scholarly journals Comparison of an oncology clinical decision-support system’s recommendations with actual treatment decisions

2021 ◽  
Author(s):  
Suthida Suwanvecho ◽  
Harit Suwanrusme ◽  
Tanawat Jirakulaporn ◽  
Surasit Issarachai ◽  
Nimit Taechakraichana ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Treatment Options ◽  
Stage Iv ◽  
Clinical Decision ◽  
Treatment Decisions ◽  
Therapeutic Options ◽  
Cancer Type ◽  
The Us ◽  
Rectal Cancers

Abstract Objective IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice. Methods This study included patients treated between 1/2017 and 7/2018 for breast, colon, lung, and rectal cancers at Bumrungrad International Hospital (BIH), Thailand. Treatments selected by clinicians were paired with therapeutic options presented by the CDSS and coded to mask the origin of options presented. The panel rated the acceptability of each treatment in the pair by consensus, with acceptability defined as compliant with BIH’s institutional practices. Descriptive statistics characterized the study population and treatment-decision evaluations by cancer type and stage. Results Nearly 60% (187) of 313 treatment pairs for breast, lung, colon, and rectal cancers were identical or equally acceptable, with 70% (219) of WfO therapeutic options identical to, or acceptable alternatives to, BIH therapy. In 30% of cases (94), 1 or both treatment options were rated as unacceptable. Of 32 cases where both WfO and BIH options were acceptable, WfO was preferred in 18 cases and BIH in 14 cases. Colorectal cancers exhibited the highest proportion of identical or equally acceptable treatments; stage IV cancers demonstrated the lowest. Conclusion This study demonstrates that a system designed in the US to support, rather than replace, cancer-treating clinicians provides therapeutic options which are generally consistent with recommendations from oncologists outside the US.

A retrospective evaluation of treatment decision making for thyroid cancer using clinical decision support in Brazil.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19193-e19193
Author(s):  
Sergio Juacaba ◽  
Hermano Alexandre Lima Rocha ◽  
Pedro Meneleu ◽  
Rezzan Hekmat ◽  
Winnie Felix ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Decision Support ◽  
Cancer Patients ◽  
Clinical Decision Support ◽  
Expert Panel ◽  
Treatment Options ◽  
Clinical Decision ◽  
Treatment Decisions ◽  
Therapeutic Options ◽  
Recommended Treatment

e19193 Background: Artificial intelligence-driven clinical decision-support systems such as Watson for Oncology (WfO) may aid cancer care in economically challenged health systems. Evidence of the applicability of such tools in resource-constrained settings is limited. The study objective was to evaluate treatment agreement between physician-prescribed therapy and WfO recommended treatment options in thyroid cancer in Brazil. An in-depth evaluation of discordant cases by a blinded expert panel of medical oncologists and cancer surgeons was performed to identify preferred therapies and predictors of discordance. Methods: Thyroid cancer patients treated at the Instituto do Câncer do Ceará, Brazil from July 2018 to June 2019, but not processed in WfO, were selected for entry into WfO in January 2020. Blinded to treatment-plan source (i.e., WfO or historical), the expert panel reviewed all WfO therapeutic options and historical physician-prescribed treatment plans for discordant cases and selected their preferred treatment options. Clinical and demographic characteristics were analyzed using logistic regression. Results: Thyroid cancer patients (n = 83) evaluated for concordance between WfO therapeutic options and historical treatments were mostly female (91%) and between the ages of 18 - 78 years (mean 47.7). Concordance between historical physician-prescribed treatment decisions and WfO was 73.5% (61/83). Demographics and clinical characteristics associated with discordance are shown in Table. For all discordant cases (n = 22), preferred treatment decisions, as determined by the expert panel, were in agreement with WfO. Conclusions: High concordance between WfO recommended treatment options and historical treatment decisions for thyroid cancer was observed at Instituto do Câncer do Ceará. For discordant cases, a blinded expert panel agreed with WfO recommended treatment options in all cases, demonstrating there may be a role for decision support in aiding individual oncologists to make best-practice and evidence-informed treatment decisions. [Table: see text]

scholarly journals Concordance between a clinical decision-support system and treatments selected by clinicians as a function of cancer type or stage.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 95-95
Author(s):  
Suthida Suwanvecho ◽  
Harit Suwanrusme ◽  
Surasit Issarachai ◽  
Tanawat Jirakulaporn ◽  
Nimit Taechakraichana ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Colon Cancer ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Treatment Options ◽  
Stage Iv ◽  
Clinical Decision ◽  
Cancer Type ◽  
Colon And Rectal Cancer ◽  
Cancer Types

