How Big Data is transforming healthcare industry?[with Case Study]
According to Mckinsey, effectively using Big Data Analytics in the US healthcare industry could create more than $300 billion in value every year. Two third of this value will be achieved by reducing healthcare expenditure.
Isn’t it beneficial if you get to know about your future health risk depending on your current lifestyle? Maybe you are unaware of how your eating habits and your daily workout is affecting your health.
How about if you get to know the risk of getting major diseases in the future, today itself. Wouldn’t you start working on your health immediately, before it’s too late?
With the increasing number of patients related to various kinds of illness, be it from mild flu to major chronic diseases, the stakeholders must take effective steps to keep mankind healthy.
By 2050, 25% of the population in North America and Europe will be above 65 years of age. In the future, the healthcare system will have to handle more patients with complex needs. Healthcare systems will have to bear huge costs to deal with such patients.
Therefore we need a system that focuses on long-term care instead of fulfilling short term requirements.
Keeping you updated with latest technology trends, Join TechVidvan on Telegram
Big Data in Healthcare
Health expenditure has increased leaps and bounds as health awareness is increasing.
Therefore many healthcare service providers are making significant use of technological advancements to provide quality healthcare services. This has led to a huge demand for analytics professionals in the healthcare industry.
As per the Wise Guy Reports, by 2022, the Big Data Analytics industry in healthcare will be more than $34.27 billion. We can expect a CAGR of 22.07 %. The overall value of the Big Data Analytics segment globally will be more than $68.03 billion by 2024.
Big Data Analytics in the healthcare sector is providing tremendous insights about a person’s health depending on their past health records, eating habits, and lifestyle.
Potential use of Big Data in Healthcare
Big Data Analytics in the healthcare domain can prove to be a blessing for mankind. Let’s see how.
1. Health Tracking
Hiding within those mounds of data is the knowledge that could change the life of a patient, or change the world. – By Atul Butte
Big Data Analytics and the Internet of things is revolutionizing the healthcare industry. Nowadays various wearables are there to record sleep, heart rate, distance walked, exercise, etc. Along with this data, there are also the devices to monitor blood pressure, blood sugar level, oximeters and many more.
Data received from sensors and continuous monitoring of body vitals can help identify important patterns through which we can conclude the health of the overall body and thereby the potential future health risk.
People can be alerted about potential health issues before the situation gets worse. This will result in increasing life expectancy and better control over chronic illnesses and infectious diseases.
2. Prevent Fraud and Abuse
Predictive analytics helped prevent more than $210.7 million in healthcare fraud for medicare and Medicaid services.
Major area of fraud and abuse in the healthcare industry is false claims and erroneous billing. Big data analytics can help in identifying fake documents along with understanding potential patterns of fraud.
Keeping a strict eye on the mismatch of the product sales with the billing data can help identify erroneous billing. Big Data Analytics can expedite claim processing and also eliminate false claims.
3. Predictive Analytics
Developed economies like Europe could save more than $149 billion by improving operational efficiency through Big Data Analytics.
We can increase capacity utilization through predictive analysis. Analyzing the patients’ admission rate with the help of past data can help increase/decrease the number of beds. This way hospitals can serve more patients with the same capacity.
With the help of Big Data Analytics, we can manage hospital staff effectively through demand forecasting. Other examples of predictive modeling:
- Predicting the chances of a heart attack in the patient
- Regression models can help predict the cost a patient will incur during treatment. Similarly, hospitals can forecast the demand for their medical supplies to avoid stockout.
4. Customized Care (for the high-risk patients)
Predictive analytics can help save more than 25% of the annual cost of healthcare institutions.
Through predictive analytics, we can identify the patients who visit frequently to the hospital. We can classify these patients based on their health condition. Patients with serious health conditions can be given priority and treated accordingly.
By analyzing their past visits we can provide them customized care and reduce their number of visits. Big data analytics plays a crucial role in delivering all these kinds of service benefits to the patients.
