There is a plethora of various clinical classification systems and applications, especially when it comes to such an area of healthcare as nursing. One of these is the Clinical Care Classification System – or CCC. According to HCA Healthcare (n.d.), it consists of two parts: standardized coded terminology and an information model for electronically documenting clinical practice. CCC supports capturing discrete data elements representing patient care concepts and measuring the relationship between the data and the outcomes by documenting the so-called “essence of care” (HCA Healthcare, n.d.). The system’s terminological concepts use a code to monitor patient care in its entirety and measure care in all healthcare environments. There are no particular challenges in terms of system management, and no prior training is needed to use it. However, according to Standard Nursing Terminologies (2017), the implementation of CCC into several software applications from different developers might be difficult. That is if an organization uses different software applications, each of them would require its own strategy to customize the CCC terminology and information model.
Among other standardized nursing terminologies are the Nursing Interventions Classification (NIC) and the Nursing Outcomes Classification (NOC). Iowa College of Nursing (n.d.) reports that these systems provide tools for the collection of nursing data, which are systematically analyzed within healthcare contexts. NIC and NOC deliver global coding systems and terminologies and materials on other Classifications. Additionally, they advise on implementing various Classifications and their use in clinical practice and educational environments. No specific training is needed for the systems’ users; as per Standard Nursing Terminologies (2017), one major challenge is the licensing process. It is complex for NIC and NOC since a large proportion of licenses come through vendors, which complicates the process of the quantification of user numbers and the differentiation of licenses purchased from licenses implemented. In its turn, it poses a challenge of securing the correct terminology implementation; if the terminology changes, it might be demanding to normalize the data to ensure system interoperability.
With that in mind, the CCC system seems to be a better option than NIC and NOC. Granted, they are both able to evaluate quality coding practices, neither requires training for managing it, and there are no challenges connected to system governance. However, implementation issues of NIC and NOC related to the licensing process cause more problems than the operation of CCC’s integration process. Therefore, the CCC system reasonably seems preferable in use.
When it comes to a clinical documentation improvement (CDI) program, system guidelines include selecting an appropriate code, correctly identifying the principal diagnosis, and determining the DRG payment. As a program, CDI provides a common interface for real-time data exchange and monitoring, which allows clinicians to communicate more efficiently. CDI’s value lies in its promotion of coding as a quality assurance tool, which is important since coding is the basis for accurate sales and compensation due to its offering of quality records. In their turn, quality records help maintain patient coordination in hospitals and, thus, help an organization meet quality standards.
In terms of the CDI process challenges, there are a few. These, as per Krauss (2020), are the things that need to be done for the challenges to be eliminated:
- Discrepancies between clinical terms employed by healthcare providers and medical terminology used for reimbursement are to be addressed;
- The best people available are to be hired for CDI management. In this case, the best people are those who understand coding guidelines and concerns connected to the process of requesting a healthcare provider.
- Correct and accurate documentation for all phases of treatment is to be maintained.
Appropriate auditing, accurate diagnostic and procedural coding, and careful sorting processes are CDI practitioners’ essential tools in helping them track their practices and support program integrity on the highest level. Among the best practices to ensure compliance are the thorough examination of records, prompt and complete responses by physicians to CDI practitioners’ questions, and physicians’ overall active cooperation.
Moreover, one of the top priorities of healthcare policymakers is ensuring healthcare interoperability towards which they work. However, there are still a number of issues that continue to prevent stakeholders from achieving improved interoperability, among which are the information blocking and impediments to data sharing. As per Monica (2017), although information blocking is deemed illegal, it is still a common problem: providers are coerced to adopt specific HIE or EHR technologies. The most effective practices addressing the issue include the recognition of the practice as illegal, greater transparency of the activities of EHR vendors, and bigger fiscal stimuli for providers so that they share information.
Furthermore, in terms of disaster recovery purposes, some health information systems (HIS) and data storage designs are better than others. In terms of HIS, electronic medical records (EMRs) allow clinicians to track all data available and monitor care’s overall quality. However, electronic health records – EHRs – through collecting data beyond particular health organizations and sharing it with other care providers, have more chances of successful disaster recovery. In terms of data storage designs, there are files in the form of which client information is stored and saved in EHRs. However, more reliable in terms of disaster recovery purposes are databases, as there is a variety of database-specific products making it possible.
In addition to that, there are more managerial challenges when it comes to navigating databases and registries. One of these is the necessity of understanding data sources: as per Prometheus Research (2018), the greatest barriers to efficient data management are incompatible formats, non-aligned structures, and inconsistent semantics. Among the tools to help one organize it all are various conversion instruments and universal standards. Another challenge is the necessity to ensure that study protocols evolve: it is often not feasible to standardize one research workflow for different projects (Prometheus Research, 2018). To achieve flexibility, the research staff is to operate the variability across sites and domains by configuring the workflows.
When it comes to data warehousing, there are two major design approaches: top-down and bottom-up. One is to choose either of these for their project based on this project’s requirements. Vithal (2018) states that, in accordance with the top-down approach, first comes the design of the data warehouse – and then the data mart is built on top of it. As per the bottom-up approach, data marts are created first to arrange for a particular business process’s reporting and analytics capabilities. After that, these data marts help in the process of creating of the data warehouse.
Finally, there is the life cycle of data: it starts with the creation of information and ends with its destruction. As per AHIMA (n.d.), when reflecting on it simply, there are four stages of the entire data retention process: creation – utilization – maintenance – destruction. Organization’s goal is to manage each step throughout the cycle to ensure that records are available. Some of the issues connected to it are a lack of file space and information volumes – and they necessitate the maintenance of a record retention schedule. This schedule is to ensure that patient health data meets the needs of the organization’s legitimate uses and that guidelines and clear destruction policies are created and respected.
References
AHIMA. (n.d.). Retention and destruction of health information. Web.
HCA Healthcare. (n.d.). Clinical Care Classification system: About. Web.
Iowa College of Nursing. (n.d.). Center for Nursing Classification and Clinical Effectiveness (CNC). Web.
Krauss, G. (2020). Three steps to improve CDI for good. ICD10monitor. Web.
Monica, K. (2017). Top 5 challenges to achieving healthcare interoperability. EHR Intelligence. Web.
Prometheus Research. (2018). 7 challenges of mastering clinical data registries. Web.
Standard nursing terminologies: A landscape analysis. (2017). The Office of the National Coordinator for Health Information Technology [PDF file]. Web.
Vithal, S. (2018). Various data warehouse design approaches: Top-down and bottom-up. DWgeek. Web.