Health systems are confronting a crisis of sustainability, with aging populations, rising rates of chronic diseases and mounting budgetary pressures, writes Aidan Meagher.
An explosion in big data and mobile health technology is enabling real-time information creation. These trends are starting to drive a fundamentally different approach, moving beyond the traditional delivery of healthcare to the management of health, focusing on healthy behaviours, prevention and real-time care.
The healthcare environment offers an abundance of new opportunities for improvement using big data and analytics — from patient centricity delivered through smart watches and cloud computing to executing radically smaller, faster and cheaper clinical trials by combining genomic markers and real-world big data analytics, right through to enhanced control over regulatory and compliance risks.
As risk-based approaches focused on specific patient cohorts lead to a world where demonstrating actual outcomes is as important as initial, pre-market evidence, life sciences organisations have an opportunity to improve R&D productivity using big data analytical capabilities. By combining real-world outcomes data with clinical data, mining genetic data, and more broadly understanding regional and population data, analytics-savvy organisations can gain insights to recognise research failures faster, design more efficient streamlined clinical trials, and speed the discovery and approval of new medicines, while reducing the cost burden.
MOBILE HEALTH
Within two years (or sooner, based on recent news reports), consumers will be universally adopting mobile devices with medical data capabilities. Soon thereafter, healthcare providers will be crafting plans and offering financial incentives to customers who agree to have their digital vitals beamed to the cloud — in much the same way automobile insurers provide discounts to drivers who agree to use a device that monitors driving habits.
Once technology, as well as the customers who use it, takes this giant leap, it is only a small step for healthcare providers to use the data in the cloud to predict wellness, anticipate preventive interventions and prescribe personalised medicine or devices that produce positive outcomes.
From the proliferation of digital data to the fragmented nature of most enterprise system implementations, large, complex organisations everywhere are suffering from data chaos. The good news is that increased access to powerful analytics, combined with the maturing capabilities of open architecture, cloud computing and predictive analytics, is helping more organisations become good with data. The bad news is that many organisations simply aren’t moving fast enough to keep up and these opportunities accompany some tough choices, calculated risks and significant challenges.
INNOVATIVE INSIGHTS
As life sciences organisations face these challenges, being effective with data becomes essential for sustained success now and into the future. Those who understand how to manage both the internal and external data relevant to their products, markets and customers, will create the opportunity for competitive advantage based on improved insight.
The first step in harnessing the power of big data and advanced analytics capabilities is to manage the data and analytics projects as a portfolio of assets. By doing this – much like individual investors manage their financial portfolios – life sciences and healthcare organisations can use an agile analytics approach to balance value, growth and assets. A typical portfolio may include research and discovery, clinical development, manufacturing and supply chain and sales and marketing.
As data management and analytics technologies evolve, life sciences and healthcare organisations have new opportunities to turn data into innovative insights. However, typical software development life cycles require lengthy validations and quality control testing prior to deployment.
An agile analytics framework allows organisations to rapidly capture the value identified through analytics implementation, significantly accelerate their analytics project delivery, increase the engagement from the business and enhance the ability to deliver data-driven insights in all areas where the organisation uses analytics. A typical analytics life cycle requires four initiatives:
- Innovation – Identify key business problems and drive innovation to develop a solution that produces a proof of concept or prototype.
- Incubation – Using the proof of concept, scale the analytics initiative to evaluate value across larger target beneficiaries and test the model across additional cycles. Determine whether there are enough benefits in the project to proceed to the next stage.
- Industrialisation – Move the solution from proof of concept to validation, deployment and monitoring. Provide a production scale solution that proves value and benefits.
- Sustainability – Maintain the analytics solution. Provide support to solution consumers and enable continued value delivery.
It is only a small step for healthcare providers to use the data in the cloud to predict wellness
AGILE ANALYTICS
Aidan Meagher, head of Life Sciences, EY Ireland
Because the business environment is changing so rapidly, organisations seeking to develop agile analytics are demanding more creative problem-solving to accelerate the innovation and incubation periods.
They can do this by: improving competencies of available talent within the organisation and positioning them to add value and be successful; implementing a lean governance model that supports the collection, sharing and reuse of analytics assets; defining processes to maintain data and enhance data quality; defining data technology capabilities and establishing adaptable procedures to access technology assets; developing and continuously maintaining a portfolio of analytics opportunities; and creating a value realisation framework by which to measure both qualitative and quantitative benefits to drive accountability and demonstrate value.
To take advantage of value opportunities, such as accelerating R&D productivity, organisations assume the focus should be on effectively managing the data. Yet to truly capitalise, an effective organisational structure, governance processes and supporting enterprise architecture are also necessary. This would enable companies to mine their collective wisdom in historical trials rather than solely focusing on current studies.
Once organisations have developed a business case that supports personalising big data in terms of strategic analytics capabilities and outlining the value of advanced analytics, it’s time to implement. Organisations can take a number of steps to position their strategic analytics capabilities.
Adopt data science as a cross-functional discipline. Just as every function typically receives support from IT and finance, organisations should offer data science or analytics support across the entire enterprise.
By managing analytics as a portfolio, organisations that allow big data and analytics to remain siloed within separate business units rarely realise the value they seek. Analytics delivered as a shared service that is governed using portfolio management discipline is a common characteristic of high performing organisations that are good with data.
Implementing an enterprise analytics Centre of Excellence (CoE) is an effective way to drive analytics portfolio management. A CoE can increase analytics productivity, improve decision effectiveness, simplify data management, analytics performance and decision-making, and facilitate continuous analytics learning and innovation.
A common analytics network managed by the CoE enables the organisation to share methods, tools, data and models in an environment where results of prior analyses can be factored into new analytics projects. Organisations spend less time seeking the right data and the right tools and more time performing analyses and driving decisions.
Transitioning an organisation to a CoE-oriented operating model takes leadership, alignment, adoption and execution, from the boardroom to day-to-day operations managers. A formal change management process helps successfully manage the shift and offers an opportunity to identify analytics talent within the company and the possibility of retraining them for specific roles in the CoE.
Whether determining an analytics strategy, implementing an analytics CoE or sourcing data scientists, few organisations are capable of going it alone. Partnering with a third party can drive new analytics innovation and add capacity for existing analytics capabilities. Success in the transforming healthcare ecosystem demands that we reimagine our approach to health.
