IT Efficiency: Ontology Programming Holds the Key
The seamless integration of knowledge and data is indispensible to today’s modern healthcare decision support systems (DSS). A healthcare organization that thoroughly understands its patients and is able to respond quickly to their needs,Healthcare Decision Support Systems Articles scores highly with them—and this has become an extremely important competitive component in today’s ever-more interconnected world where patient feedback can positively or negatively affect an organization’s reputation and bottom line.
The patient care world is complex, with various information systems being utilized to streamline and automate patient care processes. Fortunately, there is a new approach to IT efficiency vis-à-vis ontological engineering—or ontology programming—that is possibly the most significant benefit to ensuring accurate data integration, which fosters a better understanding of patient needs, thus resulting in better patient care and excellent patient outcomes.
Ontological engineering excels at extracting knowledge and critical information from the various information systems within a healthcare decision support system (or its organizational databases). Ontology programming reduces often difficult data integration issues and promotes data reuse, data sharing, and common vocabularies between the information systems, from patient intake to patient discharge.
For healthcare organizations to understand their patients better, data across the entire organization or spectrum of information systems involved in patient care must to be analyzed. Knowledge from different areas or “domains” (e.g., the patient-entry process domain, hospitalization and treatment domains, and billing and insurance domains) must to be extracted in order to accurately interpret quality of care.
Detailed knowledge is also required to interpret patient responses to the various care options exercised from the time of entry into the healthcare facility through final discharge. In addition, quality healthcare organizations strive to improve their existing processes and analyze post-care data in order to determine areas of improvement and initiate appropriate programs. Therefore, the accurate compilation and correlation of patient data is essential during the care process—both individually and in aggregate with other patient data—to determine potential process improvement steps.
As mentioned previously, healthcare organizations also benefit from their patients’ recovering better and more quickly as a result of higher quality care. This is, in no small part, driven by efficient information systems. Patient care results are reflected in quality reports issued by premier organizations such as JCAHO (Joint Commission for Accreditation for Healthcare Organizations). As of 2009, JCAHO reports include patient satisfaction data, as well, thus making it even Healthcare supply near me more important to understand patient information effectively and utilize to it to render care that leads to better patient satisfaction.
Accurate knowledge across intra-organizational domains can only be extracted when healthcare decision support systems are able to exchange relevant data with each other—which is not always possible with current configurations. Even if the numerous systems within an organization can connect to each other through common computer interfaces, they may have stored patient data differently, rendering information exchange virtually impossible and creating a silo effect. Additionally, the context in which the information is used may vary from system to system, making it even more difficult to correlate data across various platforms and systems within the organization. Finally, data consistency and data integrity issues arise as each silo information system is further customized to optimize the information system’s performance.
Therefore, to achieve a comprehensive and accurate individual patient view across the entire patient care spectrum of an organization, different information systems-based reports may have to be compiled separately with data correlated between them. The results will then need to be represented in a single, coherent report. This type of data correlation may include the mapping of various customer names for a single patient, as an example. Obviously, this type of system is not only vulnerable to error and to data integrity and consistency issues, but it is also quite inefficient and, therefore, needlessly costly.