The healthcare industry has already experienced progress in digitising medical records, as pharmaceutical companies , clinical research organisation and other related organisations and stakeholders aggregate years of research and development data in electronic databases and cloud solution systems. The government and other public stakeholders have also accelerated the move toward transparency by making decades of stored data usable, searchable, and actionable by the healthcare sector as a whole. Together, these increases in data liquidity have brought the industry to the tipping point. Here is a snapshot of what you need to know in big data.
What does it mean?
The term “big data” was introduced only within the decade and is the result of the incredible amount of information flowing into our current digital world. Big data is usually defined as huge sets of data that can only be analysed by computers (due to its sheer volume) to reveal patterns, trends and associations, especially with regards to human behaviour.
The term “big data”, with regards to healthcare, sees it as the intersection of mathematics, statistics, computer science and healthcare. The adoption of this technology is not only about delivery better quality care for patients, but sustainability of healthcare systems on the whole. Healthcare systems are often characterised by waste, variation and harm and only with the application if information and data science in real-time, will we drive up efficiency and effectiveness.
Big Data is commonly defined in terms of there 3 V's: volume (how much data), velocity (how fast the data is generated or processed), and variety (the different types or formats of data). A small volume of complex data, a huge volume of simple data, or sophisticated analytics and predictions from any of your data can still benefit from the Big Data technology innovations.
Where does all these data come from?
In the healthcare sphere, the amount of patient and consumer health data has grown exponentially because of new computer-based information systems. In recent years, the adoption of wearable technology, biosensors and mHealth has increase the amount of biological data being captured. In a clinical setting, these data includes ones that come from electronic patient records outcomes (ePRO), electronic health records (EHR) and various other software sources. It is estimated that there are over 10.7 billion objects and devices connected to the internet today. That number is expected to grow to 50 billion by 2020 according to a recent report by Cisco and DHL.
Big Data is often focused on three main varieties:
- Transactional data—these include data from invoices, payment orders, storage records, delivery records, claim activites and cost data. These are useful for payers and providers in healthcare.
- Machine or clinical data—this can be data gathered from industrial equipment, real-time data from sensors and wearable techs (including sensors on your smartphone or your heart rate monitor) as well as web logs that track user behaviours online.
- Social data—this could be data coming from social media services, such as Facebook Likes, Tweets and YouTube views which gives insights on patient behaviour and sentiment data
In many cases, the data on its own (once collected) is meaningless. Real business value often comes from combining these Big Data ‘feeds’ with ‘traditional’ (relational) data such as patient records, medical history, location data, and medication management to generate new insights, decisions and actions.
What is the purpose of big data? Why do we need it?
Big Data in healthcare is being used to predict epidemics, cure disease, improve quality of life and avoid preventable deaths. It is also used to inform consumers or lifestyle choices that promote well-being and the active engagement of consumer in their own care. In a clinical setting, big data is used in evidence based care which has proven to deliver needed outcomes for each patient while ensuring safety. It also allows a care provider a setting that is most appropriate to deliver prescribed clinical impact. They could collectively help the industry address problems related to variability in healthcare quality and escalating healthcare spend
From a patient centric view, a clinician is able to take data from various sources (such as medical and insurance records, wearable sensors, genetic data and even social media use) to draw a comprehensive picture of the patient as an individual, in order to offer a tailored healthcare package. Big data can also be used in detecting diseases at earlier stages when they can be treated more easily and effectively; managing specific individual and population health and detecting health care fraud more quickly and efficiently. Numerous questions can be addressed with big data analytics.
Big data allows for sustainable approaches that continuously enhance healthcare value by reducing cost at the same or better quality. It also drives innovation to advance the frontiers of medicine and boost R&D productivity in discovery, development and safety.
How to manage and leverage big data?
Over the past decade, the healthcare industry has made tremendous strides in the collection of health-related data and in the implementation of technology to analyse and create actionable items from it. However, the actual power of health-related “Big Data” is not just the data that is collected, but how that data is applied and what that data will mean in terms of its impact on efficiency and productivity of our healthcare system.
Given the huge potential for big data applications in the future, there are ways for healthcare organisations to leverage the big data captured:
- Implement a robust digital health platform: In order to get value from the connected digital health environment for the purpose of big data analytics, it is important to have a platform on which to create and manage applications, to run analytics, and to receive, store and secure your data. Like an operating system for a laptop, a platform does a lot of things in the background that makes life easier and less expensive for developers, stakeholders, users and you.
- Allow integration and interoperability between systems and stakeholders: The potential of big data in healthcare lies in combining and sharing data from various sources, systems and stakeholders. The fact that multiple health data sources remained siloed and separated is a drawback towards progression in big data. With different databases and software systems holding different subsets of data, integration and interoperability is key.
- Foster competition and transparency- Health care organisations are attaching monetary incentives to measuring and looking at data; displaying peer and colleague data with respect to patient satisfaction and quality metrics; and using dashboards, all in an effort to leverage competition and improve performance among clinicians.
What are the challenges in big data?
One of the biggest hurdles standing in the way to use big data in healthcare is how medical data is spread across many sources governed by different states, hospitals, and administrative departments. Integration of these data sources would require developing a new infrastructure where all data providers collaborate with each other.
Equally important is implementing new data analysis tools and strategies. The ability to analyse this quantity of data is the centre of gravity for “big data” in health care. The industry needs to catch up with other industries that have already moved from standard regression-based methods to more future-oriented like predictive analytics, machine learning, and graph analytics.
There is also the regulatory and compliance issue as Health care organisations should be aware of the various legal issues that can surface in the process of managing high volume of sensitive information. Organisations implementing big data analytics as a part of their information systems will have to comply with a significant amount of standards and regulatory compliance issues specific to health care.
Security is another challenge. In security, there are considerable privacy concerns regarding the use of big data analytics, specifically in health care given the enactment of Health Insurance Portability and Accountability Act (HIPAA) legislation since health data is highly vulnerable and becomes target for attacks. or these reasons, enabling privacy and security is very important
Data inaccuracies can be a problem in big data analytics. Self reported data is used extensively in healthcare hence the hardware and software used in capturing the data needs to be accurate, not to mention consistent at the same time.
Big data and Internet of “medical” things
Big data will really become valuable to healthcare in what’s known as the internet of things (IoT) or in this case the IoT of healthcare. For healthcare, any device that generates data about a person’s health and sends that data into the cloud will be part of this IoT. Wearables are perhaps the most familiar example of such a device.
In order to get value from the connected digital health environment i.e. IoT, it is important to have a platform on which to create and manage applications, to run analytics, and to receive, store and secure your data. Like an operating system for a laptop, a platform does a lot of things in the background that makes life easier and less expensive for developers, stakeholders, users and you.
The term “connected health” is the term used to described how healthcare is connecting in the digital health industry. A connected health system will maximise healthcare resources and provide increased, flexible opportunities for consumers to engage with clinicians and vice versa to better self-manage their care in the digital health industry. A platform is vital in a connected health system for the digital health industry.
Health information systems show great potential in improving the efficiency in the delivery of care, a reduction in overall costs to the health care system, as well as a marked increase in patient outcomes. With the implementation of this legislation as well as the technologies associated with it, it is imperative to effectively organize and process the ever-increasing quantity of data that is digitally collected and stored within health care organizations.