Data analytics is a much sought after technological advancement right now, right across the healthcare industry. In the healthcare field, data analytics has a vital role to play in enhancing various aspects of care like diagnosis, patient care, feedback on treatments, etc.
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.
There are many real life problems with healthcare where data analytics can have a positive impact. We will briefly look at few of these use cases.
- Patient care
Electronic Health Records (EHR) will provide additional information for the physicians about the health of their patient. Wearable devices can collect more vital parameters and provide inputs for improved patient engagement.
- Feedback on practitioners
Data analytics provides new ways to gather information about the effectiveness of treatments given by practitioners. Also, with these methods the performance of practitioners can be determined. Data analytics can be used to evaluate physicians and their practices and to improve patient care.
- Predictive diagnosis
AI enables the use of various algorithms and models to predict diseases early based on the health data of a patient. AI will also aid in learning for practitioners by aggregating and analysing practices and their effectiveness.
- Prescriptive analytics
Where predictive analytics helps in predicting diseases or risks, prescriptive analytics enables practitioners with capability to do something about the prediction. It helps remove the guesswork from the decision-making process and to further improve patient care. Prescriptive analytics provides remedies/suggestions to prevent or mitigate negative developments.
- Medical imaging
Every day, innumerable patients undergo some sort of medical imaging (X-Ray, MRI) for the purpose of diagnosis. For radiologists, medical images are essential in reaching their diagnoses about symptoms and diseases. Technology can bring in accuracy in diagnosing, given that it would focus on all the tiny ‘bits and bytes’. This can also be used to predict whether there is a risk of fracture.
- Operational excellence
Analytics can provide prediction models for disease spread, the required level of healthcare staffing (doctors and support staff), number of patients in need of care, etc. This will help the administrative bodies and hospitals to be better prepared for providing care.
- Real-time alerts
The objective is to help patients with treatment even before their symptoms cause them problems. Many lives are lost due to late treatment or arriving at the hospital too late. With the help of data collected on patients, doctors can be informed about conditions, and the necessary steps can be taken at the right time.
- Improved patient engagement
With the advent of wearable devices and technologies and their use with patients, future disease can be predicted. It also helps doctors to understand the symptoms and make a diagnosis and provide better service.
- Health data and strategic plans
Use of healthcare devices and data collected promotes consultation between medical practitioners, which in turn helps in identifying problems that are not immediately apparent or completely understood.
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.
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.