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Background /ObjectivesHealthcare 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.DesignA pilot randomised controlled cross-over study where a social robot and a nurse administered three PROM questionnaires with a total of 52 questions.SettingA clinical outpatient setting with community-dwelling older adults.ParticipantsForty-two community-dwelling older adults (mean age: 77.1 years, SD: 5.7 years, 45% female).MeasurementsThe 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.ResultsThe 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%).ConclusionSocial robots may effectively and acceptably assist HCPs by interviewing older adults.

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Ecosystem services are increasingly incorporated into explicit policy targets and can be an effective tool for informing decisions about the use and management of the planet's resources, especially when trade-offs and synergies need to be taken into account. The challenge is to find meaningful and robust indicators to quantify ecosystem services, measure changes in demand and supply and predict future direction. 39 collecting such observations and data on ecosystem services. Biodiversity regulates the ability of the ecosystem to supply ecosystem services, can be directly harvested to meet people's material needs, and are valued by societies for its non-tangible contributions to well-being. Societies are deeply embedded within ecosystems, depending on and influencing the ecosystem services they produce. The different types of ecosystem services (provisioning, regulating, and cultural), and their different components (supply, delivery, contribution to well-being, and value) can be monitored at global to local scales. Different data sources are best suited to account for different components of ecosystem services and spatial scales and include: 40 P. Balvanera et al.census data at national scales, remote sensing, field-based estimations, community monitoring, and models. Data availability, advantages and limitations of each are discussed. Progress towards monitoring different types of services and gaps are explored. Ways of exploring synergies and trade-offs among services and stakeholders, using scenarios to predict future ecosystem services, and including stakeholders in monitoring ecosystem services are discussed. The need of a network for monitoring ecosystem services to synergise efforts is stressed. Monitoring ecosystem services is vital for informing policy (or decision making) to protect human well-being and the natural systems upon which it relies at different scales. Using this information in decision making across all scales will be central to our endeavours to transform to more sustainable and equitable futures.

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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.

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. Trial Registration ClinicalTrials.gov NCT03857789; https://clinicaltrials.gov/ct2/show/NCT03857789

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, answerprocessing 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.