Towards a collaborative filtering approach to medication reconciliation.
A physicians prescribing decisions depend on knowledge of the patients medication list. This knowledge is often incomplete, and errors or omissions could result in adverse outcomes. To address this problem, the Joint Commission recommends medication reconciliation for creating a more accurate list of a patients medications. In this paper, we develop techniques for automatic detection of omissions in medication lists, identifying drugs that the patient may be taking but are not on the patients medication list. Our key insight is that this problem is analogous to the collaborative filtering framework increasingly used by online retailers to recommend relevant products to customers. The collaborative filtering approach enables a variety of solution techniques, including nearest neighbor and co-occurrence approaches. We evaluate the effectiveness of these approaches using medication data from a long-term care center in the Eastern US. Preliminary results suggest that this framework may become a valuable tool for medication reconciliation.
Hasan
Sharique
S
The Heinz School, Carnegie Mellon University, Pittsburgh, PA, USA.
Duncan
George T
GT
Neill
Daniel B
DB
Padman
Rema
R
D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D008487:Medical History Taking / Q000379:methods*; D008508:Medication Errors / Q000517:prevention & control*; D010363:Pattern Recognition, Automated / Q000379:methods*; D017751:Safety Management / Q000458:organization & administration*; D012815:Signal Processing, Computer-Assisted; D014481:United States