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Big Data in high rates of Diabetes and high blood pressure in Minnesota

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Big Data in high rates of Diabetes and high blood pressure in Minnesota


Diabetes and high blood pressure are major global issues that affects the health of many people in the world. It contributes substantially to the global deaths. The major diseases condition in Minnesota are high blood pressure and high rates of diabetes. Dietary measures, physical exercise and pharmacological interventions can be utilized to manage the condition This means that without proper management, it is likely to cause a crisis to Minnesota which has over 70% of people above 60 years living with the above conditions . The challenge that comes with diabetes is that it is an expensive process to manage it.

Big data in diabetes and high blood pressure management

Preventive care and predictive analysis

The population health analytics is used in the identification of the people living with diabetes and high blood pressure gaps in their healthcare and would benefit substantially when given additional support. The population health analytics will be an important aspect in Minnesota to sustain the behaviour change and continuous support outside the typical clinical setting (Han et al., 2020). The population health analytics will provide the outreach targeted Minnesota at the appropriate time that leverages the behavioural analytics. The population risk stratification integrates the chronic conditions, the general costs and other social determinants, disparate sources and risk models in identification of the people likely to reap from the proactive care management programs. Diabetes and high blood pressure management is all about the high risky people changing their lifestyles.

The regular physical activity, avoiding tobacco use, maintaining healthy body weight are some of the techniques used in delaying or preventing the onset of these chronic conditions. Diabetes and high blood can be treated, and their results delayed with things like physical activity, diet, regular screening and medication and total treatment for the complications. But the lifestyle as you know is not easy and therefore, they need continuous encouragement and support (Linnen, 2016). A host of fitness applications and wearables are developed in supporting the wellness through reporting and tracking data that is related to activity and nutrition. This kind of data will help the care team and users in assessing the progress that has been taken and allow the program changes as they are needed. This kind of intervention will be excellent because over 97% of hospitals in Minnesota have patient portals where the patients can be monitored remotely.

Thanks to technology advancement, the wearables now lead as the most efficient and effective tools for diabetes and high blood pressure prevention. The scientists have now developed the new approach that is driven by data to the population health whereby they use the machine learning in developing the risk factors and predictive models for these conditions’ onset (Linnen, 2016). This approach bases the claims on the pharmacy records, data, the laboratory results and healthcare utilization information. The model will identify the new factors of risk for the type conditions and predicts nearly with half-chances more at the onset of the diseases as compared to a model that is based on the risk factors only for comparison.

The objective was to use the data analytics to test and create a basic model for the doctors which can predict the prediabetes and high blood patients that will benefit the best from the treatment administered with a drug that prevents the conditions or even from the changes of lifestyles like the regular exercise and weight loss. The prediabetes or prehigh blood pressure is the status where the patient does not show all the signs and symptoms to be categorized as diabetic but still his or her blood pressure unreasonably high. The above model has been demonstrated that for those with the highest diabetes and high blood pressure risk, the lifestyles changes would substantially reduce the diseases by a significant margin (Senthilkumar et al., 2018). The healthcare data analytics leverages the data in existence for the Minnesota citizens with diabetes and high blood in anticipation of which patients might not take their medications and also to predict the effective lifestyle changes to influence every patient in taking their medications. Through data analytics, the health care gaps will be identified, and the necessary preventive measures are taken in mitigating the risks (Ünalir et al., 2017). The payers and the health care service providers work together for monitoring the activities across the whole program of care provision to identify the gaps and manage the care transitions better quickly.

There are real benefits that come with using these data sources and their associated analytic tools. They focus on the complete view of the patient and the patient population as well, they identify the patient at risk, preserve the patient care, prevent complications, care to monitor and to lower personal healthcare expenditures.

The population health, diabetes and high blood pressure.

Diabetes and high blood pressure need everyday strategies and treatment to control it. The task of managing and monitoring needs the information on several factors like the insulin level, the sleep and the food. The Smart technology exists as implants, mobile applications in tracking the glucose levels, communicating with the health care providers, accessing the relevant information, sharing of data and finally making that individual that better decision. The wearable technology comes with gadgets that can get worn out and equipped with sensors and the wireless connectivity to the internet that will help in monitoring the blood sugar levels, personalizing treatment, connecting with health care providers and even delivering the medication to the body (Wang & Alexander,2016). It is a massive deviation from the old technique of finger pricking of glucose monitoring.

The mHealth wearables give the real-time collection of data and transmission to the health care providers. Any change to the essential indicators will trigger the notifications and prompt to the health care team, which then allows for changes and interventions in treatment options (Wang & Alexander,2016). Therefore, the technology techniques will assist the healthcare givers to closely monitor the Minnesota patients and the progress along the way to offset the diseases as early as possible.

Data sources such as the ICD-10 codes, the ADT alerts devices, and the demographics help even before we deploy the latest technology to manage diabetes and high blood pressure. This is because family history plays an essential role in the diabetes and blood pressure attack (Ünalir et al., 2017). Those who have chronic conditions history in their family have 20-fold chances of being attacked as compared to those who have zero accountability. Those data sources help in locating the population and places where there could prevalent cases could be even before you deploy the technology.


Han, A., Isaacson, A., & Muennig, P. (2020). The promise of big data for precision population health management in the US. Public Health185, 110-116.

Minnesota. State Demographic Center. (2018). The economic status of Minnesotans: A Chartbook with data for Minnesota’s largest cultural groups

Linnen, D. (2016). The promise of big data: Improving patient safety and nursing practice. Nursing201946(5), 28-34.

Senthilkumar, S. A., Rai, B. K., Meshram, A. A., Gunasekaran, A., & Chandrakumarmangalam, S. (2018). Big data in healthcare management: A review of the literature. American Journal of Theoretical and Applied Business4(2), 57-69.

Ünalir, M. O., Can, Ö., Sezer, E., Bursa, O., & Ak, H. (2017, September). Big data-aware diabetes management: Requirements, solutions and reviews. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1-6). IEEE.

Wang, L., & Alexander, C. A. (2016). Big data analytics as applied to diabetes management. European Journal of Clinical and Biomedical Sciences2(5), 29-38


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