To improve overall profitability and bottom line, payers have a renewed focus on quality improvement programs. In today’s consumer-oriented environment, quality needs to be defined much more holistically. Soon, quality of care will not only be measured by the quality of an acute care episode, but also by the number of episodes that were prevented. As such, clinical quality measurement can be viewed as the core driver of overall business performance.
To be successful in this new context, payers must first define a clear vision for both quality and health informatics. This should also include objectives for managing wellness and seamless member engagement and support. Second, payers must define the technology architecture required to achieve this vision. Payers also need to embark on the daunting task of operationalizing this vision, while maintaining business continuity and managing existing investments and commitments in IT. Such competing demands, along with regulatory requirements to curtail administrative costs, can make a payer chief informatics officer’s (CIO) path ahead difficult to navigate.
Given all of the above, health plans and their CIOs will need sound evaluation criteria to decipher between competing vendors that can deliver such technology and related services. Before evaluating the technical and functional aspects of any given software, health plans should consider the below parameters for determining the type of technology required to fulfill its quality improvement vision:
1. Technology should facilitate free flow of information with accepted standards. Technology should seamlessly aggregate data from diverse resources. Resources contain information about medical history from electronic medical records or personal health records, member behaviors (e.g., mobile or social networks), community health (e.g., disease registries, Center for Disease Control (CDC) and National Center for Health Statistics (NCHS), etc.). Hadoop is a prime example of a success story for collecting vast amounts of structured and unstructured data from disparate sources. It will be critical to utilize solutions developed with similar concepts.
2. Technology should provide reliable information to inform quality metrics and measures. While interoperability is a significant challenge, current interoperability standards such as Health Level 7 (HL7) and Fast Healthcare Interoperability Resources (FHIR) have proven successful in taking disparate data and leveraging it for impactful analytics. These analytics efforts should focus on the ability to record and generate quality metrics (National Committee for Quality Assurance (NCQA) and Healthcare Effectiveness Data and Information Set (HEDIS), Centers for Medicare and Medicaid Services (CMS) and Agency for Healthcare Research and Quality (AHRQ), etc.) with appropriate standards to ensure ‘apples-to-apples’ comparisons.
3. Systems should be configured to provide information as quickly and/or close to real time as possible. In a payment system sensitive to patient satisfaction and quality, payers should be able to receive data as fast as possible to enable informed decision-making. For example, if three months into a reporting year, a payer’s score is low on appeals and grievances measures, it should be able to rapidly identify, isolate, analyze and improve on those scores for the last six to eight months of the reporting year.
4. Technology intervention should support quality measure reports (and components) for proactive decision-making. Protocols should be developed based on the technology’s ability to deliver required information for developing or identifying solution, in addition to tracking necessary data elements.
5. Technology solutions should have built-in quality measures currently prescribed by NCQA/HEDIS and CMS/AHRQ. If the technology has these quality measures already set-up (as the baseline), it will be easier for payers to execute their vision of a comprehensive quality improvement program.
Achieving this comprehensive vision of quality improvement programs require full potential of existing technologies as well as the promise of leading-edge technologies. Indeed, these parameters will help frame health plans’ evaluation criteria and filter the best available options in the market.