This is a guest article by tech blogger Luke Smith
Eighty percent of financial services executives report the success of their big data investments, but the value of big data in a variety of industries transcends the financial. In healthcare, for instance, big data can play a real role in saving lives through disease prevention.
Big data, the data gathered en masse through the digitization of records and devices connected to the Internet of Things, is changing every industry it touches. In healthcare, imagine the electronic health records and massive databases of disease symptoms and treatment information that never could have been amassed at such a scale before.
And in a world ravaged by the coronavirus pandemic, such data and the solutions it could provide in disease prevention have never been more crucial and beneficial.
Medical professionals and data scientists are wielding the incredible power of big data to prevent disease through two primary and industry-altering tools: genetic mapping and big data tools.
When it comes to what genetic testing can tell you, there are a few different and highly useful screening methods that each contribute to beneficial healthcare solutions. These are
● Carrier — screening used to determine the presence of a gene mutation that causes a disorder.
● Diagnostic — screening used to determine a specific genetic disorder based on the presence of symptoms.
● Predictive — screening used to determine the presence of mutations that correspond to genetic disorders that may appear later on.
Through these methods of screening, a person’s genome can be plotted in relation to other known factors about a disorder or mutation. This allows medical professionals and data scientists to connect the dots. If pre-existing factors overwhelmingly occur alongside certain disorders and mutations, medical professionals can spot those risk factors sooner, giving them enhanced tools to combat disease before it even becomes a problem.
For example, a mutation in a gene referred to as LRRK2 has been linked to the development of Parkinson’s Disease. Someone who inherits a mutation in this gene statistically has a 28 percent chance of developing Parkinson’s by age 54. But with clear genetic testing and potential treatments, that risk can be reduced.
Many other diseases and disorders that are connected to genes and cell mutation can be learned about through genetic mapping. Breast cancer, celiac disease, psoriasis, and even bipolar disorder can be screened for and mapped applying genetic predispositions. If you know the risk, you are much better situated to prevent the disease or prepare for it.
Beyond that, the tools generated by the predictive models of expansive genome mapping allow for the correction and cure of genetic issues in some circumstances. Gene-editing techniques like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) have arisen through the help of big data analysis to give medical professionals the knowledge and understanding of a genome and how diseases interact with it in order to edit genomes for disease protection.
It works like this: A ribonucleic acid (RNA) guide binds itself to a target sequence, where the CRISPR enzyme then serves as an entry point for scientists to readjust the genetic material for disease prevention coded into a person’s very DNA.
Without a broad understanding of genetic material, this method of helping people would be impossible. Scientists require big data solutions to generate the kind of personal treatment and application of healthcare tools, such as CRISPR, that push medicine into the healthcare quality of the future.
Widespread and easily affordable use of genetic corrections for disease prevention may be a decade or more away. But luckily, genetic mapping isn’t the only place big data is helping in the fight against diseases.
Here are some of the emergent tools and policies that big data has given the medical field and its ability to prevent disease.
On the one hand, the storage of these records in digital form allows for the broad analysis and widespread treatment of every disease imaginable. The data medical professionals collect can be de-identified — stripped of the markers that bind it to an individual, such as social security number or address — then processed in huge quantities to better observe and understand the correlation of symptoms, treatments, and demographics.
For example, the knowledge of how person, place, and time interact in both the emergence and successful treatment of disease is useful for medical professionals in understanding what works for different populations. With data analyzed too broadly — lumped together by state or country — the nuance of geography, moment, and population might be lost to data scientists. EHRs enable the recording of all these factors for analysis, per HIPAA privacy mandates. Then, risk factors and at-risk demographics can be targeted with proven treatment strategies crafted specifically for them, helping prevent and treat disease through precision public health.
On the other hand, medical records in digital form are more at risk than ever. A 2017 report showed 34 percent of all cyber attacks directed at the healthcare industry, targeted by cybercriminals with ransomware due to the lucrative nature of medical records. These risks, among other problems posed by EHR systems, make for a tricky — if highly useful — integration of big data.
In relation to disease prevention, however, the ability of EHRs and big data to improve both accessible information and hospital practices means life-saving, invaluable innovations.
Take immediate, face-to-face care provided by hard-working nurses, for example. Nursing has changed with big data to improve:
● Documentation of patient history and every instance of care
● Anticipation of staffing needs and hospital resources
● Patient outcomes and safety
● Efficiency of workflow
Dependent on the collection and utilization of data, these innovations can literally translate into saved lives.
In disease prediction and prevention, the IoHT is utilizing big data to create comprehensive responses and accurate treatment that are already helping people.
For example, smart thermometers can track flu symptoms and report data through a tracking app back to a person’s physician. The doctor can then use those symptoms to determine if additional treatment is needed, while the accumulated data paints a broad picture of regions where flu outbreaks might be occurring. This information helps healthcare and disease control professionals understand, treat, and prevent outbreaks.
