How lab data can inform health equity care models: FAQ with Labcorp and CHESS Health Solutions
Below, Robert Schmidt, MD, PhD, MBA, medical director for health systems and head of population analytics at Labcorp, and Melissa Pollock, MDiv, CHC, director of ACO compliance and regulatory affairs for CHESS Health Solutions, answer common questions around health equity, ACO REACH, strategies and interventions, as well as discuss how a diagnostics partner such as Labcorp can help ACOs make an impact.
Q: Why is health equity important?
A: Health equity involves breaking down barriers—such as discrimination and lack of resources—that lead to inequities. It recognizes that some groups of people require additional resources and opportunities to reach their full health potential.
Offering equitable healthcare leads to a more efficient healthcare system, as a healthier population requires less medical care. More importantly, however, healthcare is an essential human right and it is our duty to expand access for all.
Q: What’s the latest news in health equity care models?
A: The COVID-19 pandemic exposed many systemic problems in the U.S., but none more prominently than existing health disparities. To address these problems, CMS announced it will incorporate health equity in all its care models by 2030. To test a payment approach to better support these underserved communities, the Innovation Center introduced ACO Realizing Equity, Access, and Community Health (REACH).
Q: How is CMS promoting health equity through programs like ACO REACH?
A: ACO REACH is the Innovation Center’s first foray into health equity in any type of Medicare contract. ACOs participating in this redesigned total cost of care model are looking at communities through a health equity lens to determine how to make care more accessible.
ACO REACH has three major pillars to reduce health disparities:
- Social determinants of health (SDoH) data collection
- The Health Equity Plan (HEP)
- A benchmark adjustment
To address health inequities, an ACO must identify the disparity (i.e., outcome of interest), the group affected and the geographic location. Using a variety of data, including the collected SDoH data, we begin to identify the areas where there is a subset of the population with drastically different health outcomes than the general population.
The HEP then pinpoints and implements initiatives to reduce health disparities in underserved communities. A benchmark adjustment helps to offset the additional resources needed to impact health equity.
Q: What are the potential ways an ACO can start to address health equity?
A: Start with data and a spirit of curiosity. Aggregate data from disparate sources—EMR, social indices, claims data, CMS beneficiary files, lab data, patient-reported data, etc.—and rely on that data to form a holistic picture of a population to discover the biggest impact on care.
It’s important to combine clinical, claims and patient-reported data; a patient could live in a high socioeconomic status ZIP code but still live paycheck to paycheck. Laboratory data is an essential part of this aggregation, as it is a key factor in clinical decision making.
There will be identifiable trends and outliers when combining data in this new approach. Explore and visualize the data to uncover differences or identify patterns to investigate further. Are there any trends that show a certain subset of the population might be experiencing a disparity? Unearthing insights is going to take careful questioning and analysis.
Simultaneously, it is extremely important to begin outreach to build trust with underserved communities. Often in underserved communities, there is an epidemic of medical mistrust. To impact SDoH and overall care, close ties to the community and critical connections are necessary. Discover ways to bolster communications within those populations through community-based organizations that are already paving the way.
Q: What chronic conditions might be considered high priority when developing plans?
A: The high-priority chronic conditions will depend on the ACO, their populations, their communities and the disparities they uncover using data. Based on their prevalence, it is likely that diabetes, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary artery disease and hypertension will be on that list.
Laboratory, clinical and claims data can provide insights into the distribution and severity of these chronic diseases, which can help guide resource allocation.
Q: How could ACOs use lab data to identify these populations?
A: Lab data can help assess disease prevalence and severity against already identified target geographic areas. Using lab data allows for deeper exploration and identification of patients who are high risk due to their location. It can also reveal where there is a lack of lab data and why that gap exists.
Aggregated Labcorp patient results and data in Insight AnalyticsTM, Labcorp’s interactive population health dashboards, can be used to support patient management and value-based care contracting. ACOs gain actionable insights using built-in filters, insight summaries and the ability to drill down and reveal details to the provider and patient level. Further, ZIP code filters can help analyze geographic health trends, identify high/rising-risk patients in a geographic area, target care gaps and assess needed interventions.
