The coronavirus pandemic has accelerated the development of biotechnology and the entire medical field. Big data helps to research more efficiently, to diagnose more quickly, and to treat more effectively. Machine learning and data analysis in medicine allow you to create data warehouses and services, update and optimize the infrastructure of registries, and engage in advanced research in evidence-based medicine, pharmaceuticals, and pharmacology. Still, some things could be improved with big data in medicine.
Big data has become a topic of special interest for healthcare because of the great potential that is hidden in it. It includes various sources for big data such as patients’ medical records, hospital records, telehealth devices, results of medical examinations, etc. This data requires proper analysis and management to gain meaningful impact. Many challenges are associated with each step of working with big data. Customized software for analyzing big data can help to solve those problems.
Developers have to create an appropriate infrastructure capable of systematically generating and analyzing information to provide relevant healthcare solutions. Efficient analysis, management, and interpretation of data can open new opportunities for modern medical care. That is why the healthcare industry improves financial advantages and services. Strong integration of healthcare data can revolutionize medical therapies and personalized medicine.
Big data describes a large amount of healthcare information that collects patients’ records and helps to manage hospital performance. This task is too complex to solve without the adoption of digitalization.
Big data analytics in healthcare offers a lot of essential benefits. It will use specific medical data of a particular individual or a whole population and help cure disease, cut down costs, avoid preventable diseases, nip epidemics in the bud, and better the general life quality.
Related: Challenges and benefits of Big Data in healthcare
Health professionals can collect massive amounts of data and look for the best strategies to solve new challenges of today’s treatment delivery methods caused by the increasing average human lifespan.
Thanks to the work of data analysts, the development of new drugs is cheaper and faster, diagnosis is more accurate, and treatment recommendations are more individualized according to the characteristics of each patient. In the future, medicine will become personalized.
It is necessary to collect more data to gain a deeper understanding of the internal mechanisms of the human body. The biomedical field needs data scientists dealing with problems in big data.
There are already enough examples of the successful use of big data in medicine. Data science tools help fight “impersonal healthcare” when the standardization of treatments leads to a decrease in their effectiveness.
Analysis and processing of information help to level such errors in medicine. Express Scripts analyzes millions of pharmacy prescriptions every year. In the long term, this will lead to the fact that the medical staff will know about the possible side effects of the drug even before prescribing it to the patient.
For example, there are startups engaged in analyzing blood and human microbiota composition. Based on the analysis results, a personal diet is selected, considering each user’s unique characteristics.
EHR belongs to the most widespread applications in modern medical care. Each patient has a digital record that includes medical history, demographics, laboratory test results, allergies, etc. Providers can share EHRs, which offers many opportunities such as adding data into records over time, avoiding paperwork, the danger of data replication, and saving time.
EHRs can also remind a patient about the necessity of following doctor’s orders or about new prescriptions or lab tests.
Another crucial functionality of big data apps for healthcare is real-time alerting. Wearables can gather patients’ medical data continuously and send this data to the cloud to keep patients away from hospitals and avoid costly in-house treatments. The software can analyze medical data in real-time and give providers pieces of advice for most optimized prescriptions.
For example, if a patient’s blood pressure increases, the system can send a real-time alert to the doctor, who will help in lowering the blood pressure. Besides, this information can be added to the database, which will allow doctors to use this data and modify the medical care delivery strategies.
The term telemedicine means the delivery of remote clinical services using technology. Telemedicine has been known for over 40 years, but today it has come into full bloom with wireless devices, wearables, online video conferences, smartphones, etc.
Telemedicine is helpful for remote patient monitoring, primary consultations, and initial diagnosis. Telesurgery allows robots to perform operations using high-speed real-time data delivery. Physicians are not physically present in the same location with an operated patient.
Clinicians use telemedicine for medical education for health professionals or provide personalized treatment plans and prevent readmission or hospitalization. Telemedicine allows providers to prevent deterioration of patients’ conditions and predict acute medical events in advance.
Telemedicine helps keep patients away from hospitals, reduce costs, and improve the quality of delivered service. Doctors save time on avoiding unnecessary consultations and paperwork. Patients avoid waiting in lines. Telemedicine can significantly enhance the availability of care as patients’ condition can be monitored and consulted anywhere and anytime.
Related: Big data implementation: roadmap
Using big data for telemedicine enhances the patient’s engagement as well. Patients can be directly involved not only in the monitoring of their health but in changing their lifestyle to be more healthy.
The collected medical information can be used to identify potential health risks. For example, an elevated heart rate and chronic insomnia can signal a chance for future heart disease.
