A recent survey by KPMG found that 38 percent of CIOs plan to invest in EHR system optimization over the next three years—in fact, they plan to spend more on EHR optimization than any other area of HIT. The reason? Physicians are dissatisfied with their EHRs. So, what’s the solution: Spend millions to replace current EHRs or optimize the EHRs that already exist?
In our new infographic, “Unleash the Value of EHR Data at the Point of Care,” we explore the pain points physicians face, particularly under value-based care and risk-sharing models, and discuss how technology and analytics can unlock value within EHRs, driving higher quality and improved financial performance with minimal disruption to physicians’ workflows.
Let’s look first at quality measures, which are tied directly to patient outcomes and health system compensation. Physicians are tasked with keeping track of hundreds of quality measures, most of which vary considerably by patient. A 2013 study of 48 state and regional measure-sets identified more than 1,300 measures, 509 of which were distinct.
It should come as no surprise then that measuring quality at the point of care is time-consuming and expensive. In fact, physician practices spend an average of 15 hours per week and $15.4 billion annually reporting quality measures. This needn’t be such a burden, however—physicians already have access to data through the EHR that could quickly and cost-effectively reveal quality gaps and how to close them.
Diagnostic coding is another pain point for physicians. Today’s physicians are responsible for inputting and verifying more than 70,000 diagnostic codes, and errors can affect the accuracy of patients’ risk scores. According to Health Services Research, the rate of diagnostic coding errors is reported to be as high as 80 percent—that could equate to millions of dollars in lost revenue annually and an overwhelming amount of paperwork.
Patient-specific analytics can help physicians and practices by alerting them to possible coding inaccuracies. Spotting these potential errors not only leads to improved care and outcomes, but also maximum reimbursement per patient. According to data from Inovalon[i], for a Medicare Advantage patient population, higher risk score accuracy and documentation can yield an average of $2,300 per closed Hierarchical condition category (HCC) code. HCC coding is part of the model used by the Centers for Medicare and Medicaid Services to calculate payments to providers and health plans.
Physicians may be dissatisfied with EHRs, but they mustn’t give up on getting real value from them. Instead, they should optimize them with complementary data and analytics that provide actionable, patient-specific insights. Solutions such as Data Diagnostics can reveal costly and frustrating gaps in quality, risk and medical history, and they’re available through EHRs within physicians’ workflows. For many, this can transform the EHR into something far more valuable than it is now.
[i] The following average MA health plan factors, using public resources, were used in determining this value: average MA Plan Enrollment (July 2016) = 6,300, average MA Premium (2016) = $32.60, average Plan Bid (2014) = $750, average Risk Score (2014) = 1.01, average Attrition Rate (2013) = 9.17%.
MA bid and risk score data: https://www.cms.gov/Medicare/Medicare-Advantage/Plan-Payment/Plan-Payment-Data.html
2016 average premium: https://www.cms.gov/Newsroom/MediaReleaseDatabase/Press-releases/
MA Enrollment data: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/ MCRAdvPartDEnrolData/
Attrition rate: https://www.healthpocket.com/healthcare-research/infostat/medicare-enrollee-lapse-and-complaintsstrong-predictor-of-plan-quality#.V6SfmPkrJaQ
2013 Star Rating distribution: https://aishealth.com/sites/all/files/2014_star_ratings_factsheet_092713.pdf