Examples of lab-based metrics that organizations may want to analyze include:
- Colorectal cancer screening
Q: What are some interventions an ACO can implement using lab data?
A: The lowest-hanging fruit is using lab data to identify patients with abnormal lab values that have not been tested recently, such as an A1C for diabetes. An ACO can use that list to prioritize outreach to patients with a high A1C. This lab value informs recommendations for medication adjustments by a pharmacist. If a patient is routinely getting their prescription filled, but their levels are not changing, it can be a red flag to review further.
ACOs increase access to care for high/rising-risk patients when collaborating with data-forward partners like Labcorp to design a lab strategy that reaches patients regardless of their location. Help close care gaps and improve health and economic outcomes with Labcorp’s national footprint of more than 2,000 patient service centers (including retail spaces through Labcorp at Walgreens), phlebotomists located in approximately 6,000 medical offices and clinics, and configurable and scalable home collection options.
Q: What are some additional data sources an ACO might use to identify underserved populations within their communities?
A: The Innovation Center has suggested several public sources for data to drive a holistic picture: the Area Deprivation Index (ADI), and the CDC Social Vulnerability Index (SVI), CMS OMH Mapping Medicare Disparities and the Rural-Urban Disparities in Health Care in Medicare to name a few. These sources are available for free.
ADI reflects a geographic area’s level of socioeconomic deprivation, determining whether a patient is in an underserved community. SVI is used by the CDC to locate the most vulnerable populations that need assistance after a natural disaster or during a disease outbreak. When ADI is combined with SVI, the resulting score calls out smaller underserved communities, which can help to determine how to affect care specifically for those patients. These sources, overlaid with beneficiary demographic data, can then be combined with clinical quality metrics to locate disparities.
Q: What ACO-led interventions have been successful in the past?
A: Care coordination outreach, especially for those patients with chronic diseases, targets the most complex patients to deliver high-touch care. By extending the traditional reach of providers through education, social work services, coordination of care, and support, we decrease unnecessary utilization while ensuring quality of care. Care coordination outreach is extremely important in ACO REACH since follow-up within a certain period after discharge is one of the four quality measures for the model.
Transition clinics also effectively manage chronic conditions. These clinics act as a hub for care once a patient is discharged from a care facility. Patients can see a provider for more frequent, extended visits while also receiving services like medication and care management. This multidisciplinary approach allows for comprehensive, personalized care, which can decrease unnecessary utilization. Chronic Care Management is another high-touch service where care teams can spend more time with patients in between provider visits, driving positive health outcomes.
To learn more about lab data and population health initiatives at Labcorp, visit: labcorp.com/organizations/resources/hospitals
Melissa Pollock, MDiv, CHC, is the director of ACO compliance and regulatory affairs for CHESS and brings over 10 years of experience in value-based healthcare compliance. Melissa has extensive knowledge and experience related to the formation and management of Medicare ACOs. She serves as the CHESS liaison to CMS, engaging in frequent discussions with the Center for Medicare and Medicaid Innovation and other CMS leaders. More recently, Melissa has become increasingly involved in advocacy efforts to help inform value-based policymaking based on CHESS experience and perspective, at both the state and federal level. Melissa holds degrees in mathematics/economics and Spanish from Furman University and a Master of Divinity from Regent University.
Robert Schmidt, MD, PhD, MBA is the medical director for health systems and head of population analytics at Labcorp. His work focuses on the use of laboratory data to identify and close gaps in care. He is also responsible for Labcorp’s laboratory stewardship initiative. Prior to joining Labcorp, Dr. Schmidt was a professor of pathology at the University of Utah where he was the director of the Center for Effective Medical Testing where he conducted studies on cost-effectiveness, utilization analysis and evidence-based evaluation of diagnostic testing. Dr. Schmidt has over 160 peer-reviewed publications and frequently presents at national meetings.
Dr. Schmidt received his MD and MS in clinical epidemiology, and graduate diploma in biostatistics from the University of Sydney. He also earned an MBA from the University of Chicago, an MS from the Massachusetts Institute of Technology, as well as his PhD in operations management from the University of Virginia.
The statements contained in this document are solely those of the authors and do not necessarily reflect the views or policies of CMS. The authors assume responsibility for the accuracy and completeness of the information contained in this document.