With more than 80% of the health care data being unstructured, it is no surprise that healthcare industry is unable to make use of the data to improve research, treatments or clinical operations.
Human lifespan is on a rise along with the constant increase in the world population which poses challenges to the existing system of working and delivery. This is where big data analytics can bring significant breakthroughs.
After all, big data is turning out to be the new “choice of poison” for innovators bringing together patients, doctors, health professionals and developers.
While no one doubts the ability of “Big Data” to integrate disparate information and identify patterns, predict outcomes and move towards a more valuable healthcare; the realization that healthcare is of utmost importance and that a move towards Electronic Health Records (EHR) alone is not enough to ensure quality care, have given it the much-needed impetus.
No wonder, stakeholders have been hoping that big data innovations will result in a pool of change in the way healthcare systems work especially decision-making processes, diagnosis, interaction with patients and the final delivery.
Not only does big data provides the ability to improve quality of life, increase preventive care, enhance patient-to-patient interaction but it also helps improve selectivity in clinical trials, prescribing medicines according to the gene and providing actionable insights to improve various management aspects (like that of discharge and admissions, staff management, performance reporting, quality assurance, etc.) of a hospital.
With the advent of big data, healthcare professionals have begun to see an advantage in sharing the data with each other and structuring it.
You may ask, why?
One of the major reasons is that more and more healthcare professionals are now paid based on the outcomes and not the service they provide. Hence, they value information related to the patient which helps them decide which treatment suits best and gives optimal outcome.
Not only will it result in quality patient care and reduction of expenses but it has the potential to significantly improve reporting, data management, processing claims, increasing support for the payments and automation of processes.
The efforts of doctors, insurers, hospitals and other relevant actors could be easily streamlined to bring together a patient-centric system which focuses on personalized care, reasonable bills and transparency.
Beside this, physicians are moving towards evidence-based treatment which means they have started relying on data (researches and clinical data) much more than before. Hence, the need of data analytics is increasing day by day.
IoT and big data has the ability to revolutionize healthcare.
It refers to smart and interconnect devices which can gather relevant data and transfer it to the relevant person. However, this “relevant data” is mostly unstructured thereby increasing the demand for Hadoop or Kafka platforms to handle the data in a required manner
While you have ample devices which can be used by patients to monitor conditions like diabetes, insulin levels, and blood pressure but you cannot avoid visiting the hospitals. IoT devices have the capacity to turn these frequent visits into phone calls, short visits or even boost the concept of telemedicine.
Let us see a few examples to understand IoT better.
Roche has acquired rights to a glucose monitoring system which uses a sensor below the skin of the patient. This sensor communicates with a transmitter which in turn sends glucose levels to an app on patients’ phone.
In 2016, Roche launched a Bluetooth enabled coagulation system which could tell the patient how quickly their blood clots.
Another area of IoT usage, is adherence. Sensor based technology help doctors to oversee their patients’ adherence to prescriptions.
A McKinsey report states, “After more than 20 years of steady increases, health care expenses now represent 17.6 percent of GDP —nearly $600 billion more than the expected benchmark for a nation of the United States’ size and wealth.
Healthcare is a $2.7 trillion industry in the U.S. alone, and it is estimated that one-third is lost due to different forms of waste, mismanagement, and abuse. It is no different in a lot of other countries.
But, what does it tend to include?
Redundant testing, prescribing meds and devices which are expensive and no better than the cheaper ones available are a few examples.
The ability to store humungous amounts of patient data and to be able to use machine learning algorithms to identify patterns and developing rules to flag certain items which helps detecting discrepancies has resulted in a reduction of waste and frauds.
For example, it can detect if the hospitals over utilized their services or if the patient receives service from different hospitals at the same time etc.
Clubbed with AI, it can also help in identifying new fraudulent patterns and develop new “rules”.
Big data can also help in managing day to day functions of the hospital. These four hospitals have been using data to predict admission rates. The data scientists made use of the hospital admission records to carry on a time series analysis to predict patterns and staff accordingly.
Broadening uses of EHR and knowledge of its potential (also, enactment of HITECH in the USA) has provided adequate incentives to adopt EHR and encourage sharing the data.
Other countries can take a cue from Kaiser Permanente which is a leading healthcare company in the US. They have implemented a system which shares EHRs amongst all the facilities.
A McKinsey report on big data in healthcare states that “The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.”
Predictive analysis uses technology and machine learning algorithms to analyze data (mostly, medical history and latest medical research) to predict outcomes for patients. After all, the machine can analyze many more factors than doctors by adding additional features.
Hence, it can be effectively used for early diagnosis and help reduce mortality rates from problems like heart failure. The earlier a condition is diagnosed, the better it gets treated (or prevented).
For example, Asthampolis has started to use GPS enabled inhalers. It allows them to identify trends in asthma patients and develop better treatment plans.
Not only that, predictive analysis in genome allows a doctor to identify at-risk patients and insist on lifestyle changes to avoid health issues in future.
On a much higher level, analysis and monitoring of health data of the entire population can help in identifying outbreaks of disease or an epidemic.
There is no doubt big data is the answer to a lot of worries in the healthcare sector. Not only can it improve the delivery mechanisms, it can also help improve the management and other associated factors.
Providers and developers need to work together on their big data analytics competencies in order to gain maximum value.