Clinical classification and coding are assisted by several coding software that is designed to improve efficiency in the collection of data and database management in the health industry. In this proposal, I will evaluate encoders and their roles in coding for health systems. Additionally, I will appraise various implications of their pros and cons. At the same time, the issues, challenges, accuracy, and operability of these selected coding soft wares will be discussed in this paper.
Systems for Clinical Classification and Coding
Two commonly used clinical coding applications are ICD 10 and CAC. Computer Assisted Code systems analyze health documentation and isolate key terminology and suggest relevant codes for the particular treatment or service (Grider & American Medical Association, 2010). ICD 10 is a clinical analyzing tool that provides clinicians with a medical cataloging platform that stores clinical data for specific care provided to patients. CAC systems can efficiently evaluate documents and provide code for clinicians. This makes it essential in the collection of clinical data. ICD 10 can also handle the same issues with ease. It provides clinicians with relevant codes for each condition that is under study.
One of the main issues affecting these systems is the lack of ability by clinicians to reliably remake raw information from the information that is encoded. Many clinicians in the world often lack the technical know-how to work with such software. Another issue is that there is a lack of a formally approved atomic nuclear vocabulary for clinicians (Mary, 2011). One of the challenges for these two systems is, therefore, the lack of conformity in the type of data that clinicians provide. Thus, there is a conflicting level of reflection on information that often is found unfriendly by clinicians. All in all, Computer Assisted Coding is the best method for capturing data since it enhances continuity in the data by providing principles that guide competent coding of health procedures for clinicians.
The accuracy of Diagnostic and Procedural Coding
For most clinical Documentation Improvement experts, bridging the gap between clinical data collected by physicians and diagnostic coding terminology is a strenuous exercise. With such a challenge it is harder to work with new clinical coding software since they pose new issues to the way that data is collected and analyzed. Therefore, CDI experts must continue to encourage providers to document the necessary reports specifically. One of the issues is that MS-DRG experts still use older ICD software to validate data from newer builds of ICD, i.e., ICD 9 is used for the study of ICD 10 (Grider & American Medical Association, 2010). Another challenge is that coding and sequencing of data for secondary diagnoses often require careful consideration. Newer builds of ICD, ICD-10-PCS, need clarification by physicians. This complicates the whole coding process. At the same time, ICD 10 has slowed CDI expert productivity.
With these classification systems, auditing of data often requires careful analysis of different condition. An accurate diagnostic is guaranteed if the physician has good knowledge of the coding of clinical problems (Mary, 2011). Thus, procedural coding ensures that the whole process of database creation and management is fast and accurate to the needs of the clinicians for future use. Coordination of these three elements is vital, and therefore CDI programs should be put into place to manage the flow of information between parties involved in the coding procedures.
Information Operability and Information Exchange
One of the issues in interoperability of health systems and information exchange is enforcing industry-wide interoperability review standards (Roza, 2010). With new initiatives for improvement of interoperability in health information systems, there exists frequent sprouting of federal agencies' and industries' need to measure the progress of the efforts created consistently. Interoperability standards vary regarding the stakeholders.
One of the solutions to the problem is creating a framework that defines interoperability standards measurement (Suzuki et al. 2011). This designed framework should evaluate the industry's progress regarding the way that the implementation of interoperability standards have been used in the past. Another solution is to assess how the standards that are currently used conform to those of other health care systems in the world. Understanding the differences builds the need to create a basic system that is easily comprehendible on a large scale by clinicians. Another solution to this problem is promoting nationwide consistent standards measurement for health information systems. Consistency breeds similarities which enables clinicians to work with health information from different systems reliably.
Health Information Systems and Data Storage Designs.There is a wide range of health database systems that are used by clinicians and hospitals around the world. These systems include operational and strategic HIS, clinical and administrative HIS, financial HIS and decision support HIS. Operational and strategic HIS use tactical divisions to handle information and knowledge that deal with specific kinds of information. Clinical and administrative health systems allow the production of letters for patients and make follow up on the ways that data was addressed in the hospital. Financial HIS manage cash transactions and payments for health facilities while decision supports HIS define methods in which clinical decisions should be made. Examples of data storage designs are on-premise, cloud and hybrid structures (Roza, 2010). On-premise storage designs store data in hard drives, flash disks, and other storage media for use by clinicians that are onsite, that is, physically available. Cloud storage stores health data in servers that are accessed through the internet on request, on the other hand, hybrid structures, employ both cloud and onsite structures to store health data. Hybrid data storage is the best methods since it allows clinicians to access data anytime anywhere hence recovery of data is enhanced.
Management of clinical databases
One of the main challenges that affect management in clinical databases is management is security. Since the inception of the use of technology such as mobile and computers in health care management, the problem of security has increased since the possibility of compromising the integrity of data is higher. Managing health information systems, therefore, requires patient data is not accessed by unauthorized persons. Another challenge is the lack of conformity in the link between clinical and administrative systems (Suzuki et al. 2011). Patient care and administration often lack integration. This makes sharing information between these levels even harder. Another challenge is the lack of analytics talent. Often, health care provides a struggle to find analytics experts to analyze data for clinical use efficiently.
For best practice relating to secondary data, clinicians should ensure that integrity of patient data is upheld at all times. Securing information is, therefore, the primary role of the clinicians and healthcare facility (Mary, 2011). Another practice is creating good database designs for secondary data that would ensure that individuals can easily access and use the data when the need arises. Moreover, clinicians should enhance the structures for secondary data by providing the data is cleaned. This will improve the relevance of the data to the study of conditions.
Approaches to Database Warehouse Design
There are two main approaches to database warehouse design that is top-down and bottom-up designs. Top-down design starts from the general and narrows down to specific parts of the database (Roza, 2010). This means that in the top-down design, definitions and types for each attribute of an entity are put in the general idea of what is needed and specifics are defined after that. The bottom-up design starts with specifics and moves to a general approach to the design of a database. Both of these approaches deal with different data types.
Clinical classification and coding are assisted by several coding software that is designed to improve efficiency in the collection of data and database management in the health industry. Database design in healthcare systems is a vital addition to the handling of clinical data. It is therefore crucial that CDI specialists and Clinicians understand the implications of procedural coding and other aspects in the creation of health databases. Enhancing data integrity ensures security for coded data in the healthcare system. To improve continuity in the structure of coded data, standards for measurement of progress in the handling of clinical data should be created.
Grider, D. J., & American Medical Association. (2010). Preparing for ICD-10-CM: Make thetransition manageable. Chicago, Ill.: American Medical Association.
Mary, J. B. (2011). Understanding ICD-10-CM and ICD-10-PCS Coding: A Worktext. CliftonPark, N.Y: Delmar.
Roza, G. (2010). Databases. New York: Rosen Publishing.
Suzuki, K., Gruszauskas, N. P., Drukker, K., Giger, M. L., Banik, S., Rangayyan, R. M.,Desautels, J. E. L. & Jiang, S. B. (2012). Machine learning in computer-aided diagnosis:Medical imaging intelligence and analysis. Hershey, PA: Medical Information Science.
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