What is the Role of Data Analytics in Healthcare

What is the Role of Data Analytics in Healthcare – Overview

The Healthcare industry is one of the most challenging industries when it comes to data analytics and management. The massive influx of complex data in the healthcare industry makes it almost intimidating for organizations that don’t have the right data analytics and management tools to handle it. This also results in the failure of such organizations to squeeze out meaningful insights from the data, which otherwise can be used to improve the quality of healthcare offered to patients, as well as, to increase the efficiency of the organization.

That’s where the Advance Data Analytics tools or Big Data comes into play.

The healthcare data may just seem like a jumble of numbers and words that is until this data is converted into meaningful information by using advanced data analytics tools. According to one survey, over 90% of healthcare organizations strongly agreed to the growing influence of data analytics for their organizations in the next few years. This shows that just like every other industry, data analytics is slowly but surely starting to influence the healthcare industry for the good, transforming not just the clinical and operational side of the practice but also the administrative and financial aspects of the healthcare industry.

What is the Role of Data Analytics in Healthcare?

The data analytics and management for the healthcare industry are concerned with collecting data from all different departments of the organization to create a central structured data repository. This central and structured data repository enables healthcare practitioners to offer better quality services to patients by adapting a holistic treatment approach and offering personalized healthcare services.

So, when one asks what is the role of data analytics in the healthcare industry, the role is deeply rooted in the potential of the data analytics technology to enable healthcare organizations to stay competitive in the increasingly complex industry by offering better quality services to patients, as well as, by improving the efficiency of services.

Today, healthcare organizations need advanced software as a medical device tools and methods to transform complex data into insightful information. The data analytics technology thus enables organizations to get maintain a highly-structured data repository that helps them make well-informed decisions for greater efficiency and improved quality of services. Once the healthcare organizations implement data analytics into their systems and get comfortable leveraging the capability of the technology, they start seeing the bigger picture of the healthcare services with receiving detailed and structured patients’ information, which subsequently enable them to offer a completely personalized, holistic and precise treatment for the disease.

What is Healthcare Analytics Data? 

Data analytics in the healthcare industry represents the automation of collection, processing, and analyzing the complex healthcare data, to gain better insights and enable healthcare practitioners to make well-informed decisions. The impact of data analytics in the healthcare industry is massive, from the reduction in the medical costs to personalized treatment and from patients’ behavior analysis to pharmaceuticals, the data analytics in healthcare influence the practice at both macro and micro levels; developing automated operations for improved quality services and lower costs.

Now, if we are to look at what is the role of healthcare analytics data, it is to be noted that healthcare data is one of the most complex data generated in any industry. From recording the vital signs in real-time or telemedicine to electronic health care records (EHR), there is a plethora of complex data generated by each patient which is required to be stored, structured, and analyzed to ensure a holistic healthcare treatment. To make things even harder, there are strict government regulations for the healthcare industry, which obligate healthcare organizations to ensure the storage and security of the data.

With all of these complexities, the healthcare data analytics technology equips organizations with the best tools to ensure compliance with the industry regulations, while also ensuring high-quality healthcare services to patients.

Why is Data Analytics Important in healthcare? 

There are various reasons why is data analytics important in the healthcare industry. For the past many years, the healthcare industry has transformed significantly. While previously the industry operated as a volume-based business, today it is more about value-added business; looking to maximize patients’ experience with personalized services. This transformation means that doctors, physicians, and all related staff are under continuous pressure to offer the best and value-added services to patients with better outcomes. This has also affected the financial structure of the industry, where healthcare organizations are competing with competitors by offering more valuable and affordable services to patients.

Now, the role of data analytics isn’t just restricted to benefit the patients, rather the technology also offers significant relieves and incentives to healthcare organizations, insurance companies, as well as, governmental bodies. For instance, the healthcare insurance companies have been adapting to the value-based data-driven payments instead of their traditional fee-for-service model using Electronic Medical Records (EMRs) to offer better and more customized services to their insurers.

What is the Role of Data Analytics in Healthcare for Practitioners?

Disease Prediction and Prevention 

One of the biggest and most beneficial roles of data analytics in healthcare for practitioners is the ability of the practice management technology to predict diseases at an early stage. The powerful analyzing features of the technology backed by machine learning capabilities enable the technology to analyze the massive volume of historical patients’ records and identify specific patterns in patients for a serious disease. This gives doctors and physicians a better chance to take preventive measures for patients to minimize the complications before they become deadly and too complex to handle.

