Many of us make important healthcare decisions with a surprising lack of rigor. Instead of conducting in-depth research one would expect with a potentially life-and-death decision, we often just ask those close to us for their advice or recommendations.
Why do we follow this route? Because, once we peel back the layers of the healthcare onion, we quickly encounter disparate and disconnected data points and information, much of which may not be relevant to our individual situation. In fact, often there is very little relevant to our specific situation to help us make a clear decision. Moreover, we can be easily overwhelmed.
Clinicians across the care continuum also are inundated with data; but does that really get them the “relevant” 360-degree view of their patient—the holistic patient profile of sorts—to ensure that care decisions are based upon the most pertinent, complete, and timely patient data? After all, making a successful transition from volume- to value-based care requires a mastery of disparate data sources, including granular patient-specific information, but the data needs to be relevant and the insights prioritized.
Leveraging interoperability through natural language processing
With the application of natural language processing (NLP), medical groups “taking risk” from the Centers for Medicare & Medicaid Services (CMS) and other payers or participating in payment programs such as the Merit-based Incentive Payment System can greatly benefit from reducing the need to exhaust scarce and costly resources to manually read and analyze thousands of patient medical records as required by CMS and other regulatory authorities. Applications of NLP—powered by machine learning model training (ML)—now can be used to evaluate the relevant clinical information found within medical records with a greater efficiency and completeness. Conversely, traditional approaches that rely on human clinical review of often lengthy medical records are more time-consuming and much costlier.
Furthermore, many platforms enable medical groups to properly analyze structured and unstructured data found within a continuity of care document. This further improves the efficiency and completeness of the analytically driven patient profile upon which the clinical teams rely.
Accelerating the value of quality healthcare data
Using systems and platforms that integrate and aggregate disparate real-time data from historically fragmented sources, and then 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 growing value-based care environment.
Today’s ML platforms expand upon classic regression techniques typically found in traditional predictive analytic engines to derive a more dynamic, patient-specific understanding of healthcare systems and, most important, individuals. For data scientists, though, the “Holy Grail” is doing all of this in real time in a broad-spectrum manner that leverages disparate data. The capability to deploy sophisticated platforms that analyze thousands, if not millions, of unique and evolving data points (i.e., big data’s high volume, variety, and velocity trifecta) to create patient-level insights that drive more precise treatments is a game changer for many clinical conditions.
Empowering clinicians with real-time, on-demand data-driven insights
By bringing together disparate data sources into one platform, it’s possible to perform real-time calculations of a comprehensive patient profile that deliver patient-specific analytics with actionable insights. Clinicians can order these insights on-demand at the point of care, and this intelligence, which is powered by ML and other approaches, 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. While many organizations strive to do this within their unique workflow, most still rely on “outside” systems for support.
Final thoughts
In healthcare, AI is now well-supported by scalable cloud-based platforms. Once integrated, diverse data sets will impact clinical organizations by shifting the role of the provider from one of diagnostician informed by training and evidence-based practices alone to decision-makers informed by training and practices as well as real-time patient specific analytics that guide clinicians at the point of care.
Ultimately, clinicians want to—and should—spend more quality time with patients. Relevant and focused data-driven insights further allow clinicians to address this need, yet there remains a steep learning curve for some. 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 primary drivers for its progression.
