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Hindelang M, Sitaru S, Zink A. Transforming Health Care Through Chatbots for Medical History-Taking and Future Directions: Comprehensive Systematic Review. JMIR Med Inform 2024; 12:e56628. [PMID: 39207827 PMCID: PMC11393511 DOI: 10.2196/56628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/08/2024] [Accepted: 07/11/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence-driven conversational agents, chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice. OBJECTIVE This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of chatbots in medical history-taking. It also examines potential challenges and future opportunities for integration into clinical practice. METHODS A systematic search included PubMed, Embase, MEDLINE (via Ovid), CENTRAL, Scopus, and Open Science and covered studies through July 2024. The inclusion and exclusion criteria for the studies reviewed were based on the PICOS (participants, interventions, comparators, outcomes, and study design) framework. The population included individuals using health care chatbots for medical history-taking. Interventions focused on chatbots designed to facilitate medical history-taking. The outcomes of interest were the feasibility, acceptance, and usability of chatbot-based medical history-taking. Studies not reporting on these outcomes were excluded. All study designs except conference papers were eligible for inclusion. Only English-language studies were considered. There were no specific restrictions on study duration. Key search terms included "chatbot*," "conversational agent*," "virtual assistant," "artificial intelligence chatbot," "medical history," and "history-taking." The quality of observational studies was classified using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria (eg, sample size, design, data collection, and follow-up). The RoB 2 (Risk of Bias) tool assessed areas and the levels of bias in randomized controlled trials (RCTs). RESULTS The review included 15 observational studies and 3 RCTs and synthesized evidence from different medical fields and populations. Chatbots systematically collect information through targeted queries and data retrieval, improving patient engagement and satisfaction. The results show that chatbots have great potential for history-taking and that the efficiency and accessibility of the health care system can be improved by 24/7 automated data collection. Bias assessments revealed that of the 15 observational studies, 5 (33%) studies were of high quality, 5 (33%) studies were of moderate quality, and 5 (33%) studies were of low quality. Of the RCTs, 2 had a low risk of bias, while 1 had a high risk. CONCLUSIONS This systematic review provides critical insights into the potential benefits and challenges of using chatbots for medical history-taking. The included studies showed that chatbots can increase patient engagement, streamline data collection, and improve health care decision-making. For effective integration into clinical practice, it is crucial to design user-friendly interfaces, ensure robust data security, and maintain empathetic patient-physician interactions. Future research should focus on refining chatbot algorithms, improving their emotional intelligence, and extending their application to different health care settings to realize their full potential in modern medicine. TRIAL REGISTRATION PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero.
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Affiliation(s)
- Michael Hindelang
- Department of Dermatology and Allergy, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilian University, LMU, Munich, Germany
| | - Sebastian Sitaru
- Department of Dermatology and Allergy, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Alexander Zink
- Department of Dermatology and Allergy, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Division of Dermatology and Venereology, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
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Soni H, Morrison H, Vasilev D, Ong T, Wilczewski H, Allen C, Hughes-Halbert C, Ritchie JB, Narma A, Schiffman JD, Ivanova J, Bunnell BE, Welch BM. User experience of a family health history chatbot: A quantitative analysis. Health Informatics J 2024; 30:14604582241262251. [PMID: 38865081 PMCID: PMC11391477 DOI: 10.1177/14604582241262251] [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] [Indexed: 06/13/2024]
Abstract
OBJECTIVE Family health history (FHx) is an important tool in assessing one's risk towards specific health conditions. However, user experience of FHx collection tools is rarely studied. ItRunsInMyFamily.com (ItRuns) was developed to assess FHx and hereditary cancer risk. This study reports a quantitative user experience analysis of ItRuns. METHODS We conducted a public health campaign in November 2019 to promote FHx collection using ItRuns. We used software telemetry to quantify abandonment and time spent on ItRuns to identify user behaviors and potential areas of improvement. RESULTS Of 11,065 users who started the ItRuns assessment, 4305 (38.91%) reached the final step to receive recommendations about hereditary cancer risk. Highest abandonment rates were during Introduction (32.82%), Invite Friends (29.03%), and Family Cancer History (12.03%) subflows. Median time to complete the assessment was 636 s. Users spent the highest median time on Proband Cancer History (124.00 s) and Family Cancer History (119.00 s) subflows. Search list questions took the longest to complete (median 19.50 s), followed by free text email input (15.00 s). CONCLUSION Knowledge of objective user behaviors at a large scale and factors impacting optimal user experience will help enhance the ItRuns workflow and improve future FHx collection.
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Affiliation(s)
- Hiral Soni
- Doxy.me Research, Doxy.me Inc., Rochester, NY, USA
| | | | | | - Triton Ong
- Doxy.me Research, Doxy.me Inc., Rochester, NY, USA
| | | | - Caitlin Allen
- Biomedical Informatics Center, Public Health and Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Chanita Hughes-Halbert
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Jordon B Ritchie
- Biomedical Informatics Center, Public Health and Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Alexa Narma
- Doxy.me Research, Doxy.me Inc., Rochester, NY, USA
| | | | | | - Brian E Bunnell
- Doxy.me Research, Doxy.me Inc., Rochester, NY, USA
- Innovation in Mental Health Lab., Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA
| | - Brandon M Welch
- Doxy.me Research, Doxy.me Inc., Rochester, NY, USA
- Biomedical Informatics Center, Public Health and Sciences, Medical University of South Carolina, Charleston, SC, USA
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Allen CG, Neil G, Halbert CH, Sterba KR, Nietert PJ, Welch B, Lenert L. Barriers and facilitators to the implementation of family cancer history collection tools in oncology clinical practices. J Am Med Inform Assoc 2024; 31:631-639. [PMID: 38164994 PMCID: PMC10873828 DOI: 10.1093/jamia/ocad243] [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: 05/16/2023] [Revised: 10/30/2023] [Accepted: 12/19/2023] [Indexed: 01/03/2024] Open
Abstract
INTRODUCTION This study aimed to identify barriers and facilitators to the implementation of family cancer history (FCH) collection tools in clinical practices and community settings by assessing clinicians' perceptions of implementing a chatbot interface to collect FCH information and provide personalized results to patients and providers. OBJECTIVES By identifying design and implementation features that facilitate tool adoption and integration into clinical workflows, this study can inform future FCH tool development and adoption in healthcare settings. MATERIALS AND METHODS Quantitative data were collected using survey to evaluate the implementation outcomes of acceptability, adoption, appropriateness, feasibility, and sustainability of the chatbot tool for collecting FCH. Semistructured interviews were conducted to gather qualitative data on respondents' experiences using the tool and recommendations for enhancements. RESULTS We completed data collection with 19 providers (n = 9, 47%), clinical staff (n = 5, 26%), administrators (n = 4, 21%), and other staff (n = 1, 5%) affiliated with the NCI Community Oncology Research Program. FCH was systematically collected using a wide range of tools at sites, with information being inserted into the patient's medical record. Participants found the chatbot tool to be highly acceptable, with the tool aligning with existing workflows, and were open to adopting the tool into their practice. DISCUSSION AND CONCLUSIONS We further the evidence base about the appropriateness of scripted chatbots to support FCH collection. Although the tool had strong support, the varying clinical workflows across clinic sites necessitate that future FCH tool development accommodates customizable implementation strategies. Implementation support is necessary to overcome technical and logistical barriers to enhance the uptake of FCH tools in clinical practices and community settings.
