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What is the purpose of looking at different types of data?

What is the purpose of looking at different types of data?

The Purpose of Looking at Different Types of Data

In healthcare, looking at different types of data is essential in optimization of patient care. According to Bruce et al., (2018), data from various sources provides health care professionals with deep understanding about specific health conditions. Moreover, understanding different data types is crucial in helping health care professionals to establish how the data can be integrated and used together to provide solutions to the questions that data analysts and researchers may be pursuing concerning data. Also, looking for different types of data is an important prerequisite when conducting Exploratory Data Analysis because it enables the utilization of certain statistical measurements only for particular types of data.

What is the purpose of looking at different types of data?

On the other hand, the clinical terminology connotes systems that are utilized in coding the whole healthcare domain. Clinical terminologies work as a common reference system that uses the concept of compositional grammar, which can be coded and queried. Clinical terminology plays significant role in data aggregation, normalization, and reconciliation. In data aggregation, after the data is collected, the clinical terminology helps in harmonizing it into a standard framework of understanding. Once, aggregated, areas with issues can be identified and comprehended, and the health care system can utilize the information to make informed decisions (Schulz et al., 2019). Concerning data normalization, clinical terminology helps to eradicate ambiguity of data and to understand earlier non-standard and un-coded data. Data normalization helps in

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establishing foundation that enables health care settings to find solutions to critical questions, enhance patient care, and provide accurate reports to regulatory bodies and registries (Wang & Hajli, 2017). Finally, clinical terminology plays a critical role in data reconciliation by helping to interpret the information and settle on the ones that still require further normalization. Clinical terminologies also provide workflows to address the vital mapping from local to standard content and to recognize content that can potentially be captured wrongly or missed (Wang & Hajli, 2017).

Also Check Out: Why does health care data need to be aggregated? In the data aggregation process, what is the impact of using information that is assigned a code as opposed to free text data?

References

Bruce, N., Pope, D., & Stanistreet, D. (2018). Quantitative methods for health research: a practical interactive guide to epidemiology and statistics. John Wiley & Sons.

Schulz, S., Stegwee, R., & Chronaki, C. (2019). Standards in healthcare data. Fundamentals of Clinical Data Science, 19-36. https://doi.org/10.1007/978-3-319-99713-1

Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287-299. https://doi.org/10.1016/j.jbusres.2016.08.002

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