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Aaron Han, MD, PhD, FCAP, Chief of Laboratory, American Hospital Dubai, UAE, Cherie Pardue, MBA, Deputy CIO, Adventist Healthcare, Maryland, USA, and President-elect, HIMSS Virginia, USA and Carlo Kaabar, CEO, Futurelab Medical Laboratories, KSA, 16 January 2017
Over a decade ago, predictions were made about the rapid growth of data on the World Wide Web, and the ability to gain meaningful insight from this information. Big data is the moniker that is popular today to describe the meaningful analysis of the increasing growth of information that is collected. Originally, Big Data was defined by possessing 3 V’s - Volume, Velocity, and Variety. Today many include Veracity and Value as two additional characteristics of Big Data.
Volume refers to the sheer amount of information that is “stored” in the world. Most scientists would agree that the total amount of “data” in the world on the web is in the realm of several zettabytes (a trillion gigabytes). The amount of storage required for this amount of information would require over 50 billion smart phones with 32 GB storage capacity.
Velocity is the exponential rate of data growth. Every day over two exabytes (or a billion gigabytes) of data is created, and over 90% of the data in the world was generated in the last two years.
Variety refers to the myriad sources from which data is collected, and also the various types of data collected including text, voice, and video. Such unstructured data require more storage space and compound the velocity of data growth. More and more data is now collected on mobile devices and sensors. These are all part of what will become the “Internet of Things” and add to Variety and Velocity.
Veracity in Big Data is important to sort out the “noise” from useful information. Whether the data is consistent, accurate and validated is important to gain meaningful insight into data, and build analytic and predictive capabilities.
Finally, Value is increasingly the focus for most businesses trying to exploit Big Data. The goal is to find meaningful information that is actionable and achieving positive outcomes as the goal.
Healthcare organisations are being pushed to rapidly implement Big Data analytics incorporating the characteristics outlined above to meet changing reimbursement and population health goals. A new Healthcare Analytics Maturity Model has been suggested to assist healthcare organisations in understanding the steps required to move from retrospective scenarios (answering the questions of “What happened?”) to forward predictive and then prescriptive scenarios to understand “what needs to happen” to achieve specific outcomes.
Many healthcare organisations have not yet started on their journey. In 2011, only 10% of healthcare organisations worldwide had adopted data warehousing and analytic capabilities. However, if the US is a guide, according to Premier's Fall 2015 Economic Outlook survey, over 64% of Hospital C-Suite executives have increased capital budgets. Of which, 39% increased their budget by over 10% for technological investments to achieve value-based payment models, demonstrating future growth for Big Data initiatives in healthcare.
Big Data can be useful to address problems in healthcare, right from improving operational efficiency, to ensuring quality outcomes and empowering patients toward better disease management. Big Data projects require having an appropriate governance framework in place, hardware configuration and support, and use of appropriate software to generate useful and actionable information. Healthcare providers can also learn from other industries that have leveraged Big Data and analytics to achieve organisational improvement. For laboratories, there is significant potential to contribute and leverage laboratory information. In the US, it is estimated that less than 2% of the healthcare expenditure is on the laboratory, however over 70% of clinical decisions are made based on the result of lab tests. Big Data and analytics can provide insight into all aspects of disease management across the acute and chronic care setting, and for all phases of testing.
Information that is captured in the lab and Lab Information System may include patient testing results, specimen tracking information, and information across time and different points of service. These can be used to monitor individual patient condition and need for treatment intervention; populations of patients with specific conditions and optimise outcomes; operational improvements related to pre-analytical and post-analytical data points.
There is no doubt that Big Data will disrupt current business models. And, as always with technology, we will not reap its full benefits without proper adoption and commitment by the people using it. The human side of this technology deployment cannot be overemphasised. Automating parts of the jobs of healthcare providers will make a permanent change in their roles and responsibilities. We are already seeing this with the increasing adoption of EMR worldwide, for example. Providers document and order electronically, which affects the patient-physician interaction.
For Big Data analytics to be successful, it is necessary to focus, not only on the new tools but also on how jobs are being redefined and how organisations will adapt to this culture change. Using and analysing data for evidence-based decision-making will require new competencies and even a new management style. Successful change management will result not only in adoption of the new technology but commitment—there is no going back to the “good ol’ days.” This requires strong leadership and clear communication to all stakeholders, being data-driven as an organisation (adopting a “single source of truth”), as well as achieving cross-functional cooperation.
Healthcare occupies a unique role and essential function in society. Involvement of both private and public sectors are important to achieve optimal services and outcomes. Government have essential functions in the deployment of Big Data capabilities including regulation, governance, and oversight. Protection of the patient and healthcare “consumer” requires commitment from all parties in this partnership. Often technological advances will outpace discourse related to the ethics, privacy and security issues that will continue to present risks in addition to the rewards of new and potentially disruptive technologies. One specific area of concern is privacy of patient's records. In many countries, laws are established to protect consumer and patient rights. This is an increasingly challenging field, as the need for access and portability of information will come into conflict with the need to protect patients from unauthorised use of health information that is not related to treatment of any medical conditions. The comprehensive nature of information collected in medical and non-medical fields, ability to collect information from varying devices, and the growth of personalised and genetic medicine will all present challenges to the ethics of data collection and analysis.
Big Data will be an area of on-going interest for healthcare in the foreseeable future. The challenge will be for providers to build the appropriate technological framework and governance structure to achieve better outcomes for patients. Innovation and improvement will be the outcome of exploiting healthcare information.
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