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Affiliation(s)
- Caitlin G Allen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Grace Neil
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Chanita Hughes Halbert
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States
| | - Katherine R Sterba
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Paul J Nietert
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Brandon Welch
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Leslie Lenert
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
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Rana HQ, Stopfer JE, Weitz M, Kipnis L, Koeller DR, Culver S, Mercado J, Gelman RS, Underhill-Blazey M, McGregor BA, Sweeney CJ, Petrucelli N, Kokenakes C, Pirzadeh-Miller S, Reys B, Frazier A, Knechtl A, Fateh S, Vatnick DR, Silver R, Kilbridge KE, Pomerantz MM, Wei XX, Choudhury AD, Sonpavde GP, Kozyreva O, Lathan C, Horton C, Dolinsky JS, Heath EI, Ross TS, Courtney KD, Garber JE, Taplin ME. Pretest Video Education Versus Genetic Counseling for Patients With Prostate Cancer: ProGen, A Multisite Randomized Controlled Trial. JCO Oncol Pract 2023; 19:1069-1079. [PMID: 37733980 PMCID: PMC10667014 DOI: 10.1200/op.23.00007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/10/2023] [Accepted: 08/07/2023] [Indexed: 09/23/2023] Open
Abstract
PURPOSE Germline genetic testing (GT) is recommended for men with prostate cancer (PC), but testing through traditional models is limited. The ProGen study examined a novel model aimed at providing access to GT while promoting education and informed consent. METHODS Men with potentially lethal PC (metastatic, localized with a Gleason score of ≥8, persistent prostate-specific antigen after local therapy), diagnosis age ≤55 years, previous malignancy, and family history suggestive of a pathogenic variant (PV) and/or at oncologist's discretion were randomly assigned 3:1 to video education (VE) or in-person genetic counseling (GC). Participants had 67 genes analyzed (Ambry), with results disclosed via telephone by a genetic counselor. Outcomes included GT consent, GT completion, PV prevalence, and survey measures of satisfaction, psychological impact, genetics knowledge, and family communication. Two-sided Fisher's exact tests were used for between-arm comparisons. RESULTS Over a 2-year period, 662 participants at three sites were randomly assigned and pretest VE (n = 498) or GC (n = 164) was completed by 604 participants (VE, 93.1%; GC, 88.8%), of whom 596 participants (VE, 98.9%; GC, 97.9%) consented to GT and 591 participants completed GT (VE, 99.3%; GC, 98.6%). These differences were not statistically significant although subtle differences in satisfaction and psychological impact were. Notably, 84 PVs were identified in 78 participants (13.2%), with BRCA1/2 PV comprising 32% of participants with a positive result (BRCA2 n = 21, BRCA1 n = 4). CONCLUSION Both VE and traditional GC yielded high GT uptake without significant differences in outcome measures of completion, GT uptake, genetics knowledge, and family communication. The increased demand for GT with limited genetics resources supports consideration of pretest VE for patients with PC.
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Affiliation(s)
- Huma Q. Rana
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA
| | - Jill E. Stopfer
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA
| | - Michelle Weitz
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Lindsay Kipnis
- Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA
| | - Diane R. Koeller
- Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA
| | - Samantha Culver
- Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA
| | - Joanna Mercado
- Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA
| | | | - Meghan Underhill-Blazey
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA
| | - Bradley A. McGregor
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Christopher J. Sweeney
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA
| | | | | | | | - Brian Reys
- University of Texas Southwestern Medical Center, Dallas, TX
| | - Arthur Frazier
- Karmanos Cancer Institute at McLaren Clarkston, Clarkston, MI
| | - Andrew Knechtl
- Karmanos Cancer Institute at McLaren Clarkston, Clarkston, MI
| | - Salman Fateh
- Karmanos Cancer Institute at McLaren Clarkston, Clarkston, MI
| | | | - Rebecca Silver
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Kerry E. Kilbridge
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Mark M. Pomerantz
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Xiao X. Wei
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Atish D. Choudhury
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Guru P. Sonpavde
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Olga Kozyreva
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | | | | | | | | | | | | | - Judy E. Garber
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA
| | - Mary-Ellen Taplin
- Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA
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Al-Hilli Z, Noss R, Dickard J, Wei W, Chichura A, Wu V, Renicker K, Pederson HJ, Eng C. A Randomized Trial Comparing the Effectiveness of Pre-test Genetic Counseling Using an Artificial Intelligence Automated Chatbot and Traditional In-person Genetic Counseling in Women Newly Diagnosed with Breast Cancer. Ann Surg Oncol 2023; 30:5990-5996. [PMID: 37567976 DOI: 10.1245/s10434-023-13888-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/04/2023] [Indexed: 08/13/2023]
Abstract
BACKGROUND Alternative service delivery models are critically needed to address the increasing demand for genetics services and limited supply of genetics experts available to provide pre-test counseling. METHODS We conducted a prospective randomized controlled trial of women with stage 0-III breast cancer not meeting National Comprehensive Cancer Network (NCCN) criteria for genetic testing. Patients were randomized to pre-test counseling with a Chatbot or a certified genetic counselor (GC). Participants completed a questionnaire assessing their knowledge of breast cancer genetics and a survey assessing satisfaction with their decision regarding pre-test counseling. RESULTS A total of 39 patients were enrolled and 37 were randomized to genetic counseling with an automated Chatbot or a GC; 19 were randomized to Chatbot and 18 to traditional genetic counseling, and 13 (38.2%) had a family member with breast cancer but did not meet NCCN criteria. All patients opted to undergo genetic testing. Testing revealed six pathogenic variants in five patients (13.5%): CHEK2 (n = 2), MSH3 (n = 1), MUTYH (n = 1), and BRCA1 and HOXB13 (n = 1). No patients had a delay in time-to-treatment due to genetic testing turnaround time, nor did any patients undergo additional risk reducing surgery. There was no significant difference in median knowledge score between Chatbot and traditional counseling (11 vs. 12, p = 0.09) or in median patient satisfaction score (30 vs. 30, p = 0.19). CONCLUSION Satisfaction and comprehension in patients with breast cancer undergoing pre-test genetic counseling using an automated Chatbot is comparable to in-person genetic testing. Utilization of this technology can offer improved access to care and a much-needed alternative for pre-test counseling.
