The complex challenges inherent in today’s healthcare business operating models are compounded by massive volumes of data emanating from a myriad of structured and unstructured sources. Enterprises must harmonize data elements from large repositories such as databases, data warehouses, data lakes, and even spreadsheets to adequately feed analytics and AI engines that can generate actionable insights.
Deriving meaning, networking canonical relationships, and creating “evidence-based-knowledge” from disparate vocabularies stored in EHRs, claims & financial systems, hidden in messages from medical devices, labs, pharmacies, consumer apps, etc. is more than daunting for healthcare IT shops who are burdened with overwhelming fixed costs (and time) sunk into “keeping the lights on”. On top of that, with each passing week the endless streams of critical data increase in volume, depth, and velocity. Without the right information management capabilities enabled by the right enterprise interoperability tools, a definitive path to positive change for healthcare organizations is simply not attainable. This reality thwarts corporate investments intended to; improve clinical & operating performance, deploy advanced care-delivery models, benefit from collaborative strategies such as population risk/gain-sharing arrangements, and/or exercise personalized interactions with consumers (aka patients). It is clearly evident from any objective perspective, that healthcare has become a data problem.
From “Meaningful Use” to “Promoting Interoperability”
After a decade or so of acquiring and implementing EHR/EMRs and many other Health Information Technology (HIT) solutions, organizations must now employ enterprise platforms that facilitate simplified/costeffective data ingestion, integration, cataloguing, curation / augmentation, and trusted provenance for multidisciplinary tactical and strategic purposes. Along with establishing competencies around information management, the need for a platform that delivers semantic interoperability across the operating landscape is an imperative. Only then, can the enterprise execute digitally-enabled business strategies and begin to benefit from investments in enterprise analytics, informatics, and AI-fueled advancements. This imperative becomes amplified as emerging care delivery models expand the scope of “Patient 360” to include source of data that relate social determinants and life-style/behavioral choices which have significant effect on health, well-being, protocol compliance, and eventually cost of care. These data, including messaging from IoT wearables and remote monitors of movement, bio-metrics, and cardio performance present new challenges regarding interoperability, yet managed as assets may soon evolve to present most value when integrated into clinical decision support and augmented-knowledge (AI) capabilities.
Speaking the Same Language
In order to attain simplified precision in the competency of enterprise information management, healthcare organizations must assemble platforms of integrated tools/components/services that perform in unison to ingest, catalog, curate/augment, and present knowledge using all sources of data available. This is akin to fundamental plumbing in an enterprise - table-stakes if you will. The secret sauce however, is to choose carefully and assure you’ve coalesced software that: automates mundane IT tasks, modernizes/future-proofs your infrastructure via open standards, leverages flexible data structures such as graph, and produces semantically interoperable layers of actionable knowledge. Any CIO worth their salt wants to do this only one time for the enterprise – e.g. make a single investment and viably reap multiple payback streams. The IT division can then leverage all data sources as business-monetizable assets by digitally-enabling corporate agility. From that point forward, the enterprise may pursue any and all strategies they deem fit for their model without fear of data-language barriers that typify today’s healthcare information domain and hamper transformation of the industry.
Big Data Fabrics – Integrating Healthcare Knowledge
Tools such as Big Data Fabrics come to mind where all of the above are delivered with additional benefits such as achieving reduced fixed IT costs, improving operating efficiencies / collaboration across the enterprise, and accelerating speed-to-insight for business and clinical decisions. The new wave of integration and data interoperability platforms and tools is here today. Do not wait for industry standards to evolve – we will all be too old to enjoy the outcomes of such. Let’s use modern technology at the enterprise level to transcend data-language challenges and get on with the advancements in healthcare that we’ve been expecting for years.
To learn more about Big Data Fabrics, download the Forrester Wave™: Big Data Fabric, Q2 2018 report.