Helping Life Science & Healthcare Organisations Get Ready For A Data-Driven Future

There is little doubt that data volumes in Life Sciences and Healthcare are growing, and indeed, growing to a huge extent. New data sources abound. However, what is perhaps hidden behind the volume growth is how it manifests.

Organisations today must deal not just with data volume growth but also increasing variety of data sources (including sources previously not encountered, such as cameras, wearable devices and sensors) and the pace with which new data events arrive. Staff timesheet systems may create a new data event once a day for each team member, whereas patient wearable devices may create several events every second, for each patient.

Whilst not all data is easy to get at, and not all data points have the same utility, organisations can expand the amount of high-quality data that is: current, available, useful and critically, can be acted upon to improve business operations or to improve patient outcomes.

The growth, variety and pace of data acquisition will increase in the future and developing skills and expertise at managing it is a task for organisations today. Skills and capabilities acquired today will serve organisations well for the future.

The Visual Future

Once organisations master the tools to acquire and manage the wealth of new data, they need to consider new methods to represent the data. The powerful insight gained in visualising data has the potential for improved decision-making, ultimately leading to improved outcomes. And this is the reason Life Science and Healthcare leaders are now investing heavily in better data science practice and better data management.

With ever increasing data volumes building up in separate silos across different healthcare settings, from Emergency Services, to Pharmacies, GPs and Specialist Treatment Centres, it’s challenging to view these sources in an integrated manner. Wholesale re-platforming to remove silos may be the grand design, but what about integrating them today? In-silo reporting is the norm, whereas horizontal reporting, spanning multiple silos will create wide benefits to planning and execution across the organisation. Teams need to visualise and chart dissimilar data, from many sources, in a single place. Staffing systems generating staffing reports and finance systems generating separate finance reports is not sufficient; leaders need to bring integrated data to their decision-making teams.

Digital dashboards are fundamentally different to reports that are circulated as pdfs. Their digital form allows capabilities not present in analogue. For example, interactivity – dynamic filtering and selection across dozens of data attributes, personalised to your particular and precise needs. And in close to real time. Properly configured, dashboards can tell you what’s happening now, not just what happened last week. Faster and better decision-making results from digital integration and visualisation.

Considerations for Building Dashboards

There are a wide range of customisable off-the-shelf dashboard tools, from a variety of providers. Some may be more extensible than others, some may be easier for novice users – the ideal choice is most likely different for different organisations. The ‘secret sauce’ does not lie in the application of one tool over the other, rather, the value is derived from the data workflow that ends up in a dashboard, largely irrespective of the visualising tool selected.

Some organisations choose to build their own, either heavily customising one or more tools, or going fully custom. With a custom build, organisations have more control over all aspects of digital dashboard design and construction. This is the route organisations take when:

Deeper integration with source systems is required. For example, where users wish to both visualise data and to engage (and control) source systems, placing build orders, validating a batch, requesting new tests, etc.
Ultra-granular data security controls are required to lock down data access at, for example, a record level or where a regulated system negates use of an unvalidated cloud platform
Complexity of the scientific process being modelled, which may not lend itself to representation in a generalist tool

The Agile Culture of Successful Information Managers

Building digital dashboards (and the data pipelines that power them) does not, in itself, guarantee successful interpretation of data and the correct onward action to yield improved outcomes. The supporting technology is a key component, but these factors must also be considered:

Connections: is there work required to change the data connections to bring error-free, gap-free, consistent data to the surface?
Culture: does the team adopt agile practices to constantly Test, Measure & Learn from their dashboarding activity – moving from static reports requires an upgrade to culture & digital practice
Expertise: can the internal team handle the workload, in terms of bandwidth and skills – might there be data science roles to recruit for or should you partner with an expert consultancy?
Technology: abstraction is an important design decision when creating digital dashboards so that the dependency between source and surface is managed – this allows legacy re-platforming to take place without too much up-stream re-work

With the volume and variety of data sources increasing, organisations should expand their digital & data capabilities to embed good data practice in their operations. Capabilities learned today will repay their investment in the future as successful operations increasingly run on data and the complexity of that data is only going to increase.

Michelle Waddell – Wyoming Interactive