A Predictive Method to Determine Incomplete Electronic Medical Records
| Autor: | Luvai Motiwalla, Amir Talaei-Khoei, S. Farzan Kazemi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Data records
Artificial neural network Computer science business.industry health care facilities manpower and services Medical record 02 engineering and technology Missing data medicine.disease digestive system behavioral disciplines and activities Confidence interval Support vector machine 03 medical and health sciences 0302 clinical medicine health services administration Secondary analysis Health care 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing 030212 general & internal medicine Medical emergency business health care economics and organizations |
| Zdroj: | SIGMIS-CPR |
| DOI: | 10.1145/3209626.3209706 |
| Popis: | This paper is utilizing predictive models to determine missing electronic medical records (EMR) at general practice offices. Prior research has addressed the missing values problem in the EMRs used for secondary analysis. However, health care providers are overlooking the missing records problem that stores the patients' medical visits information in EMRs. Our study provides a technique to predict the number of EMR entries for each practice based on their past data records. If the number of EMR entries is less than predicted, it warns the occurrence of missing records with the 95% confidence interval. The study uses seven years of EMRs from 14 general practice offices to train the predictive model. The model predicts EMR data entries and accordingly identified missing EMRs for the following year. We compared the actual visits illustrated by de-identified billing data to the predictive model. The study found auto-correlation method improves the performance of identifying missing records by detecting the period of prediction. In addition, artificial neural networks and support vector machines perform better than other predictive methods depending on whether the analysis aims at detecting missing EMRs or when identifying complete EMRs with no missing records. Results suggest that clinicians and medical professionals should be mindful of the potential missing records of EMRs prior any secondary analysis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|