Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial

Abstract

Objective The use of evidence-based guidelines can improve the care for asthma patients. We implemented a computerized asthma management system in a pediatric emergency department (ED) to integrate national guidelines. Our objective was to determine whether patient eligibility identification by a probabilistic disease detection system (Bayesian network) combined with an asthma management system embedded in the workflow decreases time to disposition decision. Methods We performed a prospective, randomized controlled trial in an urban, tertiary care pediatric ED. All patients 2–18 years of age presenting to the ED between October 2010 and February 2011 were screened for inclusion by the disease detection system. Patients identified to have an asthma exacerbation were randomized to intervention or control. For intervention patients, asthma management was computer-driven and workflow-integrated including computer-based asthma scoring in triage, and time-driven display of asthma-related reminders for re-scoring on the electronic patient status board combined with guideline-compliant order sets. Control patients received standard asthma management. The primary outcome measure was the time from triage to disposition decision. Results The Bayesian network identified 1,339 patients with asthma exacerbations, of which 788 had an asthma diagnosis determined by an ED physician-established reference standard (positive predictive value 69.9%). The median time to disposition decision did not differ among the intervention (228 minutes; IQR=(141, 326)) and control group (223 minutes; IQR= (129, 316));(p=0.362). The hospital admission rate was unchanged between intervention (25%) and control groups (26%); (p=0.867). ED length of stay did not differ among intervention (262 minutes; IQR=(165, 410)) and control group (247 minutes; IQR=(163, 379));(p=0.818). Conclusions The control and intervention groups were similar in regards to time to disposition; the computerized management system did not add additional wait time. The time to disposition decision did not change; however the management system integrated several different information systems to support clinicians’ communication.

Development and implementation of a mobile device-based pediatric electronic decision support tool as part of a national practice standardization project

“…We previously analyzed our regional deployment of the iGuideline ECDS tool using Apsalar and found that, from its release in June 2013, it was downloaded by 937 users in the first year, resulting in 3013 user sessions and a median session length of 31.8 seconds 32 . Multiple previous reports have demonstrated the value of integrating ECDS into EHR systems to disseminate evidence-based practice recommendations 33–37 . However, these reports are based primarily on individual health systems or integrated networks that utilize the same basic EHR infrastructure.…”

Paediatric ED BiPAP continuous quality improvement programme with patient analysis: 2005–2013

“…A Pediatric Asthma Severity (PAS) score was used and recorded by the PED staff (physicians, nurses and respiratory therapist (RT)) from 2005 to 2008. From 2009 to 2013, an individualised component input-derived PAS score was used by the triage nurse, RT, nurses and physicians for initial assessment, and to drive asthma therapeutic interventions10 18 19 (table 1). …”

“…In recent paediatric asthma trials, ventilation heterogeneity and individualistic β-2 receptor response to medications have been proposed as the aetiology of patient's variable response to bronchodilator therapy 7–9. Current aerosol systems can be ineffective in certain asthmatics with large airway obstruction and poor lung ventilation while CPAP or BiPAP has shown to be beneficial in these asthmatics 10–14. In acute asthmatics, BiPAP has a direct bronchodilator effect, achieves greater peripheral bronchodilator dispersal while unloading fatigued inspiratory muscles, offsetting auto positive end-expiratory pressure (auto-PEEP), recruiting collapsed alveoli all causing a reduction in their work of breathing and achieving their total lung capacity quicker 11–15…”

A Novel Decision Model to Support the Prediction of Asthma among the Big Data’s of Various Different Patient's

“…Asthma maladies analyze is absolutely subject to the sound that is made by the patient amid cough.A non asthmatic patient when hacks delivers a sound of recurrence (206 (14) Hz) where as an asthmatic patient on hack creates a sound with recurrence (239(19)Hz).There are different sound recording gadgets now a days that records the sound and demonstrates the recurrence of the sound from which it can be unmistakably analyze whether the sound is of a typical individual or of an asthmatic patient.On the premise of bronchial excessive touchiness the asthma has the accompanying sorts: hypersensitive asthma (atopic, extraneous, brought on by immunologic jolt of an antigen), natural (non-unfavourably susceptible, impelled by contamination, physically or artificially), practice affected, medication instigated asthma, occupative asthma and asthmatic bronchitis [3]. There are distinctive sort of sounds as indicated by diverse hypotheses.…”