Background Supportive oncology practice can be enhanced by integrating brief and validated electronic patient-reported outcome (ePRO) assessment into the electronic health record (EHR) and clinical workflow. Methods 636 women receiving gynecologic oncology outpatient care received instructions to complete clinical assessments through Epic MyChart, the EHR patient communication portal. PROMIS computer adaptive tests (CATs) were administered to assess fatigue, pain interference, physical function, depression, and anxiety. Checklists identified psychosocial concerns, informational and nutritional needs, and risk factors for inadequate nutrition. Assessment results, including PROMIS T-scores with documented severity thresholds, were immediately populated in the EHR. Clinicians were notified of clinically elevated symptoms through EHR messages. EHR integration was designed to provide automated triage to social work providers for psychosocial concerns, health educators for information, and dietitians for nutrition-related concerns. Results Of 4,042 MyChart messages sent, 3,203 (79%) were reviewed by patients. The assessment was started by 1,493 (37%) patients, and once started 93% completed (1,386 patients). Using first assessments only, 49.8% of patients who reviewed the MyChart message completed the assessment. Mean PROMIS CAT T-scores indicated a lower level of physical function and elevated anxiety compared to the general population. Fatigue, pain, and depression scores were comparable to the general population. Impaired physical functioning was the most common basis for clinical alerts, occurring in 4% of patients. Conclusions We used PROMIS CATs to measure common cancer symptoms in routine oncology outpatient care. Immediate EHR integration facilitated the use of symptom reporting as the basis for referral to psychosocial and supportive care.

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Summary Background High numbers of patients discharged from psychiatric hospital care are readmitted within a year. Peer support for discharge has been suggested as an approach to reducing readmission post-discharge. Implementation has been called for in policy, however, evidence of effectiveness from large rigorous trials is missing. We aimed to establish whether peer support for discharge reduces readmissions in the year post-discharge. Methods We report a parallel, two-group, individually randomised, controlled superiority trial, with trial personnel masked to allocation. Patients were adult psychiatric inpatients (age ≥18 years) with at least one previous admission in the preceding 2 years, excluding those who had a diagnosis of any organic mental disorder, or a primary diagnosis of learning disability, an eating disorder, or drug or alcohol dependency, recruited from seven state-funded mental health services in England. Patients were randomly assigned (1:1) to the intervention (peer support plus care as usual) or control (care as usual) groups by an in-house, online randomisation service, stratified by site and diagnostic group (psychotic disorders, personality disorders, and other eligible non-psychotic disorders) with randomly permuted blocks of randomly varying length to conceal the allocation sequence and achieve the allocation ratio. The peer support group received manual-based, one-to-one peer support, focused on building individual strengths and engaging with activities in the community, beginning during the index admission and continuing for 4 months after discharge, plus care as usual. Care as usual consisted of follow-up by community mental health services within 7 days of discharge. The primary outcome was psychiatric readmission 12 months after discharge (number of patients readmitted at least once), analysed on an intention-to-treat basis. All patients were included in a safety analysis, excluding those who withdrew consent for use of their data. The trial is registered with the ISRCTN registry, ISRCTN10043328. The trial was complete at the time of reporting. Findings Between Dec 1, 2016, and Feb 8, 2019, 590 patients were recruited and randomly assigned, with 294 allocated to peer support (287 included in the analysis after withdrawals and loss to follow-up), and 296 to care as usual (291 in the analysis). Mean age was 39·7 years (SD 13·7; range 18–75). 306 patients were women, 267 were men, three were transgender, and two preferred not to say. 353 patients were White, 94 were Black, African, Caribbean, or Black British, 68 were Asian or Asian British, 48 were of mixed or multiple ethnic groups, and 13 were of other ethnic groups. In the peer support group, 136 (47%) of 287 patients were readmitted at least once within 12 months of discharge. 146 (50%) of 291 were readmitted in the care as usual group. The adjusted risk ratio of readmission was 0·97 (95% CI 0·82–1·14; p=0·68), and the adjusted odds ratio for...

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Background Patient-reported outcomes are increasingly utilized in routine orthopedic clinical care. Computer adaptive tests (CATs) from the Patient-Reported Outcomes Measurement Information System (PROMIS) offer a brief and precise assessment that is well suited for collection within busy clinical environments. However, software apps that support the administration and scoring of CATs, provide immediate access to patient-reported outcome (PRO) scores, and minimize clinician burden are not widely available. Objective Our objective was to design, implement, and test the feasibility and usability of a Web-based system for collecting CATs in orthopedic clinics. Methods AO Patient Outcomes Center (AOPOC) was subjected to 2 rounds of testing. Alpha testing was conducted in 3 orthopedic clinics to evaluate ease of use and feasibility of integration in clinics. Patients completed an assessment of PROMIS CATs and a usability survey. Clinicians participated in a brief semistructured interview. Beta-phase testing evaluated system performance through load testing and usability of the updated version of AOPOC. In both rounds of testing, user satisfaction, bugs, change requests, and performance of PROMIS CATs were captured. Results Patient feedback supported the ease of use in completing an assessment in AOPOC. Across both phases of testing, clinicians rated AOPOC as easy to use but noted difficulties in integrating a Web-based software application within their clinics. PROMIS CATs performed well; the default assessment of 2 CATs was completed quickly (mean 9.5 items) with a satisfactory range of measurement. Conclusion AOPOC was demonstrated to be an easy-to-learn and easy-to-use software application for patients and clinicians that can be integrated into orthopedic clinical care. The workflow disruption in integrating any type of PRO collection must be addressed if patients’ voices are to be better integrated in clinical care.