With doctors using the data from dedicated wearable fitness devices to personalize healthcare services to a patient, big data analytics is at the core of healthcare operations today.Data-driven decision making is changing models of treatment delivery in healthcare sectors. Healthcare institutions are now able to understand and analyse the probability of a person picking up serious illness at a very early stage that treatment is affordable and simple than if it is spotted until later. Read on to understand how big data in healthcare is making the world a better place with the ability to analyse data in various dimensions through descriptive, prescriptive and predictive analytics for enhancing the quality of healthcare services.
Big Data Analytics in Healthcare
Let's re-imagine Big Data Healthcare world.
Today healthcare comes with many challenges.
1. Access to quality healthcare, medicines, and care centers
2. Reducing costs of healthcare
3. Better patient experience
Big Data analytics in healthcare has taken a huge step in solving these issues.Today if a person visits the doctor, they inadvertently generate a lot of healthcare data which is then recorded and stored for future use. This healthcare data allows the care center and the doctor to prescribe customized wellness programs and medicines for the patient. Not only can the doctor check past records, but can also track the progress of the patient, recommend medicines which are suitable for a specific individual and advise other non medical solutions for the person's get well program.
There are various medical apps which are helping more people get fitter and more engaged with their own health. These apps help the doctors and the patients:
1. Measure progress of the patient.
2. Track social sentiment.
3. Reduce the hospital/doctor visits, thereby lowering the cost of healthcare.
We have come a long way, but have we been able to harness the full power of Big Data analytics in healthcare ?
Some elements are already in practice. Slowly but steadily the healthcare industry is becoming much more connected and more patient centric, due to Big Data in healthcare.
Healthcare informatics is one of the fastest growing areas of healthcare that deals with getting the right information to the right person at the right time.Healthcare informatics is on the nib of stepping into a new era as technology starts to handle healthcare data with great potential for information growth. Big data healthcare industry is making technological advancements in healthcare informatics with various subfields like clinical informatics, bio-informatics, Neuro-informatics and Public Health informatics leveraging big data analytics.
Big Trends in Healthcare Industry
- 50 years back healthcare services were mostly physician centric. This has not changed much. You go to the doctor, he prescribes a care regime and medicines, then you go to the pharmacist to fill your prescription, you might also need to go to the diagnostics lab to get your tests done. You had to run around everywhere and everyone just operated within the premises of their own enterprise. Today the US healthcare industry constitutes nearly 18% of the overall US GDP. It is no longer possible for the different healthcare sectors to work in silos - ignoring what happens to the patient outside their departments. Now, driven by the healthcare reforms and the emergence of digital technologies, the healthcare service model is becoming more patient centric.
- Today we see the emergence of models like “pay for performance”- where the healthcare institutes get paid more, for better quality of healthcare provided to the patients. This has given rise to a lot of collaborative care models like ACO (Accountable Care Organizations) and PCMH (Patient Centered Medical Homes) – where healthcare institutes come together and work with the patient for better healthcare practices. These kind of models allow the medical insurance companies to keep a patient healthy, than to treat a sickness.
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History of Big Data in Healthcare
Healthcare data is more voluminous in nature than data generated in any other industry. EMRs alone generate around 1000 tables of healthcare data. An average sized hospital will generate some petabytes of healthcaredata every day.
The variety of big data in healthcare industry is mind boggling. You have the digital sensors, notes of physician, electronic records, lab data, pharmacy data, etc.
With the advent of wearable devices, it is now possible to monitor patients 24/7. This generates huge volumes of healthcare data at a very rapid speed.
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Big Data Analytics in Healthcare Sector
There is a wide spectrum of big data analytics in healthcare sector.
1.At the bottom of the spectrum, there will be applications that will be producing some reports. This kind of analytics is widely used and is present in all kinds of healthcare companies. For example, fragmented point solutions, operational reports for hospitals, etc. The databases and the RDBMS are creating these records.
2. At the top of the spectrum, we will have predictive or suggestive/descriptive and prescriptive analytics. This is where Big Data is bringing in tremendous value in healthcare descriptive, prescriptive and predictive analytics.
Looks at the past performance records and mines historical data to look at the reasons of past structures of failures. Things like operational reports, financial reports which do a post mortem analysis of performance are of descriptive nature.
Deals with - What will happen? When historical data combines with statistical models and mathematical algorithms and combines outside data to figure out what will be the outcome of an event.
It not only anticipates what will happen and when it will happen but also why it needs to happen. This analytics provides decision options for taking advantage of any opportunity and mitigating risks.
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Predictive and Prescriptive Analytics
We will look at some examples of predictive and prescriptive analytics.
- Real – time alerts are an example of predictive analytics.
- Analyzing socio-economic data allows the healthcare companies to understand which patient can come in regularly for follow up appointments and which patients do not have the resources to do so.
- Predicting the “flight-path” of the patient is another great example of predictive analytics. As in using historical data and bringing in data from other sources, now it can be predicted, at which time the patient is likely to have complications, or what could be the timeline of the patient recovering.
Prescriptive analytics can potentially look at all the patterns of research studies that are happening for a particular therapy, all the medical records, physician notes, claims, socio economic data and behavioral data – this will affect the eventual outcome of the treatment. With prescriptive analytics, it is possible to accurately predict what the final result of the treatment would be.
This is where Big Data in healthcare makes a difference – the ability to look at wide variety of data from all dimensions and facilitate actionable insights to improve the quality of healthcare.