Population Health Management Dashboard (2 page in power point with visual data charts on the information)

Running head: BIG DATA IN DIABETES MANAGEMNT

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BIG DATA IN DIABETES MANAGEMENT

Big Data in Diabetes Management

Student’s Name

Institutional Affiliation

Many organizations use data for the purpose of data analytics. However, before organizations can get valuable information about big data, they may need knowledge of various significant data sources. As a PHM program leader, I would focus on diabetes management and find out the significant data sources used to analyze patients who have diabetes. Examples of substantial data sources include the media, cloud, the web, IoT, and databases (Russom, 2011). The different sources of big data are aimed at providing data for purposes of customer analytics, industrial analytics, business process analytics, and analytics for fraud detection. In our case, healthcare information can be found in many sources throughout the web.

The best big data that may use in diabetes management would be artificial intelligence and IoT. Digital health takes into account advanced medical technologies and digital communication (Al-Turjman, 2019). Machine learning enables us to take into account the identification, prediction of patterns, and inductive reasoning. Today, diabetes management is facing a whole lot of challenges, including the decreased number of diabetologists and an increase in the number of patients (Eswari, Sampath, & Lavanya, 2015). With the use of artificial intelligence, diabetologists can take full responsibility for their patients (Ross, Anderson, Kodate, Thompson, Cox, & Malik, 2014). Robust data analysis will ensure that gaps in care are identified, and the necessary measures are taken to mitigate risks.

Less digital ways of acquiring patient information in the past included ADT alerts, demographics, and ICD-10 codes (Nyenwe, Ashby, Tidwell, Nouer, & Kitabchi, 2011). Although there are effective ways, they may not provide varied data on patients with diabetes. They also do not help in proper analysis, and therefore integrating artificial intelligence and IoT may help in obtaining and analyzing big data for diabetes patients.

References

Al-Turjman, F. (Ed.). (2019). Artificial Intelligence in IoT. Springer.

Eswari, T., Sampath, P., & Lavanya, S. (2015). Predictive methodology for diabetic data analysis in big data. Procedia Computer Science, 50, 203-208.

Nyenwe, E. A., Ashby, S., Tidwell, J., Nouer, S. S., & Kitabchi, A. E. (2011). Improving diabetes care via telemedicine: Lessons from the Addressing Diabetes in Tennessee (ADT) project. Diabetes Care, 34(3), e34-e34.

Ross, A. J., Anderson, J. E., Kodate, N., Thompson, K., Cox, A., & Malik, R. (2014). Inpatient diabetes care: complexity, resilience and quality of care. Cognition, technology & work, 16(1), 91-102.

Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.

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