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Affiliation(s)
- Zahraa Al-Hilli
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - Ryan Noss
- Center for Personalized Genetic Healthcare, Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jennifer Dickard
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Wei Wei
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Anna Chichura
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Benign Gynecology, Women's Health Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Vincent Wu
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kayla Renicker
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Holly J Pederson
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
- Center for Personalized Genetic Healthcare, Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Charis Eng
- Center for Personalized Genetic Healthcare, Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
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Webster EM, Ahsan MD, Perez L, Levi SR, Thomas C, Christos P, Hickner A, Hamilton JG, Babagbemi K, Cantillo E, Holcomb K, Chapman-Davis E, Sharaf RN, Frey MK. Chatbot Artificial Intelligence for Genetic Cancer Risk Assessment and Counseling: A Systematic Review and Meta-Analysis. JCO Clin Cancer Inform 2023; 7:e2300123. [PMID: 37934933 PMCID: PMC10730073 DOI: 10.1200/cci.23.00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/25/2023] [Accepted: 09/11/2023] [Indexed: 11/09/2023] Open
Abstract
PURPOSE Most individuals with a hereditary cancer syndrome are unaware of their genetic status to underutilization of hereditary cancer risk assessment. Chatbots, or programs that use artificial intelligence to simulate conversation, have emerged as a promising tool in health care and, more recently, as a potential tool for genetic cancer risk assessment and counseling. Here, we evaluated the existing literature on the use of chatbots in genetic cancer risk assessment and counseling. METHODS A systematic review was conducted using key electronic databases to identify studies which use chatbots for genetic cancer risk assessment and counseling. Eligible studies were further subjected to meta-analysis. RESULTS Seven studies met inclusion criteria, evaluating five distinct chatbots. Three studies evaluated a chatbot that could perform genetic cancer risk assessment, one study evaluated a chatbot that offered patient counseling, and three studies included both functions. The pooled estimated completion rate for the genetic cancer risk assessment was 36.7% (95% CI, 14.8 to 65.9). Two studies included comprehensive patient characteristics, and none involved a comparison group. Chatbots varied as to the involvement of a health care provider in the process of risk assessment and counseling. CONCLUSION Chatbots have been used to streamline genetic cancer risk assessment and counseling and hold promise for reducing barriers to genetic services. Data regarding user and nonuser characteristics are lacking, as are data regarding comparative effectiveness to usual care. Future research may consider the impact of chatbots on equitable access to genetic services.
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Frey MK, Ahsan MD, Webster E, Levi SR, Brewer JT, Lin J, Blank SV, Krinsky H, Nchako C, Wolfe I, Thomas C, Christos P, Cantillo E, Chapman-Davis E, Holcomb K, Sharaf RN. Web-based tool for cancer family history collection: A prospective randomized controlled trial. Gynecol Oncol 2023; 173:22-30. [PMID: 37062188 PMCID: PMC10310435 DOI: 10.1016/j.ygyno.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/31/2023] [Accepted: 04/01/2023] [Indexed: 04/18/2023]
Abstract
OBJECTIVES Approximately 1% of individuals have a hereditary cancer predisposition syndrome, however, the majority are not aware. Collecting a cancer family history (CFH) can triage patients to receive genetic testing. To rigorously assess different methods of CFH collection, we compared a web-based tool (WBT) to usual care (clinician collects CFH) in a randomized controlled trial. METHODS New gynecologic oncology patients (seen 9/2019-9/2021) were randomized to one of three arms in a 2:2:1 allocation ratio: 1) usual care clinician CFH collection, 2) WBT completed at home, or 3) WBT completed in office. The WBT generated a cancer-focused pedigree and scores on eight validated cancer risk models. The primary outcome was collection of an adequate CFH (based on established guidelines) with usual care versus the WBT. RESULTS We enrolled 250 participants (usual care - 110; WBT home - 105; WBT office - 35 [closed early due to COVID-19]). Within WBT arms, 109 (78%) participants completed the tool, with higher completion for office versus home (33 [94%] vs. 76 [72%], P = 0.008). Among participants completing the WBT, 63 (58%) had an adequate CFH versus 5 (5%) for usual care (P < 0.001). Participants completing the WBT were significantly more likely to complete genetic counseling (34 [31%] vs. 15 [14%], P = 0.002) and genetic testing (20 [18%] vs. 9 [8%], P = 0.029). Participant and provider WBT experience was favorable. CONCLUSIONS WBTs for CFH collection are a promising application of health information technology, resulting in more comprehensive CFH and a significantly greater percentage of participants completing genetic counseling and testing.
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Affiliation(s)
- Melissa K Frey
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America.
| | - Muhammad Danyal Ahsan
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Emily Webster
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Sarah R Levi
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Jesse T Brewer
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Jenny Lin
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Stephanie V Blank
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Icahn School of Medicine at Mount Sinai, United States of America
| | - Hannah Krinsky
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Corbyn Nchako
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Isabel Wolfe
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Charlene Thomas
- Population Health Sciences, Division of Biostatistics and Epidemiology, Weill Cornell Medicine, New York, NY, United States of America
| | - Paul Christos
- Population Health Sciences, Division of Biostatistics and Epidemiology, Weill Cornell Medicine, New York, NY, United States of America
| | - Evelyn Cantillo
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Eloise Chapman-Davis
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Kevin Holcomb
- Department of Obstetrics and Gynecology, Division of Gynecology Oncology, Weill Cornell Medicine, New York, NY, United States of America
| | - Ravi N Sharaf
- Division of Gastroenterology, Department of Medicine, Weill Cornell Medicine, New York, NY, United States of America; Division of Epidemiology, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States of America
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Allen C. User experience of a family health history chatbot: A quantitative analysis. RESEARCH SQUARE 2023:rs.3.rs-2886804. [PMID: 37205400 PMCID: PMC10187455 DOI: 10.21203/rs.3.rs-2886804/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Objective Family health history (FHx) is an important tool in assessing one's risk towards specific health conditions. However, user experience of FHx collection tools is rarely studied. ItRunsInMyFamily.com (ItRuns) was developed to assess FHx and hereditary cancer risk. This study reports a quantitative user experience analysis of ItRuns. Methods We conducted a public health campaign in November 2019 to promote FHx collection using ItRuns. We used software telemetry to quantify abandonment and time spent on ItRuns to identify user behaviors and potential areas of improvement. Results Of 11065 users who started the ItRuns assessment, 4305 (38.91%) reached the final step to receive recommendations about hereditary cancer risk. Highest abandonment rates were during Introduction (32.82%), Invite Friends (29.03%), and Family Cancer History (12.03%) subflows. Median time to complete the assessment was 636 seconds. Users spent the highest median time on Proband Cancer History (124.00 seconds) and Family Cancer History (119.00 seconds) subflows. Search list questions took the longest to complete (median 19.50 seconds), followed by free text email input (15.00 seconds). Conclusion Knowledge of objective user behaviors at a large scale and factors impacting optimal user experience will help enhance the ItRuns workflow and improve future FHx collection.
