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  • 1
    In: The Lancet, Elsevier BV, Vol. 401, No. 10387 ( 2023-05), p. 1499-1507
    Type of Medium: Online Resource
    ISSN: 0140-6736
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    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2067452-1
    detail.hit.zdb_id: 3306-6
    detail.hit.zdb_id: 1476593-7
    SSG: 5,21
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  • 2
    Online Resource
    Online Resource
    American Public Health Association ; 2015
    In:  American Journal of Public Health Vol. 105, No. S3 ( 2015-07), p. S369-S371
    In: American Journal of Public Health, American Public Health Association, Vol. 105, No. S3 ( 2015-07), p. S369-S371
    Type of Medium: Online Resource
    ISSN: 0090-0036 , 1541-0048
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    Language: English
    Publisher: American Public Health Association
    Publication Date: 2015
    detail.hit.zdb_id: 2054583-6
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  • 3
    Online Resource
    Online Resource
    American Public Health Association ; 2016
    In:  American Journal of Public Health Vol. 106, No. 4 ( 2016-04), p. 637-639
    In: American Journal of Public Health, American Public Health Association, Vol. 106, No. 4 ( 2016-04), p. 637-639
    Type of Medium: Online Resource
    ISSN: 0090-0036 , 1541-0048
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    Language: English
    Publisher: American Public Health Association
    Publication Date: 2016
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  • 4
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 15_suppl ( 2019-05-20), p. e18067-e18067
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 15_suppl ( 2019-05-20), p. e18067-e18067
    Abstract: e18067 Background: Factors affecting cancer treatment may include evidence for effectiveness, cost, and preference. These influences can lead to treatment variation across institutions and populations. Decision-support systems have been proposed as tools to reduce variation. This study quantified concordance between treatment provided by oncologists in China and therapeutic options presented by a decision-support tool. Methods: We identified and analyzed concordance studies in nine unique institutions located in seven provinces in China, published in 2017-2018 using Watson for Oncology (WFO), a clinical decision-support tool. Published rates of concordance were compared by cancer type and institution. Results: Concordance of all combined cases was 59% (2012/3388). Concordance rates varied by cancer type and institution (Table). Concordance rates were highest for ovarian (96%), rectal (94%) and breast (89%) cancers but lowest in gastric (12%), ovarian (43%) and breast (55%) cancers. Conclusions: Concordance between treatments and therapeutic options from an oncology decision-support tool varied significantly across cancer types and institutions in China, suggesting significant practice variation. Without established guidelines for treatment, clinical decisions may be influenced by preferences and local factors. Future studies are needed to identify reasons for variation and improve adherence to regional evidence-based guidelines. [Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
    detail.hit.zdb_id: 2005181-5
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  • 5
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 35, No. 15_suppl ( 2017-05-20), p. e18204-e18204
    Abstract: e18204 Background: IBM Watson for Oncology (WFO) is a Memorial Sloan Kettering-trained cognitive computing system that provides oncologists with evidence-based treatment options for cancer. Treatments are presented in three categories: “Recommended”, “For Consideration” and “Not Recommended”. We examined the concordance of treatment options between WFO and the tumor board from Gachon University Gil Medical Centre (GMC), Incheon, South Korea. GMC is an urban center that cares for 50,000 cancer patients annually. Methods: We enrolled 340 patients with stage II, III and IV colon cancer and 185 with chemotherapy-naïve advanced gastric cancer, all treated between 2012 and 2016. Cases were processed using WFO, and the output was compared to blinded tumor board recommendations. Treatment options were considered concordant when the GMC recommendation was included in the “Recommended” or “For Consideration” categories. Results: Treatment recommendations were concordant in 248 (73%) of the 340 evaluated colon cancer cases. Of 250 patients treated in the adjuvant setting, 212 (85%) were concordant. Of 90 patients with metastatic disease, 36 (40%) were concordant. Treatment recommendations were concordant in 90 (49%) of 185 chemotherapy-naïve gastric cancer patients. Low concordance rates in gastric cancer were explained by two observations: (1) The trastzumab/FOLFOX regimen is not covered by the Korean National Health Insurance System, and (2) A regimen known as S-1 (tegafur, gimeracil, and oteracil) plus cisplatin is routinely used in Korea and is not used in the U.S. Conclusions: Treatment options suggested by WFO were concordant with the therapeutic decisions of GMC in the large majority of colon cancer patients treated in the adjuvant setting. Lower degrees of concordance were seen in patients with metastatic colon and gastric cancer, reflecting differences in practice patterns between the United States, where WFO was trained, and GMC, in Korea. Geography-specific customization is available in WFO and should enable physicians and patients to benefit from WFO worldwide. WFO's ability to learn from gastric cancer cases in a part of the world with increased incidence may reveal insights that are applicable elsewhere.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2017
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  • 6
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 35, No. 15_suppl ( 2017-05-20), p. 6501-6501
