How data architects can improve patient services

As life sciences companies have developed enhanced pharma analytics capabilities to support their new business models, they have begun to better understand the role that information and data play in helping them achieve their objectives. For...


As life sciences companies have developed enhanced pharma analytics capabilities to support their new business models, they have begun to better understand the role that information and data play in helping them achieve their objectives.

For instance, data from patient-specific electronic medical records (EMRs), genomic and genetic data, financial data and electronic patient-reported data offer practical insight into how to optimise healthcare management and best determine the therapies that provide the highest overall value to patients and health care systems.

The importance of converging data
But the data challenge for life sciences companies goes beyond gathering patient data from EMRs and e-prescriptions to help develop new therapeutics. The picture becomes more complex as these companies work to organise and harness data from several sources to optimise their research & development, sales and marketing efforts, as well as other enterprise-wide operations.

In the Accenture Technology Vision 2012 research, Accenture identified six cross industry technology trends that forward-looking life science technology leaders should master over the next three to five years. One of these trends involves converging data architectures. We see that rebalancing a company's data architecture portfolio and blending structured data with increasing amounts of unstructured data, is key to turning data into new streams of value.

Unstructured data now make up a very significant portion of the data portfolio. Unstructured data can be a powerful untapped resource used to provide deeper insights into customer characteristics and business operations and ultimately help drive competitive advantage, when combined with structured data.

Using new techniques for new challenges
The continued growth of big data has forced many companies to seek new ways to acquire, organise, manage and analyse these data. Companies should investigate technologies for analysing these data to gain a competitive advantage, such as new distributed computing paradigms, analytics and in-memory technologies that can analyse unstructured data in its raw state.

A clear focus needs to be on recruiting data specialists and data scientists in such areas as alternative database technologies, analytics and data life cycle management. What will be looked-for are data architects with business and technical skills that can be applied to dimensional and relational modelling, data warehousing, and business analytics. Data architects must verify that a company's data assets are supported by a data architecture that achieves the company's strategic goals. The data architect must have an overall vision, and foresee how data will flow.

Data architects should work with the data owners in research, development, sales and marketing - to identify the potential business opportunities, while addressing data privacy concerns. The architect must know how to design the appropriate solution for data owners: relational databases are no longer the only tool in the toolkit. They will have to use bridge technologies, such as hybrid solution architectures, to constructively manage the convergence of data and enable all forms of data management to coexist. Data architects will have to understand the complexity of varied types of data and the increasing velocity with which that data necessities to move. Not all of this data has value, and the architects have to help data scientists sift through huge amounts to find the “needle in a haystack” needed to create business insights.

Rebalancing data portfolios

Accenture sees the ability to converge structured and unstructured data together as a real game changer for the life sciences industry. To get the most business value and practical use out of both structured and unstructured data, life sciences companies have to rebalance their data architecture portfolios and converge, or blend, structured data with the unstructured data.

  • Life sciences companies should look to combine data from EMRs, e-prescriptions, diagnostic tests and insurers to:

  • Identify and develop therapeutics that offer the best outcomes and the most value for specific groups of patients.

  • Use the data to support reimbursement models.

  • Further optimise the use of the therapeutic by patients.
Being able to combine information from EMRs and e-prescribing, the computer-based electronic generation and filling of a doctor's prescription, makes it possible to track a patient’s outcomes over the care continuum. This is critical to a provider’s ability to demonstrate outcomes that result from care for which they should be reimbursed.

Future outcome-based reimbursement models require complete insightful longitudinal data that clearly illustrates positive patient outcomes.

Data centres will need to manage big data platforms, blending data from internal sources with the cloud. It is essential they manage the provisioning and orchestration of services across these numerous nodes, and integrate the big data management with traditional data management. Big data will place new demands on the network infrastructure, which will need to move terabyte-sized data sets.

The big data storage infrastructure will need multi-petabyte capacity and because unstructured data represents an increasingly valuable business asset, companies will have to take security steps to keep it protected yet available. This may require new approaches, because the volumes involved may be too large to back up and restore through conventional methods. Life sciences companies will need to consider implementing adequate controls to address how to prevent data from being compromised or stolen, as well as to how to back it up and respect data privacy concerns.

Harnessing new data to bring new value to a life sciences company's business will require a comprehensive strategy that entails new architectures, new services, and new data platforms. It is important to think in terms of new skills that will be needed in the organisation. This effort will demand prompt, disciplined and sustained execution.

Posted by Sunil Rao, Global Managing Director/Technology, Accenture Life Sciences
and Rajesh Bhasin, Senior Manager/Technology,
Accenture Life Sciences

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