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Wang C, Lu H, Bowen DJ, Xuan Z. Implementing digital systems to facilitate genetic testing for hereditary cancer syndromes: An observational study of 4 clinical workflows. Genet Med 2023; 25:100802. [PMID: 36906849 DOI: 10.1016/j.gim.2023.100802] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 02/12/2023] Open
Abstract
PURPOSE National efforts have prioritized the identification of effective methods for increasing case ascertainment and delivery of evidence-based health care for individuals at elevated risk for hereditary cancers. METHODS This study examined the uptake of genetic counseling and testing following the use of a digital cancer genetic risk assessment program implemented at 27 health care sites in 10 states using 1 of 4 clinical workflows: (1) traditional referral, (2) point-of-care scheduling, (3) point-of-care counseling/telegenetics, and (4) point-of-care testing. RESULTS In 2019, 102,542 patients were screened and 33,113 (32%) were identified as at high risk and meeting National Comprehensive Cancer Network genetic testing criteria for hereditary breast and ovarian cancer, Lynch syndrome, or both. Among those identified at high risk, 5147 (16%) proceeded with genetic testing. Genetic counseling uptake was 11% among the sites with workflows that included seeing a genetic counselor before testing, with 88% of patients proceeding with genetic testing after counseling. Uptake of genetic testing across sites varied significantly by clinical workflow (6% referral, 10% point-of-care scheduling, 14% point-of-care counseling/telegenetics, and 35% point-of-care testing, P < .0001). CONCLUSION Study findings highlight the potential heterogeneity of effectiveness attributable to different care delivery approaches for implementing digital hereditary cancer risk screening programs.
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Affiliation(s)
- Catharine Wang
- Department of Community Health Sciences, Boston University School of Public Health, Boston, MA.
| | | | - Deborah J Bowen
- Department of Bioethics and Humanities, School of Public Health, University of Washington, Seattle, WA
| | - Ziming Xuan
- Department of Community Health Sciences, Boston University School of Public Health, Boston, MA
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10
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Wang A, Qian Z, Briggs L, Cole AP, Reis LO, Trinh QD. The Use of Chatbots in Oncological Care: A Narrative Review. Int J Gen Med 2023; 16:1591-1602. [PMID: 37152273 PMCID: PMC10162388 DOI: 10.2147/ijgm.s408208] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 04/18/2023] [Indexed: 05/09/2023] Open
Abstract
Background Few reports have investigated chatbots in patient care. We aimed to assess the current applications, limitations, and challenges in the literature on chatbots employed in oncological care. Methods We queried the PubMed database through April 2022 and included studies that investigated the use of chatbots in different phases of oncological care. The search used five different combinations of the specific terms "chatbot", "cancer", "oncology", and "conversational agent". Inclusion criteria were chatbot use in any aspect of oncological care-prevention, patient education, treatment, and surveillance. Results The initial search yielded 196 records, 21 of which met inclusion criteria. The identified chatbots mostly focused on breast and ovarian cancer (n=8), with the second most common being cervical cancer (n=3). Good patient satisfaction was reported among 14 of 21 chatbots. The most reported chatbot applications were cancer screening, prevention, risk stratification, treatment, monitoring, and management. Of 12 studies examining efficacy of care via chatbot, 9 demonstrated improvements compared to standard care. Conclusion Chatbots used for oncological care to date demonstrate high user satisfaction, and many have shown efficacy in improving patient-centered communication, accessibility to cancer-related information, and access to care. Currently, chatbots are primarily limited by the need for extensive user-testing and iterative improvement before widespread implementation.
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Affiliation(s)
- Alexander Wang
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Zhiyu Qian
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Logan Briggs
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexander P Cole
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Leonardo O Reis
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, and Immuno-Oncology Division, Pontifical Catholic University of Campinas, PUC-Campinas, Sao Paulo, Brazil
| | - Quoc-Dien Trinh
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Correspondence: Quoc-Dien Trinh, Surgery, Harvard Medical School, Division of Urological Surgery, Brigham and Women’s Hospital, 45 Francis St, ASB II-3, Boston, MA, 02115, USA, Tel +1 617 525-7350, Fax +1 617 525-6348, Email
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11
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Liebermann E, Taber P, Vega AS, Daly BM, Goodman MS, Bradshaw R, Chan PA, Chavez-Yenter D, Hess R, Kessler C, Kohlmann W, Low S, Monahan R, Kawamoto K, Del Fiol G, Buys SS, Sigireddi M, Ginsburg O, Kaphingst KA. Barriers to family history collection among Spanish-speaking primary care patients: a BRIDGE qualitative study. PEC INNOVATION 2022; 1:100087. [PMID: 36532299 PMCID: PMC9757734 DOI: 10.1016/j.pecinn.2022.100087] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Objectives Family history is an important tool for assessing disease risk, and tailoring recommendations for screening and genetic services referral. This study explored barriers to family history collection with Spanish-speaking patients. Methods This qualitative study was conducted in two US healthcare systems. We conducted semi-structured interviews with medical assistants, physicians, and interpreters with experience collecting family history for Spanish-speaking patients. Results The most common patient-level barrier was the perception that some Spanish-speaking patients had limited knowledge of family history. Interpersonal communication barriers related to dialectical differences and decisions about using formal interpreters vs. Spanish-speaking staff. Organizational barriers included time pressures related to using interpreters, and ad hoc workflow adaptations for Spanish-speaking patients that might leave gaps in family history collection. Conclusions This study identified multi-level barriers to family history collection with Spanish-speaking patients in primary care. Findings suggest that a key priority to enhance communication would be to standardize processes for working with interpreters. Innovation To improve communication with and care provided to Spanish-speaking patients, there is a need to increase healthcare provider awareness about implicit bias, to address ad hoc workflow adjustments within practice settings, to evaluate the need for professional interpreter services, and to improve digital tools to facilitate family history collection.