    Abstract: 6501 Background: IBM Watson for Clinical Trial Matching (CTM) is a cognitive computing solution that uses natural language processing (NLP) to help increase the efficiency and accuracy of the clinical trial matching process. This solution helps providers locate suitable protocols for their patients by reading the trial criteria and matching it to the structured and unstructured patient characteristics when integrated with the Electronic Medical Record (EMR). It is also designed to determine which sites have the most viable patient population and identify inclusion and exclusion criteria that limit enrollment. Methods: This project was a collaboration among Highlands Oncology Group (HOG), Novartis and IBM Watson Health to explore the use of CTM in a community oncology practice. HOG is in Northeast Arkansas and has 15 physicians and 310 staff members working across 3 sites. During the 16-week pilot period, data from 2,620 visits by lung and breast cancer patients were processed by the CTM system. Using NLP capabilities, CTM read the clinical trial protocols provided by Novartis, and evaluated the patient data against the protocols’ inclusion and exclusion criteria. Watson excluded ineligible patients, determined those that needed further screening, and assisted in that process. Feedback on the user experience was also obtained. Results: In an initial pre-screening test, the HOG clinical trial coordinator (CTC) took 1 hour and 50 minutes to process 90 patients against 3 breast cancer protocols. Conversely, when the CTM screening solution was used, it took 24 minutes. This represents a significant reduction in time of 86 minutes or 78%. Watson excluded 94% of the patients automatically, providing criteria level evidence regarding the reason for exclusion, thus reducing the screening workload dramatically. Conclusions: IBM Watson CTM can help expedite the screening of patient charts for clinical trial eligibility and therefore may also help determine the feasibility of protocols to optimize site selection and enable higher and more efficient trial accruals.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2017
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  • 7
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 15_suppl ( 2019-05-20), p. 10532-10532
    Abstract: 10532 Background: Evidence-based medicine (EBM) requires applying literature evidence to inform practice. Students from Taipei Medical University Hospital, trained in EBM concepts, participated in a preliminary study using Watson for Oncology (WfO), an evidence-based decision-support system to enhance the EBM skills of medical students. Methods: A class of 50 medical students compared traditional search methods (TSM) and WfO in a workshop divided into 2 sequential sessions on colon and lung cancer, respectively. All students were trained on WfO, and 2 groups of 25 students each were randomly assigned to either TSM or WfO in the first session. Those groups were then assigned to the alternate approach in the second session. Students completed a profile that included their clinical experience with each cancer type. Students used either WfO or TSM to help answer a series of questions related to colon or lung cancer. Students then completed a survey of attitudes towards the technology, followed by a constructed-response learning assessment without the aid of TSM or WfO. Assessments were scored and results compared using a Mann-Whitney U Test; outcomes at two different experience levels, based on student profiles, were compared using a Kruskal-Wallis test. Results: In this preliminary study, more than 70% of students reported limited clinical experience with either cancer. On the colon cancer assessment, students in the WfO group performed significantly better than the TSM group ( p = 0.0001); there was no significant difference detected for lung cancer. Students with more clinical experience felt that TSM was easier to learn than WfO ( p= 0.005); students with less experience felt that WfO was clearer and more understandable than TSM ( p= 0.002). Conclusions: These preliminary results are consistent with better learning outcomes for students using WfO in the colon cancer module. Students with more clinic experience reported that TSM was easier to learn than WfO, however it is unknown if this might be due to a potentially greater familiarity with TSM in this more experienced group. More studies are needed to determine what features, if any, of WfO can facilitate EBM approaches in oncology education.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
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  • 8
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  Journal of Clinical Oncology Vol. 38, No. 15_suppl ( 2020-05-20), p. 534-534
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. 534-534
    Abstract: 534 Background: Adjuvant treatment after breast conserving surgery (BCS) has been shown to improve outcomes, but the degree of uptake varies considerably. We sought to examine factors associated with post-BCS receipt of and time to treatment (TTT) for adjuvant radiation therapy (ART), cytotoxic chemotherapy (ACT) and endocrine therapy (AET) among women with breast cancer. Methods: IBM MarketScan claims data were used to select women diagnosed with non-metastatic invasive breast cancer from 01/01/2012 to 03/31/2018, who received primary BCS without any neoadjuvant therapy, and who had continuous insurance eligibility 60 days post-BCS. Logistic and quantile regressions were used to identify factors associated with receipt of adjuvant therapy (ART, ACT, AET) and median TTT in days for ART (rTTT), ACT (cTTT), and AET (eTTT), respectively, after adjustment for covariates including age, year, region, insurance plan type, comorbidities, and a vector of ZIP3-level measures (e.g., community race/ethnicity-density, education level) from the 2019 Area Health Resource Files. Results: 36,270 patients