Improving Communication Between the Emergency Department and Radiology Department With a Novel Web-Based Tool in an Urban Academic Center

Background Despite the acceptability and efficacy of e–patient-reported outcome (ePRO) systems, implementation in routine clinical care remains challenging. Objective This pragmatic trial implemented the PROMPT-Care (Patient Reported Outcome Measures for Personalized Treatment and Care) web-based system into existing clinical workflows and evaluated its effectiveness among a diverse population of patients with cancer. Methods Adult patients with solid tumors receiving active treatment or follow-up care in four cancer centers were enrolled. The PROMPT-Care intervention supported patient management through (1) monthly off-site electronic PRO physical symptom and psychosocial well-being assessments, (2) automated electronic clinical alerts notifying the care team of unresolved clinical issues following two consecutive assessments, and (3) tailored online patient self-management resources. Propensity score matching was used to match controls with intervention patients in a 4:1 ratio for patient age, sex, and treatment status. The primary outcome was a reduction in emergency department presentations. Secondary outcomes were time spent on chemotherapy and the number of allied health service referrals. Results From April 2016 to October 2018, 328 patients from four public hospitals received the intervention. Matched controls (n=1312) comprised the general population of patients with cancer, seen at the participating hospitals during the study period. Emergency department visits were significantly reduced by 33% (P=.02) among patients receiving the intervention compared with patients in the matched controls. No significant associations were found in allied health referrals or time to end of chemotherapy. At baseline, the most common patient reported outcomes (above-threshold) were fatigue (39%), tiredness (38.4%), worry (32.9%), general wellbeing (32.9%), and sleep (24.1%), aligning with the most frequently accessed self-management domain pages of physical well-being (36%) and emotional well-being (23%). The majority of clinical feedback reports were reviewed by nursing staff (729/893, 82%), largely in response to the automated clinical alerts (n=877). Conclusions Algorithm-supported web-based systems utilizing patient reported outcomes in clinical practice reduced emergency department presentations among a diverse population of patients with cancer. This study also highlighted the importance of (1) automated triggers for reviewing above-threshold results in patient reports, rather than passive manual review of patient records; (2) the instrumental role nurses play in managing alerts; and (3) providing patients with resources to support guided self-management, where appropriate. Together, these factors will inform the integration of web-based PRO systems into future models of routine cancer care. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12616000615482; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370633 International Registered Report Identifier (IRRID) RR2-10.1186/s12885-018-4729-3

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BE PREPARED - USING WEB-BASED TECHNOLOGY TO PREPARE NURSING STUDENTS FOR A CLINICAL PLACEMENT IN THE EMERGENCY DEPARTMENT

ICERI2019 Proceedings ◽

10.21125/iceri.2019.0165 ◽

2019 ◽

Author(s):

Darren Falconer ◽

Helene Metcalfe ◽

Jeffrey Hamdorf

Keyword(s):

Emergency Department ◽

Nursing Students ◽

Clinical Placement ◽

Web Based

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Emergency patient flow forecasting in the radiology department

Health Informatics Journal ◽

10.1177/1460458220901889 ◽

2020 ◽

Vol 26 (4) ◽

pp. 2362-2374

Author(s):

Yumeng Zhang ◽

Li Luo ◽

Fengyi Zhang ◽

Ruixiao Kong ◽

Jianchao Yang ◽

...

Keyword(s):

Emergency Department ◽

Nonlinear Models ◽

Patient Flow ◽

Mean Absolute Percentage Error ◽

Radiology Department ◽

Percentage Error ◽

Support Vector ◽

Emergency Patient ◽

Adaptive Boosting ◽

Absolute Percentage Error

The accurate forecast of radiology emergency patient flow is of great importance to optimize appointment scheduling decisions. This study used a multi-model approach to forecast daily radiology emergency patient flow with consideration of different patient sources. We constructed six linear and nonlinear models by considering the lag effects and corresponding time factors. The autoregressive integrated moving average and least absolute shrinkage and selection operator (Lasso) were selected from the category of linear models, whereas linear-and-radial support vector regression models, random forests and adaptive boosting were chosen from the category of nonlinear models. The models were applied to 4-year daily emergency visits data in the radiology department of West China Hospital in Chengdu, China. The mean absolute percentage error of six models ranged from 8.56 to 9.36 percent for emergency department patients, whereas it varied from 10.90 to 14.39 percent for ward patients. The best-performing model for total radiology visits was Lasso, which yielded a mean absolute percentage error of 7.06 percent. The arrival patterns of emergency department and total radiology emergency patient flows could be modeled by linear processes. By contrast, the nonlinear model performed best for ward patient flow. These findings will benefit hospital managers in managing efficient patient flow, thus improving service quality and increasing patient satisfaction.

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