IT Efficiency: Ontology Programming Holds the Key
The seamless integration of knowledge and data is indispensible to today’s new healthcare decision assist systems (DSS) . A healthcare organization that thoroughly understands its patients and is able to answer rapidly to their needs, scores highly with them-and this has become an extremely well-known 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 original reach to IT efficiency vis-a-vis ontological engineering-or ontology programming-that is possibly the most well-known wait on to ensuring just data integration, which fosters a better thought of patient needs, thus resulting in better patient care and obliging patient outcomes.
Ontological engineering excels at extracting knowledge and necessary information from the various information systems within a healthcare decision assist system (or its organizational databases) . Ontology programming reduces often difficult data integration issues and promotes data reuse, data sharing, and favorite 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 alive to 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 elaborate quality of care.
Detailed knowledge is also required to account for 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 resolve areas of improvement and begin appropriate programs. Therefore, the true compilation and correlation of patient data is considerable during the care process-both individually and in aggregate with other patient data-to choose potential process improvement steps.
As mentioned previously, healthcare organizations also attend from their patients’ recovering better and more quick as a result of higher quality care. This is, in no runt portion, 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 more indispensable to understand patient information effectively and consume to it to render care that leads to better patient satisfaction.
moral knowledge across intra-organizational domains can only be extracted when healthcare decision relieve systems are able to exchange relevant data with each other-which is not always possible with unusual configurations.Even if the numerous systems within an organization can connect to each other through approved computer interfaces, they may have stored patient data differently,rendering information exchange virtually impossible and creating a silo attain. Additionally, the context in which the information is veteran 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 accomplish a comprehensive and good individual patient concept 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 portray. 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.
Data correlation, integrity, and integration issues are not confined within an organization’s systems only. Health care organizations rely on HIE (Healthcare Information Exchange) to communicate with external entities. HIE is archaic to depart clinical information between different information systems from various providers (i.e. test labs, insurance companies, and other healthcare facilities) without losing the meaning of the information exchanged. These systems typically consume established standards for data exchange, such as SNOMED CT, ICD-9 and -10, and other HIE standards.
Periodic updates are required, and organizations must ensure that they are in compliance in order to participate in data exchanges with other providers. Naturally, whenever any data changes occur, the cost and time required to modify multiple systems within an organization can be staggering, but without the exercise of ontological engineering, the higher costs must be borne, as system modifications are mandatory.
Whether the data reside internally or external sources are employed for HIE, a healthcare organization faces the accepted issues of data mapping, data integration, reuse, and data sharing. Whenever data change, or fresh relationships between data are discovered, organizations utilize principal resources in time and money adjusting databases across various systems in an attempt to hold them aligned with each other. This absorbs vital resources, taking them away from the core focus and value proposition of the organization-that of providing quality patient care.
When data change, especially internal organizational data, stale technologies (as in “relational” databases) require changes to their database structures and schemas, potentially leading to major regression testing of the systems after the changes have been completed. This must be accomplished in order to ensure that nothing is deleted or corrupted after the changes are made, and is quite naturally, another costly step-both in terms of time and resources.
Information Technology departments have tried to reply to data integrity and data integration issues across various systems within an organization by building a data warehouse that acts as a central repository for most, or all, of the inter-related systems. However, the solution is only partially successful. Often times, competing interests from various internal “stakeholders” in different information systems can lead to data that is stored in a manner is great to some information systems, but not others. This, of course, potentially compromises data access and reuse by other systems.
In addition, since the entire organization’s data cannot be migrated to a data warehouse simultaneously, some systems are migrated before others, and the entire migration process may prefer as long as a year or more to complete in a titanic health care organization. In the interim, data across the enterprise changes, and the whole cycle of re-aligning data must commence anew. There have been proposed solutions to address this and other related problems, but they each leave something to be desired.
Ontology programming can aid nick data integration, sharing, and reuse trouble to quite an extent. By definition, ontologies are a formal representation of knowledge by a dwelling of concepts within a domain. They not only store data in a database, but also store relationships, including hierarchical relationships, between data.
This ability distinguishes ontological engineering from standard relational databases and provides the flexibility of updating data and relationships between them. Ontologies are also able to add newly discovered relationships without the necessity of significantly changing the core database or requiring extensive programming efforts-unlike typical databases currently in exercise. They also excel at removing term confusion and providing data mapping capabilities, which vastly promotes improved data fragment and data reuse across an organization’s information systems.
For healthcare organizations, as well as other enormous business enterprises, the practical, time-saving applications of a system built on ontology programming are quite extensive. We know that ontological engineering provides the ability to extract knowledge contained within applications and information systems across the various domains within an organization, but it is also very useful for capturing “right world decisions” made by humans and converting it into computer format. The result of this capturing of knowledge across domains by SMEs (Subject Matter Experts) and healthcare providers leads to distinguished more consistent ask results whenever similar conditions are encountered in the future.
Such information system architecture can significantly slash medical errors and enhance patient care. This can be accomplished, for instance, by the capturing of a healthcare professional’s diagnosis of a particular medical condition and other relevant data. Once the data are entered into the ontological system, it will consistently provide the same results for similar conditions in the future and offer the diagnostics and conclusions as an help to other healthcare professionals.
Subsequently, a healthcare professional may resolve to use the same diagnostics (or treat the patient differently according to differences in patient circumstances), but the healthcare decision help system’s information can now provide an distinguished, relevant checkpoint based upon the previous diagnostic information.
In conclusion, the utilize of ontology programming in the healthcare field provides a vital reduction in data integration issues and-because these technologies are salubrious extractors of knowledge across multiple information systems and can add unique relationships between such systems with relative ease-they provide the flexibility to change data with far less anguish and cost than standard systems now require.
Consequently, ontological engineering is able to provide an invaluable component to improved patient care and outcomes by supporting necessary healthcare processes and decision-making. The beneficial integration of knowledge and data within healthcare organizations may at first appear prosaic, but it is nothing short of revolutionary in its potential to affect organizational performance and quality care.