In the past decade, many large-scale EHR implementations have led to drops in revenue and operating income, and reductions in physician productivity have been one factor in these declines. Faced with negative ROI on a sizable investment, many organizations have responded by attempting to rebuild productivity through targeted initiatives. Common efforts include provider training, EHR system improvements, and workflow tweaks.
However, this single-focus response presents three problems. First, experience has shown that even the strongest process-improvement efforts never fully regain previous levels of production. Second, the continued focus on workflow change runs the risk of exacerbating provider frustrations with technology. Third, although productivity clearly remains important to revenue, the transition to value-based contracts is altering the basic landscape of revenue generation and patient care management.
For these reasons, healthcare leaders need to think in terms of the EHR’s impact on delivering high-quality outcomes, managing the medical spend, and ensuring efficient medical utilization. The industry must begin to think of an EHR as a tool for managing the health of a patient population, rather than as a technology for documenting a patient encounter.
Such an approach to realizing the value of an EHR involves a complex process that requires new structures and capabilities. Financial and clinical leaders can facilitate the process by helping their organizations grow along three vectors: building a data platform, developing an information framework, and evolving new care models.
Build the Data Platform
Under the productivity model, the value of an EHR is its ability to capture patient encounter data that can then be used to generate a charge. Under the new value model, an EHR is used to manage the health of patient populations through intelligence and insights gained from claims, clinical, and social-determinants-of-health (SDOH) data from across the continuum that are brought into the EHR for providers to use at the point of care.
Assembling the data platform for a high-value EHR is an evolution. Healthcare organizations that have done it successfully have constructed their platform one layer at a time, using the three tiers discussed below.
Claims data. The first tier comprises health plan claims data for a managed population across the entire continuum of care. The value of these data is in providing a comprehensive picture of the health conditions within an organization’s population. This information allows a healthcare organization to begin generating risk scores and segmenting its population based on risk levels, which helps identify potential patient issues. The organization can then begin to design programs and interventions for managing high-risk patients and preventing rising-risk patients from entering the high-risk group. Through full claims data provided by insurers, an organization can measure cost and resource-utilization efficiency opportunities that can be brought back into the EHR for follow-up care.
Clinical data. The second tier consists of clinical data, including data from ambulatory and inpatient EHRs, pharmacy data, clinical laboratory data, and other information sources. Layering this clinical information into the data platform enables the organization to move beyond what is happening in the population to why it is happening. For example, an organization might use claims data to identify its rising-risk diabetic population. It could then use physician EHR and lab data to identify specific care management issues and specific actions that could be taken at the point of care to modify patient risk.
An important part of the second tier is using the clinical data within the EHR to calculate standard quality measures and aligning these measures to the performance measures of the organization’s payer contracts. These measures allow an organization to provide real-time performance dashboards at the point of care, enabling providers and the care team to manage performance and fill identified care gaps.
Interoperability is essential in this tier. EHRs need to capture, exchange, and present data in a way that can drive action.
SDOH data. The third tier of this platform is SDOH data, including data on genetics, behavior, environment, and social circumstances. These data could include information about income, housing status, transportation access, and other issues that partially predict costs and outcomes in a patient population. Some advanced healthcare organizations are beginning to use SDOH data to proactively manage patient cohorts by creating models that predict disease prevalence and total spending in rising-risk populations.
Develop the Performance Analytics Framework
The clinical data repository described above is essentially a part of the overall data management platform integrating the claims and SDOH data to manage and monitor performance. The depository’s function is to ingest, normalize, and analyze the data from across the continuum to derive intelligence. This intelligence is then used to define a performance analytics framework, which consists of performance domains, each of which encompasses multiple measures. One key organizing principle is quality. However, organizations should be aware that clinical quality is not the only focus of an effective analytic framework of patient care. Healthcare leaders should think in terms of performance. The goal is to develop a system of integrated clinical insights focused on value-driven performance measures not just for quality and outcomes, but also for care-gap management, cost efficiency, resource utilization, patient access, patient engagement, provider satisfaction, and other aspects of care value.
The key is to look at value-based contracts as a series of use cases and problem statements. For example, consider a health system that has entered a value-based contract for managing its diabetic population. One of the key elements of the contract is cost reduction for this patient cohort. The corresponding question framing the problem is, “Can we reduce the cost of care for our diabetic population?”
The next step is to determine what information is required to “solve” this problem. First, the organization will need a measure such as per-member-per-month (PMPM) costs for the diabetic population. Second, the organization will need data points to generate the measure. Key elements could include data on physician services, tests, drugs, and procedures. These data will come from inpatient and ambulatory EHRs, as well as from billing systems.
Essentially, the process of creating an information framework is identifying problems in value-based care and then working backward to the measures and data needed to solve those problems.
Shaillee Chopra is principal and CDIO for Lumina Health Partners. This article was originally published as the cover story of the March 2019 edition of HFM magazine. View the article in its entirety here.
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