95 Background: Watson for Oncology (WFO) is an artificial intelligence (AI) based clinical decision-support tool trained by Memorial Sloan Kettering. This retrospective observational study of breast, lung, colon and rectal cancer examined the concordance of treatment options provided by WFO to treatments selected by clinicians at Bumrungrad International Hospital (BIH) as a function of stage or cancer type. Methods: Concordance between WFO treatment options and treatments selected by BIH clinicians (WFO-BIH concordance) was defined as identical or equally acceptable treatments, as determined by a panel of experts blinded to the source of treatment. Relationships between stage or type of cancer and WFO-BIH concordant treatments were evaluated by Chi-squared analysis. Results: Analysis revealed a statistically significant association ( P = 0.02) between cancer stage and concordance. For all 4 cancer types combined, stages I-III demonstrated higher concordance than stage IV. A highly significant association ( P < 0.001) between concordance and cancer type was identified. Colon cancer demonstrated the highest concordance, followed by rectal, lung and breast cancer. Reasons for discordance, when given, related to oncologist or patient preferences, and treatment availability. Conclusions: BIH clinicians tended to agree more with WFO therapeutic options for stage I-III cancers and colon cancer in general, as compared to relatively less agreement for stage IV cancers and breast cancer in general, suggesting the need to understand reasons for discordance among all cancer types and stages. An AI tool, trained by experts in the U.S., provides treatment options consistent with some therapies selected in international settings, but preferences and treatment availability may affect choices made in practice. [Table: see text]

A Targeted Review of Breast Cancer Studies of Concordance for an Internationally-Implemented Artificially Intelligent Clinical Decision-Support System

2020 ◽  
Author(s):  
Yull Edwin Arriaga ◽  
Rezzan Hekmat ◽  
Karlis Draulis ◽  
Suwei Wang ◽  
Anita M Preininger ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Treatment Decisions ◽  
Therapeutic Options ◽  
Weighted Mean ◽  
Tumor Boards

Abstract Background: Breast cancer has the highest incidence and is the leading cause of cancer-related mortality among women worldwide. IBM Watson® for Oncology (WfO), an artificial intelligence-based clinical decision-support system, provides therapeutic options for consideration to cancer-treating physicians. We conducted a targeted review of studies evaluating concordance of therapeutic options offered by the system with treatment decisions by practicing clinicians in breast cancer. Methods: PubMed, EMBASE, Cochrane, trial registers, conference abstracts, and an internal publication database were searched to identify studies evaluating the concordance of system-generated therapeutic options with treatment decisions by individual clinicians and multidisciplinary tumor boards for breast cancer patients reported in peer-reviewed abstracts or papers published in English between 01/01/2015 and 11/15/2019.Results: Ten breast cancer concordance studies (4703 patients) that met the inclusion criteria were identified and analyzed; the identified studies were from China, India, and Thailand. The weighted mean concordance for all studies was 67.4% (SD 16.0%, range 55.0% - 98.0%). The weighted mean concordance of the system with multidisciplinary tumor boards was 88.2%, (SD 9.7%, range 76.5% - 98.0%), which was substantially higher than concordance between the system and individual clinicians (61.5% , SD 10.1%, range 55.0% -76.0%).Conclusion: Concordance between system-generated therapeutic options and treatment decisions of multidisciplinary tumor boards or individual clinicians for breast cancer demonstrated overall agreement between the system and decisions of practicing cancer-treating physicians in China, India and Thailand. As multidisciplinary tumor boards may lead to higher quality clinical decision-making compared to those of individual clinicians in practice, the relatively higher concordance of the system with multidisciplinary tumor boards suggests a role for clinical decision support to inform clinicians of evidence-informed treatment options.

