Challenges in Healthcare Technology – Health System Example

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"Challenges in Healthcare Technology" is a great example of a paper on the health system. One of the greatest challenges for medical information technology is in defining a controlled terminology to ensure standards interoperability (Fidahussein, Friedlin & Grannis, 2011). The original goal of standardization efforts was to establish a simple data interchange standard. However, today, standardization efforts are keenly focusing on defining a controlled terminology to ensure standards interoperability. This is the greatest challenge for medical technology because many different terms are used in expressing identical or similar concepts.

Such usage of a wide variety of terms brings up the question of inconsistency, which in turn complicates data integration, comparison, and sharing between different departments. The implications that health care providers are facing in the light of the National Health IT agenda are broad-reaching and the impact cannot be underestimated. Logical Observations Indexes Names and Codes (LOINC) defines codes for information such as clinical observations and blood chemistry laboratory tests (LOINC, 2011). LOINC makes the health care domain extremely dynamic and complex. It enables computer systems to receive the data to generate exact interpretations (semantic interoperability). In 1999, the Clinical Care Classification (CCC) of Nursing Diagnosis and Outcomes was integrated into the LOINC standard (Saba, 2011), and later presented to the National Committee on Viral and Health Statistics (U. S.

National Library of Medicine, 2011). In 2003, The CCC system was recognized, confirmed, and approved as a terminology meeting the two reference terminology model structures, nursing diagnoses, and nursing actions. The integrated system uses three knowledge sources and it facilitates interoperability between systems. The system uses Metathesaurus, Semantic Network, and SPECIALIST Lexicon and Lexical Tools to combine standards and terminologies that are used in health care (U. S.

National Library of Medicine, 2011). The usage of these standards and terminology eliminates the risk of erroneous interpretation. There are three types of interoperability (data, process, and context) (LOINC, 2011) and their related challenges. The greatest challenge of interoperability arises in data reconciliation for multiple providers in health care delivery. Second, there is a challenge of communication being non-prospectively mapped to archetypes. Thirdly, due to complexity that arises from wide usage of terms, there is poor integration of hard and soft aspects of interoperability.

These challenges are very disruptive to work processes and work cultures and this may result in significant harm to patients. There are various strategies that health care organizations can adopt to address the challenges of interoperability (Fidahussein, Friedlin & Grannis, 2011). Standards are valuable only when they are adopted, shared, consistent, and stable. Health care organizations must reconcile the differences within them to achieve semantic interoperability. The popular workflow-based mediation framework provides an interoperable healthcare information system environment within different organizations. This framework enables integrated and secure access to data and information irrespective of the health care organization it resides. Workflow management systems are important in organizations and research fields because they are designed to automate processes by efficiently controlling and coordinating the flow of work between participants.

However, they only increase interoperability problems. Therefore, this paper proposes a distributed workflow authorization model that has no central WFMS. This new framework is built on top of a secure collaboration framework for mediator-free environments and this ensures secure interoperability. For example, the proposed framework enables physicians in a particular health organization to access services provided by other organizations in different locations.


Fidahussein, M., Friedlin, J. & Grannis, S. (2011). Practical challenges in the secondary use of real-world data: the notifiable condition detector. In Logical Observation Identifiers Names and Codes. Retrieved from

Logical Observation Identifiers Names and Codes. (2011). Retrieved from

Saba, V. (2011). Clinical Care Classification System. Retrieved from

U.S. National Library of Medicine (2011). Unified Medical Language System (UMLS). Retrieved from

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