A Clinical Classification and Coding Proposal

Executive Summary

It is important to note that a large hospital organization’s Director of Health Information needs to be highly competent in developing and proposing essential operations, procedures, and policies. Any major organizational change on a policy level requires collaboration with committee teams, which will be substantively presented to the CEO and Board of Directors. The given seven-part proposal will address clinical classification and coding, principles and applications of classification systems, interoperability, health information systems, managerial challenges, data warehousing, and data and information management. Ensuring compliance should reflect the need for proper compliance from involved parties, such as CDI specialists, physicians, and coders. For data warehousing, the best approaches are an agile data warehouse and a data flow diagram.

Comparison of Applications/Systems for Clinical Classification and Coding

The selected applications or systems for clinical classification and coding are Computer-assisted coding (CAC) software by ForeSee Medical and an encoder by The TruCode Encoder. The first vendor has the capacity to evaluate quality coding practices by operating on Current Procedural Terminology or CPT coding and classification system (ForeSee Medical, 2022). The second vendor primarily functions with a capacity for the use of the International Classification of Diseases or ICD-10 coding application. In the case of implementation, the ForeSee Medical will require utilizing secure cloud computing, whereas The TruCode Encoder needs the 3M Coding and Reimbursement System Plus (CRS+) (High Tech Guide Inc., 2022). The first vendor has fewer training needs due to a more intuitive design, but the alternative is more complex. The best option is ForeSee Medical’s CAC using CPT, because it can be adjusted to other systems and is easier to implement due to cloud-based features.

The Principles Used Within a Clinical Documentation Improvement Program

The overall value of a clinical documentation improvement (CDI) program cannot be overstated. It is a process that is designed for “bridging the gap between clinical terminology used by physicians and diagnostic coding terminology used for reimbursement is an arduous task” (Fee, 2022, para. 1). However, there are serious challenges, which require a major consideration when implementing such a program. For the accurate diagnostic and procedural coding with classification systems, the first issue is the fact that “even despite a less than one percent observed shift in MS-DRGs from ICD-9 to ICD-10, changes are not always intuitive” (Fee, 2022, para. 4). For the coding, auditing, and CDI programs, the second challenge is that “coding and sequencing secondary diagnoses requires careful consideration” (Fee, 2022, para. 5). These factors need to be considered due to the need for proper compliance from involved parties, such as CDI specialist, physicians, and coders.

Interoperability Issue That Is Possible Within the Health Information Exchange

It should be noted that interoperability is a major problem within the health information exchange (HIE). It is stated that “interoperability is the capacity for healthcare providers to access and share clinical information and the most up-to-date and complete patient medical records, regardless of the healthcare system where they work” (Bauza, 2021, para. 3). The selected interoperability issue of interest is a consumer-mediated exchange, which is about providing access to patients to their health information. The two best practices to address the problem are benefited to patient approach and access to data enhancement (Bauza, 2021). The first one refers to “often or nearly always having access to necessary data through interoperable means” (Bauza, 2021, para. 14). The second practice is “that the patient data accessed in the EHR benefits the patient care outcome” (Bauza, 2021, para. 14). In other words, both focus on ensuring that only useful data is given to the patients to make the data sharing simpler, and access to available constantly by having interoperable methods of delivery.

Evaluation of Health Information Systems and Data Storage Designs

The selected two health information systems are Electronic Health Records (EHR) and Patient Portal. A thorough evaluation reveals that EHR “increases efficiency by eliminating paperwork and giving immediate access to patients’ data … improves the quality of patient care … avoiding any medical errors” (Shurshti & Saxena, 2020, para. 18). However, Patient Portal provide patients with access to their health information, such medical history, medications, or treatments. It additionally allows “to schedule appointments, view bills, and make payments online. They can use their personal devices … to access it” (Shurshti & Saxena, 2020, para. 26). In the case of data storage designs, cloud storage is both scalable and cost-effective, whereas hybrid storage design offers a balanced approach by combining on-site and cloud storage (IBM, 2022). The best HIS would be EHR because it is more comprehensive and a prerequisite for Patient Portals since the latter retrieves information from the EHR. A hybrid data storage design is preferred because it is more resilient to risks and threats. For example, if physical storage units become damaged due to fire, the healthcare organization can access the cloud, and if the latter becomes hacked, there is always a physical backup.

Managerial Challenges Related to Clinical Indices, Databases, and Registries

The two managerial challenges to clinical indices, databases, and registries from the perspective of a hospital’s health information management function include data completeness and data inconsistency (Pharma Intelligence, 2018). For the former, it is recommended to properly monitor the implementation as well as data collection processes on the policy level, which means that supervision needs to be strict and authoritative (Pharma Intelligence, 2018). For data inconsistency, duplicate or absent data collection sources need to be identified and addressed through more rigidly defined practices.

Data Warehouse Design Approaches

The first approach in data warehouse design that supports quality data management from varying sources, processing/storage of data throughout the warehouse model, and meaningful output into the presentation layer are having a data flow diagram. It is stated that “knowing where all the business’ data repositories are and how the data travels within the company in a diagram format allows everyone to determine the best steps for moving forward” (Hoyle, 2018, para. 4). The second measure is to utilize an agile data warehouse. In other words, “data warehouses no longer have to be large, monolithic … with proper planning aligning to a single integration layer, data warehouse projects can be broken down into smaller, faster deliverable pieces” (Hoyle, 2018, para. 7). The latter allows for an organizational be more flexible, whereas the former is about a more structured approach.

Data Creation, Collection, Management, Storage, and Transformation

The analysis reveals that data collection needs to be conducted under specific conditions to avoid future disruptions and issues. Data creation or collection needs to be purposeful and targeted in order to avoid collecting unnecessary ‘noisy’ data. The management process involves access and usage stages, where information was secured, processed, analyzed, and utilized (Romadhoni, 2020). The storage phase is about data sovereignty, where the security and safety of data are of paramount criticality. Subsequently, data is transferred through network micro-segmentation and user access enablement. Lastly, the retention process arrives, where information needs to be destroyed, eliminated, or deleted (Romadhoni, 2020). The duration of retention is tied to specific needs of a particular organization, but three years should be the most optimal time period (RCR, 2022). Data has the highest utility in a healthcare setting within its first three years of retention.


Bauza, D. (2021). Why is interoperability important in healthcare? Audacious Inquiry.

Fee, J. P. (2022). Four CDI challenges Emerge in ICD-10. The American Health Information Management Association. 

ForeSee Medical. (2022). Computer-assisted coding software. 

High Tech Guide Inc. (2022). What are the two types of encoders in medical coding? Web.

Hoyle, K. (2018). Top 9 best practices for data warehouse development. Snowflake. 

IBM. (2022). What is data storage? 

Pharma Intelligence. (2018). Challenges and opportunities in clinical data management [PDF document].

RCR. (2022). Retention of data

Romadhoni, F. (2020). What is data lifecycle management and what phases would it pass through? Medium. 

Shurshti, W., & Saxena, P. (2020). 8 types of health information technology & healthcare software system. Software Suggest. 

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