Given the current reality of the coronavirus pandemic, the potential of IoT devices to track COVID-19 symptoms, alert users to potential infection in the area, or even remind them to wear a mask could be especially useful in disease prevention.
Without big data giving artificial intelligence (AI) the tools to understand dangers and symptoms, however, IoT would not be as significantly helpful.
One of the most useful approaches of AI in medical disease prevention technology is that of machine learning. This is the ability of AI to execute a function without explicit programming, having learned from its environment and accumulated data sets to draw informed conclusions and act on them. Machine (or deep) learning in medical diagnosis has been used to save lives and cut treatment costs—and it is a primary function of the future of disease prevention.
A project conducted by two major Boston organizations is looking to apply machine learning algorithms to better identify patients at risk for heart disease and diabetes. This work is a carryover project from previous work with machine learning and EHRs to predict hospitalizations up to a year in advance at an 82 percent accuracy rate.
These machine learning algorithms scan for risk factors in patient medical data to determine when intervention is needed, helping alert physicians, prescribe treatment plans, and prevent diseases from emerging altogether, when possible.
This marriage of AI and big data to improve healthcare outcomes represents the future of disease prevention and treatment, one in which technology helps us save lives and live better.
With this understanding comes the tools to prevent disease before they even emerge. Whether it be a burgeoning pandemic or the trending of diabetes, big data enables the awareness and identification of risks, allowing us to combat them with better preparation. In dealing with the COVID-19 pandemic of 2020, the value and application of such big data tools should be clearer than ever.
Luke Smith is a writer and researcher turned blogger. Since finishing college he has been trying his hand at being a freelance writer. He enjoys writing on a variety of topics but technology and digital marketing topics are his favorite. When he isn’t writing you can find him traveling, hiking, or gaming.
Want to write an article for our blog? Read our requirements and guidelines to become a contributor.
Eighty percent of financial services executives report the success of their big data investments, but the value of big data in a variety of industries transcends the financial. In healthcare, for instance, big data can play a real role in saving lives through disease prevention.
Big data, the data gathered en masse through the digitization of records and devices connected to the Internet of Things, is changing every industry it touches. In healthcare, imagine the electronic health records and massive databases of disease symptoms and treatment information that never could have been amassed at such a scale before.
And in a world ravaged by the coronavirus pandemic, such data and the solutions it could provide in disease prevention have never been more crucial and beneficial.
Medical professionals and data scientists are wielding the incredible power of big data to prevent disease through two primary and industry-altering tools: genetic mapping and big data tools.
Genetic Mapping
The gathering and utilization of vast amounts of data in the field of genetics stand to benefit all of humankind. Alongside the boom in popularity of direct-to-consumer genetic testing and mapping products like those offered by Ancestry DNA or 23andMe, the availability and accessibility of this data for medical professionals could make a world of difference in disease prevention.When it comes to what genetic testing can tell you, there are a few different and highly useful screening methods that each contribute to beneficial healthcare solutions. These are
● Carrier — screening used to determine the presence of a gene mutation that causes a disorder.
● Diagnostic — screening used to determine a specific genetic disorder based on the presence of symptoms.
● Predictive — screening used to determine the presence of mutations that correspond to genetic disorders that may appear later on.
Through these methods of screening, a person’s genome can be plotted in relation to other known factors about a disorder or mutation. This allows medical professionals and data scientists to connect the dots. If pre-existing factors overwhelmingly occur alongside certain disorders and mutations, medical professionals can spot those risk factors sooner, giving them enhanced tools to combat disease before it even becomes a problem.
For example, a mutation in a gene referred to as LRRK2 has been linked to the development of Parkinson’s Disease. Someone who inherits a mutation in this gene statistically has a 28 percent chance of developing Parkinson’s by age 54. But with clear genetic testing and potential treatments, that risk can be reduced.
Many other diseases and disorders that are connected to genes and cell mutation can be learned about through genetic mapping. Breast cancer, celiac disease, psoriasis, and even bipolar disorder can be screened for and mapped applying genetic predispositions. If you know the risk, you are much better situated to prevent the disease or prepare for it.
Beyond that, the tools generated by the predictive models of expansive genome mapping allow for the correction and cure of genetic issues in some circumstances. Gene-editing techniques like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) have arisen through the help of big data analysis to give medical professionals the knowledge and understanding of a genome and how diseases interact with it in order to edit genomes for disease protection.
It works like this: A ribonucleic acid (RNA) guide binds itself to a target sequence, where the CRISPR enzyme then serves as an entry point for scientists to readjust the genetic material for disease prevention coded into a person’s very DNA.
Without a broad understanding of genetic material, this method of helping people would be impossible. Scientists require big data solutions to generate the kind of personal treatment and application of healthcare tools, such as CRISPR, that push medicine into the healthcare quality of the future.
Widespread and easily affordable use of genetic corrections for disease prevention may be a decade or more away. But luckily, genetic mapping isn’t the only place big data is helping in the fight against diseases.
