The Benefits of Data Analytics in Healthcare

What is data analytics in healthcare?
 

Data collected via various healthcare activities is at the heart of data analytics. Data is collected by wearable devices (like smart watches and fitness trackers), medical devices (like pacemakers, implants, etc.), electronic health records (EHR), imaging (like X-Ray, MRI scans, etc.), research at hospitals, and through other means.

This data when studied and analysed provides greater insights into the health of patients and patterns concerning certain health conditions. This collection and analysis of the data is referred to as data analytics in healthcare.

Data analytics in healthcare relies on analysis of historical and new data to make trend predictions, create models to manage disease spread, increase the reach of care to larger groups of people, facilitate the feedback for/from practitioners, help in the early diagnosis of diseases, and much more.

Advantages and use cases
 

The application of data analytics in healthcare has a huge positive impact on mankind. Analytics helps in improving patient care and treatments. With the help of early diagnosis, patients can be provided treatments in good time, thus enhancing effectiveness. As predicted by Gartner Research, by the year 2023 most large organisations will adopt decision intelligence and decision modelling.

In the year 2020, mankind was hit by the COVID-19 pandemic and we are still grappling with it. We have often heard terms like contact tracing, and there are many platforms that will notify you if you have come into contact with an infected patient. All these solutions rely on data analytics to provide you with preventive measures or tell you what steps you need to take, to get tested for example.

Specific health parameters derived from a person’s electronic health record can aid in early diagnosis of cancer, especially breast cancer.

For administration, this data can play a crucial role in policy formation for public healthcare and management systems.

Challenges
 

There are many challenges for adoption and leveraging the full potential of data analytics. Some of these are described below.

The first and foremost challenge is that the healthcare data is distributed across various solutions, making it impractical to apply analytics. Also, the data storage facilities and conventions used makes it difficult to build an insightful and granular database.

Many organisations collect a lot of data via their applications using the traditional means of data collection. They lack adequate tools, expertise and the right talent to harness the potential of heterogenous and inconsistent data. Using the right tools and algorithms on this data can result in insightful actions.

Another very important challenge is the security of data. Data has become the ‘new oil’, and with the advent of technology and its ready availability it has become easier to target and steal data. Organisations need to set up higher security to protect it from such attacks. The next frontier concerns combining big data with blockchain technology: ‘blockchain data analytics’.

Trust in analytics is lacking: that’s the conclusion from the KPMG Guardians of Trust survey. According to this survey, only 35% of the respondents trust analytics as used by their organisations.

Conclusion
 

The full potential and benefits of data analytics is still unknown and new research may open doors for further advancements for analytics for healthcare. The most important challenge is to have patient data stored so that it enables exchange of information between various stakeholders. However, the major concern that hampers the exchange of information is the fear of breach of privacy and confidentiality.

Governments and industry bodies are now working on standardisation, with standards like FHIR, to enable secure data exchange. Adoption of standards across industries and countries holds huge potential for data analytics. The need for the right solutions, expertise and talent is higher now than ever if we are to be ready for tomorrow.