Dataset Information

A real-time electronic symptom monitoring system for patients after discharge following surgery: a pilot study in cancer-related surgery.

ABSTRACT:

Background

Advances in peri-operative care of surgical oncology patients result in shorter hospital stays. Earlier discharge may bring benefits, but complications can occur while patients are recovering at home. Electronic patient-reported outcome (ePRO) systems may enhance remote, real-time symptom monitoring and detection of complications after hospital discharge, thereby improving patient safety and outcomes. Evidence of the effectiveness of ePRO systems in surgical oncology is lacking. This pilot study evaluated the feasibility of a real-time electronic symptom monitoring system for patients after discharge following cancer-related upper gastrointestinal surgery.

Methods

A pilot study in two UK hospitals included patients who had undergone cancer-related upper gastrointestinal surgery. Participants completed the ePRO symptom-report at discharge, twice in the first week and weekly post-discharge. Symptom-report completeness, system actions, barriers to using the ePRO system and technical performance were examined. The ePRO surgery system is an online symptom-report that allows clinicians to view patient symptom-reports within hospital electronic health records and was developed as part of the eRAPID project. Clinically derived algorithms provide patients with tailored self-management advice, prompts to contact a clinician or automated clinician alerts depending on symptom severity. Interviews with participants and clinicians determined the acceptability of the ePRO system to support patients and their clinical management during recovery.

Results

Ninety-one patients were approached, of which 40 consented to participate (27 male, mean age 64?years). Symptom-report response rates were high (range 63-100%). Of 197 ePRO completions analysed, 76 (39%) triggered self-management advice, 72 (36%) trigged advice to contact a clinician, 9 (5%) triggered a clinician alert and 40 (20%) did not require advice. Participants found the ePRO system reassuring, providing timely information and advice relevant to supporting their recovery. Clinicians regarded the system as a useful adjunct to usual care, by signposting patients to seek appropriate help and enhancing their understanding of patients' experiences during recovery.

Conclusion

Use of the ePRO system for the real-time, remote monitoring of symptoms in patients recovering from cancer-related upper gastrointestinal surgery is feasible and acceptable. A definitive randomised controlled trial is needed to evaluate the impact of the system on patients' wellbeing after hospital discharge.

SUBMITTER: Richards HS

PROVIDER: S-EPMC7285449 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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A real-time electronic symptom monitoring system for patients after discharge following surgery: a pilot study in cancer-related surgery.

Richards H S HS Blazeby J M JM Portal A A Harding R R Reed T T Lander T T Chalmers K A KA Carter R R Singhal R R Absolom K K Velikova G G Avery K N L KNL

BMC cancer 20200610 1

<h4>Background</h4>Advances in peri-operative care of surgical oncology patients result in shorter hospital stays. Earlier discharge may bring benefits, but complications can occur while patients are recovering at home. Electronic patient-reported outcome (ePRO) systems may enhance remote, real-time symptom monitoring and detection of complications after hospital discharge, thereby improving patient safety and outcomes. Evidence of the effectiveness of ePRO systems in surgical oncology is lackin ...[more]

PMID: 32522163

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Project description:BACKGROUND:Hospitalized patients with cancer experience a high symptom burden, which is associated with poor health outcomes and increased health care utilization. However, studies investigating symptom monitoring interventions in this population are lacking. We conducted a pilot randomized trial to assess the feasibility and preliminary efficacy of a symptom monitoring intervention to improve symptom management in hospitalized patients with advanced cancer. PATIENTS AND METHODS:We randomly assigned patients with advanced cancer who were admitted to the inpatient oncology service to a symptom monitoring intervention or usual care. Patients in both arms self-reported their symptoms daily (Edmonton Symptom Assessment System and Patient Health Questionnaire-4). Patients assigned to the intervention had their symptom reports presented graphically with alerts for moderate/severe symptoms during daily team rounds. The primary end point of the study was feasibility. We defined the intervention as feasible if >75% of participants hospitalized >2?days completed >2 symptom reports. We observed daily rounds to determine whether clinicians discussed and developed a plan to address patients' symptoms. We used regression models to assess intervention effects on patients' symptoms throughout their hospitalization, readmission risk, and hospital length of stay (LOS). RESULTS:Among 150 enrolled patients (81.1% enrollment), 94.2% completed >2 symptom reports. Clinicians discussed 60.4% of the symptom reports and developed a plan to address the symptoms highlighted by the symptom reports 20.8% of the time. Compared with usual care, intervention patients had a greater proportion of days with lower psychological distress (B?=?0.12, P?=?0.008), but no significant difference in the proportion of days with improved Edmonton Symptom Assessment System-physical symptoms (B?=?0.07, P?=?0.138). Intervention patients had lower readmission risk (hazard ratio?=?0.68, P?=?0.224), although this difference was not significant. We found no significant intervention effects on hospital LOS (B?=?0.16, P?=?0.862). CONCLUSIONS:This symptom monitoring intervention is feasible and demonstrates encouraging preliminary efficacy for improving patients' symptoms and readmission risk.ClinicalTrials.gov identifier NCT02891993.

