Data Analysis

Data analysis involves examining raw data to determine trends, make conclusions and identify potential areas of improvement. Healthcare analytics utilizes historical and current data to gain insights into healthcare service delivery and support healthcare decision-making. The utilization of health data analytics enables healthcare providers to improve patient care, promote accurate diagnosis, promote preventive healthcare, informed decision-making, and implementation of patient-centered care (Islam et al., 2018). It also lowers business costs and optimizes internal operations.

To understand the importance of health data analytics, it is important to define health data. Healthcare data is any data related to a patient’s health or collective population (Dash et al., 2019). This data is gathered from health information systems and other tools such as electronic health records, E-prescribing, and patient portals. This data allows healthcare professionals to holistically view each patient and analyze trends linked with race, predisposition, location, and socioeconomic status.

There is no benefit of collecting if we don’t know how to use it. Data collection and use have been streamlined in recent years to better patient care and enhance daily operations. The data can be used in predictive modeling. Historical and current data can be used to analyze trends and make predictions (Islam et al., 2018). This data can help the healthcare population implement preventive healthcare services and track outcomes. There is a growing demand for value-based care hen, hence the shift in preventive and predictive models in healthcare. Instead of simply treating presenting symptoms, healthcare professionals can identify patients at higher risk of developing long-term healthcare conditions and help address the problems before they surface. This will help lower healthcare costs for the patient, healthcare provider, and insurance payers. Data analytics can also help predict risks of admission and infection, which can help improve healthcare outcomes.

References

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis, and prospects. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0217-0

Islam, M., Hasan, M., Wang, X., Germack, H., & Noor-E-Alam, M. (2018). A systematic review on healthcare analytics: Application and theoretical perspective of data mining. Healthcare, 6(2), 54. https://doi.org/10.3390/healthcare6020054

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