Big Data Analytics in Healthcare

ART01x - edited feature imageBig data and data analytics are changing the way we operate in many areas across the globe, from business to government to mobile app development. The world of healthcare is no exception, as data can support the needs of the healthcare sector and improve its ability to serve and treat people. Similar to many other sectors, big data in healthcare has changed the management and analysis of data and has given us the ability to leverage the information to improve what we do.


Healthcare analytics can use big data to improve its services in ways such as predicting outbreaks of epidemic diseases, using the information to avoid preventable diseases, reducing the costs of treatment and Improving quality of life

Healthcare professionals, similar to their business counterparts, can collect and analyze data to improve their work.

Why Big Data

There are various reasons why data analysis is gaining importance in healthcare. One is the rising cost of healthcare and changing the priority from treating disease to improving patient outcomes.

For instance, if healthcare providers are paid by an insurance company based on patient outcomes, they have more incentive to share patient information across the board with every provider that comes in contact with a patient. With an incentive to share patient information, it makes it easier to utilize the power of data analytics to improve the lives of their patients.

Therefore doctors want to gather as much information as possible from across the healthcare spectrum to the level of the individual patient, in order to pick up warning signs of diseases. For instance, allowing treatment to begin at an early stage to make it simpler, more effective, and less expensive.

Healthcare data analytics aid in prevention, which is always better than waiting until a disease occurs and needs a cure. In this way, physicians are making decisions based more often on evidence and not solely on their professional opinion and schooling.

Therefore, like with other businesses and industries, data gathering and management in the healthcare sector is gaining steam. Let’s look at five ways that data analytics can be and are being used in the healthcare sector.

Big Data – Examples of How it is Being Used

Electronic Health Records

One of the most powerful applications of big data is the collection of health records, in which each patient has their own digital record. This record includes medical history, family history and demographics, any allergies, all test results, and more.

Records are shared among health care providers through secure information systems. Doctors can also add to records and implement changes without time-consuming paperwork. Such records can also serve patients by tracking prescriptions, or triggering reminders for lab work, for instance.

Health Alerts in Real Time

A new innovation in healthcare is the creation of wearable devices which provide real-time alerts on patient health. Similar to software used in hospitals to analyze medical data on the spot, new personal analytics devices can provide the same functionality away from hospital or clinical settings. This can help reduce costs of in-house treatments and also provide quick healthcare advice to patients.

These wearables collect health data on a continuous basis and store it in the cloud. Besides providing real-time data for individual patient care, the collection of information can be accessed and applied to analyzing the state of health at the larger population level, allowing for comparisons based on geography, demographics, or even at the socioeconomic level.  Treatment and prevention strategies can then be developed and adjusted.

One example is an asthma inhaler with a GPS tracker, which allows for tracking asthma trends at an individual level but also at the larger level of populations and by geography, to allow for better treatment plans.

Another example is a blood pressure tracker that can send an alert to a doctor if there is an alarming spike in blood pressure. The doctor can then react by contacting the patient immediately and ensuring a change in care before waiting for an appointment – or before it’s too late.

Wearables that are already in common use by consumers, such as step/fitness trackers, sleep trackers or home blood pressure monitors, can also add vital information to the healthcare database. Data can be mashed together and analyzed to look at health risks, like an elevated heart rate plus difficulty sleeping, which could signal impending heart disease. Trackers can also create incentives for patients to be involved in monitoring their own health, such as insurance incentives for wearing such smart devices.

Predicting Patient Needs to Determine Staffing

Since determining appropriate staffing is one way to support cost effectiveness in delivering healthcare, the ability to predict staffing needs would be a powerful one for hospitals and other healthcare facilities.

This seems like a nearly impossible task, but big data is helping a few hospitals to figure this out. In Paris, there are four hospitals that are using data from multiple sources to predict daily and hourly rates of patients at each hospital.

To do this, they are analyzing data sets of hospital admission records for the last 10 years, crunching the numbers using a technique called “time series analysis.” This allows researchers to see patterns in admission rates and use machine learning to find algorithms that predict future trends in admission rates.

The data is then provided to hospital staff to forecast the next 15 days and allow for adequate staffing, which in turn provides better care to patients, lower waiting times, and staff that have appropriate workloads.

Big Data and Artificial Intelligence

Another use of data in healthcare is the rising use of Artificial Intelligence (AI). Simply defined, artificial intelligence is using algorithms and software to approximate human cognition in analyzing complex medical data. In this way, AI allows computer algorithms to estimate conclusions without direct human input.

For instance:

  • Brain-computer interfaces that are backed by AI can help restore fundamental experiences such as speaking and interacting that are lost through neurological diseases and trauma to the nervous system. Creating direct interfaces between the human mind and computers, without the use of keyboards or monitors or a mouse, could mean a drastic improvement in quality of life for people with spinal cord injuries, ALS or stroke damage.
  • AI is part of the next generation of radiology tools, allowing for “virtual biopsies” to help analyze tumors in their entirety, rather than through one small, invasive biopsy sample. Using AI innovations in the field of radiomics, work is being done to harness image-based algorithms to characterize the properties of tumors.
  • A shortage of healthcare providers, particularly in developing nations, is an issue especially in areas such as radiology or ultrasound. AI can help mitigate this by assuming some diagnostic duties that are usually handled by humans. For instance, AI imaging tools can screen X-rays, reducing the need for a trained radiologist on site.
  • AI may help enhance the speed and ability to document information for Electronic Health Records. After all, the need to document data means that healthcare providers now spend time on the entry of information related to each patient. Enhancements in AI could mean that a patient visit could be recorded on video, and then AI and machine learning could index videos for future information retrieval. In addition, virtual assistants like Amazon’s Alexa could be used at a patient’s bedside for order entry, or AI could process routine requests to healthcare providers like prescription refills or notification of test results. In this way, AI can reduce the administrative workload of healthcare professionals.

Integrating Medical Imaging and Big Data

Medical imaging in the form of X-rays, MRIs, ultrasounds, etc. makes up a big part of medical tests and procedures in healthcare. Radiologists need to individually examine each of these results, which can result in overwork and delays in diagnosis. But big data could be changing how they are analyzed.

For instance, the analysis of hundreds of thousands of images can develop algorithms that identify patterns in the pixels of the images. Those patterns could in turn develop into a numbering system to help physicians with diagnosis. It could even be possible that it would go as far as allowing radiologists to analyze the algorithms rather than examine individual images. The algorithms will be based on studying more images than any radiologist could look at in a career.

Final Thoughts

These five examples provide a snapshot into the amazing trends developing in the use of big data to support effective healthcare.

Using data to the advantage of healthcare providers can improve patient experience, including quality of treatment and increased satisfaction. It should also support an improvement in the quality of the health of the overall population. And finally, it can also support a reduction in costs.

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Danielle Canstello

Danielle Canstello

Danielle Canstello works on the content team for Pyramid Analytics and enjoys writing about the analytics, marketing and business analytics industries.

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