inform 2024; 189:105507. [PMID: 38870885 DOI: 10.1016/j.ijmedinf.2024.105507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 05/14/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND The prevalence of chronic diseases has shifted the burden of disease from incidental acute inpatient admissions to long-term coordinated care across healthcare institutions and the patient's home. Digital healthcare ecosystems emerge to target increasing healthcare costs and invest in standard Application Programming Interfaces (API), such as HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) for trusted data flows. OBJECTIVES This scoping review assessed the role and impact of HL7 FHIR and associated Implementation Guides (IGs) in digital healthcare ecosystems focusing on chronic disease management. METHODS To study trends and developments relevant to HL7 FHIR, a scoping review of the scientific and gray English literature from 2017 to 2023 was used. RESULTS The selection of 93 of 524 scientific papers reviewed in English indicates that the popularity of HL7 FHIR as a robust technical interface standard for the health sector has been steadily rising since its inception in 2010, reaching a peak in 2021. Digital Health applications use HL7 FHIR in cancer (45 %), cardiovascular disease (CVD) (more than 15 %), and diabetes (almost 15 %). The scoping review revealed that references to HL7 FHIR IGs are limited to ∼ 20 % of articles reviewed. HL7 FHIR R4 was most frequently referenced when the HL7 FHIR version was mentioned. In HL7 FHIR IGs registries and the internet, we found 35 HL7 FHIR IGs addressing chronic disease management, i.e., cancer (40 %), chronic disease management (25 %), and diabetes (20 %). HL7 FHIR IGs frequently complement the information in the article. CONCLUSIONS HL7 FHIR matures with each revision of the standard as HL7 FHIR IGs are developed with validated data sets, common shared HL7 FHIR resources, and supporting tools. Referencing HL7 FHIR IGs cataloged in official registries and in scientific publications is recommended to advance data quality and facilitate mutual learning in growing digital healthcare ecosystems that nurture interoperability in digital health innovation.
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Affiliation(s)
- Roberta Gazzarata
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; Healthropy Srl, Corso Vittorio Veneto 14B, Savona, 17100, Italy.
| | - Joao Almeida
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; MEDCIDS - Faculty of Medicine of University of Porto, Porto, Portugal; PDH - Pharma Data Hub, Porto, Portugal.
| | - Lars Lindsköld
- European Federation for Medical Informatics, Ch de Maillefer 37, CH-1052 Le Mont-sur-Lausanne, Switzerland; SciLifeLab Datacenter, University of Uppsala, S-752 37 Uppsala, Sweden.
| | - Giorgio Cangioli
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium.
| | - Eugenio Gaeta
- Life Supporting Technologies, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
| | - Giuseppe Fico
- Life Supporting Technologies, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
| | - Catherine E Chronaki
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; European Federation for Medical Informatics, Ch de Maillefer 37, CH-1052 Le Mont-sur-Lausanne, Switzerland.
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Harris A, Bather JR, Kawamoto K, Fiol GD, Bradshaw RL, Kaiser-Jackson L, Monahan R, Kohlmann W, Liu F, Ginsburg O, Goodman MS, Kaphingst KA. Determinants of Breast Cancer Screening Adherence During the COVID-19 Pandemic in a Cohort at Increased Inherited Cancer Risk in the United States. Cancer Control 2024; 31:10732748241272727. [PMID: 39420801 DOI: 10.1177/10732748241272727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2024] Open
Abstract
BACKGROUND We examined neighborhood characteristics concerning breast cancer screening annual adherence during the COVID-19 pandemic. METHODS We analyzed 6673 female patients aged 40 or older at increased inherited cancer risk in 2 large health care systems (NYU Langone Health [NYULH] and the University of Utah Health [UHealth]). Multinomial models were used to identify predictors of mammogram screening groups (non-adherent, pre-pandemic adherent, pandemic period adherent) in comparison to adherent females. Potential determinants included sociodemographic characteristics and neighborhood factors. RESULTS Comparing each cancer group in reference to the adherent group, a reduced likelihood of being non-adherent was associated with older age (OR: 0.97, 95% CI: 0.95, 0.99), a greater number of relatives with cancer (OR: 0.80, 95% CI: 0.75, 0.86), and being seen at NYULH study site (OR: 0.42, 95% CI: 0.29, 0.60). More relatives with cancer were correlated with a lesser likelihood of being pandemic period adherent (OR: 0.89, 95% CI: 0.81, 0.97). A lower likelihood of being pre-pandemic adherent was seen in areas with less education (OR: 0.77, 95% CI: 0.62, 0.96) and NYULH study site (OR: 0.35, 95% CI: 0.22, 0.55). Finally, greater neighborhood deprivation (OR: 1.47, 95% CI: 1.08, 2.01) was associated with being non-adherent. CONCLUSION Breast screening during the COVID-19 pandemic was associated with being older, having more relatives with cancer, residing in areas with less educational attainment, and being seen at NYULH; non-adherence was linked with greater neighborhood deprivation. These findings may mitigate risk of clinically important screening delays at times of disruptions in a population at greater risk for breast cancer.
