Policymakers around the world were generally unprepared for the global COVID-19 pandemic. As a result, the virus has led to millions of cases and hundreds of thousands of deaths. Theoretically, the number of cases and deaths did not have to happen (as demonstrated by the results in a few countries). In this pandemic, as in other great disasters, policymakers are confronted with what policy analysts call Decision Making under Deep Uncertainty (DMDU). Deep uncertainty requires policies that are not based on 'predict and act' but on ‘prepare, monitor, and adapt’, enabling policy adaptations over time as events occur and knowledge is gained. We discuss the potential of a DMDU-approach for pandemic decisionmaking.@en

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Background: Society is facing a global shortage of 17 million health care workers, along with increasing health care demands from a growing number of older adults. Social robots are being considered as solutions to part of this problem. Objective: Our objective is to evaluate the quality of care perceived by patients and caregivers for an integrated care pathway in an outpatient clinic using a social robot for patient-reported outcome measure (PROM) interviews versus the currently used professional interviews. Methods: A multicenter, two-parallel-group, nonblinded, randomized controlled trial was used to test for noninferiority of the quality of care delivered through robot-assisted care. The randomization was performed using a computer-generated table. The setting consisted of two outpatient clinics, and the study took place from July to December 2019. Of 419 patients who visited the participating outpatient clinics, 110 older patients met the criteria for recruitment. Inclusion criteria were the ability to speak and read Dutch and being assisted by a participating health care professional. Exclusion criteria were serious hearing or vision problems, serious cognitive problems, and paranoia or similar psychiatric problems. The intervention consisted of a social robot conducting a 36-item PROM. As the main outcome measure, the customized Consumer Quality Index (CQI) was used, as reported by patients and caregivers for the outpatient pathway of care. Results: In total, 75 intermediately frail older patients were included in the study, randomly assigned to the intervention and control groups, and processed: 36 female (48%) and 39 male (52%); mean age 77.4 years (SD 7.3), range 60-91 years. There was no significant difference in the total patient CQI scores between the patients included in the robot-assisted care pathway (mean 9.27, SD 0.65, n=37) and those in the control group (mean 9.00, SD 0.70, n=38): P=.08, 95% CI -0.04 to 0.58. There was no significant difference in the total CQI scores between caregivers in the intervention group (mean 9.21, SD 0.76, n=30) and those in the control group (mean 9.09, SD 0.60, n=35): P=.47, 95% CI -0.21 to 0.46. No harm or unintended effects occurred. Conclusions: Geriatric patients and their informal caregivers valued robot-assisted and nonrobot-assisted care pathways equally.

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Patient reported outcome measures (PROMs) are an essential means for collecting information on the effectiveness of hospital care as perceived by the patients themselves. Especially older adult patients often require help from nursing staff to successfully complete PROMs, but this staff already has a high work load. Therefore, a social robot is introduced to perform the PROM questioning and recording task. The study objective was to design a multimodal dialogue for a social robot to acquire PROMs for older patients. The primary outcomes were the effectiveness, the efficiency, and the subjective usability as perceived by older adults of acquiring PROMs by a social robot. The robot dialogue design included a personalized welcome, PROM questions, confirmation requests, affective statements, use of a support screen on the robot displaying the answer options, and accompanying robot gestures. The design was tested in a crossover study with 31 community-dwelling persons aged 70 years or above. Answers obtained with the robot were compared with those obtained by a questionnaire taken by humans. First results indicated that PROM data collection in older persons may be carried out effectively and efficiently by a social robot. The robot’s subjective usability was on average scored as 80.1 (± 11.6) on a scale from 0 to 100. The recorded data reliability was 99.6%. A first relevant step has been made on the design trajectory for a robot to obtain PROMs from older adults. Practice variation in subjective usability scores still asks for technical dialogue improvements.

