Boyle, Venessa L.

Background: Mayo Clinic’s Care Hotel is a virtual hybrid care model which allows postoperative patients to recover in a comfortable environment after a low-risk procedure. Hospitals need to understand the key patient factors that promote acceptance of the Care Hotel if they are to benefit from this innovative care model. This study aims to identify factors that can predict whether a patient will stay at Care Hotel. Materials and methods: This retrospective chart review of 1065 patients was conducted between 23 July 2020, and 31 December 2021. Variables examined included patient age, sex, race, ethnicity, Charlson comorbidity index, distance patient travelled to hospital, length of surgery, day of the week of surgery, and surgical service. Associations of patient and surgery characteristics with the primary outcome of staying at the Care Hotel were assessed using unadjusted and multivariable logistic regression models. Results: Of the 1065 patients who met criteria for admission to the Care Hotel during the study period, 717 (67.3%) chose to stay at the Care Hotel while 328 (32.7%) choose to be admitted to the hospital. In multivariable analysis, there was a significant association between surgical service and staying at the Care Hotel (P<0.001). Specifically, there was a higher likelihood of staying at the Care Hotel for patients from Neurosurgery [odds rato (OR)=1.86, P=0.004], Otorhinolaryngology (OR=2.70, P<0.001), and General Surgery (OR=2.75, P=0.002). Additionally, there was a higher likelihood of staying at the Care Hotel with distance travelled over 110 miles [OR (per each doubling)=1.10, P=0.007]. Conclusion: When developing a post-surgical care model for patients following outpatient procedures, the referring surgical service is a primary factor to consider in order to ensure patient acceptance, along with patient distance. This study can assist other healthcare organizations considering this model, as it provides guidance on which factors are most indicative of acceptance.

Abstract Objectives: Cognitive computing has the potential to improve efficiency and accuracy of clinical trial enrollment using artificial intelligence. The cognitive computing clinical trial matching (CTM) system used for this study utilizes natural language processing to derive patient and tumor attributes from structured and unstructured electronic medical record (EMR) data. The attributes are matched to complex eligibility criteria in trial protocols. This CTM system has been implemented in our gynecologic oncology practice, for which there is lower representation of ethnic minorities, elderly, and uninsured in National Cancer Institute-sponsored trials. Methods: A clinical research coordinator (CRC) used the CTM system to screen patients for potential clinical trials one day prior to their clinic visit. Trial matches were shared with gynecologic oncology clinicians to raise awareness for study opportunities and assist in treatment decisions. Clinicians were surveyed regarding their experience with the CTM system prepared matches. The identical patients were evaluated by a clinician using the traditional manual screening method. Results: Seventeen patients with new diagnosis, recent resection, or restaging scans were screened for 41 potential gynecologic, phase I, and supportive care clinical trials. Trial screening by the CRC using the CTM system resulted in a total of 119 matched trials (mean: 7 trials/patient) compared to the clinician-generated list of 271 matched trials (mean: 16 trials/patient). The CRC using the CTM system spent an average of 18 minutes/patient (range: 7 to 40 minutes) compared to the clinician average of 22 minutes/patient (range: 7 to 37 minutes). A survey of 8 gynecologic oncology clinicians reported they were willing to spend an average of 8 minutes/patient (range: 1 to 15 minutes) to screen patients for trial eligibility. On independent review, discrepancies occurred when the clinician identified trials that the CRC excluded due to inclusion/exclusion criteria or were unknowingly closed to accrual. In addition, the CRC identified trials that were inadvertently missing from the clinician’s list of active protocols. Consistent with these observations, 100% of surveyed clinicians (8 of 8) agreed that “The CTM system has helped to include and exclude trials prior to discussion with patients.” Conclusions: Use of the CTM system by a CRC was superior to a clinician alone for the accuracy and efficiency of screening clinical trial eligibility. This process improved the identification of trials that a clinician might otherwise miss and the exclusion of trials that were not available or appropriate for patients. The CTM system can reduce the burden of clinicians and research staff to screen patients. Additional research is needed to determine if the process can improve clinical trial enrollment in gynecologic cancers. Citation Format: Thanh P. Ho, Jane M. Helgeson, Angela L. Andring, Jennifer C. Reed, Venessa L. Boyle, Nigar Sofiyeva, Konstantinos Leventakos, Tufia C. Haddad, Andrea E. Wahner Hendrickson, Saravut (John) Weroha. Implementation of cognitive computing to match clinical trials in gynecologic cancers: A single-institution experience [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 01.