2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology

Format:

Book

Author/Creator:

IEEE Staff, author, issuing body.

Contributor:

IEEE Staff, Contributor.

Language:

English

Subjects (All):

Technology.

Physical Description:

1 online resource

Place of Publication:

[Place of publication not identified] IEEE 2012

Language Note:

English

Summary:

Ambulatory care sensitive conditions (ACSCs) are characterized as health conditions for which good outpatient care can potentially prevent the need for hospitalization, or for which early intervention can prevent complications or more severe disease. Currently, there are 16 identified ACSCs within the US health system: diabetes short-term complication, perforated appendix, diabetes long-term complication, pediatric asthma, chronic obstructive pulmonary disease, pediatric gastroenteritis, hypertension, congestive heart failure, low birth weight rate, dehydration, bacterial pneumonia, urinary tract infection, angina admission without procedure, uncontrolled diabetes, adult asthma, and lower-extremity amputation among patients with diabetes. Potentially preventable acute health events (PPEs) for such diagnosis codes represent a straightforward opportunity for reducing medical costs while concomitantly improving quality of care. While claims data have previously been used to predict future health outcomes of patients, we report here a novel approach, using data mining techniques, towards supplementing such data with patients' electronic health records (EHR) to develop a clinical decision support system that satisfactorily predicts the onset of PPEs in a large population of patients.

Notes:

Bibliographic Level Mode of Issuance: Monograph

ISBN:

9780769549217

0769549217