A blinded evaluation of a clinical decision-support system at a regional cancer care center.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6553-6553
Author(s):  
Suthida Suwanvecho ◽  
Edward H Shortliffe ◽  
Harit Suwanrusme ◽  
Surasit Issarachai ◽  
Tanawat Jirakulaporn ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Expert Panel ◽  
Care Center ◽  
Clinical Decision ◽  
Treatment Decisions ◽  
Therapeutic Options ◽  
Colon And Rectal Cancer ◽  
Significant Difference ◽  
Made In

6553 Background: Clinical decision-support systems (CDSS) such as Watson for Oncology (WFO) may reduce treatment variation in oncology, provided options offered by the system are at least as acceptable as expert, evidence-based options. Deviation from expert consensus in practice is not well documented. In this blinded study, WFO therapeutic options and treatment decisions made by individual oncologists at Bumrungrad International Hospital (BIH) were evaluated by expert panel. Methods: Treatments selected by BIH that were labeled as either “for consideration” or “not recommended” by WFO were evaluated by a panel of 3 oncologists in 2018. The panel evaluated WFO options and previous BIH treatments for prospective cases from 2016-2018, blinded to the source of treatment option. Consensus of panel rated treatment pairs as: identical; both acceptable and roughly equivalent; both acceptable, but one preferred; one is acceptable and the other, unacceptable; neither is acceptable. The results of 321 treatment choices for breast, lung, colon and rectal cancers were analyzed, and McNemar’s test, a modified pairwise chi-square, was applied to identify differences between BIH and WFO. Results: 71% of both BIH and WFO treatments across all 4 cancer types were considered acceptable or identical by the panel. In 18 cases (5.6%), WFO treatments were preferred; in 14 cases (4.4%), BIH cases were preferred. Unacceptable treatments by either BIH or WFO were identified in 15% and 23% of treatments, respectively. Statistical analysis of treatment pairs revealed no significant difference between BIH and WFO treatments for breast, colon and rectal cancer. Treatment for lung cancer differed significantly ( p = 0.004); in 6% of cases, WFO was unacceptable and BIH acceptable; in 1% of cases, BIH was unacceptable and WfO was acceptable. Conclusions: This study is one of the first to compare therapeutic options from CDSS to treatment decisions made in practice, evaluated in a blinded fashion by an expert panel. 71% of treatments suggested by WFO CDSS were as acceptable as those selected by clinicians at the point of care, and some were considered superior. Decisions made in practice were unacceptable to the panel in 15% of cases, suggesting a role for CDSS.

Artificial intelligence-based clinical decision-support system improves cancer treatment and patient satisfaction.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18303-e18303
Author(s):  
Zuochao Wang ◽  
Zhonghe Yu ◽  
Xuejing Zhang
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Treatment Options ◽  
Clinical Decision ◽  
Diagnostic Model ◽  
Consultation System

e18303 Background: Traditional diagnostic model for cancer heavily relies on physicians and their teams’ knowledge. However, under this diagnostic model, patients’ source of information is quite limited. Cancer patients usually fill with negative emotion. Lack of knowledge to the disease and treatment options further leads to less confidence to their treatment outcome. Methods: In order to improve their faith in getting proper treatment and the hope for surviving the deadly disease, we has introduced an artificial intelligence based clinical decision-support system, the Watson for Oncology (WFO), since May-2018. WFO is developed by IBM, it assesses information from a patient’s medical record, evaluates medical evidence, and displays potential treatment options. Our oncologist can then apply their own expertise to identify the most appropriate treatment options. We have generated a new 7-step consultation system with the help of WFO. That include 1: introduce the WFO to patients, 2: patients express their demands and expectations, 3: the oncologist presents patient’s medical condition, 4: discussion with other members in the consultation team, 5: input patients’ information into WFO system and review treatment options, 6: discuss and finalize treatment options with patients, 7: feedbacks form patients after consultation. 70 patients who were hospitalized from May-2018 to Dec-2018 were divided into two groups, 50 patients volunteered to be assigned to the new 7-step consultation system and 20 patients stayed with the traditional diagnostic method to find them treatment options. All patients were followed up by questionnaire. Results: The results showed that patients in the 7-step consultation group presented significantly higher satisfaction rate towards treatment options, confidence level to their health care workers, and willingness to follow the treatment option when compared to patients in the traditional diagnostic group. Conclusions: The WFO assisted 7-step consultation system not only provides a more efficient way to find treatment options, but also improves patients’ understanding to their disease and possible side effects towards the treatment. Most importantly, patients build stronger confidence with their health care team and are willing to believe they will benefit from the treatment plans.

