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Kamil D, Wojcik KM, Smith L, Zhang J, Wilson OWA, Butera G, Jayasekera J. A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States. MDM Policy Pract 2024; 9:23814683241236511. [PMID: 38500600 PMCID: PMC10946080 DOI: 10.1177/23814683241236511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/04/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools. Highlights There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.
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Affiliation(s)
- Dalya Kamil
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Kaitlyn M. Wojcik
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Laney Smith
- Frederick P. Whiddon College of Medicine, Mobile, AL, USA
| | | | - Oliver W. A. Wilson
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Gisela Butera
- Office of Research Services, National Institutes of Health Library, Bethesda, MD, USA
| | - Jinani Jayasekera
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
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Li X, Kahn RM, Wing N, Zhou ZN, Lackner AI, Krinsky H, Badiner N, Fogla R, Wolfe I, Bergeron H, Nelson BB, Thomas C, Christos PJ, Sharaf RN, Cantillo E, Holcomb K, Chapman-Davis E, Frey MK. Leveraging Health Information Technology to Collect Family Cancer History: A Systematic Review and Meta-Analysis. JCO Clin Cancer Inform 2021; 5:775-788. [PMID: 34328789 PMCID: PMC8812651 DOI: 10.1200/cci.21.00004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Collection of family cancer histories (FCHs) can identify individuals at risk for familial cancer syndromes. The aim of this study is to evaluate the literature on existing strategies whereby providers use information technology to assemble FCH. METHODS A systematic search of online databases (Ovid MEDLINE, Cochrane, and Embase) between 1980 and 2020 was performed. Statistical heterogeneity was assessed through the chi-square test (ie, Cochrane Q test) and the inconsistency statistic (I2). A random-effects analysis was used to calculate the pooled proportions and means. RESULTS The comprehensive search produced 4,005 publications. Twenty-eight studies met inclusion criteria. Twenty-seven information technology tools were evaluated. Eighteen out of 28 studies were electronic surveys administered before visits (18, 64.3%). Five studies administered tablet surveys in offices (5, 17.8%). Four studies collected electronic survey via kiosk before visits (4, 14.3%), and one study used animated virtual counselor during visits (1, 3.6%). Among the studies that use an FCH tool, the pooled estimate of the overall completion rate was 86% (CI, 72% to 96%), 84% (CI, 65% to 97%) for electronic surveys before visits, 89% (CI, 0.74 to 0.98) for tablet surveys, and 85% (CI, 0.66 to 0.98) for surveys via kiosk. Mean time required for completion was 31.0 minutes (CI, 26.1 to 35.9), and the pooled estimate of proportions of participants referred to genetic testing was 12% (CI, 4% to 23%). CONCLUSION Our review found that electronic FCH collection can be completed successfully by patients in a time-efficient manner with high rates of satisfaction.
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Affiliation(s)
- Xuan Li
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Ryan M Kahn
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Noelani Wing
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Zhen Ni Zhou
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Andreas Ian Lackner
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Hannah Krinsky
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Nora Badiner
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Rhea Fogla
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Isabel Wolfe
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Hannah Bergeron
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Becky Baltich Nelson
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Charlene Thomas
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Paul J Christos
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Ravi N Sharaf
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Evelyn Cantillo
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Kevin Holcomb
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Eloise Chapman-Davis
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
| | - Melissa K Frey
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, New York, NY
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