Big data has taken over many industries, and the health industry has not been left behind. The adaptation of big data in the healthcare industry has a significant effect that it does in other industries. It is becoming a vital part of patient diagnosis, care, treatment, and scheduling medication, among others.
Big data refers to the large amounts of health data collected in numerous ways, including medical imaging, electronic health records (EHRs), X-rays, diagnostic reports, and genomic sequencing. The use of data analytics has played a huge role in how healthcare professionals collect, analyze and use large amounts of data to provide better healthcare services. Here is a look at the main benefits of big data in the healthcare industry.
Reduction of Cost
One of the main benefits of using big data in the healthcare industry is reducing costs in various industries, including medication, staffing, operational procedures, and admission rates.
Hospitals are now using predictive analysis with the staff to predict admission rates over a specific period. This helps reduce overstaffing issues, decrease patient wait time, and increase hospital efficiency. These are all things that most hospitals across the globe are trying to improve.
Data analytics and big data are now making it possible to diagnose diseases faster and more accurately. Typically, doctors will examine patients by talking to them and comparing their symptoms with what they have encountered. Often they refer to research or consult their literature.
Big data, on the other hand, provides a smarter and more accurate way for physicians to diagnose patients. They collect data from the patient and feed it into an algorithm that will suggest the likely diagnoses. The algorithms will also suggest high-value tests and help reduce unnecessary tests, which might be expensive for no reason.
Real-Time Patient Monitoring
Big data has popularized IoT devices and wearables, which help improve patient care. These devices will automatically collect health vitals such as blood pressure, heart rate, oxygen concertation, pulse, temperature, and blood sugar levels, among others.
Modeling And Forecasting Outcomes
Predictive analytics and big data have made it easy for healthcare professionals to make clinical decisions. Many individuals in the healthcare industry use predictive modelling for different purposes, including predicting the outcomes of treatments and some diseases.
Other models also forecast the spread of diseases, while others predict the risk of developing a disease. For instance, many countries have used predictive analytics to identify undiagnosed diabetes, forecast the spread of COVID-19 and predict the survival of patients after in-hospital cardiopulmonary resuscitation.
Big data plays a huge role in telemedicine. With high-speed real-time data and robots, physicians can perform their operations while away from their patients. It also plays a vital role in robot-assisted surgery, remote patient monitoring, initial diagnosis, and virtual nursing assistance.
Telemedicine has made it easier for patients and doctors:
- Doctors no longer have to waste time with unnecessary paperwork and consultations
- Patients do not have to wait in line
- Prediction of acute medical events and help prevent deterioration of the patient’s condition
- Reduced healthcare costs and improved quality of services
Electronic Health Records (EHRs)
EHRs are the biggest sources of data in the healthcare industry. These records give doctors a clear picture of a patient’s health history. They are also shared through a secured system and are available for the private and public sectors.
Physicians can implement the required changes without paperwork and with the danger of data replication. It also sends reminders to patients when they need new lab tests and tracks prescriptions to see if they have followed the doctor’s instructions.
Big data has made it easier for healthcare professionals to understand the industry and spot opportunities they can derive from them. It has also played a huge role in discovering and developing new drugs. With predictive analysis, real-time and historical data and visual analytics, healthcare professionals can identify a process or trial’s potential strengths and weaknesses and help discover a new drug.