Ruth A Bush1, Vijaya M Vemulakonda2, Sean T Corbett3, George J Chiang4. 1. University of San Diego, San Diego, 92110, USA; Clinical Research Informatics, Rady Children's Hospital-San Diego, 7910 Frost Street, Suite #325, San Diego, CA 92123, USA. 2. Department of Pediatric Urology, Children's Hospital Colorado, 13123 East 16th Avenue, Box 463, Aurora, CO 80045, USA. 3. Division of Pediatric Urology, University of Virginia, PO Box 800422, Charlottesville, VA 22908, USA. 4. University of California, San Diego, La Jolla, CA 92093, USA; Clinical Research Informatics, Rady Children's Hospital-San Diego, 7910 Frost Street Suite #325, San Diego, CA 92123, USA.
Abstract
BACKGROUND: Non-attendance at paediatric urology outpatient appointments results in the patient's failure to receive medical care and wastes health care resources. OBJECTIVE: To determine the utility of using routinely collected electronic health record (EHR) data for multi-centre analysis of variables predictive of patient noshows (NS) to identify areas for future intervention. METHODS: Data were obtained from Children's Hospital Colorado, Rady Children's Hospital San Diego and University of Virginia Hospital paediatric urology practices, which use the Epic® EHR system. Data were extracted for all urology outpatient appointments scheduled from 1 October 2010 to 30 September 2011 using automated electronic data extraction techniques. Data included appointment type; date; provider type and days from scheduling to appointment. All data were de-identified prior to analysis. Predictor variables identified using χ(2) and analysis of variance were modelled using multivariate logistic regression. RESULTS: A total of 2994 NS patients were identified within a population of 28,715, with a mean NS rate of 10.4%. Multivariate logistic regression determined that an appointment with mid-level provider (odds ratio (OR) 1.70 95% CI (1.56, 1.85)) and an increased number of days between scheduling and appointment (15-28 days OR 1.24 (1.09, 1.41); 29+ days OR 1.70 (1.53, 1.89)) were significantly associated with NS appointments. CONCLUSION: We demonstrated sufficient interoperability among institutions to obtain data rapidly and efficiently for use in 1) interventions; 2) further study and 3) more complex analysis. Demographic and potentially modifiable clinic characteristics were associated with NS to the outpatient clinic. The analysis also demonstrated that available data are dependent on the clinical data collection systems and practices.
BACKGROUND: Non-attendance at paediatric urology outpatient appointments results in the patient's failure to receive medical care and wastes health care resources. OBJECTIVE: To determine the utility of using routinely collected electronic health record (EHR) data for multi-centre analysis of variables predictive of patient noshows (NS) to identify areas for future intervention. METHODS: Data were obtained from Children's Hospital Colorado, Rady Children's Hospital San Diego and University of Virginia Hospital paediatric urology practices, which use the Epic® EHR system. Data were extracted for all urology outpatient appointments scheduled from 1 October 2010 to 30 September 2011 using automated electronic data extraction techniques. Data included appointment type; date; provider type and days from scheduling to appointment. All data were de-identified prior to analysis. Predictor variables identified using χ(2) and analysis of variance were modelled using multivariate logistic regression. RESULTS: A total of 2994 NS patients were identified within a population of 28,715, with a mean NS rate of 10.4%. Multivariate logistic regression determined that an appointment with mid-level provider (odds ratio (OR) 1.70 95% CI (1.56, 1.85)) and an increased number of days between scheduling and appointment (15-28 days OR 1.24 (1.09, 1.41); 29+ days OR 1.70 (1.53, 1.89)) were significantly associated with NS appointments. CONCLUSION: We demonstrated sufficient interoperability among institutions to obtain data rapidly and efficiently for use in 1) interventions; 2) further study and 3) more complex analysis. Demographic and potentially modifiable clinic characteristics were associated with NS to the outpatient clinic. The analysis also demonstrated that available data are dependent on the clinical data collection systems and practices.
Authors: William R Hersh; Mark G Weiner; Peter J Embi; Judith R Logan; Philip R O Payne; Elmer V Bernstam; Harold P Lehmann; George Hripcsak; Timothy H Hartzog; James J Cimino; Joel H Saltz Journal: Med Care Date: 2013-08 Impact factor: 2.983
Authors: Richard D Neal; Mahvash Hussain-Gambles; Victoria L Allgar; Debbie A Lawlor; Owen Dempsey Journal: BMC Fam Pract Date: 2005-11-07 Impact factor: 2.497