An active 40% of executives in the healthcare sector are putting money into AI, ML, and predictive analytics tools. By 2026, these solutions could yield annual savings of up to $150 billion.
An increasing variety of industries are making sizeable bets on sophisticated solutions like cloud computing, IoT, and data science to future-proof their business. And there is a great deal of data generated in today’s healthcare system — and it is both more complex and sensitive than data produced in other sectors.
The ongoing pandemic has highlighted the critical need to update healthcare IT, and as a result, stakeholders are examining all possible opportunities with haste. For this reason, data analytics has become of even greater importance for a rapidly evolving sector.
In order to delve deeper into the use of data analytics in the healthcare sector, we must first understand the reason for taking the data-driven approach.
Why Data Analytics in Healthcare?
Despite the fact that the healthcare industry produces a lot of data, it frequently struggles to combine that data into insights that can improve workflows and patient outcomes. By incorporating data into this slowly reviving industry, healthcare providers can circumvent the following challenges:
Healthcare data is becoming less cumbersome to store, transfer, and make available to interested parties and external partners — and more readily accessible to the public through visualization.
Precise in Forecasts
Providing stakeholders with up-to-the-moment data-driven predictions, thus enabling them to adapt more quickly to the ever-changing demands of healthcare markets and environments.
Automated processes make light work of data management chores, facilitating communication and collaboration between healthcare institutions and transforming analytics-backed data into actionable business intelligence.
The Types of Healthcare Analytics
No doctor, nurse, or nutritionist could ever sift through all the data being produced in the healthcare industry. Therein lies the value of healthcare analytics. In order to make better decisions and provide better care for all patients, healthcare analytics applies mathematical tools to massive amounts of data. Healthcare providers and patients alike can reap real-world benefits from the following data analytics.
The Use Cases of Data Analytics in Healthcare
With multiple stakeholders involved, namely patients, doctors, hospitals, pharma firms, and decision-makers, healthcare is a highly convoluted yet dynamic industry. However, as data analytics becomes more prevalent in healthcare, the traditional patient-doctor relationship is dwindling.
Now that we have discussed some of the fundamental reasons why data is crucial in the modern healthcare system, we move on to exploring the ways in which data analytics is being used in the healthcare industry.
Using clinical Data for Medical Research
The global market for electronic health records (EHRs) is expected to grow at a CAGR of 6.5% from its current level to USD 44.18 Bn by 2026. The EHRs now dominate as the most popular healthcare data management tool with their capabilities of storing a gazillion medical records such as patient’s medical history and other vital pieces of health-associated information in a more organized manner. As an added bonus, electronic health records (EHRs) are helpful because they can notify patients and doctors of upcoming appointments and other health-related events.
The following are some ways that medical professionals gain from using EHR-backed data in treatments:
- The data gathered aids in individualized healthcare delivery, allowing for more precise diagnosis and treatment.
- Researchers can use the findings as a guide — as they explore various methods to improve the effectiveness of healthcare operations and clinical procedures.
Refining Drug Supply Chains
The healthcare industry would collapse without the supply chain infrastructure. One instance of poorly coordinated or delayed logistics can have catastrophic effects. Through the use of analytics, the healthcare industry can better monitor the flow of supplies and medications to facilities like individual clinics and hospitals. It has helped the healthcare industry’s major players manage their operations more efficiently and reduced supply-chain fluctuations.
Prevention of Fraud
According to the Institute of Medicine (IOM), an estimated $74 billion is lost each year due to healthcare fraud and abuse. Not only have healthcare analytics aided in foresight and decision-making, but they have also aided in data security by revealing unsuspected patterns in network traffic, which has helped with things like breach prediction, vulnerability detection, and more.
Healthcare fraud and abuse are major contributors to unnecessary spending, which has serious consequences for the many interested parties (from providers to patients). It is possible to anticipate when anomalies will occur by using predictive analytics equipped with trained machine-learning models.
As with other applications of data analytics, healthcare data analytics have a bright future. Considering the recent spread of COVID-19, this is more important than ever. But as the volume and complexity of healthcare data continue to increase, it becomes increasingly difficult for the current system to effectively aid healthcare professionals in gaining insights from the data and enhancing the quality of their services.
As a data analytics services company, our data analysts are well-versed in the intricacies of data analytics and are equipped to assist healthcare organizations in adopting and exploiting its benefits, which in turn boosts the efficiency, productivity, and profitability of the healthcare industry as a whole.
For help figuring out how to put data to use in your healthcare system’s digital transformation process, chat with one of our experts now!