Big Data Analytics in Health Care Can Save People (Reasons and Examples)
The most promising area where big data can be applied to make a significant change in 2019 is healthcare. If there is one industry that reaches everybody, it is the healthcare industry and therefore, it must be on a forever path towards excellence. Healthcare analytics can reduce the costs of treatment, predict and avoid preventable diseases, notify healthcare providers about any outbreak of epidemics, and improve the overall quality of health.
Since the average lifespan of a human being is increasing along with the population, healthcare providers are accepting new challenges to current treatment delivery system. In order to do so, healthcare providers, just like any other enterprise, need to collect a vast amount of medical data and identify new strategies and solutions.
Being a healthcare software development company ourselves, and having comprehensive expertise in Big Data and Healthcare Analytics, we have addressed the need for big data, how it can help, and provide you with several big data examples in healthcare that can save people.
After reading this article, do not forget to contact us to find out how you can boost your healthcare business with Big Data Analytics.
Big Data in Healthcare
Big data is the large quantity of information created by digitization – in healthcare, it is the specific health data of the patients, which is potentially used to plan further treatments, cure diseases, reduce care cost, and prevent epidemics.
Big data has driven many changes in treatment models. Since human lifespan has increased, doctors want as much information as early as possible about a patient for a number of reasons – pick any early warning signs of serious illness, diagnose and treat disease early, and provide effective but less expensive care.
Healthcare data analytics attempts to tackle the silos problems of patients’ data, such as collected bits of patient information scattered in hospitals, surgeries, labs, and clinics’ archives that is impossible to bring together and communicate properly. Unlike yesteryears, with today’s new custom healthcare software, collecting such massive data, and converting it into critical insights for better treatment plans becomes easier.
Reasons Why We Need Big Data
There’s a huge need for Big Data Analytics in healthcare, especially in rising cost nations like the USA. “After more than 20 years of steady increases, healthcare expenses now represent 17.6 percent of GDP —nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth” –McKinsey Report.
With current incentives changing as well (insurance companies making a switch from fee-for-service plans to custom-made plans for patients), we clearly are in need of smart, data-driven thinking in healthcare.
Previously, healthcare providers had no direct incentive for sharing patient data with another, which made implementing the power of analytics harder. Now that more providers are being paid based on patients’ outcomes, they receive financial incentive to share data, which can be used to plan better care treatment, improve patients’ lives, and cut the cost of insurance companies.
Additionally, physicians are making decisions based on large bands of clinical data and research as opposed to the traditional system – relying on their schooling and professional opinion. In other words, physicians, surgeons, pharmacists’ decisions are becoming more event-based than book-based.
This new change guarantees that there will be greater demand for big data analytics in healthcare, just like other booming industries, in 2019 and henceforward. And the emergence of SaaS BI tools will also answer the needs.
Examples of Big Data Applications in Healthcare
Patient Flow Prediction
Time Series Analysis techniques, along with machine learning, can help solve the problem of overstaffing and understaffing in hospitals and medical facilities. Through big data analytics, providers can find accurate algorithms that show future patient admission trends.
EHRs or EMRs are the best and widespread application as of now, where every patient will have his own digital record containing demographic, treatment history, medical and diagnosis history, allergies, lab reports, and prescriptions. The records, which are shared via a secure information system, are available to care providers from both the private and public sectors.
Improve Patient Engagement
Big data involves patients to directly monitor their own health. In most cases, potential patients take interest in smart devices that record every step they take, such as heartbeats, calories burnt, BP, sleeping habits, and sugar count, on a permanent basis. All this information can be installed in trackable software and help providers identify potential health risks. Financial incentives from insurance companies will further augment people to lead a healthy lifestyle.
Clinical Decision Support Software (CDS) will analyze medical data in real-time and provide suitable advice on prescriptive decisions to doctors. Wearable devices will collect health data continuously and send this via the cloud, thus ensuring doctors treat patients without the need for admissions and readmissions.
Prevent Misused Opioids Abuse
In the US, overdoses fromOpioids have caused more deaths than road accidents, and therefore, analytics experts from Fuzzy Logix Analysts and Blue Cross Blue Shield Data Scientists have started working on this. They are trying to use past insurance and pharmacy data to identify risk factors that predict and help providers flag off individuals with risk for abusing opioids. In fact, in the future, Big Data will play a major role in tackling this serious problem from the root.
Big Data Might Cure Cancer
All thanks to President Obama’s program on curing cancer, medical researchers used a large amount of data on treatment plans and recovery rates of cancer patients to find trends and treatments that have the highest rate of success. This data research led to an impeccable benefit, such as finding an antidepressant, Imipramine, having the abilityto cure a certain type of lung cancer.
Another potential use case could be the sequencing of cancer tissue samples genetically from clinical trial patients and make the data available to wider cancer databank. Although there are several obstacles to this as of now,there are precedents to navigate these problems.
Predictive Analytics in Healthcare
We already know predictive analytics is the biggest breakthrough in business intelligence trend, but little did we know that its potential applications are reaching far and wide to the healthcare industry, much further in the future.
Predictive analytics and healthcare business intelligence trends will be very crucial in cases of patients with complex and long medical histories and patients suffering from multiple conditions. Latest predictive analytics tools would also predict patients who are at risk of diabetes, and advise those individuals with preventive measures.
These were the big examples of big data analytics that will empower healthcare in 2019. Other big data applications that you will see working their way up steadily in the coming years include:
– Reduction of data threats and enhanced cyber-security
– Vast usage of telemedicine to provide personalized treatment plans and reduce cost
– Integration of big data and medical imaging to help physicians in the diagnosis
– Prevention of unnecessary ER visits, which will save cost, time, & energy
– Usage of health data from across the world for informed strategic planning
To conclude, we have seen three main trends from the above big data application examples: a) improve patient experience and expectation dramatically by enhancing the quality of treatment and patient satisfaction, b) improve the overall health of people over time, and c) reduce the general cost for both providers and patients. These applications should be the focal point of data science for they not only have the potential to save money but also to enhanced living.