Healthcare Data Quality Elements - Special Academic Support

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Reducing the number of medical errors and making sure that patient satisfaction levels are high are two main objectives of healthcare providers. With the development of new technologies, it becomes simultaneously less and more difficult for physicians and nurses to follow all the guidelines and avoid mistakes. In order to do that, a special database was created: a National Guideline Clearinghouse (NGC) website with valuable information for healthcare providers. Generally, such guidelines are statements that include recommendations intended to optimize patient care that are informed by a systematic review of evidence (Agency for Healthcare Research and Quality, 2018a, para. 1). To be included in the database, clinical practice guidelines need to meet various criteria.

It is fair to notice that irrelevant and poor-quality information cannot be published for the use of medics. Consequently, developers have a list of criteria that allows them to check data quality before including clinical practice guidelines in NGC. For example, the first data quality element is whether the statements include recommendations that can enhance patient care and help healthcare practitioners make better decisions for particular clinical circumstances. Second, to be considered quality, the data cannot be produced by an individual. Rather, it has to be prepared under the auspices of any authorized and reliable specialty association, such as a government agency, healthcare organization, or relevant professional society.

Evidence and support from many trustworthy sources are also proof of the validity of the information. In other words, it is required to demonstrate that the clinical practice guideline is based on a systematic review of the evidence, which should be accurately documented (Agency for Healthcare Research and Quality, 2018a, para. 2). For instance, it is required to provide a statement that the data is based on a systematic review and a description of the search strategy, including databases used a summary of search terms, and the time period covered. It is also necessary to add a summary of evidence from the selected studies and particular criteria that were used to select those studies.

Further, there are three more significant elements of data quality. One of them is whether the data contains an evaluation of the harms and benefits of the recommended care options. Another data quality element is that the full text and possibly the supporting documents have to be available in English (Agency for Healthcare Research and Quality, 2018a). Finally, the last criterion is that the data needs to be not older than five years.

Considering the number of data quality elements, it is possible to discuss the actual effectiveness of these criteria. The most important factor is that these data quality elements help data analytics prepare visualization products that meet the highest level of data quality and validity. To be more precise, when developers receive an extended number of proposed clinical practice guidelines, they cannot publish them all without checking the data for accuracy, relevance, timeliness, and other vital parameters. Otherwise, the use of published data that has not been checked for quality can lead to reduced healthcare outcomes and worsen patient satisfaction.

The list of particular data quality elements allows data analytics to check the information faster, identify lacking elements, and ask the provider to add the necessary information. The result is a visualization product that contains only high-quality and relevant data (Agency for Healthcare Research and Quality, 2018b). Therefore, if one asks whether these elements are helpful for health data visualization, the answer will be yes. Data analytics use these elements as criteria to delete irrelevant or poor-quality information to then visualize the valuable data.

References

Agency for Healthcare Research and Quality. (2018a). NGC and NQMC inclusion criteria. AHRQ. Web.

Agency for Healthcare Research and Quality. (2018b). NQMC measure domain framework. AHRQ. Web.

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