Health Information Technology the Development of Patient

Health Information Technology

The development of patient management systems continues to revolutionize the field of healthcare, specifically in the areas of treatment plans, content and records management systems, and predictive analytics. These areas are all making healthcare information and knowledge management a critical success factor in creating effective healthcare information systems globally (Epstein, Fiscella, Lesser, Stange, 2010). Implicit in the areas of records management and predictive analytics is also the need for more effectively managing standardized terminologies that are critical to document patient care. The greater the depth of insight that healthcare information systems can deliver, including the acuity of analysis and availability of predictive analytics to measure the progress of treatment plans, the greater the need for an agile, flexible taxonomy of standardized methodologies as well (Schmitt, 2002). The intent of this analysis is to evaluate how taxonomies can be used for defining and managing standardized terminologies used for documenting patient care. Also included in this analysis is a discussion of how the taxonomy of standardized terminology is distributed across the continuum of knowledge, data analysis, formation and use of frameworks and how healthcare professionals can gain greater insights through the use of the healthcare information systems.

The Importance Of Standardized Terminologies In Patient Care

Previous generations of patient care systems and processes focused more on functional aspects of system development and use (Cliff, 2012). This has cratered a highly fragmented, silo-like approach to the management of every aspects of the patient care experience. Often done specifically to keep costs low, the effectiveness of terminologies and the taxonomies supporting them were often split by the specific functional areas of a healthcare treatment center, not aligned to the specific process areas and needs of a given patient's treatment (Schmitt, 2002). Aligning terminology structures and taxonomies in a more static approach led to a more abbreviated aspect of the data and knowledge continuum, where only the most used terminology and resulting patient care programs were integrated into the initial systems that includes these functional enhancements (Epstein, Fiscella, Lesser, Stange, 2010).

Moving from a functionally siloed approach to creating a more roles-based analysis and definition of healthcare management systems including patient care systems and data structures led to greater levels of patient-driven metrics and analytics over just the reliance on terminology and static work processes (Simpson, 2003). Redefining the taxonomies of healthcare information systems to be more focused on patient outcomes and less on the static, often didactic approach to how terminology was used as a means to manage complex patient care programs to completion have been replaced with a more efficient approach that is based on overall patient outcomes first (Cliff, 2012). The continual adoption of wireless technologies including Wi-Fi has assisted with the proliferation of these technologies throughout healthcare providers as well (Claudio, Velazquez, Bravo-Llerena,, et.al., 2010). This shift to a more patient-centered approach to aligning the many processes throughout healthcare facilities has led to a corresponding re-shifting of the standardized terminologies to also support a more patient-driven data taxonomy and model throughout healthcare providers as well (Simpson, 2003).

This taxonomy-based shift is also driving a reformation of how healthcare information systems are designed and used. Accelerated by the pervasive adoption of Wi-Fi throughout healthcare systems and provider locations, the aligning of patient-driven taxonomies of terms and the processes they use are becoming far more prevalent than ever before (Claudio, Velazquez, Bravo-Llerena,, et.al., 2010). Today physicians and services providers can create and manage cross-functional teams that allow for rapid patient prognosis and the development of treatment plans that are based on the collective intelligence of the entire team (Cliff, 2012). This practice of using Wi-Fi enabled networks within healthcare providers shows significant potential for alleviating the bottlenecks that a lack of terminology definition and clarity also has caused in the past.

By aligning standardized terminologies to patient treatment-driven taxonomies first, the level of understanding of specific terms and their relative context can also be more efficiently managed as well. The contextual intelligence of standardized terminologies becomes more accurate, clearer and easier to learn when put into the context of a patient outcome-driven scenario. When taxonomies are created that specifically address these aspects of managing patient workflows and treatment programs terminology can also be significantly abbreviated over time, as well, as there is greater simplicity in patent-based process workflows over complex administrative ones (Schmitt, 2002). The point of standardized terminologies being more oriented towards a common foundation of patient-driven outcomes is also accelerating them towards the more outcome-based areas of the data, information and knowledge continuum as well (Cliff, 2012). This has significantly helped to create a more effective base of knowledge to evaluate new potential treatment programs on, and has in turned fueled a significant knowledgebase that has been used for over a decade to further streamline terminologies to be more aligned to patient outcomes for greater contextual clarity and focus (Schmitt, 2002).