Name
Abstract | ||
|---|---|---|
Pediatric asthma exacerbations account for >1.8 million emergency department (ED) visits annually. Asthma guidelines are intended to guide time-dependent treatment decisions that improve clinical outcomes; however, guideline adherence is inadequate. We examined whether an automatic disease detection system increases clinicians' use of paper-based guidelines and decreases time to a disposition decision.We evaluated a computerized asthma detection system that triggered NHLBI-adopted, evidence-based practice to improve care in an urban, tertiary care pediatric ED in a 3-month (7/09-9/09) prospective, randomized controlled trial. A probabilistic system screened all ED patients for acute asthma. For intervention patients, the system generated the asthma protocol at triage for intervention patients to guide early treatment initiation, while clinicians followed standard processes for control patients. The primary outcome measures included time to patient disposition.The system identified 1100 patients with asthma exacerbations, of which 704 had a final asthma diagnosis determined by a physician-established reference standard. The positive predictive value for the probabilistic system was 65%. The median time to disposition decision did not differ among the intervention (289 min; IQR = (184, 375)) and control group (288 min; IQR = (185, 375)) (p=0.21). The hospital admission rate was unchanged between intervention (37%) and control groups (35%) (p = 0.545). ED length of stay did not differ among the intervention (331 min; IQR = (226, 581)) and control group (331 min; IQR = (222, 516)) (p = 0.568).Despite a high level of support from the ED leadership and staff, a focused education effort, and implementation of an automated disease detection, the use of the paper-based asthma protocol remained low and time to patient disposition did not change. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.ijmedinf.2012.11.006 | International Journal of Medical Informatics |
Keywords | Field | DocType |
Asthma,Medical informatics,Bayesian network,Decision support | Disease,Asthma,Emergency department,Pediatrics,Randomized controlled trial,Triage,Guideline,Medicine,Disposition,Evidence-based practice | Journal |
Volume | Issue | ISSN |
82 | 4 | 1386-5056 |
Citations | PageRank | References |
6 | 0.88 | 6 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Judith W. Dexheimer | 1 | 25 | 10.59 |
| Thomas J. Abramo | 2 | 15 | 2.28 |
| Donald H. Arnold | 3 | 15 | 2.62 |
| Kevin B. Johnson | 4 | 337 | 39.11 |
| Yu Shyr | 5 | 171 | 21.81 |
| Fei Ye | 6 | 17 | 3.44 |
| Kang-Hsien Fan | 7 | 11 | 1.51 |
| Neal Patel | 8 | 11 | 2.19 |
| D Aronsky | 9 | 211 | 35.38 |