Care Coordination 

Data analytics also comes extremely useful for emergency care, especially at times when doctors are required to take quick actions. The crucial and real-time insights offered by the technology give doctors a second chance to save the patient’s life. Apart from its care coordination role in emergency situations, the technology can also help caregivers to take better care of the patients and send alerts of patient’s health.

Customer Service 

Since the healthcare industry is increasingly adopting a value-added business model, it is becoming more important than ever to offer patients better customer services like personalized treatment, faster processing time, and overall better & accurate healthcare services. Data analytics has a great influence when it comes to customer servicing. By offering organizations with structured and filtered data, the technology enables healthcare practitioners to add a personalized touch to the treatment, as well as, develop seamless healthcare experience by streamlining the billing and payment process.

Financial Risk Management 

Healthcare organizations come with various financial risks including unpaid bills, under-utilized records, lower reimbursements, and much more. Now, data analytics backed by the powerful Artificial Technology can help organizations implement predictive analytics which is able to determine the accounts owing payments, identify unpaid bills, identify the insurance coverage of the patients, and much more to improve the overall cash flow for the organization.

Fraud & Abuse 

Apart from improving the quality of services and efficiency of the organization, the data analytics can also help healthcare organizations to detect and avoid fraud and abuse. The Healthcare industry is highly prone to many fraudulent activities, ranging from human errors in billing to unnecessary lab tests, improper payments, insurance claims, and much more. Data analytics technology enables hospitals and healthcare organizations to identify potential patterns of fraudulent activities (honest or abusive) and avoid them.

What is the Role of Data Analytics in Healthcare for Patients?

Population Health Management 

The rise of data analytics technologies in the healthcare industry including machine learning, deep learning, artificial intelligence, and others have derived the development of Population Health Management (PHM) service in the industry. The rise of PHM has re-shifted the focus of the healthcare services from treatment and response to prediction and prevention. The powerful predictive analytics technology is able to identify risk patterns for chronic diseases in patients at early stages, thereby, giving healthcare practitioners a chance to act in time and avoid complexities of the later stages. The predictive analysis isn’t just a life-saving technology, but it also helps in lowering the cost of the treatment and cure for patients.

Health Tracking

Today the key to healthcare services is the ability to predict the health risks for patients before it becomes a chronic illness. This can only be achieved by implementing powerful data analytics technology with sufficient training data to train the models, which then would be able to closely analyze the medical records of patients and identify key patterns predicting potential risks of diseases, thus acting as a healthcare tracking system; offering faster and better services to patients.

With healthcare tracking technology, the healthcare organizations are now able to track the vital statistics for patients’ health, while developing preventive care methodologies for early treatment and cure. The technology also helps patients in developing chronic illnesses by giving them the right care at the right time.

Industry Advancement

Apart from the massive benefits of the technology and its potential to overcome current healthcare industry problems, data analytics technology also has massive potential to develop Health BI solutions for the future. The technology can be used to analyze a massive amount of complex medical data to come up with the best treatment options personalized concerning the patients’ medical history.

What is Build vs Buy Data Analytics Solution in Healthcare?

While there is a massive potential for Business Intelligence solutions in the healthcare industry, still the sector falls well behind when it comes to leveraging the technology for advancement. A closer look at the problem reveals that one of the biggest reasons for the healthcare industry not adapting the powerful business intelligence solutions is the inherent complexity of the initiative. The complexities including the connectivity, security, integration, and implementation of the technology act as the biggest barrier for the adaptation of BI solutions in the healthcare industry.

Thereby, instead of building an in-house analytics solution, a better alternative is to buy the data analytics solution from a professional tech business, which can seamlessly integrate all required systems with the existing infrastructure and give healthcare organizations an easy way to implement and leverage the power of data analytics technologies.

Folio3 Offer Custom Healthcare Intelligence and Data Analytics

Healthcare is undoubtedly one of the most dynamic and complicated industries when it comes to data management. The continuous growth of the complex data makes it almost impossible for the legacy system to assist healthcare practitioners to get insights from the data and improve the quality of their services.

With proven experience and expertise to design, develop and implement robust clinical decision support systems (CDSS), Folio3 is one of the leading tech partners for healthcare organizations to implement and leverage the power of data analytics and assist healthcare practitioners to improve efficiency, maximize productivity and profitability.

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