The value of pathways on drug costs.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 327-327
Author(s):  
Valerie Pracilio Csik ◽  
Michael J. Ramirez ◽  
Adam F Binder ◽  
Nathan Handley
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Weighted Average ◽  
Clinical Decision ◽  
Treatment Decisions ◽  
Drug Costs ◽  
Cancer Center ◽  
Significant Variable ◽  
Oncology Care ◽  
The Impact

327 Background: Oncology care represents a significant portion of US healthcare spending. Cost of Part B drugs has increased at a rate 5.7x that of overall Medicare spending. As a participant in the Oncology Care Model, drug costs represent a majority of our total costs. Pathways are a clinical decision-support tool that use evidence-based care maps accounting for efficacy, toxicity and cost. Our NCI-designated cancer center implemented pathways in July 2018 to reduce care variation and decrease costs. Methods: We reviewed costs related to pathway utilization over a two year period, analyzing differences in total annual drug cost for patients in three categories: On-Pathway (aligned with pathway recommendation), Off-Pathway (not aligned with recommendation), and No Pathway (not used). Per Member Per Month (PMPM) costs were calculated and a weighted average applied to account for changes in annual drug costs. Results: PMPM drug costs decreased -8% in year 1 (FY19) and -4% in year 2 (FY20) when pathways were used (On- and Off-Pathway). When pathways were followed (On-Pathway) in making treatment decisions, the drug costs were 11% lower than when pathways were not used. The annual impact on drug costs when pathways were used amounted to $2.45 million in year 1 and $1.77 million in year 2 (Table). Conclusions: Pathway use reduced drug costs, a significant variable in oncology value-based care models. This finding highlights the value of clinical decision support tools in reducing care variability, a known contributor to health care costs, in making treatment decisions. Further assessment is needed to determine if these results are similar at other cancer centers to fully realize the impact of pathways on drug costs.[Table: see text]

A review of gynecological cancers studies of concordance with individual clinicians or multidisciplinary tumor boards for an artificial intelligence-based clinical decision-support system.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14070-e14070
Author(s):  
Yull Edwin Arriaga ◽  
Rezzan Hekmat ◽  
Karlis Draulis ◽  
Suwei Wang ◽  
Anita M Preininger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Therapeutic Options ◽  
Gynecological Cancers ◽  
Ovarian Cancers ◽  
Tumor Boards

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]

scholarly journals Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial

2018 ◽  
Vol 25 (9) ◽  
pp. 1137-1146 ◽  
Author(s):  
JoAnn M Sperl-Hillen ◽  
A Lauren Crain ◽  
Karen L Margolis ◽  
Heidi L Ekstrom ◽  
Deepika Appana ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Treatment Options ◽  
Clinical Decision ◽  
Clinic Visit ◽  
Rate Of Change ◽  
Annual Rate ◽  
Primary Care Visit ◽  
Intention To Treat

