Data – how you capture it, how you analyze it and how you leverage the insights gleaned from it – is one of the most valuable tools healthcare organizations have in their arsenal to meaningfully improve the patient experience and overall health outcomes while reducing costs.
The ability to use data to identify an individual patient’s unique needs, refine care plans, speed drug discovery and commercialization, reduce waste, expand the value proposition of medications, and streamline clinician workflow is driving consumer-centric care and the value-based strategies that are delivering improved quality outcomes and lower costs. And as the journey toward value-based care continues, taking new and evolving forms, those who can appropriately leverage data are positioning themselves as leaders within the industry and differentiating themselves from the rest of the pack.
Successful healthcare organizations are those that can efficiently and effectively leverage the copious amounts of data they collect to help them provide the right care for the right patient at the right time. But that alone is only half the battle. The ability to leverage advanced predictive and comparative analytics to benchmark quality performance against the market and inform strategy in real time is becoming necessary for health plans to remain competitive in the market. With insight into performance trends, health plans can ensure they are targeting the right quality metrics to inform overall plan strategy, rate setting and budgeting. And increasingly, those quality metrics most in need of focus are those that matter most for providing patient-centered care.
Exemplifying just how important patient input is to the industry, NCQA is working with four U.S. healthcare payers and providers to pilot new patient-centered outcomes measures – measures that according to project funders John A. Hartford Foundation and the SCAN Foundation are truly important to patients. For the project, NCQA and its partner organizations will identify those measures that patients consider to be the most important and use those to measure clinical quality performance.
Data-driven approaches that demonstrate value and do so with the engagement and support of both the provider and the patient lead are also key to success. Such approaches include automated medical records retrieval and review solutions that leverage Artificial Intelligence (AI), including Natural Language Processing (NLP) and Machine Learning (ML) – valuable tools when you consider that there are approximately 100 million medical record reviews performed each year on patients who receive care from an average of 6.2 different physicians and that more than 75% of that patient data appears in unstructured form. Managing and streamlining patient data in an automated, cost-effective way is essential to enabling informed clinical decision making, enhanced care coordination and a more valuable patient experience.