Project description:BackgroundThe rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs.ObjectiveThe aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes.MethodsData were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality.ResultsThe EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS.ConclusionsIn this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities.

Project description:ObjectiveTo evaluate effects of remote monitoring of adjuvant chemotherapy related side effects via the Advanced Symptom Management System (ASyMS) on symptom burden, quality of life, supportive care needs, anxiety, self-efficacy, and work limitations.DesignMulticentre, repeated measures, parallel group, evaluator masked, stratified randomised controlled trial.SettingTwelve cancer centres in Austria, Greece, Norway, Republic of Ireland, and UK.Participants829 patients with non-metastatic breast cancer, colorectal cancer, Hodgkin's disease, or non-Hodgkin's lymphoma receiving first line adjuvant chemotherapy or chemotherapy for the first time in five years.InterventionPatients were randomised to ASyMS (intervention; n=415) or standard care (control; n=414) over six cycles of chemotherapy.Main outcome measuresThe primary outcome was symptom burden (Memorial Symptom Assessment Scale; MSAS). Secondary outcomes were health related quality of life (Functional Assessment of Cancer Therapy-General; FACT-G), Supportive Care Needs Survey Short-Form (SCNS-SF34), State-Trait Anxiety Inventory-Revised (STAI-R), Communication and Attitudinal Self-Efficacy scale for cancer (CASE-Cancer), and work limitations questionnaire (WLQ).ResultsFor the intervention group, symptom burden remained at pre-chemotherapy treatment levels, whereas controls reported an increase from cycle 1 onwards (least squares absolute mean difference -0.15, 95% confidence interval -0.19 to -0.12; P<0.001; Cohen's D effect size=0.5). Analysis of MSAS sub-domains indicated significant reductions in favour of ASyMS for global distress index (-0.21, -0.27 to -0.16; P<0.001), psychological symptoms (-0.16, -0.23 to -0.10; P<0.001), and physical symptoms (-0.21, -0.26 to -0.17; P<0.001). FACT-G scores were higher in the intervention group across all cycles (mean difference 4.06, 95% confidence interval 2.65 to 5.46; P<0.001), whereas mean scores for STAI-R trait (-1.15, -1.90 to -0.41; P=0.003) and STAI-R state anxiety (-1.13, -2.06 to -0.20; P=0.02) were lower. CASE-Cancer scores were higher in the intervention group (mean difference 0.81, 0.19 to 1.43; P=0.01), and most SCNS-SF34 domains were lower, including sexuality needs (-1.56, -3.11 to -0.01; P<0.05), patient care and support needs (-1.74, -3.31 to -0.16; P=0.03), and physical and daily living needs (-2.8, -5.0 to -0.6; P=0.01). Other SCNS-SF34 domains and WLQ were not significantly different. Safety of ASyMS was satisfactory. Neutropenic events were higher in the intervention group.ConclusionsSignificant reduction in symptom burden supports the use of ASyMS for remote symptom monitoring in cancer care. A "medium" Cohen's effect size of 0.5 showed a sizable, positive clinical effect of ASyMS on patients' symptom experiences. Remote monitoring systems will be vital for future services, particularly with blended models of care delivery arising from the covid-19 pandemic.Trial registrationClinicaltrials.gov NCT02356081.

Dataset Information

A real-time electronic symptom monitoring system for patients after discharge following surgery: a pilot study in cancer-related surgery.

Background

Methods

Results

Conclusion

Publications

A real-time electronic symptom monitoring system for patients after discharge following surgery: a pilot study in cancer-related surgery.

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