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Affiliation(s)
- Erica Liebermann
- College of Nursing, University of Rhode Island, RINEC, 350 Eddy Street, Providence, RI 02903, USA
| | - Peter Taber
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT 84108, USA
| | - Alexis S Vega
- Department of Communication, University of Utah, 255 S. Central Campus Drive, Salt Lake City, UT 84112, USA
| | - Brianne M Daly
- Huntsman Cancer Institute, 2000 Circle of Hope Drive, Salt Lake City, UT 84112, USA
| | - Melody S Goodman
- School of Global Public Health, New York University, 726 Broadway, New York, NY 10012, USA
| | - Richard Bradshaw
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT 84108, USA
| | - Priscilla A Chan
- Perlmutter Cancer Center, NYU Langone Health, 160 E. 34th Street, New York, NY 10016, USA
| | - Daniel Chavez-Yenter
- Department of Communication, University of Utah, 255 S. Central Campus Drive, Salt Lake City, UT 84112, USA
- Huntsman Cancer Institute, 2000 Circle of Hope Drive, Salt Lake City, UT 84112, USA
| | - Rachel Hess
- Department of Population Health Sciences, University of Utah, 295 Chipeta Way, Salt Lake City, UT, 84108, USA
| | - Cecilia Kessler
- Huntsman Cancer Institute, 2000 Circle of Hope Drive, Salt Lake City, UT 84112, USA
| | - Wendy Kohlmann
- Huntsman Cancer Institute, 2000 Circle of Hope Drive, Salt Lake City, UT 84112, USA
| | - Sara Low
- Huntsman Cancer Institute, 2000 Circle of Hope Drive, Salt Lake City, UT 84112, USA
| | - Rachel Monahan
- Perlmutter Cancer Center, NYU Langone Health, 160 E. 34th Street, New York, NY 10016, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT 84108, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT 84108, USA
| | - Saundra S Buys
- Huntsman Cancer Institute, 2000 Circle of Hope Drive, Salt Lake City, UT 84112, USA
- Department of Internal Medicine, University of Utah, 30 N 1900 E, Salt Lake City, UT 84132, USA
| | - Meenakshi Sigireddi
- Perlmutter Cancer Center, NYU Langone Health, 160 E. 34th Street, New York, NY 10016, USA
| | - Ophira Ginsburg
- Center for Global Health, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA
| | - Kimberly A Kaphingst
- Department of Communication, University of Utah, 255 S. Central Campus Drive, Salt Lake City, UT 84112, USA
- Huntsman Cancer Institute, 2000 Circle of Hope Drive, Salt Lake City, UT 84112, USA
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12
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Meadows RJ, Figueroa W, Shane‐Carson KP, Padamsee TJ. Predicting breast cancer risk in a racially diverse, community-based sample of potentially high-risk women. Cancer Med 2022; 11:4043-4052. [PMID: 35388639 PMCID: PMC9636513 DOI: 10.1002/cam4.4721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 02/28/2022] [Accepted: 03/11/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Identifying women with high risk of breast cancer is necessary to study high-risk experiences and deliver risk-management care. Risk prediction models estimate individuals' lifetime risk but have rarely been applied in community-based settings among women not yet receiving specialized care. Therefore, we aimed: (1) to apply three breast cancer risk prediction models (i.e., Gail, Claus, and IBIS) to a racially diverse, community-based sample of women, and (2) to assess risk prediction estimates using survey data. METHODS An online survey was administered to women who were determined by a screening instrument to have potentially high risk for breast cancer. Risk prediction models were applied using their self-reported family and medical history information. Inclusion in the high-risk subsample required ≥20% lifetime risk per ≥1 model. Descriptive statistics were used to compare the proportions of women identified as high risk by each model. RESULTS N = 1053 women were initially eligible and completed the survey. All women, except one, self-reported the information necessary to run at least one model; 90% had sufficient information for >1 model. The high-risk subsample included 717 women, of which 75% were identified by one model only; 96% were identified by IBIS, 3% by Claus, <1% by Gail. In the high-risk subsample, 20% were identified by two models and 3% by all three models. CONCLUSIONS Assessing breast cancer risk using self-reported data in a community-based sample was feasible. Different models identify substantially different groups of women who may be at high risk for breast cancer; use of multiple models may be beneficial for research and clinical care.
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Affiliation(s)
- Rachel J. Meadows
- Center for Epidemiology & Healthcare Delivery ResearchJPS Health NetworkFort WorthTexasUSA
| | - Wilson Figueroa
- The Ohio State UniversityCenter for Health Outcomes and Policy Evaluation Studies, College of Public HealthColumbusOhioUSA
- Division of Health Services Management & PolicyCollege of Public Health, The Ohio State UniversityColumbusOhioUSA
| | - Kate P. Shane‐Carson
- Division of Human Genetics, Department of Internal MedicineOhio State University Comprehensive Cancer CenterColumbusOhioUSA
| | - Tasleem J. Padamsee
- Division of Health Services Management & PolicyCollege of Public Health, The Ohio State UniversityColumbusOhioUSA
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13
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Soni H, Ivanova J, Wilczewski H, Bailey A, Ong T, Narma A, Bunnell BE, Welch BM. Virtual conversational agents versus online forms: Patient experience and preferences for health data collection. Front Digit Health 2022; 4:954069. [PMID: 36310920 PMCID: PMC9606606 DOI: 10.3389/fdgth.2022.954069] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/16/2022] [Indexed: 11/07/2022] Open
Abstract
Objective Virtual conversational agents, or chatbots, have emerged as a novel approach to health data collection. However, research on patient perceptions of chatbots in comparison to traditional online forms is sparse. This study aimed to compare and assess the experience of completing a health assessment using a chatbot vs. an online form. Methods A counterbalanced, within-subject experimental design was used with participants recruited via Amazon Mechanical Turk (mTurk). Participants completed a standardized health assessment using a chatbot (i.e., Dokbot) and an online form (i.e., REDCap), each followed by usability and experience questionnaires. To address poor data quality and preserve integrity of mTurk responses, we employed a thorough data cleaning process informed by previous literature. Quantitative (descriptive and inferential statistics) and qualitative (thematic analysis and complex coding query) approaches were used for analysis. Results A total of 391 participants were recruited, 185 of whom were excluded, resulting in a final sample size of 206 individuals. Most participants (69.9%) preferred the chatbot over the online form. Average Net Promoter Score was higher for the chatbot (NPS = 24) than the online form (NPS = 13) at a statistically significant level. System Usability Scale scores were also higher for the chatbot (i.e. 69.7 vs. 67.7), but this difference was not statistically significant. The chatbot took longer to complete but was perceived as conversational, interactive, and intuitive. The online form received favorable comments for its familiar survey-like interface. Conclusion Our findings demonstrate that a chatbot provided superior engagement, intuitiveness, and interactivity despite increased completion time compared to online forms. Knowledge of patient preferences and barriers will inform future design and development of recommendations and best practice for chatbots for healthcare data collection.
