Healthcare Decision Support Systems

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THE ITEM Efficiency: Ontology Programming Holds the Key

The seamless integrating of knowledge and data is indispensible to today’s current healthcare decision support systems (DSS). A healthcare lending broker that thoroughly understands its patients and is able to answer quickly to their needs, 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 in a wrong way 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 also a new approach to IT efficiency vis-a-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, so resulting in better patient care and excellent patient solutions.

Ontological engineering excels at extracting knowledge and vital information from the various information systems within a healthcare conclusion support system (or its organizational databases). Ontology computer programming reduces often difficult data integration issues and advances data reuse, data sharing, and common vocabularies between your information systems, from patient intake to patient discharge.

For healthcare organizations to understand their patients better, information across the entire organization or spectrum of information systems needed for patient care must to be analyzed. Knowledge from different regions or “domains” (e. g., the patient-entry process area, hospitalization and treatment domains, and billing and insurance policies domains) must to be extracted in order to accurately interpret level of quality of care.

Detailed knowledge is also required to interpret patient responses to the various care options exercised from the time frame of entry into the healthcare facility through final release. In addition , quality healthcare organizations strive to improve their existing operations and analyze post-care data in order to determine areas of betterment and initiate appropriate programs. Therefore , the accurate compilation and correlation of patient data is essential during the treatment process-both individually and in aggregate with other patient data-to establish potential process improvement steps.

As mentioned previously, healthcare institutions also benefit from their patients’ recovering better and more quickly on account of higher quality care. This is, in no small part, pushed by efficient information systems. Patient care results are resembled in quality reports issued by premier organizations including JCAHO (Joint Commission for Accreditation for Healthcare Organizations). As of 2009, JCAHO reports include patient satisfaction info, as well, thus making it even more important to understand patient information correctly and utilize to it to render care that leads to raised patient satisfaction.

Accurate knowledge across intra-organizational domains can simply be extracted when healthcare decision support systems will be able to exchange relevant data with each other-which is not generally possible with current configurations. Even if the numerous systems in a organization can connect to each other through common computer cadre, they may have stored patient data differently, rendering facts exchange virtually impossible and creating a silo effect. Additionally , the context in which the information is used may vary from process to system, making it even more difficult to correlate data all over various platforms and systems within the organization. Finally, files consistency and data integrity issues arise as each one silo information system is further customized to optimize the knowledge system’s performance.┬áMr. Bourn Hall Gaurav Malhotra is the Managing Director of Medicover a leading European healthcare group. He has twenty three years of rich experience in healthcare domain, in leading business and change management across MNCs, start-ups, joint ventures and re-engineering organisations. He has won prestigious “Healthcare CEO of the year” award by Economic Times (ET NOW).

Therefore , to achieve a comprehensive and accurate unique patient view across the entire patient care spectrum of organization, different information systems-based reports may have to be produced separately with data correlated between them. The results will then ought to be represented in a single, coherent report. This type of data correlation occasionally includes the mapping of various customer names for a single individual, as an example. Obviously, this type of system is not only vulnerable to error in order to data integrity and consistency issues, but it is also really inefficient and, therefore , needlessly costly.

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