were identified: 11,996 (33%) received ART only, 4,837 (13%) received ACT only, 3,458 (10 %) received AET only, 5,752 (16%) received both ART and AET, and 9,909 (27%) received no adjuvant therapy within 6 months of BCS. (318) 1% of patients received combinations of either ART, AET or ACT. Relative to having no adjuvant therapy, patients 〉 80 years were significantly less likely to receive ART only (relative risk ratio [RRR] 0.65), ACT only (RRR 0.05), or combination ART/AET (RRR 0.66) but more likely to receive AET alone (RRR 3.61) (all p 〈 .001). Patients from communities with high proportions of Black (RRR 0.14), Asian (RRR 0.13), or Hispanic (RRR 0.45) residents were significantly less likely to receive combination ART and AET (all p 〈 .001). Having HIV/AIDS (+11 days; p = .01) and residing in highly concentrated Black (+8.5 days; p = .01) and Asian (+12.2 days; p = .04) communities were associated with longer rTTT. Longer cTTT was associated with having comorbidities of cerebrovascular disease (+6.0 days; p 〈 .001), moderate to severe liver disease (+12.3 days; p 〈 .001) and residing in high-density Asian communities (+18.0 days; p 〈 .001). Shorter eTTT (-11.4 days; p = .06) and cTTT (-14.8 days; p 〈 .001) was observed in patients with comorbidities of dementia. Conclusions: Results from this cohort of privately insured patients demonstrate disparities in receipt of post-BCS adjuvant radiation and systemic therapy along multiple demographic dimensions and expose opportunities to promote timely receipt of care.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 9
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. e14070-e14070
    Abstract: e14070 Background: Watson for Oncology (WfO) is an artificial intelligence-based clinical decision-support system that offers potential therapeutic options to cancer-treating physicians. We reviewed studies of concordance between therapeutic options offered by WfO and treatment decisions made by individual clinicians (IC) and multidisciplinary tumor boards (MTB) in practice in gynecological cancers. Methods: We searched PubMed and an internal database to identify peer-reviewed WfO concordance studies of gynecological cancers published between 01/01/2015 and 06/30/2019. Concordance was defined as agreement between therapeutic options recommended or offered for consideration by WfO and treatment decisions made by IC or MTB. Mean concordance was calculated as a weighted average based on the number of patients per study. Statistical significance was evaluated by z-test of two proportions. Results: Our search identified 5 retrospective studies with 635 patients with cervical and ovarian cancers in China and Thailand; 4 compared WfO to MTB and 1 to IC. Overall WfO concordance with MTB and IC for both cancers was 77.2% (SD 11.6%). The concordance between MTB and WfO in cervical and ovarian cancers was 80.5% and 86.2%, respectively ( P = .21); IC concordance with WfO in cervical and ovarian cancers was 65.2% and 73.2%, respectively ( P = .18). MTB concordance with WfO for both cancers combined was 81.5%, significantly higher than the 67.9% IC concordance with WfO for both cancers ( P = .01). Conclusions: Studies of cervical and ovarian cancers demonstrated a statistically significantly higher concordance of MTB and WfO than IC and WFO, suggesting a role for WfO in supporting treatment-decision making in gynecological cancers that aligns with decisions made by MTB. Larger prospective studies are needed to evaluate the technical performance, usability, workflow integration, and clinical impact of WfO in gynecological cancers.[Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    RVK:
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
    detail.hit.zdb_id: 2005181-5
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  • 10
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. e14114-e14114
    Abstract: e14114 Background: Watson for Oncology (WfO) is an artificial intelligence-based clinical decision-support system which provides therapeutic options and associated scientific evidence to cancer-treating physicians. Oncologists at Bumrungrad International Hospital (BIH) have used WfO since 2015. We examined the association between concordance of WfO therapeutic options and BIH treatment decisions with short-term clinical outcomes for lung cancer patients. Methods: This study included lung cancer patients seen at BIH for treatment and follow-up care and for whom WfO was used from 2015 to 2018. Charts were reviewed for concordance with WfO, documentation of disease progression, response to treatment, and survival. We evaluated concordance between oncologists’ treatments and therapeutic options listed as “recommended” by WfO. We evaluated association between WfO concordance and partial or complete response rates over a 24-month period by comparison of proportions with odds ratio. Progression-free survival (PFS, time from diagnosis until progression or death) was evaluated by Kaplan-Meier log-rank test. Results: Seventy-nine lung cancer patients were included. We identified a trend towards higher response rates in concordant cases (59.2%, N = 32), as compared to discordant (48.0%, N = 12), with an odds ratio of 1.56 (see table). There was not a significant difference in PFS between concordant and discordant cohorts. Conclusions: In this small-cohort, retrospective study, lung cancer patients receiving treatments that are concordant with WfO recommended therapeutic options trended towards higher response rates than patients with discordant treatments. Use of a clinical decision-support system may help support cancer-treating physicians in delivering best practice and evidence-based care that may improve short-term outcomes. Prospective studies with larger samples and other cancer types are underway. [Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
    detail.hit.zdb_id: 2005181-5
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