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Affiliation(s)
- Hiral Soni
- Doxy.me Research, Doxy.me Inc., Rochester, NY, United States,Correspondence: Hiral Soni
| | - Julia Ivanova
- Doxy.me Research, Doxy.me Inc., Rochester, NY, United States
| | | | | | - Triton Ong
- Doxy.me Research, Doxy.me Inc., Rochester, NY, United States
| | - Alexa Narma
- Doxy.me Research, Doxy.me Inc., Rochester, NY, United States
| | - Brian E. Bunnell
- Doxy.me Research, Doxy.me Inc., Rochester, NY, United States,Department of Psychiatry and Behavioral Neurosciences, Innovation in Mental Health Lab, University of South Florida, Tampa, FL, United States
| | - Brandon M. Welch
- Doxy.me Research, Doxy.me Inc., Rochester, NY, United States,Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
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14
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Chavez-Yenter D, Goodman MS, Chen Y, Chu X, Bradshaw RL, Lorenz Chambers R, Chan PA, Daly BM, Flynn M, Gammon A, Hess R, Kessler C, Kohlmann WK, Mann DM, Monahan R, Peel S, Kawamoto K, Del Fiol G, Sigireddi M, Buys SS, Ginsburg O, Kaphingst KA. Association of Disparities in Family History and Family Cancer History in the Electronic Health Record With Sex, Race, Hispanic or Latino Ethnicity, and Language Preference in 2 Large US Health Care Systems. JAMA Netw Open 2022; 5:e2234574. [PMID: 36194411 PMCID: PMC9533178 DOI: 10.1001/jamanetworkopen.2022.34574] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/12/2022] [Indexed: 11/14/2022] Open
Abstract
Importance Clinical decision support (CDS) algorithms are increasingly being implemented in health care systems to identify patients for specialty care. However, systematic differences in missingness of electronic health record (EHR) data may lead to disparities in identification by CDS algorithms. Objective To examine the availability and comprehensiveness of cancer family history information (FHI) in patients' EHRs by sex, race, Hispanic or Latino ethnicity, and language preference in 2 large health care systems in 2021. Design, Setting, and Participants This retrospective EHR quality improvement study used EHR data from 2 health care systems: University of Utah Health (UHealth) and NYU Langone Health (NYULH). Participants included patients aged 25 to 60 years who had a primary care appointment in the previous 3 years. Data were collected or abstracted from the EHR from December 10, 2020, to October 31, 2021, and analyzed from June 15 to October 31, 2021. Exposures Prior collection of cancer FHI in primary care settings. Main Outcomes and Measures Availability was defined as having any FHI and any cancer FHI in the EHR and was examined at the patient level. Comprehensiveness was defined as whether a cancer family history observation in the EHR specified the type of cancer diagnosed in a family member, the relationship of the family member to the patient, and the age at onset for the family member and was examined at the observation level. Results Among 144 484 patients in the UHealth system, 53.6% were women; 74.4% were non-Hispanic or non-Latino and 67.6% were White; and 83.0% had an English language preference. Among 377 621 patients in the NYULH system, 55.3% were women; 63.2% were non-Hispanic or non-Latino, and 55.3% were White; and 89.9% had an English language preference. Patients from historically medically undeserved groups-specifically, Black vs White patients (UHealth: 17.3% [95% CI, 16.1%-18.6%] vs 42.8% [95% CI, 42.5%-43.1%]; NYULH: 24.4% [95% CI, 24.0%-24.8%] vs 33.8% [95% CI, 33.6%-34.0%]), Hispanic or Latino vs non-Hispanic or non-Latino patients (UHealth: 27.2% [95% CI, 26.5%-27.8%] vs 40.2% [95% CI, 39.9%-40.5%]; NYULH: 24.4% [95% CI, 24.1%-24.7%] vs 31.6% [95% CI, 31.4%-31.8%]), Spanish-speaking vs English-speaking patients (UHealth: 18.4% [95% CI, 17.2%-19.1%] vs 40.0% [95% CI, 39.7%-40.3%]; NYULH: 15.1% [95% CI, 14.6%-15.6%] vs 31.1% [95% CI, 30.9%-31.2%), and men vs women (UHealth: 30.8% [95% CI, 30.4%-31.2%] vs 43.0% [95% CI, 42.6%-43.3%]; NYULH: 23.1% [95% CI, 22.9%-23.3%] vs 34.9% [95% CI, 34.7%-35.1%])-had significantly lower availability and comprehensiveness of cancer FHI (P < .001). Conclusions and Relevance These findings suggest that systematic differences in the availability and comprehensiveness of FHI in the EHR may introduce informative presence bias as inputs to CDS algorithms. The observed differences may also exacerbate disparities for medically underserved groups. System-, clinician-, and patient-level efforts are needed to improve the collection of FHI.
