Name
Title | ||
|---|---|---|
Development and Preliminary Evaluation of a Prototype of a Learning Electronic Medical Record System. |
Abstract | ||
|---|---|---|
Electronic medical records (EMRs) are capturing increasing amounts of data per patient. For clinicians to efficiently and accurately understand a patient's clinical state, better ways are needed to determine when and how to display EMR data. We built a prototype system that records how physicians view EMR data, which we used to train models that predict which EMR data will be relevant in a given patient. We call this approach a Learning EMR (LEMR). A physician used the prototype to review 59 intensive care unit (ICU) patient cases. We used the data-access patterns from these cases to train logistic regression models that, when evaluated, had AUROC values as high as 0.92 and that averaged 0.73, supporting that the approach is promising. A preliminary usability study identified advantages of the system and a few concerns about implementation. Overall, 3 of 4 ICU physicians were enthusiastic about features of the prototype. |
Year | Venue | Field |
|---|---|---|
2015 | AMIA | Data science,Intensive care unit,Usability,Medical record,Medical emergency,Logistic regression,Medicine |
DocType | Volume | Citations |
Conference | 2015 | 0 |
PageRank | References | Authors |
0.34 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. J. C. King | 1 | 35 | 7.29 |
| Gregory F. Cooper | 2 | 3464 | 580.16 |
| Harry Hochheiser | 3 | 451 | 54.16 |
| G Clermont | 4 | 84 | 15.29 |
| Shyam Visweswaran | 5 | 231 | 30.47 |