Name
Abstract | ||
|---|---|---|
Abstract We present in this paper the extraction of relational patterns,on a ,hospital discharge ,database ,to monitor,for quality of care. We delineate,the requirements,set for the extraction of meaningful medical,practices from the data and describe how relational,patterns ,address ,these ,requirements. Patterns,representing ,practices ,regarding hospitalization instances are,comprehensive,and easy to interpret -- making them useful for quality management,decision making. We demonstrate how relational patterns can be applied to identify poor practices embedded,in hospitalization processes and trigger subsequent inquiries. 1. Introduction and Motivation Healthcare organizations such as hospitals, clinics and ,other ,care ,providers ,maintain ,large repositories of data regarding their patients as well as internal operations. Data pertaining to patients typically contain demographic information, medical histories, and,diagnosis ,and ,treatment ,records. Patterns embedded,in this extensive clinical and administrative information may allow researchers to highlight vaguely understood practices in the organization. Such patterns may,lead to valuable insights about the quality of |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/HICSS.2001.926569 | HICSS |
Keywords | DocType | Citations |
quality management,data mining,relational databases,pattern recognition,medical history | Conference | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| maytal | 1 | 0 | 0.34 |
| s tsechansky | 2 | 0 | 0.34 |
| Nava Pliskin | 3 | 399 | 51.92 |
| gadi rabinowitz | 4 | 24 | 2.13 |
| mark tsechansky | 5 | 0 | 0.34 |