Background: Active patient engagement may improve their perioperative experience and outcomes. We sought to evaluate the use of a mobile device application (App) for patient engagement and patient reported outcomes (PROs) assessment following robotic lung cancer surgery. Methods: Patients with suspected lung cancer undergoing robotic resection between January-May 2019, were offered the SeamlessMD App, which was customized to meet requirements of the thoracic enhanced recovery pathway. The App guided patients through preoperative preparation, in-hospital recovery, and postop discharge care with personalized reminders, task lists, education, progress tracking, and surveys. Results: Fifty patients participated in the study (22.1%). Of the 50 patients, 20 (40%) patients completed the preoperative compliance survey, and 31 (62%) completed the hospital satisfaction survey. A total of 62 inpatient recovery checks were completed, identifying non-compliance with incentive spirometer use in 2 (3.2%) and patient worries about self-care after discharge in 18 (29%) instances. Postoperative health-checks were completed by 27 (54%) patients with a median of 3 [0-17] completed surveys per patient. Patient reported symptom scores up to 30 days after surgery, demonstrating a significant decrease maximum pain level (P=0.002) and anxiety scores (P<0.001). The App enabled health-checks improved confidence and decreased worries in over 80% of patients. Nine patients (40.9%) reported the health-checks helped avoid 1+ calls and 4 (18.2%) reported the App helped avoid 1+ visits to the hospital. Over 74% of patients reported the App was very or extremely useful in each of the preoperative, inpatient, and post-discharge settings. Conclusions: A mobile device platform may serve as an effective mechanism to record perioperative PROs and satisfaction while facilitating patient-provider engagement in perioperative care.

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With all of the research and investment dedicated to artificial intelligence and other automation technologies, there is a paucity of evaluation methods for how these technologies integrate into effective joint human-machine teams. Current evaluation methods, which largely were designed to measure performance of discrete representative tasks, provide little information about how the system will perform when operating outside the bounds of the evaluation. We are exploring a method of generating Extensibility Plots, which predicts the ability of the human-machine system to respond to classes of challenges at intensities both within and outside of what was tested. In this paper we test and explore the method, using performance data collected from a healthcare setting in which a machine and nurse jointly detect signs of patient decompensation. We explore the validity and usefulness of these curves to predict the graceful extensibility of the system.