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Affiliation(s)
- Adrian Harris
- Center for Anti-racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY, USA
| | - Jemar R Bather
- Center for Anti-racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY, USA
- Department of Biostatistics, New York University School of Global Public Health, New York, NY, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Richard L Bradshaw
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | - Rachel Monahan
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Wendy Kohlmann
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Feng Liu
- Center for Anti-racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY, USA
| | - Ophira Ginsburg
- Center for Global Health, National Cancer Institute, Rockville, MD, USA
| | - Melody S Goodman
- Center for Anti-racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY, USA
- Department of Biostatistics, New York University School of Global Public Health, New York, NY, USA
| | - Kimberly A Kaphingst
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Communication, University of Utah, Salt Lake City, UT, USA
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Bradshaw RL, Kawamoto K, Bather JR, Goodman MS, Kohlmann WK, Chavez-Yenter D, Volkmar M, Monahan R, Kaphingst KA, Del Fiol G. Enhanced family history-based algorithms increase the identification of individuals meeting criteria for genetic testing of hereditary cancer syndromes but would not reduce disparities on their own. J Biomed Inform 2024; 149:104568. [PMID: 38081564 PMCID: PMC10842777 DOI: 10.1016/j.jbi.2023.104568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/07/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
OBJECTIVE This study aimed to 1) investigate algorithm enhancements for identifying patients eligible for genetic testing of hereditary cancer syndromes using family history data from electronic health records (EHRs); and 2) assess their impact on relative differences across sex, race, ethnicity, and language preference. MATERIALS AND METHODS The study used EHR data from a tertiary academic medical center. A baseline rule-base algorithm, relying on structured family history data (structured data; SD), was enhanced using a natural language processing (NLP) component and a relaxed criteria algorithm (partial match [PM]). The identification rates and differences were analyzed considering sex, race, ethnicity, and language preference. RESULTS Among 120,007 patients aged 25-60, detection rate differences were found across all groups using the SD (all P < 0.001). Both enhancements increased identification rates; NLP led to a 1.9 % increase and the relaxed criteria algorithm (PM) led to an 18.5 % increase (both P < 0.001). Combining SD with NLP and PM yielded a 20.4 % increase (P < 0.001). Similar increases were observed within subgroups. Relative differences persisted across most categories for the enhanced algorithms, with disproportionately higher identification of patients who are White, Female, non-Hispanic, and whose preferred language is English. CONCLUSION Algorithm enhancements increased identification rates for patients eligible for genetic testing of hereditary cancer syndromes, regardless of sex, race, ethnicity, and language preference. However, differences in identification rates persisted, emphasizing the need for additional strategies to reduce disparities such as addressing underlying biases in EHR family health information and selectively applying algorithm enhancements for disadvantaged populations. Systematic assessment of differences in algorithm performance across population subgroups should be incorporated into algorithm development processes.
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Affiliation(s)
- Richard L Bradshaw
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; University of Utah Health, Salt Lake City, UT, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; University of Utah Health, Salt Lake City, UT, USA
| | - Jemar R Bather
- Department of Biostatistics, New York University School of Global Public Health, New York, NY, USA; Center for Anti-racism, Social Justice, & Public Health, New York University School of Global Public Health, New York, NY, USA
| | - Melody S Goodman
- Department of Biostatistics, New York University School of Global Public Health, New York, NY, USA; Center for Anti-racism, Social Justice, & Public Health, New York University School of Global Public Health, New York, NY, USA
| | - Wendy K Kohlmann
- University of Utah Health, Salt Lake City, UT, USA; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Daniel Chavez-Yenter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA; Department of Communication, University of Utah, Salt Lake City, UT, USA
| | - Molly Volkmar
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | | | - Kimberly A Kaphingst
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA; Department of Communication, University of Utah, Salt Lake City, UT, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; University of Utah Health, Salt Lake City, UT, USA.
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Horvat CM, King AJ, Huang DT. Designing and Implementing "Living and Breathing" Clinical Trials: An Overview and Lessons Learned from the COVID-19 Pandemic. Crit Care Clin 2023; 39:717-732. [PMID: 37704336 PMCID: PMC9935272 DOI: 10.1016/j.ccc.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
The practice of medicine is characterized by uncertainty, and the findings of randomized clinical trials (RCTs) are meant to help curb that uncertainty. Traditional RCTs, however, have many limitations. To overcome some of these limitations, new trial paradigms rooted in the origins of evidence-based medicine are beginning to disrupt the traditional mold. These new designs recognize uncertainty permeates medical decision making and aim to capitalize on modern health system infrastructure to integrate investigation as a component of care delivery. This article provides an overview of "living, breathing" trials, including current state, anticipated developments, and areas of controversy.
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Affiliation(s)
- Christopher M Horvat
- UPMC Children's Hospital of Pittsburgh, Faculty Pavilion, 4401 Penn Avenue, Suite 0200, Pittsburgh, PA 15224, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, 603A, Pittsburgh, PA 15261, USA.
| | - Andrew J King
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, 603A, Pittsburgh, PA 15261, USA
| | - David T Huang
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, 603A, Pittsburgh, PA 15261, USA
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