A chronic disease that has affected many people in the US is Diabetes. Based on the 2020 statistical report from Diabetes Research Institute Foundation, the number of people suffering from Diabetes is 34.2 million people which is 10.5% of the current US population.1 We have existing methodologies through which we can detect if a person has diabetes, but as a software engineer, I think “is there a way we can detect or predict if a person might potentially have diabetes using current and historical patient data?” This is where my research on Diabetes management and care brings me to the platform of Big Data...

1. Using Big Data to Drive Diabetes Management and Care.
By: Govind Yatnalkar
A chronic disease that has affected many peoplein the US is Diabetes. Based on the 2020
statistical report from Diabetes ResearchInstituteFoundation, the number ofpeople suffering from
Diabetes is 34.2 million people which is 10.5%ofthe current USpopulation.1 We have existing
methodologies through which we can detect ifa person has diabetes,but as a software engineer, I think “is
there a way we can detect or predict ifa person might potentially have diabetes using current and
historical patientdata?” This is where my research on Diabetes management and care brings me to the
platform ofBig Data.
Simply, Big Data is defined as the gathering of massive data frommultiplesources, compiling and
feeding it to a computing system for analysis. In the case ofDiabetes, there are Petabytes (1 Petabyte =
1000000 GBs) of data consisting ofinformation for peoplediagnosed with Diabetes, their treatmentplans
including the list ofmedications or drugs, theirmetadata such as age, gender,lifestylehabits, and the
timelines, indicating which drug was most effective to reduce sugarlevels as quickly as possible. This data
might be collectedfrom sources such as smart glucometers, EHRs/PHRs (Electronic/ Patient Health
Records), Diabetes specific public Cloud storage,and hospital information systems.2
All this data is then fed to the Big Data tool or an algorithm such as the popular Hadoop
(Map/Reduce/Filter tool) for analysis. Ifsuch tools areintegrated with AI algorithms,the system will
learn the behaviorofmillions ofpeople along with their lifestyles to determinewhich majorelements led
to diabetes. In the end, the most significant targetofBig Data analytics is to derive conclusions and
orchestrate valid hypotheses.From a perspectiveof Diabetes care, ifthese tools areremotely connected
with insulin delivery systems, every patient data would be instantly analyzed basedon this Big Data set,
and precise medication delivery would be triggered, facilitating enhancedDigital healthcareservices. 2
While researching Big Data algorithms to manage Diabetes, I found about Data Mining which is
the actual component that performs analysis on Big Data. For a new patient, ifall patient history is
inputted in the system,the Data Mining tool would providecustomized plans including medications for
managing Diabetes. But then, I also thought what ifI rerun the same tool for existing patients. How would
that work?This would be incredible as doctors would get to know what novel methods are present to
manage Diabetes and shift patients towards new treatment plans, which are utilized by many patients
having similar lifestylepatterns and are proving useful for controlling sugarlevels. Additionally, ifsimilar
Data Mining reruns are performed on new patient data during general health checkups, this tool would
provide decisions indicating if a user might be classified as a Diabetic depending on the recorded medical
history and lifestyle patterns.3
As discussed,these Big Data tools would help classify patients as Diabetics and provide custom
treatment plans.Working in the medical device industry, I have learned any medical devicewhich helps
1 LaurenSchrier (2021). Diabetes Statistics. Retrieved on January 31st from https://www.diabetesresearch.org/diabetes-
statistics#:~:text=34.2%20million%20people%2C%20or%2010.5,%2C%20economic%2C%20and%20ethnic%20backgrounds.
2 Ky leighRoessner(April 2019) Big DataAre ChangingDiabetes Management in Big Ways. Retrieved on January31st from
https://www.abbott.com/corpnewsroom/diabetes-care/big-data-are-changing-diabetes-management-in-big-ways.html.
3 Rumbold, J. M. M., O'Kane, M., Philip, N., & Pierscionek,B. K. (2020). Big Dataand diabetes: the applicationsof Big Datafor
diabetescare now and in the future. Diabetic Medicine, 37(2), 187-193.Retrieved on January31st from
https://onlinelibrary.wiley.com/doi/full/10.1111/dme.14044.

2. in diagnosis, cure, or treatmentshould be validated and therefore, Big Data is not an exception. To put it
simply, this tool must be thoroughly testedand inspected from a quality or safety perspective which also
means following the FDA guidelines.
For Big Data tools, as it includes data collection from multiple resources, an important stepis to
check the veracity ofthese sources. All the sources through which this data is collected shouldbe validated
to check that the information is true and accurate. As the final hypothesis or decisions thrive on this data,
if there are anomalies present in datasets, these anomalies would ultimately affect final decisions.Also,
any software engineer would perform unit-to-system level testing with in-depth code inspections as it
always helps identify any risks present in the system, such as potential security breach points or
unnecessary use ofloops which affects performance. Overall,the system should be well tested and
validatedbefore it is released for clinical use.
In my opinion, Big Data is an incredible tool which is helping patients by closely analyzing their
lifestyle patterns with medical history to provide a customized Diabetes management and care plan. But
manufacturers should make sure their tool is validated. Do you have a Big Data tool that needs validation
along with an FDA approval or clearance?Our quality and softwareexperts can not only help validate
your system but also guide you to ensureyourmedical device is FDA compliant. Contactus at 248-987-
4497 orinfo@emmainternational.com for additional information.