Information Governance Considerations within a Healthcare Setting

Part 1: Electronic Systems for Clinical Classifications and Coding

Clinical coding is regarded as one of the fundamental functions in the field of health information management, and can be said to be the process where descriptions of diseases, injuries, and procedures are designated into numeric or alphanumeric designations. Classification as a general term means a certain system which organizes related entities. In healthcare, classification systems are meant to classify clinical conditions and procedures in order to support statistical data analysis across the whole of the healthcare system. They are used to set standards for comparisons of statistics of health, both nationally and internationally. Clinical classification and coding both require electronic applications or systems which need proper evaluation before they are implemented and managed. This ensures that only the best system is used. 

We will consider two systems which are used for clinical classification and coding. These will be ICD-10 and ICPC-2. The former stands for International Classification of Disease whereas the latter stands for International Classification of Primary Care and was purposefully meant to capture the interaction between a patient and the GP. This system has a biaxial structure, one which is primarily oriented around one of the body systems, represented by a letter. For instance, D stands for digestive. The second axis is represented by a number which covers the seven components contained in every body system. The resulting simple code is unique for each item and usually has a letter and two digits. During the implementation of ICPC-2 system, it is possible for issues, mainly criticism, to arise. This is because the system offers fewer diagnosis codes as compared to other systems. It however, has limited management challenges and training needs since its concepts are easily understood. ICD-10 is possibly the earliest electronic system used for clinical classification and coding and has evolved all the way from the 19th century where it was used to classify causes of death only, and now it has become an extremely rich classification system which covers signs, symptoms, procedures, and causes of injury and diseases. The possible problem that may arise from using ICD-10 is that it is too detailed for use by GPs. In addition, for the system to be effective, it needs proper management and training due to the complexity of its functions. I find ICD-10 to be the best option because it is excellent in its role, and it’s also very broad in the role that it performs (Alakrawi 2016). 

Part 2: Accuracy of Diagnostic and Procedural Coding

CDI programs are essential in a healthcare system as they facilitate accurate representations of clinical status of patients which is translated into coded data. A CDI program ensures that admissions, treatment procedures, and readmissions are properly documented. Most organizations implement CDI programs because of the need to improve on patient outcomes. The implementation of the program, however, faces challenges including the existence of many diagnosis choices, and that some hospitals face resource-constraints, thus cannot utilize CDI programs. The third possible challenge relates to quality control. Many healthcare institutions share CDI brands but they adopt different administrative practices in terms of processes which require additional professionals, thus making the sustainability of the program tricky due to increased costs. Through CDI, physicians attempt to be more accurate in their diagnosis and procedural coding to avoid any form of complication.

Part 3: Information Operability and Information Exchange

In order to reform the healthcare system successfully, it is important for health information exchange to be implemented because it enables interoperability and the meaningful use of health IT. Health information exchange faces obstacles which are viewed differently depending on an individual’s perspective. One interoperability issue that arises within health information exchange is the lack of standardization, which holds the whole system back. There are no universal standard-based EHR systems which have been adopted. Different providers use different EHR systems, with varying format and terminologies, in the exchange of information, hence stunting interoperability. This makes it almost impossible for health information exchange to be fully implemented in most healthcare institutions. Among the best practices that address the issue include having incentive programs which have increased adoption nationwide. This way, more providers will have access to EHRs, thus making interoperability between them much easier. Secondly, policymakers have continued to help other providers in installing and successfully using EHRs since this is the foundation of health information exchange. Also, the Government Accountability Office has continuously campaigned for health data standards which ought to be sufficiently specific and applicable universally in order to increase interoperability. 

Part 4: Health Information Systems and Data Storage Design

A Health information systems, just like an information system, is a system comprising all information processing as well as associated human actors in their respective information processing roles. The only distinction is that health information systems deal with information and knowledge in healthcare environments. A health information system can be subject or task-based. The former relates to health care organizations, and the latter is related with specific tasks. Subject-based information systems have been considered to be more efficient than those that are task-based because they have minimal chances of duplication. An example of a subject-based system is EHR in which basic data is collected only once, and then it flows into specified tasks. Financial and Clinical Health information System is basically concerned with monetary issues of the patients in terms of the medical costs spelt out by different departments. Costing is done using estimations and this system has the capacity to go beyond invoicing and into non-payment follow up. In order to attain smooth operations in terms of payments and the patients’ finances, financial and clinical healthcare system forms an integral part of hospitals. Another type of information system is Decision Support Health Information System which is mainly concerned with artificial intelligence. Primarily, decision support system aids the computer in performing certain processes after data has been converted into clinically relevant information. This health information system is not so vital and has not been widely adopted for professional or organizational purposes. The best system can be the EHR because it is the basis of most communications that happen in healthcare. Data storage designs include cloud, onsite, and DSS. These are simply places where one can safely store data without the threat of losing it. These data storage designs are powered by the internet, making them easy to access as compared to manual databases. Online cloud is considered one of the best data storage designs because of its efficiency and the fact that the storage amount is so large, making it impossible for one to exhaust their space.

Part 5: Clinical Indices, Databases, and Registries

Challenges 

Patient data sources can be either primary or secondary. The primary data sources are those collected for direct purpose of the registry, whereas the secondary sources are those that were originally collected for purposes other than for the registry under consideration. Secondary data such as indexes, registries, and reports typically contain patient identifiable data that contain sensitive data (Harman & AHIMA, 2006). 

One of the challenges in the management of clinical indices in the areas of secondary data sources and the medical records is that it may experience ethical compromise. Another major managerial challenge in clinical indices is the wide distribution of data across the system. The system is very complex and the data system itself also is very complex. Another challenge is the variation in the timeliness of data where there is a time range between clinical data and the coded administrative data (Institute Care, 2016). 

Recommendations

There should be processes in place to enable the protection of the privacy of the patients regarding secondary data sources. All healthcare facilities should create opportunities for their members to learn and understand the ethical responsibilities that they have regarding the management of information. There should be policies in place to protect the dignity of the users and any other persons who are directly affected by the computer system. Finally, there should be proper and authorized use of the health centres’ information resources (Harman & AHIMA, 2006). 

Part 6: Data Warehouse

A data warehouse is defined as a system that is used for reporting and data analysis. It is a central point where all data can be organized in the required standard formats. Agarwal suggests that, at the foundation of any business intelligence, is a data warehouse (2014). One of the best approaches when designing a warehouse is ensuring that it can accommodate future needs for easier management of data. Second, data warehousing should not be considered as solely an IT initiative because this may dilute its essence. A good data warehouse should have a visual appeal and should mostly focus on the ease of use (Agarwal, 2014).

References

Agarwal, A. (2014). 6 Data Warehouse Design Mistakes to Avoid . Retrieved from http://www.computerweekly.com/tip/6-data-warehouse-design-mistakes-to-avoid  

Alakrawi, Z. M. (2016). Clinical Terminology and Clinical Classification Systems: A Critique Using AHIMA’S Data Quality Management Model . Retrieved from http://perspectives.ahima.org/clinical-terminology-and-clinical-classification-systems-a-critique/

Harman, L. B., & American Health Information Management Association. (2011). Ethical challenges in the management of health information . Sudbury, Mass: Jones and Bartlett Publishers.

Institute Care (2016). U.S. Healthcare Data Today: Current State of Play . Ncbi.nlm.nih.gov . Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK54296/