The two systems for clinical classification and coding are the Computer Assisted Coding System and the Clinical Classification Encoder. The computer assisted coding system is a software that carries out the analysis of healthcare documents and provides a concise list of the medical codes for the specific phrases and terms used in the document (Tech Target, 2017). This system uses the natural language processing to decipher and highlight the key terms in the health documents. The coding in this system is done according to the ICD-9, ICD-10, and the American Association’s Current Procedural Terminology coding. This system has the capacity to distinguish between instances that require and those that do not require coding. The current switch to ICD-10-CM facilitates the easing of the system to accommodate this change in the health facility’s coding systems. The system, however, is not a complete take over from the manual coding and as such will require the use of manual coders to cross check the output of the system hence requiring the use of coding personnel to manage its outcome.
The other system for clinical classification and coding is the widely used Clinical Classification Encoder (ICD/OPCS). It promotes the generation of “accurate, consistent, complete and timely diagnostic and procedure coding” (3M Health Information Systems, 2017) and as such provides high quality coding. This coding software incorporates all the coding classification rules, regulations and recommendations and as such will require the training of personnel in rules and convention of the national standards which is very challenging and takes a lot of time. It possesses no implementation setbacks as it is compatible with the software and encoders that apply the rules and convention of the national standards.
Therefore, the Clinical Classification Encoder is the best option as it is the widely used clinical classification and coding system and it uses and relies on rules and convention of the national Standards making it compatible with the systems and software used in many other organizations.
The Clinical Documentation Improvement program is necessary in any health organization as it enhances the accuracy of the coded data. The value of this program is that it helps provide accurate data that narrates the patient’s story, reveals the complexity of the patient case and reflects on the care that the patient should be accorded in the health facility (Cindy et al, 2013). The challenges that face the Clinical Documentation Improvement process are the availability of multiple providers who cause chaos in the process. The inconsistency of the process also proves to a challenge as there is no structured guideline to use in the process and the practices of clinical documentation is not taught in the medical school or residency programs making the health professionals lack the skills of how to conduct the process and as such result in poor quality clinical documentation (Cindy et al, 2013).
The classification systems and the Clinical Documentation Improvement programs devise the correct procedural coding that make accurate diagnosis possible with affecting the correct cost for the health services offered and as such makes auditing easy (Melanie, 2014).
The Health Information Exchange (HIE) is a process that involve the sharing of data between many departments in the health organization or sharing these data among the different organizations involved in the dissemination of health services. This exchange of information is crucial to provide enough data that guides the decision making process. This process involves the operability of many software and hardware among the many departments in an organization or the many organizations in a health sector. One possible issue that might affect this interoperability between applications or systems is the possible lack of compatibility between these applications or systems.
The lack of compatibility between applications and systems might arise due to the differences in the data interfaces used by the varying applications and systems. This will cripple the exchange of the health data as the shared data cannot be picked up and used by any other application or system. This will render the data exchange process a useless one and prove to be a waste of time. To avoid this kind of scenario, three practices could be done ahead of information exchange. These practices include: the purchase of compatible hardware and software, system integration and checking for application and system’s compatibility prior to installation (aceproject.org, 2017). The purchase of compatible hardware and software saves the organization a lot of time that could be used to create intermediate data interface converters and as such limits the percentage error that arise from these data interface conversion processes. The checking for compatibility between already installed applications and systems also saves the organization a lot of time that would be lost in data traffics due to slow inefficient systems and data conversion processes taken to make the exchange of health information possible. Finally, system integration will ensure the continuous flow of data from on point or department to another. This will avoid the unnecessary data traffics created due to poor system integration.
Health Information systems (HIS) refer to any system that captures, stores, manages and transmit information that is related to the health of individuals or from organizations that are involved with the dissemination of health services (Public Health Information Network, 2017). These systems include the District Level Routine Information Systems, Hospital Patient Administration Systems, and Disease Surveillance Systems among many others. The District Level Routine Information System is a health information system that acquires health information from many health care organizations in a district and reviews them for quality data documentation as well as stores this information for future references. This enables a district to outline the common diseases affecting the people of that area and device good mechanism to combat such common diseases. The Hospital Patient Administration System is a health information System that captures, stores, and manages the health information and hence the health records of an individual who is a patient in a health facility. This information system is an example of the many information systems that feed the District Level Routine Information System. The Disease Surveillance System is similar to the District Level Routine Information System but only captures the entries of disease outbreaks, the number of affected people in the population and the fatality rate of the disease. It is kept and maintained by majorly the ministries of health in various countries.
