To ensure they make the best decisions about their patients’ care, clinicians need actionable data. But lack of data is certainly not a problem. Clinicians are inundated with data. But most often, that data is from disparate and disconnected sources, failing to provide a “relevant” 360-degree view of the patient. Making a successful transition from volume-to value-based care requires not only a mastery of disparate data sources, but the ability to rapidly access the most relevant data and prioritize the insights gleaned to take appropriate action.
Leveraging Interoperability through Natural Language Processing
With the application of natural language processing (NLP), healthcare organizations can bypass the process of manually reading and analyzing thousands of patient medical records and instead leverage machine learning (ML) model training to evaluate relevant clinical information found within patient medical records with efficiency and completeness, saving scarce and costly resources.
Furthermore, many platforms enable healthcare organizations to analyze structured and unstructured data found within a continuity of care document (CCD). This further improves the efficiency and completeness of the analytically-driven patient profile.
Accelerating the Value of Quality Healthcare Data
Using a combination of big data technologies, software and solutions that integrate and aggregate disparate real-time data from historically fragmented sources is essential in assessing both the quality and potential value of healthcare data. Historically, the standard relational database approach to data mining and analysis posed many challenges when dealing with non-standard, disparate data sources that big data technologies now solve. Making that data available to the healthcare delivery system provides a basis for artificial intelligence (AI) to change how providers, payers and other healthcare organizations engage with patients and drive toward better outcomes in a value-based care environment.
Empowering Clinicians with Real-Time, On-Demand Data-Driven Insights
Bringing together disparate data sources into one platform enables real-time calculations of a comprehensive patient profile that deliver patient-specific analytics with actionable insights. In a value-based environment, traditional batch systems and technologies must shift focus to be more transactional to reduce turnaround time and send data to clinicians in real time at the point of care. The ability to scale based on demand is necessary to ensure a performant and cost efficient solution. Cloud-native technologies enable compute elasticity within the analytics space to scale up or down, based on demand. Focusing on in-memory technologies that can perform advanced analytics is key to achieving real-time data insights, and due to recent technological advances, there are now numerous ways to achieve this goal.
Clinicians can order these insights on demand at the point of care, and this intelligence lets them identify and address gaps in quality, utilization and medical history, supporting improvement in clinical and quality outcomes and economic performance across the healthcare ecosystem. The ability to deploy sophisticated platforms that analyze millions of unique and evolving data points to create patient-level insights that drive more precise treatments is a game changer for many clinical conditions. While many organizations strive to do this within their unique workflow, most still rely on “outside” systems for support. Finding a partner that understands healthcare data and is well versed in advanced technologies is essential to success.
The Future Is Near
Diverse data sets will shift the role of the provider from one of diagnostician to decision-maker informed by training and practices as well as real-time patient specific analytics.
Ultimately, clinicians want to — and should — spend more quality time with patients. Relevant and focused data-driven insights allow clinicians to address this need. It would be best if nearly every “data point” related to a patient was aggregated and analyzed to inform and improve the patient-specific quality of care delivered. As this happens, we will have progressed along the curve toward value-based care and a true 360-degree patient view. NLP and ML-related technologies will be a primary driver for its progression.