5. Preventing human errors
In God we trust. All others must bring data. – By W. Edwards Deming, Statistician, Professor, Author, Lecturer, and Consultant
Doctors are not gods and they can make mistakes as well. Therefore to reduce human error, EHRs (Electronic health records) can come in handy. Digital health records can provide lots of data about the patient’s medical history.
Analyzing the past prescriptions and its effectiveness, analytics can keep a check on the wrong prescription and alert the patient immediately.
6. More effective diagnostic and therapeutic techniques
Medical reports and doctors’ prescriptions generate tremendous amounts of data daily. We can analyze past data to check the effectiveness of the treatment process and medicines. This will help us know which treatment process is suitable for a particular condition.
We can remove ineffective treatments and processes to achieve the desired results.
7. Computational Phenotyping
This is related to converting Electronic Health Records (EHRs) into meaningful clinical insights. We get the raw data from many sources like patient personal information, medication, lab test reports, doctor’s prescription, data collected from sensors, etc.
This raw is fed into an algorithm to generate medical insights. This data helps support clinical operations or genomic studies.
8. Patient Similarity
Patient Similarity Algorithms helps identify patients with similar characteristics based on their past health records. Through this doctors can predict treatment strategy more precisely for a particular disease.
For example, identifying which treatment strategy will work best for which groups of people.
Big data will replace the need for 80% of all doctors – By Vinod Khosla, co-founder of Sun Microsystems and founder of Khosla Ventures
These days the world is facing an acute shortage of Medical Staff. In India, the situation is worse as compared to WHO recommendations. WHO recommends that there should be 1 doctor per 1000 of the population, but in India, there is 1 doctor per 10,000 of population.
Big Data Analytics can help improve this situation. Telemedicine refers to delivering medical services to remote areas using technology. Telemedicine can be used for medical education for health professionals, remote patient monitoring, etc.
Remote medical staff can check and collect medical data from the patients. Doctors can prescribe the treatment based on the data. This helps avoid the physical presence of doctors to treat the patients.
10. Big Data Analytics and Medical Imaging
Millions of CT Scans, MRIs, X-Rays, ECGs are done daily. The Healthcare industry in recent years is leveraging this data to find patterns across millions of images. This can help study the disease more precisely and can provide a new knowledge pool in the field of medical science.
There may be the possibility that a radiologist may no longer need to look at the image and the algorithm will do all the required work for you.
Case Studies of Big data in healthcare
1. Pulse Heart
More than $48M improvement in the revenue through the data-driven decisions.
Pulse Heart also known as MultiCare Health System’s Pulse Heart Institute felt the need to further improve physician engagement, quality outcomes and recruitment, and its economic health. Organization alignment among various departments helped increase the efficiency of the Institute.
Pulse heart focused on increasing clinician engagement by providing meaningful data, actionable insights to make informed decisions. All these changes helped pulse heart drive improvement and achieve the desired results.
Big Data Analytics helped improve market share in every submarket. With the revenue of $48M, the company surpassed market share targets in 2 years instead of three.
2. Thibodaux Regional Health System
Big Data Analytics helps in reducing the mortality rate through the improvement in stroke care delivery.
In its emergency department, Thibodaux Regional Health System had implemented evidence-based stroke care interventions.
With the help of analytics along with the support of visionary leadership, the team responsible for stroke care transformation was able to identify scope for improvements, which resulted in improved care delivery through facility-wide automated alerts and reduction in the need to transfer patients to other facilities for treatment.
For the patients who have had heart stroke:
- Average variable cost reduced by around 23.70%
- Length of Stay was reduced by 19.5%
- This has led to the cost savings of $118K for those patients.
- The relative reduction in the mortality rate was of around 33.80 %
In the upcoming years, Healthcare analytics will play a central role in the field of medical science. Big Data Analytics in healthcare will open up new insights about the human body and its internal organs. This will also transform our lifestyle significantly.
This field will require a huge workforce with data analytics capabilities to leverage the full potential of Big Data Analytics.