Patient-reported assessments are transforming many facets of health care, but there is scope to modernize their delivery. Contemporary assessment techniques like computerized adaptive testing (CAT) and machine learning can be applied to patient-reported assessments to reduce burden on both patients and health care professionals; improve test accuracy; and provide individualized, actionable feedback. The Concerto platform is a highly adaptable, secure, and easy-to-use console that can harness the power of CAT and machine learning for developing and administering advanced patient-reported assessments. This paper introduces readers to contemporary assessment techniques and the Concerto platform. It reviews advances in the field of patient-reported assessment that have been driven by the Concerto platform and explains how to create an advanced, adaptive assessment, for free, with minimal prior experience with CAT or programming.

Purpose The feasibility of implementing a computerized adaptive test (CAT) system in routine clinical care in ophthalmology has not been assessed. We evaluated the implementation of a glaucoma-specific CAT (GlauCAT) in outpatients at Massachusetts Eye and Ear Institute. Methods In this implementation study (July 2020–April 2021), 216 adults (mean ± SD age 64.8 ± 15.3 years; 56.0% women) completed six adaptive GlauCAT quality of life (QOL) tests on an internet-enabled tablet at the clinic. A real-time printable report summarizing domain scores was shared with physicians prior to consultation. The implementation was evaluated using Proctor's outcomes: acceptability (patient satisfaction); appropriateness (independent complete rate [%]); feasibility (acceptance rate [%]; completion time); and fidelity (percentage of patients discussing GlauCAT results with their physician). Physician barriers/facilitators were explored using open-ended questions. Results Patients’ mean ± SD satisfaction score was 3.5 ± 0.5 of 4, with >95% of patients willing to recommend it to others. Of the 216 (89.2%) patients accepting to participate, 173 (80%) completed GlauCAT independently. Patients took 8 minutes and 5 seconds (median) to complete all 6 GlauCAT tests. Almost two-thirds ( n = 136/216) of the patients reported discussing their GlauCAT results with their doctor. Physicians described the GlauCAT summary report as helpful and user-friendly, although lack of time and uncertainty about how to action information were reported. Conclusions Pilot implementation of six GlauCAT QOL tests in glaucoma outpatient clinics was feasible and acceptable. Integration of GlauCAT with electronic medical records (EMRs) and evaluation of long-term implementation outcomes are needed. Translational Relevance GlauCAT's multiple outcomes and low test-taking burden makes it attractive for measuring glaucoma-specific QOL in routine clinical care.

BACKGROUND Remote patient-reported outcome measure (PROM) data capture can provide useful insights in research and clinical practice, and deeper insights can be gained by administering assessments more frequently. However, frequent data collection can be limited by the by the burden of multiple, lengthy questionnaires. This burden can be reduced with algorithms that select only the most relevant items from a PROM for an individual respondent. We developed "Ecological Momentary Computerized Adaptive Testing” (EMCAT), using algorithms to reduce PROM response burden and facilitate high frequency data capture via a smartphone application. OBJECTIVE To determine the feasibility of EMCAT as a system for remote PROM administration. METHODS We enrolled 40 patients with hand trauma or thumb-base arthritis, across 2 sites, between 13th July 2022 and 14th September 2022. We monitored their symptoms with a validated PROM (the Patient Evaluation Measure), via EMCAT, over a 12-week period. Patients were assessed either thrice weekly, once daily, or thrice daily. We additionally administered full-length PROM assessments at 0, 6, and 12 weeks, and the User Engagement Scale (UES) at 12 weeks. RESULTS The use of EMCAT significantly reduced the length of the PROM (median 2 vs 11 items) and the time taken to complete it (median 8.8 seconds vs 1 minute 14 seconds). Very similar scores were produced when EMCAT was administered concurrently with the full-length PROM, with a mean error of <0.01 on a logit (z-score) scale. The median response rate in the daily assessment group was 93%. The median Perceived Usability score of the UES was 4.0 (maximum possible score 5.0). CONCLUSIONS EMCAT reduces the burden of PROM assessments, enabling acceptable high-frequency, remote PROM data capture. CLINICALTRIAL ISRCTN19841416

UNSTRUCTURED Patient-reported assessments are transforming many facets of healthcare, but there is scope to modernize their delivery. Contemporary assessment techniques like computerized adaptive testing (CAT) and machine learning can be applied to patient-reported assessments to reduce burden, improve accuracy and provide individualize, actionable feedback. The Concerto platform is a highly adaptable, secure and easy-to-use console for developing and administering advanced patient-reported assessments that can harness the power of CAT and machine learning. In this paper, we introduce readers to contemporary assessment techniques and the Concerto platform. We review advances in the field of patient-reported assessment that have been driven by the Concerto platform and explain how to create an advanced, adaptive assessment, for free, with no prior experience of CAT or programming.