Active Semantic Electronic Medical Record

Abstract

Abstract. The healthcare industry is rapidly advancing towards the widespread use of electronic medical records systems to manage the increasingly large amount of patient data and reduce medical errors. In addition to patient data there is a large amount of data describing procedures, treatments, diagnoses, drugs, insurance plans, coverage, formularies and the relationships between these data sets. While practices have benefited from the use of EMRs, infusing these essential programs with rich domain knowledge and rules can greatly enhance their performance and ability to support clinical decisions. Active Semantic Electronic Medical Record (ASEMR) application discussed here uses Semantic Web technologies to reduce medical errors, improve physician efficiency with accurate completion of patient charts, improve patient safety and satisfaction in medical practice, and improve billing due to more accurate coding. This results in practice efficiency and growth by enabling physicians to see more patients with improved care. ASEMR has been deployed and in daily use for managing all patient records at the Athens Heart Center since December 2005. This showcases an application of Semantic Web in health care, especially small clinics.

Integration of an OWL-DL Knowledge Base with an EHR Prototype and Providing Customized Information

“…Sheth and colleagues [25] applied the Semantic Web technology to an electronic medical record and focused on tasks related to medications and billing. However the paper has insufficient technical details in terms of the integration.…”

10 Patient empowerment via technologies for patient-friendly personalized language

“…The goal of this NLP system is to reduce medical errors and improve physicians' efficiency, focusing on the fact that patients' limited knowledge about medications is a key factor in post-discharge adverse drug events. [35] SemAssist generates model text for the patient advice section; if the text in the advice section currently is missing important elements, it provides a critique so that appropriate corrective actions can be taken before signing off the discharge summary. SemAssist uses a model for a set of medications known to account for a high percentage of readmissions to hospital (warfarin, ACE inhibitors, amoxicillin, acetaminophen and prednisone) and their patient-related advice in an ontology that includes patients' actions (things to do or to avoid), as well as common side-effects and reminders of required follow-up.…”

“…Similarly to SemAssist, the Active Semantic Electronic Medical Record (ASEMR) is designed to use semantic annotations to facilitate documentation in health care. [35] Both SemAssist and ACEMR use NLP to produce semantic annotations that are leveraged by a clinical decision support system to alert healthcare workers if a rule is broken. These systems aim to reduce the time and effort for healthcare workers in providing necessary information to patients.…”

Multisource agent-based healthcare data gathering

“…In more detail, EMR/EHR [9][10] [11] is the digital collection of a person's health related documents used within HIS to provide an effective, reliable and costs saving health management, contains the standard medical and clinical data and is managed only by health care providers, whereas in the PHR [12] the person can directly manage his personal medical-related information and therefore it can also contain data coming from website and social networks cited previously (i.e. unstructured or semi-structured).…”