Abstract Objective To test the hypothesis that use of a clinical decision support (CDS) system in a primary care setting can reduce cardiovascular (CV) risk in patients. Materials and Methods Twenty primary care clinics were randomly assigned to usual care (UC) or CDS. For CDS clinic patients identified algorithmically with high CV risk, rooming staff were prompted by the electronic health record (EHR) to print CDS that identified evidence-based treatment options for lipid, blood pressure, weight, tobacco, or aspirin management and prioritized them based on potential benefit to the patient. The intention-to-treat analysis included 7914 adults who met high CV risk criteria at an index clinic visit and had at least one post-index visit, accounted for clustering, and assessed impact on predicted annual rate of change in 10-year CV risk over a 14-month period. Results The CDS was printed at 75% of targeted visits, and providers reported 85% to 98% satisfaction with various aspects of the intervention. Predicted annual rate of change in absolute 10-year CV risk was significantly better in CDS clinics than in UC clinics (-0.59% vs. +1.66%, −2.24%; P &lt; .001), with difference in 10-year CV risk at 12 months post-index favoring the CDS group (UC 24.4%, CDS 22.5%, P &lt; .03). Discussion Deploying to both patients and providers within primary care visit workflow and limiting CDS display and print burden to two mouse clicks by rooming staff contributed to high CDS use rates and high provider satisfaction. Conclusion This EHR-integrated, web-based outpatient CDS system significantly improved 10-year CV risk trajectory in targeted adults.

scholarly journals Contributions from the 2016 Literature on Clinical Decision Support

2017 ◽  
Vol 26 (01) ◽  
pp. 133-137
Author(s):  
V. Koutkias ◽  
J. Bouaud
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Healthcare Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Editorial Team ◽  
Bibliographic Databases ◽  
Semantic Web Technologies ◽  
The Us

Summary Objectives: To summarize recent research and select the best papers published in 2016 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and section editor evaluation. Results: Among the 1,145 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper describes machine learning models used to predict breast cancer multidisciplinary team decisions and compares them with two predictors based on guideline knowledge. The second paper introduces a linked-data approach for publication, discovery, and interoperability of CDSSs. The third paper assessed the variation in high-priority drug-drug interaction (DDI) alerts across 14 Electronic Health Record systems, operating in different institutions in the US. The fourth paper proposes a generic framework for modeling multiple concurrent guidelines and detecting their recommendation interactions using semantic web technologies. Conclusions: The process of identifying and selecting best papers in the domain of CDSSs demonstrated that the research in this field is very active concerning diverse dimensions, such as the types of CDSSs, e.g. guideline-based, machine-learning-based, knowledge-fusion-based, etc., and addresses challenging areas, such as the concurrent application of multiple guidelines for comorbid patients, the resolution of interoperability issues, and the evaluation of CDSSs. Nevertheless, this process also showed that CDSSs are not yet fully part of the digitalized healthcare ecosystem. Many challenges remain to be faced with regard to the evidence of their output, the dissemination of their technologies, as well as their adoption for better and safer healthcare delivery.

scholarly journals Evaluating the Perceived Utility of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment Using a Simulation Centre

2021 ◽  
Author(s):  
Myriam Tanguay-Sela ◽  
David Benrimoh ◽  
Christina Popescu ◽  
Tamara Perez ◽  
Colleen Rollins ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Treatment Options ◽  
Clinical Decision ◽  
Perceived Utility ◽  
Clinical Interactions

AbstractAifred is a clinical decision support system (CDSS) that uses artificial intelligence to assist physicians in selecting treatments for major depressive disorder (MDD) by providing probabilities of remission for different treatment options based on patient characteristics. We evaluated the utility of the CDSS as perceived by physicians participating in simulated clinical interactions. Twenty psychiatry and family medicine staff and residents completed a study in which each physician had three 10-minute clinical interactions with standardized patients portraying mild, moderate, and severe episodes of MDD. During these scenarios, physicians were given access to the CDSS, which they could use in their treatment decisions. The perceived utility of the CDSS was assessed through self-report questionnaires, scenario observations, and interviews. 60% of physicians perceived the CDSS to be a useful tool in their treatment-selection process, with family physicians perceiving the greatest utility. Moreover, 50% of physicians would use the tool for all patients with depression, with an additional 35% noting they would reserve the tool for more severe or treatment-resistant patients. Furthermore, clinicians found the tool to be useful in discussing treatment options with patients. The efficacy of this CDSS and its potential to improve treatment outcomes must be further evaluated in clinical trials.

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