Wouldn’t it be advantageous to know your future health risks based on your current lifestyle?
Being unaware of how your daily eating habits and exercise routine affects your health is a concern. If you were aware of the likelihood of developing significant illnesses in the future, you could take proactive measures to improve your health before it becomes too late.
As the number of patients with various illnesses, from mild to severe, increases, stakeholders must take effective action to ensure the population remains healthy.
By 2050, 25% of the population in North America and Europe will be over 65 years old. As a result, the healthcare system will be required to manage a greater number of patients with complex needs. The cost of dealing with these patients will be significant.
Therefore, we require a system that prioritizes long-term care over short-term solutions.
Big Data in Healthcare
According to Wise Guy Reports, the Big Data Analytics industry in healthcare is projected to exceed $34.27 billion by 2022, with a compound annual growth rate of 22.07%. By 2024, the global value of the Big Data Analytics segment is expected to surpass $68.03 billion. Big Data Analytics in healthcare is providing valuable insights into an individual’s health based on their medical history, eating habits, and lifestyle.
Potential Use of Big Data in Healthcare
1. Health Tracking
Data collected from sensors and continuous monitoring of vital signs can aid in identifying important patterns that indicate overall body health and potential future health risks. By alerting individuals about potential health concerns before they worsen, life expectancy can be prolonged, and individuals can gain better control over chronic and infectious diseases.
2. Prevent Fraud and Abuse
Monitoring product sales and billing data closely can aid in identifying erroneous billing
practices. Big Data Analytics can accelerate claim processing and eliminate false claims, resulting in a more efficient system.
3. Predictive Analytics
Predictive analysis can aid in increasing capacity utilization in healthcare. Analyzing past patient admission rates can assist in adjusting the number of beds available, increasing the number of patients served with the same capacity.
Big Data Analytics can be used for demand forecasting, allowing hospitals to manage staff effectively. Predictive modeling can be applied in various scenarios, such as predicting the likelihood of a patient experiencing a heart attack or utilizing regression models to forecast treatment costs for patients. Similarly, healthcare providers can use predictive modeling to anticipate medical supply demands and avoid stockouts.
4. Customized Care (for high-risk patients)
Predictive analytics can be used to identify patients who frequently visit hospitals and classify them based on their health conditions. Patients with serious health conditions can receive priority treatment tailored to their specific needs based on their past visits, resulting in reduced hospital visits. Big data analytics is crucial in providing these and other related benefits to patients.
5. Preventing Human Errors
Physicians are not infallible and are prone to making mistakes. Electronic health records (EHRs) can be useful in reducing human error by providing comprehensive data about a patient’s medical history.
Analyzing past prescriptions and their effectiveness can help identify errors and alert patients immediately, resulting in more accurate treatment.
6. More Effective Therapeutic and Diagnostic Techniques
The wide range of data generated by medical reports and doctors’ prescriptions can be analyzed to determine the effectiveness of treatment processes and medications. This analysis can help identify the most suitable treatment processes for specific medical conditions.
By eliminating ineffective treatments and processes, desired results can be achieved.
7. Computational Phenotyping
Extracting raw data from sources such as patient personal information, medication history, lab test results, doctor’s prescriptions, and sensor data is necessary to transform Electronic Health Records (EHRs) into useful clinical insights.
This unprocessed data is then run through an algorithm to produce medical insights that can aid in clinical procedures or genomic research.
8. Patient Similarity
The use of Patient Similarity Algorithms enables doctors to identify patients with similar health conditions based on their past medical records. This helps in predicting the most effective treatment strategies for a particular disease, such as determining which treatments work best for which patient groups.
The lack of medical professionals around the world is a serious problem, especially in nations like India where the ratio of doctors to the population is well below WHO norms. Yet, Big Data Analytics can be extremely helpful in overcoming this obstacle. A cutting-edge approach called telemedicine uses technology to bring medical care to far-flung locations.
Medical education for healthcare workers, remote patient monitoring, and many other applications are all possible with telemedicine. With the aid of telemedicine, distant medical professionals can examine and gather patient medical data, allowing doctors to prescribe treatments based on the data without needing to be physically present. This would make it easier for folks in remote places to receive treatment and help with the shortage of medical professionals.
Agile Soft Systems is a leading provider of healthcare software solutions in USA in the healthcare industry. Our expertise in using big data to improve patient care, streamline operations and reduce costs is helping healthcare organizations worldwide. With our advanced analytics solutions, we are helping healthcare providers make informed decisions and deliver better care to their patients. Connect with us [email protected] to know more.