Data Quality In Health And Social Care

As a data quality analyst it is ones primary responsibility to maintain a high level of data quality through a thorough analysis and review of the data on an ongoing basis. When accuracy and quality of data begins to diminish it is up to the analyst to review current processes across the organization in order to identify where enhancements or new processes need to be implemented. The analyst needs to take into consideration a multitude of factors which could be contributing to the decreasing data quality. “Poor quality data often result from difficulties in collection standards, coding standards and chart documentation and lack of training.” (Heather Richards.[CIHI], 2011). The analyst needs to review all factors which affect the processes

In large organizations is it common for coding specialist to perform their jobs within a variety of environments including different departments and even from home offices. There can be issues working from both an unsupervised home setting and also with working within a healthcare setting. “For remote coders, working in an unsupervised environment can lead to poor habits. Temptations such as checking e-mail and social media sites too frequently can spell trouble……There are other distractions at home, too, that can pull your attention away from being effective,…..If people have small children or pets at home or the television is often on, these things can take focus away from the job.” (Chapman, 2014). Also, when working from home coders may not have access to the same tools, resources and the knowledge of their fellow colleagues; working in a team environment may be of benefit with a career such as a data analyst. On the contrary, working within a team also has its challenges; an experienced coder may be distracted by questions from those with less experience. Managers need to take these environmental factors into account when reviewing productivity, they need to “monitor staff for productivity decreases and tracks coders who fail to consistently meet goals. [They need to] work with those individuals to help them see where they are not being productive and help them address

Through the use of technology the organization will be able to identify errors and inconsistencies that wouldn’t likely be caught by the coders. Investing in coding software based on natural language processing (NPL) can assist coders with extracting relevant information more efficiently as the software can “scrutinize and interpret unstructured clinicians' notes using specialized linguistic algorithms, extracting the clinical facts that support the assignment of codes. Structured input applications integrate the coding into the clinical documentation process, producing clinical documents with embedded codes.” ("Computer-assisted coding: the secret weapon," 2010). In addition, all software programs have access to validation tables and coding standers which in turn can allow the software to edit entries and reject invalid values. This built in audit will allow for coders to correct entries immediately and lessen the scope creep associated with re-entry at a later time; this will lead to an increase in productivity and accuracy within the department. In addition, the data analyst should consider increasing the frequency coding audits from quarterly to weekly until the outcomes of these audits improve. Also, the organization should consider the ‘need to implement an assessment plan to review the data collected such as “reabstraction studies