Physicians can monitor collected data and help patients suffering from asthma, blood pressure, or other chronic diseases. Patients can benefit from it, become a bit more independent, and reduce unnecessary visits to the doctor.
Big data is essential to tackle the hospitalization risk and prevent deterioration for specific patients with chronic diseases. Analyzing different insights such as symptoms, medication type, the frequency of medical visits, and many others, healthcare facilities can provide better preventative care and reduce admissions to the hospital. It helps to cut costs, but it will also ensure the availability of resources for patients who need it more. It is how big data in healthcare can save people’s lives and improve the quality of medical care.
Patient safety is crucial for the healthcare delivery system. Despite being manned and run by qualified personnel dedicated to patient safety, the healthcare sector is not immune to error. Even the most competent healthcare professionals can make mistakes and prescribe the wrong treatment or medicine, which can have grave consequences, harm the patient even if it may lead to death.
The minimization of human errors is the most significant issue for modern medical care. Latent medical errors indicate organizational inadequacies and active medical errors that need to be addressed at the individual level. All those errors can be more easily avoided with the help of big data in healthcare industry. Information can be utilized to dissect client information and the endorsed prescription. It helps doctors who deal with many patients in a day to avoid mistakes.
A most common problem for each healthcare manager is to decide how many people to put on staff at any given period. If too many workers are put on, it causes the risk of unnecessary labor costs added up. If too few workers are available, the hospitals won’t be able to handle the patients, which can even lead to death. Big data collected from different patients can be organized and help solve this problem by analyzing the information and predicting the amount of needed staff.
Customized healthcare apps use the data from previous records to determine how many patients will be likely to visit the medical facility in an hour or a day. Using those predictions, hospitals can put on a sufficient number of healthcare professionals to avoid adding up labor costs or lousy patient service. Organized data will also help define the areas that need healthcare service most and increase the medical services in the required areas.
Healthcare works with sensitive patients’ vulnerable data, with a high black market rate. Stolen personal data can be used for several criminal activities. Medical facilities need specific security procedures such as antivirus and firewall safety protocols to prevent data thefts. Customized software can help to detect any suspicious activity in the database and prevent hackers from stealing data.
In an analytical study, the international consulting company Deloitte highlights several trends that will be characteristic of future medicine. Data and problems in big data play a significant role in most of them. Medical technology companies will become leaders in the entire biomedical industry, and the development of software capable of analyzing medical data will become a priority. Big data will capture R&D departments, and advances in the field of artificial intelligence, nanotechnology, and bioinformatics will help to improve the clinical diagnosis of many diseases.
Now the data scientist community is actively engaged in creating algorithms capable of diagnosing tumors by analyzing the results of MRI, CT, and x-rays. The goal is not to put doctors out of work but to give them more information. Such assistance systems will allow specialists to determine the diagnosis faster and more accurately and minimize the error factor.
The emergence of personalized medicine and an individual approach to treating patients will soon occur and significantly increase treatment and prevention effectiveness. In principle, drugs approved by the strictest regulators cannot fail to work, which means that if they do not work somewhere, the reason is in some additional conditions. The data will help you learn how to identify them.
The basic knowledge of a data scientist in the medical field does not differ from the basic knowledge of an ordinary data scientist. However, at a more in-depth study stage, biomedical specialists move on to more specific things: molecular biology and methods of working with particular medical databases. They also deal with problems in big data in healthcare.
IT specialists in the biomedical industry work closely with the analysis of medical images. They have their specifics: tomography scans are three-dimensional and have a particular format that is not similar to the standard jpeg. Such a data scientist must be familiar with the unique architectures of neural networks for working with such data and unique algorithms and programs.
Important key points of big data in healthcare are the following:
Due to rising medical costs, there’s a huge need for big data in healthcare. According to a McKinsey report.
“healthcare expenses now represent 17.6 percent of GDP — nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth”.
It shows that costs are much higher than expected. It is why we need data-driven and intelligent thinking to solve the problem.
Big data will provide better healthcare by giving early warnings of disease conditions, introducing prediction of epidemics concerning population health, and helping discover novel biomarkers and intelligent therapeutic intervention strategies for an improved quality of life.
What are the challenges of big data?
Big data can make businesses an easier target for attackers. Even giant companies have experienced massive data breaches.
How big data helps to solve problems in healthcare
Big data in healthcare has enormous potential and can help hospitals, clinics, and medicine. Machine learning algorithms that can find statistical correlations in a vast global array of medical data will quickly issue predictions and recommendations for the patient and his attending physician.
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