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Affiliation(s)
- Daniel Chavez-Yenter
- Huntsman Cancer Institute, University of Utah, Salt Lake City
- Department of Communication, University of Utah, Salt Lake City
| | - Melody S. Goodman
- School of Global Public Health, New York University, New York, New York
| | - Yuyu Chen
- School of Global Public Health, New York University, New York, New York
| | - Xiangying Chu
- School of Global Public Health, New York University, New York, New York
| | - Richard L. Bradshaw
- Department of Biomedical Informatics, University of Utah, Salt Lake City
- School of Medicine, University of Utah Health, Salt Lake City, Utah
| | | | | | - Brianne M. Daly
- Huntsman Cancer Institute, University of Utah, Salt Lake City
| | - Michael Flynn
- School of Medicine, University of Utah Health, Salt Lake City, Utah
| | - Amanda Gammon
- Huntsman Cancer Institute, University of Utah, Salt Lake City
| | - Rachel Hess
- Department of Population Health Sciences, University of Utah, Salt Lake City
- Department of Internal Medicine, University of Utah, Salt Lake City
| | - Cecelia Kessler
- Huntsman Cancer Institute, University of Utah, Salt Lake City
| | | | - Devin M. Mann
- Department of Population Health, New York University Grossman School of Medicine, New York University, New York, New York
| | - Rachel Monahan
- Perlmutter Cancer Center, NYU Langone Health, New York, New York
- Department of Population Health, New York University Grossman School of Medicine, New York University, New York, New York
| | - Sara Peel
- Huntsman Cancer Institute, University of Utah, Salt Lake City
| | - 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, University of Utah, Salt Lake City
- Department of Internal Medicine, University of Utah, Salt Lake City
| | - Ophira Ginsburg
- Center for Global Health, National Cancer Institute, Rockville, Maryland
| | - Kimberly A. Kaphingst
- Huntsman Cancer Institute, University of Utah, Salt Lake City
- Department of Communication, University of Utah, Salt Lake City
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15
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Vanderwall RA, Schwartz A, Kipnis L, Skefos CM, Stokes SM, Bhulani N, Weitz M, Gelman R, Garber JE, Rana HQ. Impact of Genetic Counseling on Patient-Reported Electronic Cancer Family History Collection. J Natl Compr Canc Netw 2022; 20:898-905.e2. [PMID: 35948032 DOI: 10.6004/jnccn.2022.7022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 04/29/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Cancer family history is a vital part of cancer genetic counseling (GC) and genetic testing (GT), but increasing indications for germline cancer GT necessitate less labor-intensive models of collection. We evaluated the impact of GC on patient pedigrees generated by an electronic cancer family history questionnaire (eCFHQ). METHODS An Institutional Review Board-approved review of pedigrees collected through an eCFHQ was conducted. Paired pre-GC and post-GC pedigrees (n=1,113 each group) were analyzed independently by cancer genetic counselors for changes in patient-reported clinical history and to determine whether the pedigrees met NCCN GT criteria. Discrepancy in meeting NCCN GT criteria between pre-GC and post-GC pedigrees was the outcome variable of logistic regressions, with patient and family history characteristics as covariates. RESULTS Overall, 780 (70%) patients had cancer (affected), 869 (78%) were female, and the median age was 57 years (interquartile range, 45-66 years; range, 21-91 years). Of the 1,113 pairs of pre-GC and post-GC pedigrees analyzed, 85 (8%) were blank, 933 (84%) were not discrepant, and 95 (9%) were discrepant in meeting any NCCN GT criteria. Of the discrepant pedigrees, n=79 (83%) became eligible for testing by at least one of the NCCN GT criteria after GC. Patients with discrepant pedigrees were more likely to report no or unknown history of GT (odds ratio [OR], 4.54; 95% CI, 1.66-18.70; P=.01, and OR, 18.47; 95% CI, 5.04-88.73; P<.0001, respectively) and belonged to racially and/or ethnically underrepresented groups (OR, 1.91; 95% CI, 1.08-3.25; P=.02). CONCLUSIONS For most patients (84%), a standalone eCFHQ was sufficient to determine whether NCCN GT criteria were met. More research is needed on the performance of the eCFHQ in diverse patient populations.
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Affiliation(s)
- Rebecca A Vanderwall
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Alison Schwartz
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Lindsay Kipnis
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Catherine M Skefos
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Samantha M Stokes
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Nizar Bhulani
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute.,Harvard Medical School; and
| | - Michelle Weitz
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Rebecca Gelman
- Harvard Medical School; and.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Judy E Garber
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute.,Harvard Medical School; and
| | - Huma Q Rana
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute.,Harvard Medical School; and
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16
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Mavragani A, Frey LJ, Lamy JB, Bellcross C, Morrison H, Schiffman JD, Welch BM. Automated Clinical Practice Guideline Recommendations for Hereditary Cancer Risk Using Chatbots and Ontologies: System Description. JMIR Cancer 2022; 8:e29289. [PMID: 35099392 PMCID: PMC8845001 DOI: 10.2196/29289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/30/2021] [Accepted: 12/18/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Identifying patients at risk of hereditary cancer based on their family health history is a highly nuanced task. Frequently, patients at risk are not referred for genetic counseling as providers lack the time and training to collect and assess their family health history. Consequently, patients at risk do not receive genetic counseling and testing that they need to determine the preventive steps they should take to mitigate their risk. OBJECTIVE This study aims to automate clinical practice guideline recommendations for hereditary cancer risk based on patient family health history. METHODS We combined chatbots, web application programming interfaces, clinical practice guidelines, and ontologies into a web service-oriented system that can automate family health history collection and assessment. We used Owlready2 and Protégé to develop a lightweight, patient-centric clinical practice guideline domain ontology using hereditary cancer criteria from the American College of Medical Genetics and Genomics and the National Cancer Comprehensive Network. RESULTS The domain ontology has 758 classes, 20 object properties, 23 datatype properties, and 42 individuals and encompasses 44 cancers, 144 genes, and 113 clinical practice guideline criteria. So far, it has been used to assess >5000 family health history cases. We created 192 test cases to ensure concordance with clinical practice guidelines. The average test case completes in 4.5 (SD 1.9) seconds, the longest in 19.6 seconds, and the shortest in 2.9 seconds. CONCLUSIONS Web service-enabled, chatbot-oriented family health history collection and ontology-driven clinical practice guideline criteria risk assessment is a simple and effective method for automating hereditary cancer risk screening.
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Affiliation(s)
| | - Lewis J Frey
- Health Equity and Rural Outreach Innovation Center, Ralph H. Johnson Veterans Affairs Health Care System, Charleston, SC, United States
| | - Jean-Baptiste Lamy
- Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, F-93000, Bobigny, France
| | - Cecelia Bellcross
- Department of Human Genetics, Emory University, Atlanta, GA, United States
| | | | - Joshua D Schiffman
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Utah, Salt Lake City, UT, United States.,Family Cancer Assessment Clinic, Huntsman Cancer Institute, University of Utah, Salt Lake City, United States, UT, United States
| | - Brandon M Welch
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
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17
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Ritchie JB, Welch BM, Allen CG, Frey LJ, Morrison H, Schiffman JD, Alekseyenko AV, Dean B, Hughes Halbert C, Bellcross C. Comparison of a Cancer Family History Collection and Risk Assessment Tool - ItRunsInMyFamily - with Risk Assessment by Health-Care Professionals. Public Health Genomics 2021; 25:1-9. [PMID: 34872100 PMCID: PMC9167897 DOI: 10.1159/000520001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 09/28/2021] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION Primary care providers (PCPs) and oncologists lack time and training to appropriately identify patients at increased risk for hereditary cancer using family health history (FHx) and clinical practice guideline (CPG) criteria. We built a tool, "ItRunsInMyFamily" (ItRuns) that automates FHx collection and risk assessment using CPGs. The purpose of this study was to evaluate ItRuns by measuring the level of concordance in referral patterns for genetic counseling/testing (GC/GT) between the CPGs as applied by the tool and genetic counselors (GCs), in comparison to oncologists and PCPs. The extent to which non-GCs are discordant with CPGs is a gap that health information technology, such as ItRuns, can help close to facilitate the identification of individuals at risk for hereditary cancer. METHODS We curated 18 FHx cases and surveyed GCs and non-GCs (oncologists and PCPs) to assess concordance with ItRuns CPG criteria for referring patients for GC/GT. Percent agreement was used to describe concordance, and logistic regression to compare providers and the tool's concordance with CPG criteria. RESULTS GCs had the best overall concordance with the CPGs used in ItRuns at 82.2%, followed by oncologists with 66.0% and PCPs with 60.6%. GCs were significantly more likely to concur with CPGs (OR = 4.04, 95% CI = 3.35-4.89) than non-GCs. All providers had higher concordance with CPGs for FHx cases that met the criteria for genetic counseling/testing than for cases that did not. DISCUSSION/CONCLUSION The risk assessment provided by ItRuns was highly concordant with that of GC's, particularly for at-risk individuals. The use of such technology-based tools improves efficiency and can lead to greater numbers of at-risk individuals accessing genetic counseling, testing, and mutation-based interventions to improve health.