The information systems apply various data storage designs that could include onsite storage or online storage options or a combination of both. The onsite storage options often involve the storage of data in flash memory thumb drives and/or the use of external hard drives (Peter, 2017). All these storage options will require physical storage spaces that are easy to break in and could be easily destroyed by clematis such as fire outbreaks. The online storage options provide the opportunity for one to back up their onsite data in a secure, remote server such as in a Cloud. This kind of storage option is mainly free from the risk of damage by physical calamites as it makes use of virtual space. However, it requires high security profiles to make sure that it is free from spyware and viruses. The combination of the two storage option is mainly efficient and practical as it enhance security of data and [provides for a backup system in case of any accident.
Therefore, for disaster recovery purposes, the District Level Routine Information System and a combination of the onsite and online data storage designs are the best options. This because, in case a facility’s medical records department is destroyed by an accident it cannot suffer much data loss problems as the District Level Routine Information System and the online data storage servers offer backups for the facility’s data.
Health Information management is the most crucial process that guides the decision making processes involved to decipher patient treatment, determination of public health, cost management, and quality control for the many health databases (Ray, 2017). The management of health information is however faced by many challenges including the lack of understanding of the proceeds of the databases used in an organization, the lack of proficient health information management professionals, and the use of outdated database management systems. The lack of understanding of the proceeds of the databases used in an organization is brought about by the poor training of health informatics professionals and this can be elevated by the provision of adequate and proper training to students studying health informatics. The lack of health information management professional can be elevated by the provision of comprehensive courses in health information management. The use of outdated database management systems is brought about by the lack of technological proceeds and innovations done in the healthcare. This can be elevated by the setting aside of money to fund technological research in the healthcare system and hence improve on the rate of technological advances made in the field with the division of and application of up to date database management systems.
Therefore, the three best practices related to the management of secondary data sources include the provision of adequate and proper training to the students studying health informatics, provision of comprehensive courses in health information man agent to increase the number of professionals in this field, and the provision of funds to oversee extensive research in the technological aspects of the healthcare system to ensure the innovation of up to date database management systems (Veerlde et all, May 2017).
Data warehousing involves the process of creating and using systems that do carry out data analysis and data reporting. These data warehouses process data by data cleansing, integration and consolidation. Creating a data warehouse might follow two common and popular approaches. One that is referred to as the top-down design approach and the other one is the bottom-up design approach (Folks Talk, 2017). The bottom-up design approach involves the creation of the basic data marts that are specialized in specific areas of health. The marts are created first to enhance data reporting capabilities. The different data marts are then integrated together to make one complete data warehouse. This approach enhances the uniformity of the data marts in the data warehouse while making it easy and exceptionally fast to report data. This approach is, however, reverses the positions of the data warehouse and the data marts.
The top-down design approach uses the integration of all data into one data warehouse from which the data marts can be created. This approach enables the consistent dimensional view of the data in the data marts as all data marts are derived from the same data warehouse. It is also very dynamic and can accommodate changes in the health facility since the creation of data marts is very simple.
References
Ace. The Electoral Knowledge Network. Elections and Technology. Retrieved from https://aceproject.org/ace-en/topics/et/etb/etb02/etb02e Accessed on 13th May 2017
Cindy, D., Coleen E., Linda, D., and Judy, P. (2013). Improving Reimbursement through Clinical Documentation: A New Beginning. 1
Folks Talk. (2017). Data Warehouse Design Approaches. Retrieved from https://www.folkstalk.com/2011/04/data-warehouse-design-approaches.html Accessed on 13th May 2017
Melanie, E. (2014). Evolving and Adapting: The Changing Role of the CDI Professional. AHIMA: HIM Body of Knowledge. Retrieved from https://bok.ahima.org/doc?oid=301716#.WRfsq8mnwWE Accessed on 13th May 207
Peter, A. (2017). Choosing the Best Data Storage Solution. Retrieved from https://www.entrepreneur.com/article/172226 Accessed on 13th May 2017
Public Health Information Network. (2017). Health Information Systems (HIS). Retrieved from https://phinnetwork.org/resources/health-information-systems-his/ Accessed on 13th May 2017
Ray, H. (2017). Healthcare Database Management for Health Information and Information Management Students: Challenges and Instruction Strategies-Part 1. (Abstract). Retrieved from https://eduperspectives.ahima.org/healthcare-database-management-for-health-informatics-and-information-management-students-challenges-and-instruction-strategies-part-1/ Accessed on 13th May 2017
Tech Target. (2017). Computer assisted coding system (CACS). Definition. Retrieved from https://searchhealthit.techtarget.com/definition/computer-assisted-coding-system-CACS Accessed on 13th May 2017
Veerlde, Van den E., Louise, C., Matthew, W., Libby, B., and Laurence Horton. (2017). Managing and Sharing Data. UK Data Archive. [PDF]. Retrieved from www.data-archive.ac.uk/media/2894/managingsharing.pdf Accessed on 13th May 2017
3M Health Information Systems. (2017). Clinical Coding Software. [PDF]. Retrieved from Download information about the 3M™ Medicode™ Classification Encoder (PDF, 245KB) » Accessed on 13th May 2017
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