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Background /Objectives: Healthcare professionals (HCP) are confronted with an increased demand for assessments of important health status measures, such as patient-reported outcome measurements (PROM), and the time this requires. The aim of this study was to investigate the effectiveness and acceptability of using an HCP robot assistant, and to test the hypothesis that a robot can autonomously acquire PROM data from older adults. Design: A pilot randomised controlled cross-over study where a social robot and a nurse administered three PROM questionnaires with a total of 52 questions. Setting: A clinical outpatient setting with community-dwelling older adults. Participants: Forty-two community-dwelling older adults (mean age: 77.1 years, SD: 5.7 years, 45% female). Measurements: The primary outcome was the task time required for robot-patient and nurse-patient interactions. Secondary outcomes were the similarity of the data and the percentage of robot interactions completed autonomously. The questionnaires resulted in two values (robot and nurse) for three indexes of frailty, well-being and resilience. The data similarity was determined by comparing these index values using Bland-Altman plots, Cohen's kappa (κ) and intraclass correlation coefficients (ICC). Acceptability was assessed using questionnaires. Results: The mean robot interview duration was 16.57 min (SD=1.53 min), which was not significantly longer than the nurse interviews (14.92 min, SD=8.47 min; p=0.19). The three Bland-Altman plots showed moderate to substantial agreement between the frailty, well-being and resilience scores (κ =0.61, 0.50 and 0.45, and ICC=0.79, 0.86 and 0.66, respectively). The robot autonomously completed 39 of 42 interviews (92.8%). Conclusion: Social robots may effectively and acceptably assist HCPs by interviewing older adults.

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The current attention on quality monitoring instruments for hospitalized patients imposes a high data registration workload on nurses. The focus of our research was to investigate whether a social robot is able to take over some of this data collection by administering questionnaires autonomously. We performed an exploratory design experiment on the internal medicine ward of the Franciscus Gasthuis Vlietland hospital. 35 patients (mean age 64.1\pm 17.7, 20 female) participated in the study. We used the social robot Pepper to conduct five questionnaires on medical history, defecation, pain, memory and sleep. Patients and nurses found the robot reasonably acceptable in this role. Further research is needed to address concerns and optimize the nurse-robot task division.

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Medical staff uses Patient Reported Outcome Measurement (PROM) questionnaires as a means of collecting information on the effectiveness of care delivered to patients as perceived by the patients themselves. Especially for the older patient group, the PROM questioning poses an undesirable workload on the staff. This proof of concept paper investigates whether a social robot with a display can conduct such questioning in an acceptable and reliable way. A set of 15 typical questions was selected from existing PROM questionnaires. For the asking, answer-processing and responding, a multi-modal robot-dialogue was designed and implemented. In a within-subjects experiment, 31 community-dwelling older participants answered the 15 questions in two conditions: questioning by the robot, versus questioning by a human. The main part of the robot questioning provided reliable answers, but took somewhat more time compared to human questioning. The experiment demonstrated the feasibility of a social robot for an acceptable and reliable collection of PROM data from older persons.

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Patient Reported Outcome Measures (PROMs) are a means of collecting information on the effectiveness of care delivered to patients as perceived by the patients themselves. A patient's pain level is a typical parameter only a patient him/herself can describe. It is an important measure for a person?s quality of life. When a patient stays in a Dutch hospital, nursing staff needs to ask a patient for its pain level at least three times a day. Due to their work pressure, this requirement is regularly not met. A social robot available as a bed side companion for a patient during his hospital stay, might be able to ask the patient's pain level regularly. The video shows that this innovation in PROM data acquisition is feasible in older persons.

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We are designing a social robot to collect patient data in hospitals by interviewing patients. This task is crucial for improving and providing value-based care. Currently, professional caretakers administer self-reported outcome questionnaires called patient reported outcome measures (PROMs) to collect this data. By delegating this task to a robot, time spent on administration is significantly reduced. Social robots are finding applications in many domains but are particularly interesting for addressing healthcare related problems [1]. We are researching and developing a social robot as an interview robot for administering PROMs [2]. The Radboud university medical center (Radboudumc) Alzheimer Center [L1] and Delft University of Technology Interactive Intelligence Group [L2] have joined forces, combining their expertise on patient measures and providing value-based aged care with complementary expertise on social interaction between robots with patients. The design of the robot is evaluated in the actual care setting where patients are treated at the Radboudumc.@en