Developing a real-time electronic symptom monitoring system for patients after discharge following cancer-related surgery.

BACKGROUND: Patients undergoing major cancer surgery frequently require post-acute care for complications and adverse effects. Enhanced recovery after surgery programmes mean that patients are increasingly discharged home earlier. Symptom/complication detection post-discharge is sub-optimal. Systematic patient monitoring post-discharge following surgery may be optimally achieved through routine electronic patient-reported outcome (ePRO) data capture. ePRO systems that employ clinical algorithms to guide management of patients and automatically alert clinicians of clinically-concerning symptoms can improve patient outcomes and decrease hospital admissions. ePRO systems that provide individually-tailored self-management advice and integrate live ePRO data into electronic health records (EHR) may also advance personalised health and patient-centred care. This study aims to develop a hospital EHR-integrated ePRO system to improve detection and management of complications post-discharge following cancer-related surgery. METHODS: The ePRO system was developed in two phases: (1) Development of a web-based ePRO symptom-report from validated European Organisation for Research and Treatment of Cancer (EORTC) questionnaires, clinical opinion and patient interviews, followed by hospital EHR integration; (2) Development of clinical algorithms triggering symptom severity-dependent patient advice and clinician alerts from: (i) prospectively-collected patient-completed ePRO symptom-report data; (ii) stakeholder meetings; (iii) patient interviews. Patient advice was developed from: (i) clinician-patient telephone consultations and patient interviews; (ii) review of hospital patient information leaflets (PIL) and patient support websites. RESULTS: Phase 1, including interviews with 18 patients, identified 35 symptom-report items. In phase 2, 130/300 (43%) screened patients were eligible. 61 (47%) consented to participate and 59 (97%) provided 444 complete self-reports. Stakeholder meetings (9 clinicians, 1 patient/public representative) and patient interviews (n = 66) refined advice/alert accuracy. 15 telephone consultations, 7 patient interviews and review of 28 PILs and 3 patient support websites identified 4 themes to inform self-management advice. Comparisons between ePRO symptom-report data, telephone consultations and clinical events/outcomes (n = 27 patients) further refined clinical algorithms. CONCLUSIONS: A hospital EHR-integrated ePRO system that alerts clinicians and provides patient self-management advice has been developed to improve the detection and management of problems and complications after discharge following surgery. An ongoing pilot study will inform a multicentre randomised trial to evaluate the effectiveness of the ePRO system compared to usual care.

Electronic patient self-Reporting of Adverse-events: Patient Information and aDvice (eRAPID): a randomised controlled trial in systemic cancer treatment.

BACKGROUND: eRAPID (electronic patient self-Reporting of Adverse-events: Patient Information and aDvice) is an internet based system for patients to self-report symptoms and side effects (adverse events or AE) of cancer treatments. eRAPID allows AE reporting from home and patient reported data is accessible via Electronic Patient Records (EPR) for use in routine care. The system can generate alerts to clinical teams for severe AE and provides patient advice on managing mild AEs. The overall aims of eRAPID are to improve the safe delivery of cancer treatments, enhance patient care and standardise AE documentation. METHODS: The trial is a prospective randomised two-arm parallel group design study with repeated measures and mixed methods. Participants (adult patients with breast cancer on neo-adjuvant or adjuvant chemotherapy, colorectal and gynaecological cancer receiving chemotherapy) are randomised to receive the eRAPID intervention or usual care over 18 weeks of treatment. Participants in the intervention arm receive training in using the eRAPID system to provide routine weekly adverse event reports from home. Hospital staff can access eRAPID reports via the EPR and use the information during consultations or phone calls with patients. Prior to commencing the full trial an internal pilot phase was conducted (N = 87 participants) to assess recruitment procedures, consent and attrition rates, the integrity of the intervention information technology and establish procedures for collecting outcome data. The overall target sample for the trial is N = 504. The primary outcome of the trial is quality of life (FACT-G) with secondary outcomes including health economics (costs to patients and the NHS), process of care (e.g. contacts with the hospital, number of admissions, clinic appointments and changes to treatment/medications) and patient self-efficacy. Outcome data is collected at baseline, 6, 12, 18 weeks and 12 months. The intervention is also being evaluated via end of study interviews with patient participants and clinical staff. DISCUSSION: The pilot phase was completed in February 2016 and recruitment and attrition rates met criteria for continuing to the full trial. Recruitment recommenced in May 2016 and is planned to continue until December 2017. Overall findings will determine the value of the eRAPID intervention for supporting the care of patients receiving systemic cancer treatment. TRIAL REGISTRATION: Current Controlled Trials ISRCTN88520246 . Registered 11 September 2014.

Asking the right questions to get the right answers: using cognitive interviews to review the acceptability, comprehension and clinical meaningfulness of patient self-report adverse event items in oncology patients.

BACKGROUND: Standardized reporting of treatment-related adverse events (AE) is essential in clinical trials, usually achieved by using the National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE) reported by clinicians. Patient-reported adverse events (PRAE) may add value to clinician assessments, providing patient perspective on subjective toxicity. We developed an online patient symptom report and self-management system for real-time reporting and managing AE during cancer treatment integrated with electronic patient records (eRAPID). As part of this program we developed a patient version of the CTCAE (version 4.0), rephrasing terminology into a self-report format. We explored patient understanding of these items via cognitive interviews. MATERIAL AND METHOD: Sixty patients (33 female, 27 male) undergoing treatment were purposively sampled by age, gender and tumor group (median age 61.5, range 35-84, 12 breast, 12 gynecological, 13 colorectal, 12 lung and 11 renal). Twenty-one PRAE items were completed on a touch-screen computer. Subsequent audio-recorded cognitive interviews and thematic analysis explored patients' comprehension of items via verbal probing techniques during three interview rounds (n = 20 patients/round). RESULTS: In total 33 item amendments were made; 29% related to question comprehension, 68% response option and 3% order effects. These amendments to phrasing and language improved patient understanding but maintained CTCAE grading and key medical information. Changes were endorsed by members of a patient advisory group (N = 11). CONCLUSION: Item adaptations resulted in a bank of consistently interpreted self-report AE items for use in future research program. In-depth analysis of items through cognitive interviews is an important step towards developing an internationally valid system for PRAE, thus improving patient safety and experiences during cancer treatment.

Indirect iterative learning control for a discrete visual servo without a camera-robot model.

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.