Health Organization Policies: Clinical Classification and Coding

Topic: Health Tech
Words: 1208 Pages: 4

Computer-assisted coding (CAC) is a system that can accurately and automatically create medical codes from clinical documentation electronically. The system analyzes different medical documents to generate accurate medical codes for specific phrases and terms (Faes et al., 2019). The advantages of CAC facilitate matching outputs with reporting requirements, including official guidelines vital to enhancing accuracy (Faes et al., 2019). With CAC, implementation considerations are easier to analyze, thereby improving compliance. Since a computer-assisted coding system performs automatic coding assignments, system management challenges, including training needs, remain minimal.

Encoders in the health care system update medical codes, enhancing the accuracy of the coding process. While the system helps in sequencing different judgments, the system remains ineffective in executing specific rules regarding various aspects of patient care. Research offers that the system limits the utilization of the coding rules (Faes et al., 2019). The installment process’s problems include frame gathering, which involves the system, including analysis linked to patient and outpatient settings. Computer-assisted coding is the preferred system since it efficiently performs automatic assignments and requires no human effort to yield medical codes. The system can be connected to electronic health records to facilitate easy access to medical information that allows for continuity of care.

Principles and Applications of Classification Systems

The International Classification of Disease (ICD-10) is a medical coding system that has transformed procedures and healthcare diagnosis by applying universal medical codes. The classification system encompasses the HCPCS coding system, which recognizes elements including supplies, administrators, items, and supplies omitted in the current procedural terminology codes (Fung et al., 2020). The challenges within the systems include tracking epidemiological patterns and records of patients’ well-being. Considerable value in establishing viability, quality, and security that sum up beneficial human services, including therapeutic repayment options.

Clinical documentation improvement reflects the medical record documentation review process for accuracy and completeness. The issues shared with the CDI program include lack of interpretation of OASIS, lack of documentation, and lack of narratives with the electronic medical records (Fung et al., 2020). Particularly, bridging the clinical terminology gaps in the diagnostic coding process and among healthcare providers remains a challenge. Hiring individuals with coding knowledge and the program is time-consuming and costly. The right professional should also exhibit knowledge about coding guidelines, including ethics governing the care providers within the query process (Fung et al., 2020). Regarding auditing, the program offers timely auditing and correct diagnostic with classifications that help monitor and enhance the integrity of the CDI programs. Clinical documentation improves compliance with extreme quality measures that supports coding. Proper examination of conflicting data and ambiguous and timely, including active participation, remain critical in enhancing adherence. The practices allow for correct codes that reimburse the different services within the hospital.

Across the health systems to obtain an overall view of patients’ health records. The interoperability issue addressed exists within the electronic exchange of information (Fung et al., 2020). The problem influences the healthcare quality, including medication errors within the care practice. The best practices can be realized when correct identification and correct matching of accurate records are provided. Patient identity matching reflects the process of identification that links data. The different matching techniques critical to inpatient identification foster proper patient labels and minimize the likelihood of errors (Fung et al., 2020). The process is also cost-effective and guarantees quality execution of time. They reduce elements of duplication of patient records and the drive-by the health system to improve patient safety (Farooqui & Mehra, 2018).

Health Information Systems

There are numerous health information systems, such as medical practice management systems and electronic health records. The medical practice management system serves as an integral component of the health system (Farooqui & Mehra, 2018). The practice management system remains geared towards streamlining numerous activities that enhance the smooth running of the systems. The process allows for healthcare data that provides for medical management software. The software automatically provides for the automation of tasks, including routine clerical assignments. Electronic health records (EHR) are an example of healthcare management information involving patients’ medical data (Farooqui & Mehra, 2018). The EHR has facilitated easy access to information, enabling accurate and quick data access. EHR engages with data management, which consists of data on medical history that enhances the provision of high-quality data (Farooqui & Mehra, 2018). EHR improves efficiency by immediately accessing patients’ information by eliminating paperwork (Farooqui & Mehra, 2018). It also allows for information sharing across numerous departments to minimize medication errors.

Examples of data storage systems include on-premise, which involves data storage on a specific medium that enhances complete control of information. The benefit of the data system is that it remains secure and with full visibility allowing health organizations to gain power. The system’s benefits allow for complete access, storage, and security of data (Farooqui & Mehra, 2018). It enables leveraging cloud technology that enhances the flexibility and efficiency of data storage solutions. According to Farooqui & Mehra (2018), scalable cloud resources information offer health organizations access to computing resources that propel innovations within the care setting.

For disaster recovery purposes, the most appropriate health information system encompasses electronic health records, which are automated and allow easy access to information, supporting high-quality services to patients. I would choose electronic health records because it remains highly efficient and minimizes increased paperwork. The storage system entails hybrid cloud data storage system (Farooqui & Mehra, 2018). Healthcare can use the system to take advantage of various aspects of hybrid cloud deployment by developing recovery protocols that maximize data availability and uptime, making it suited for disaster recovery.

Managerial Challenges

Managerial challenges linked to databases include the potential of bias and participant confidentiality and privacy. Observational bias is a concern that affects the administrative roles in terms of accurate clinical indices, databases, and registries (Farooqui & Mehra, 2018). Allowing patients to access their data and enhancing participants to retain control over their data within the health system remains difficult (Farooqui & Mehra, 2018). Management of secondary data sources includes weighing the different risks, including benefits, to maximize the data value. Another essential component to properly managing data includes ensuring issues with provisions.

Data Warehousing

Medicare records is an essential data warehousing that allows for the organization of medical information to limit out of sight unauthorized individuals. It enables healthcare providers to sustain medical records to enhance an understanding of patients’ care, including treatment decisions critical in influencing overall health outcomes for patients (Farooqui & Mehra, 2018). Another data warehousing is administrative records which imply that records contain information related to functions, organization, policies, and procedures, including other institutions’ operations (Farooqui & Mehra, 2018). Compared to medical records, administrative records are the most effective as they enhance quality data management within the care setting, fostering meaningful connections.

Data Analysis and Information

Data collection offers vital information critical in answering different information and other outcomes that predict trends in the prospects (Farooqui & Mehra, 2018). Data is accessed at the point of collection, where data is gathered for use in clinical decision-making, including strategic planning and other processes. After data collection, data is analyzed, and essential information is separated from unwanted data. Information is then stored in appropriate systems such as electronic health records and duration for storage analysis before their dissemination.


Faes, L., Wagner, S. K., Fu, D. J., Liu, X., Korot, E., Ledsam, J. R., & Keane, P. A. (2019). Automated deep learning design for medical image classification by healthcare professionals with no coding experience: a feasibility study. The Lancet Digital Health, 1(5), e232-e242.

Fung, K. W., Xu, J., & Bodenreider, O. (2020). The new International Classification of Diseases 11th edition: a comparative analysis with ICD-10 and ICD-10-CM. Journal of the American Medical Informatics Association, 27(5), 738-746.

Farooqui, N. A., & Mehra, R. (2018). Design of a data warehouse for medical information systems using data mining techniques. In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 199-203). IEEE.

The Electronic Health Records Implementation
The Electronic Health Records Implementation