A World of Big Data Tools
Data is everything in the high tech, electronic world we have created. In the medical industry, big data resources are rapidly changing the handling of every function, from medical record storage to patient monitoring, and treatment prescriptions. Without big data and the digital tools to manage it, 21st century progress would be lessened or even stalled.Here are some of the emergent tools and policies that big data has given the medical field and its ability to prevent disease.
Electronic Health Records
The digitization of electronic health records (EHRs) has been both a victory and difficult challenge.On the one hand, the storage of these records in digital form allows for the broad analysis and widespread treatment of every disease imaginable. The data medical professionals collect can be de-identified — stripped of the markers that bind it to an individual, such as social security number or address — then processed in huge quantities to better observe and understand the correlation of symptoms, treatments, and demographics.
For example, the knowledge of how person, place, and time interact in both the emergence and successful treatment of disease is useful for medical professionals in understanding what works for different populations. With data analyzed too broadly — lumped together by state or country — the nuance of geography, moment, and population might be lost to data scientists. EHRs enable the recording of all these factors for analysis, per HIPAA privacy mandates. Then, risk factors and at-risk demographics can be targeted with proven treatment strategies crafted specifically for them, helping prevent and treat disease through precision public health.
On the other hand, medical records in digital form are more at risk than ever. A 2017 report showed 34 percent of all cyber attacks directed at the healthcare industry, targeted by cybercriminals with ransomware due to the lucrative nature of medical records. These risks, among other problems posed by EHR systems, make for a tricky — if highly useful — integration of big data.
In relation to disease prevention, however, the ability of EHRs and big data to improve both accessible information and hospital practices means life-saving, invaluable innovations.
Take immediate, face-to-face care provided by hard-working nurses, for example. Nursing has changed with big data to improve:
● Documentation of patient history and every instance of care
● Anticipation of staffing needs and hospital resources
● Patient outcomes and safety
● Efficiency of workflow
Dependent on the collection and utilization of data, these innovations can literally translate into saved lives.
Internet of Things
The Internet of Things (IoT) is a useful tool in managing our lives. Empowering smart devices and recording our preferences, this tech is a natural means of generating big data and communicating it back to the cloud. In the medical field, the Internet of Healthcare Things (IoHT) does a lot more than just add convenience, however.In disease prediction and prevention, the IoHT is utilizing big data to create comprehensive responses and accurate treatment that are already helping people.
For example, smart thermometers can track flu symptoms and report data through a tracking app back to a person’s physician. The doctor can then use those symptoms to determine if additional treatment is needed, while the accumulated data paints a broad picture of regions where flu outbreaks might be occurring. This information helps healthcare and disease control professionals understand, treat, and prevent outbreaks.
Given the current reality of the coronavirus pandemic, the potential of IoT devices to track COVID-19 symptoms, alert users to potential infection in the area, or even remind them to wear a mask could be especially useful in disease prevention.
Without big data giving artificial intelligence (AI) the tools to understand dangers and symptoms, however, IoT would not be as significantly helpful.
AI Analytics
AI is present in nearly all of the tools that make the application of big data and disease prevention possible. Without the use of AI to chart trends and draw connections across thousands if not millions of data points, medical professionals would be much less able to draw the sort of connections and predictions that allow them to diagnose and prevent disease.One of the most useful approaches of AI in medical disease prevention technology is that of machine learning. This is the ability of AI to execute a function without explicit programming, having learned from its environment and accumulated data sets to draw informed conclusions and act on them. Machine (or deep) learning in medical diagnosis has been used to save lives and cut treatment costs—and it is a primary function of the future of disease prevention.
A project conducted by two major Boston organizations is looking to apply machine learning algorithms to better identify patients at risk for heart disease and diabetes. This work is a carryover project from previous work with machine learning and EHRs to predict hospitalizations up to a year in advance at an 82 percent accuracy rate.
These machine learning algorithms scan for risk factors in patient medical data to determine when intervention is needed, helping alert physicians, prescribe treatment plans, and prevent diseases from emerging altogether, when possible.
This marriage of AI and big data to improve healthcare outcomes represents the future of disease prevention and treatment, one in which technology helps us save lives and live better.
A Future of Better Prevention and Treatment
Big data is making waves across industries, but nowhere is the importance of vast collections of information more essential than in healthcare. Through genetic mapping and the wide variety of tools used in conjunction with big data, medical professionals are in a better position than ever before to understand the full workings of symptoms and disease, risk factors and diagnoses, treatments and patients.With this understanding comes the tools to prevent disease before they even emerge. Whether it be a burgeoning pandemic or the trending of diabetes, big data enables the awareness and identification of risks, allowing us to combat them with better preparation. In dealing with the COVID-19 pandemic of 2020, the value and application of such big data tools should be clearer than ever.
Luke Smith is a writer and researcher turned blogger. Since finishing college he has been trying his hand at being a freelance writer. He enjoys writing on a variety of topics but technology and digital marketing topics are his favorite. When he isn’t writing you can find him traveling, hiking, or gaming.Want to write an article for our blog? Read our requirements and guidelines to become a contributor.