Towards a collaborative filtering approach to medication reconciliation.

Sharique Hasan, George T. Duncan, Daniel Neill, Rema Padman

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)288-292
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - Dec 1 2008

Fingerprint

Medication Reconciliation
Patient Medication Knowledge
Long-Term Care
Joints
Physicians
Pharmaceutical Preparations

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Towards a collaborative filtering approach to medication reconciliation. / Hasan, Sharique; Duncan, George T.; Neill, Daniel; Padman, Rema.

In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 01.12.2008, p. 288-292.

Research output: Contribution to journalArticle

@article{4c186b332df74906933f930b37eb8a67,
title = "Towards a collaborative filtering approach to medication reconciliation.",
abstract = "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.",
author = "Sharique Hasan and Duncan, {George T.} and Daniel Neill and Rema Padman",
year = "2008",
month = "12",
day = "1",
language = "English (US)",
pages = "288--292",
journal = "AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium",
issn = "1559-4076",
publisher = "American Medical Informatics Association",

}

TY - JOUR

T1 - Towards a collaborative filtering approach to medication reconciliation.

AU - Hasan, Sharique

AU - Duncan, George T.

AU - Neill, Daniel

AU - Padman, Rema

PY - 2008/12/1

Y1 - 2008/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=73949083943&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=73949083943&partnerID=8YFLogxK

M3 - Article

SP - 288

EP - 292

JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

SN - 1559-4076

ER -