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Affiliation(s)
- Jordon B. Ritchie
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, U.S
| | - Brandon M. Welch
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, U.S
- Hollings Cancer Center, Medical University of South Carolina, Charleston, SC US
| | - Caitlin G. Allen
- Department of Behavioral, Social, and Health Education Sciences, Emory University, Rollins School of Public Health, Atlanta, Georgia, U.S
| | - Lewis J. Frey
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, U.S
| | - Heath Morrison
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, U.S
| | - Joshua D. Schiffman
- Oncological Sciences, Huntsman Cancer Institute at the University of Utah, Salt Lake City, UT, U.S
| | | | - Brian Dean
- Computer Science, Clemson University, Clemson, SC, U.S
| | - Chanita Hughes Halbert
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, U.S
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC US
- Hollings Cancer Center, Medical University of South Carolina, Charleston, SC US
| | - Cecelia Bellcross
- Department of Human Genetics, Emory University, Atlanta, Georgia, U.S
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18
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Xu L, Sanders L, Li K, Chow JCL. Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review. JMIR Cancer 2021; 7:e27850. [PMID: 34847056 PMCID: PMC8669585 DOI: 10.2196/27850] [Citation(s) in RCA: 104] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 07/02/2021] [Accepted: 09/18/2021] [Indexed: 01/01/2023] Open
Abstract
Background Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. Objective This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. Methods A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. Results Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. Conclusions Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.
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Affiliation(s)
- Lu Xu
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
| | - Leslie Sanders
- Department of Humanities, York University, Toronto, ON, Canada
| | - Kay Li
- Department of English, York University, Toronto, ON, Canada
| | - James C L Chow
- Department of Medical Physics, Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
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19
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Ritchie JB, Bellcross C, Allen CG, Frey L, Morrison H, Schiffman JD, Welch BM. Evaluation and comparison of hereditary Cancer guidelines in the population. Hered Cancer Clin Pract 2021; 19:31. [PMID: 34274008 PMCID: PMC8285854 DOI: 10.1186/s13053-021-00188-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/28/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Family health history (FHx) is an effective tool for identifying patients at risk of hereditary cancer. Hereditary cancer clinical practice guidelines (CPG) contain criteria used to evaluate FHx and to make recommendations for genetic consultation. Comparing different CPGs used to evaluate a common set of FHx provides insight into how well the CPGs perform, the extent of agreement across guidelines, and how well they identify patients who should consider a cancer genetic consultation. METHODS We compare the American College of Medical Genetics and Genomics (ACMG) and the National Comprehensive Cancer Networks (NCCN) (2019) CPG criteria for FHx collected by a chatbot and evaluated by ontologies and web services in a previous study. Collected FHx met criteria from seven groups: Gene Mutation, Breast and Ovarian, Li-Fraumeni syndrome (LFS), Colorectal and Endometrial, Relative Meets Criteria, ACMG Only Criteria, and NCCN Testing. CPG Criteria were coded and matched across 12 ACMG sub-guidelines and 6 NCCN sub-guidelines for comparison purposes. RESULTS The dataset contains 4915 records, of which 2221 met either ACMG or NCCN criteria and 2694 did not. There was significant overlap-1179 probands met both ACMG and NCCN criteria. The greatest similarities were for Gene Mutation and Breast and Ovarian criteria and the greatest disparity existed among Colorectal and Endometrial criteria. Only 156 positive gene mutations were reported and of the 2694 probands who did not meet criteria, 90.6% of them reported at least one cancer in their personal or family cancer history. CONCLUSION Hereditary cancer CPGs are useful for identifying patients at risk of developing cancer based on FHx. This comparison shows that with the aid of chatbots, ontologies, and web services, CPGs can be more efficiently applied to identify patients at risk of hereditary cancer. Additionally this comparison examines similarities and differences between ACMG and NCCN and shows the importance of using both guidelines when evaluating hereditary cancer risk.
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Affiliation(s)
- Jordon B Ritchie
- Medical University of South Carolina, 22 WestEdge St, Ste 200, Charleston, SC, 29403, USA.
| | | | | | - Lewis Frey
- Medical University of South Carolina, 22 WestEdge St, Ste 200, Charleston, SC, 29403, USA
| | | | | | - Brandon M Welch
- Medical University of South Carolina, 22 WestEdge St, Ste 200, Charleston, SC, 29403, USA
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20
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Modernizing family health history: achievable strategies to reduce implementation gaps. J Community Genet 2021; 12:493-496. [PMID: 34028705 DOI: 10.1007/s12687-021-00531-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 05/02/2021] [Indexed: 10/21/2022] Open
Abstract
Family health history (FHH) is a valuable yet underused healthcare tool for assessing health risks for both prevalent disorders like diabetes, cancer, and cardiovascular diseases, and for rare, monogenic disorders. Full implementation of FHH collection and analysis in healthcare could improve both primary and secondary disease prevention for individuals and, through cascade testing, make at risk family members eligible for pre-symptomatic testing and preventative interventions. In addition to risk assessment in the clinic, FHH is increasingly important for interpreting clinical genetic testing results and for research connecting health risks to genomic variation. Despite this value, diverse implementation gaps in clinical settings undermine its potential clinical value and limit the quality of connected health and genomic data. The NHGRI Family Health History Group, an open-membership, US-based group with international members, believes that integrating FHH in healthcare and research is more important than ever, and that achievable implementation advances, including education, are urgently needed to boost the pace of translational utility in genomic medicine. An inventory of implementation gaps and proposed achievable strategies to address them, representing a consensus developed in meetings from 2019-2020, is presented here. The proposed measures are diverse, interdisciplinary, and are guided by experience and ongoing implementation and research efforts.
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