Apr 4
2018
Leveraging Automated Patient Interventions To Drive MIPS Performance
By Gary Hamilton, CEO, InteliChart.
Now that at least 96 percent of hospitals have implemented an electronic health record (EHR) most organizations are facing the reality that the technology has not truly helped them achieve their clinical quality and financial goals.
Electronic, enterprise-wide data is essential to manage highly complex, high-cost patients that providers care for every day. However, EHRs typically do not deliver the insight or tools providers need to manage these high-risk or the near high-risk patients when they are not in the hospital.
If the EHR does offer such population health management (PHM) capabilities, it typically requires an excessive amount of manual data access and manipulation, leading to even greater costs. That means patients who require more intensive care support at home, or who could highly benefit from timely and targeted intervention, face care delays simply due to lack of provider resources.
The Medicare Access and CHIP Re-authorization Act (MACRA) of 2015’s Merit-based Payment System (MIPS) brings this challenge into clear focus, highlighting how individual providers and healthcare organizations need automated patient interventions to efficiently deliver care throughout the continuum. Automation and more precise outreach not only helps care managers work more efficiently, but it also forges stronger engagement between providers and patients for long-term clinical quality and financial gains.
Gaps In Technology Capabilities
According to a recent survey conducted by our company of more than 800 healthcare professionals, most organizations seem to understand how crucial PHM technology is to MIPS success. Few professionals, however, are apparently taking full advantage of available opportunities to better their organization. For example, 80 percent of healthcare professionals reported they have the necessary technology for PHM or to manage MIPS performance, but only 30 percent reported they are able to automate interventions across populations.
Automating interventions is becoming a critical piece of PHM to reduce the significant resources required to analyze data and conduct outreach. Currently, a care manager can spend approximately 40 percent of their time just searching for patient data, while PCMHs require 59 percent more staff per provider to fulfill care management requirements.
Streamlining the data aggregation combined with technology that continuously analyzes data and initiates communication with the patient will eliminate the manual efforts that burden the care managers and providers assigned to PHM today. More importantly, such technology delivers consistency and predictability for patient interventions, an essential component to modify patient behavior and yield successful outcomes.
Yielding More Precise Guidance
Guidance to deliver precise and effective interventions and outreach is possible, yet very limited if confined to single-practice EHR data alone. By only utilizing a provider’s own patient data, organizations will be limited to a partial view of a designated population and the accuracy of patient care-gaps will be substantially degraded. Numerous other data sets, including EHR data captured from unaffiliated providers as well as non-clinical sources, must be included for more accurate outcome predictions and targeted interventions.
For example, by including data from community providers that co-manage patients, data from regional and national HIEs (Carequality/Commonwell), as well as other key data points concerning social determinants of health will yield much more accurate risk scoring and prioritize patients for interventions. Information such as patients’ nearby relatives, home address, and car ownership can change frequently and be incorporated into sophisticated algorithms that help predict behaviors and outcomes.
A care manager can then use those analytic capabilities to stratify these patients into risk categories for more frequent interventions that can be initiated automatically based on pre-defined rules. Patients at varying risk levels for acquiring Type-2 diabetes, for instance, may need different levels of support from the provider to help them make the healthcare and lifestyle choices to better manage their health and improve their outcomes.