Best of ASCO - 2014 Annual Meeting

 

Welcome

Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.

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

Sub-category:
Quality of Care/Quality Improvement

Category:
Health Services Research, Clinical Informatics, and Quality of Care

Meeting:
2019 ASCO Annual Meeting

Abstract No:
e18303

Citation:
J Clin Oncol 37, 2019 (suppl; abstr e18303)

Author(s): Zuochao Wang, Zhonghe Yu, Xuejing Zhang; Oncology Department, Beijing Chaoyang Integrative Medicine Emergency Medical Center, Beijing, China; Neurology Department, University of Pittsburgh School of Medicine, Pittsburgh, PA

Abstract Disclosures

Abstract:

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.

 
Other Abstracts in this Sub-Category:

 

1. Effect of montelukast and rupatadine on rituximab infusion time, rate, severity of reactions, and cost of administration.

Meeting: 2019 ASCO Annual Meeting Abstract No: 6500 First Author: Rouslan Kotchetkov
Category: Health Services Research, Clinical Informatics, and Quality of Care - Quality of Care/Quality Improvement

 

2. Comparison of normal saline versus heparin flush solutions for maintaining patency of central venous catheter in cancer patients.

Meeting: 2019 ASCO Annual Meeting Abstract No: 6501 First Author: Amy Pai
Category: Health Services Research, Clinical Informatics, and Quality of Care - Quality of Care/Quality Improvement

 

3. Preventing excess narcotic prescriptions in MIS urologic oncology discharges (PENN): A prospective cohort quality improvement initiative.

Meeting: 2019 ASCO Annual Meeting Abstract No: 6502 First Author: Ruchika Talwar
Category: Health Services Research, Clinical Informatics, and Quality of Care - Quality of Care/Quality Improvement

 

More...