Today, nearly every hospital and health system have access to their patient and performance data. The emergence of artificial intelligence and machine learning applications and solutions are charged with determining key insights gleaned from claims and EHR data and the path toward improved performance, lower costs and better care and outcomes.
Our health systems are paralyzed by the enormity of data, the difficulty in developing correlations, and the elusiveness of key actions that result in sustained improvement for providing continuity of quality care. Healthcare organizations need to trust the outputs generated from its EHRs – from evaluating the clinical effectiveness of products to securing reimbursements. Explicitly connecting this information to the provider’s strategic goals helps them operationalize data driven decision making and performance metrics to make their communities safer, healthier and more efficient in managing their healthcare.
Artificial intelligence and machine learning will drive efficiency in areas where data is easily captured and the patterns can be emulated to reduce margins of error and create inroads to clinical standards of care.
The hidden secret in most healthcare systems is this challenge of "data orphanization", terminology created and coined by Value Bridge Consulting. Data Orphanization is the process that clinicians and providers either omit, or err in entering and accounting for patient level detail through clinical systems, ERP’s, and EHR’s (Epic, Cerner and MEDITECH) causing disruption to the evolutionary trends in healthcare for AI, Machine learning and predictive analytics.
Supply and finance data that lays “orphaned” within EHR’s and ERP systems will be omitted from algorithms that impact the analytic enablement of treatment protocol, prevention treatment and patient outcomes. This orphaned data impedes organizations to see the true causation of variations in care that lead to undesirable outcomes, cost overruns, elongated lengths-of-stay, readmissions and other metrics impacting patient care, patient outcomes and future technological advances.
To effectively mine, curate, integrate and audit data, there must be true commitment. Commitment and dedication to ongoing support of industry standards, data governance, accountability, and innovation. Healthcare systems are the best stewards to intervene and control their data destiny and shape healthcare delivery for the future.