Accelerated digitalization is a boon for the healthcare industry. With electronic healthcare records (EHRs), provider organizations can more easily manage population health and meet the needs of stakeholders. However, the increased use of electronic records also means that providers need to adjust their data management strategies to meet patient expectations and ensure integrity, interoperability, and security while complying with policies and regulations. To do that, they need to move away from data silos and leverage holistic care models and secure data.
Overcoming healthcare data challenges through connected enterprise data management
Disconnected healthcare data results in poor accessibility to records and translates into fragmented care delivery for patients. One must navigate from a primary care physician to a specialist, from labs to pharmacies, and from claims to payers—all across uncoordinated systems that often have little visibility into the conditions of the patients they’re treating. The result is hundreds—if not thousands—of disconnected systems across the industry. Bringing them all together, analyzing data records, and making sure these systems communicate with each other present major issues for today’s healthcare industry.
The key challenges that healthcare enterprises face while implementing successful data management capabilities are captured in the four V’s of big data:
– Volume: The amount of global healthcare data is huge, with numerous sources, patients, departments, and care settings. Projections indicate that there could be as much as 2,314 exabytes of new data generated in 2020.
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– Velocity: Healthcare data needs to be ingested, analyzed, and updated in real-time to ensure the highest quality of care.
– Variety: In order to create a holistic patient profile, an organization should capture a variety of data types from a variety of sources. This could include standard sources like EHR and claims, in addition to social determinants of health, surveys, and social media.
– Veracity: And most importantly, the quality and accuracy of data leading the way for clinical decisions have to solve the problems of data uncertainty, data incompleteness, and data inconsistency.
How can healthcare organizations ensure that their data types and volumes are integrated on an enterprise level to meet the needs of all care stakeholders—and to enable truly holistic decision-making?
Source: Hitconsultant