7 Essential Types of Data Analytics for Payers
Written by: Katie Wilson
Healthcare payer data is more than just numbers. The right information can help optimize operations, manage risk, and enhance patient outcomes. Pulling actionable insights from large amounts of data requires data analytics—software programs that use algorithms to identify patterns.
Here, we’ll share the essential types of data analytics that can specifically benefit payers, along with practical tips for selecting the right analytics solutions tailored to your needs.
The Importance of Healthcare Payer Data
According to data from the National Association of Insurance Companies, payer profit margins have decreased in recent years—from 5.3% in 2020 to 3.3% in 2023. Similarly, the average simple loss ratio, the amount payers spend reimbursing claims, has climbed over the minimum threshold by multiple percentage points for both small and large payer groups. In short, the money payers have left to cover administrative costs and to grow financially has shrunk.
Adopting data-driven technology can reverse this trajectory. Payers can benefit from data analytics in many ways, depending on their needs. Some payers may focus on enhancing operational efficiency. Analyzing historical and present data can help payers uncover inefficient workflows, such as manual claims processing. Payers can use this information to know exactly where to incorporate automation or other tools for increased profitability.
Other payers may use data-driven insights to make more informed decisions, such as which ancillary services best serve their member population. Data analytics minimizes guesswork and reduces errors in decision-making. Healthcare analytics solutions can also help payers pinpoint gaps in care that may contribute to larger problems.
For example, a lack of access to pharmacies can contribute to failure to adhere to medications. For patients with chronic conditions, not taking medications as directed can result in higher hospital admissions, heightened morbidity and mortality rates, and increased healthcare expenses. In contrast, meeting patients’ needs outside their presenting illness leads to better health outcomes.
Healthcare data analytics can parse through revenue cycle data to identify cost-saving opportunities, such as reducing claim resubmission rates, and to expose risks in Center for Medicare and Medicaid Services (CMS) audits. Minimizing unnecessary costs and mitigating risks of failing audits helps ensure sustainable operations.
Ultimately, data-driven technology ensures that payers remain competitive despite changes in the healthcare industry. Regulatory changes, technology advancements, and evolving member expectations mean payers must remain agile to adjust and adapt. Data analytics provides payers the tools to evaluate and improve service offerings and operations to meet challenges.
Essential Healthcare Payer Data Analytics
The healthcare system generates enormous amounts of data. Sources include electronic health records (EHRs), lab results, medical and pharmacy claims submissions, health assessments, and more. In the past, this data had siloed uses. With the help of machine learning, artificial intelligence, and advanced modeling techniques, payers can now use healthcare data for practical purposes across many applications.
Claims Analytics
Claims analytics identify trends and insights contained in claims data. Payers can use the findings to improve claim submission processes, identify claim cycle trends, and detect healthcare insurance fraud. In recent years, fraud detection has been complicated by the complexity of health insurance data and improvements by fraudsters. Advanced algorithms use patient medical histories, expenditure details, and related items to identify suspicious scenarios accurately.
Claims analytics can also reduce denied claims, which cost hospitals and health systems billions annually. Claims are often denied because of diagnostic coding errors and insufficient supporting documentation. Analytics solutions can uncover errors in claims submissions and identify solutions, creating a more efficient, profitable reimbursement process.
Risk Adjustment Analytics
Risk adjustment analytics enhance risk assessments, helping payers identify high-risk cases and manage population health. These analytic solutions use data to confirm members’ chronic illnesses and preexisting health conditions and detect unconfirmed or unsubmitted patient diagnoses. By accurately understanding patients’ health needs, payers can predict the cost of their care and offer more appropriate treatment earlier.
Risk adjustment analytics can also uncover opportunities to close care gaps. Veradigm Risk Adjustment Analytics, for example, can help payers identify high-risk health conditions, opportunities to address those conditions and intervention strategies. The solution combines multiple predictive models, big data (e.g., medical records and pharmacy claims), and clinical inferencing (e.g., treatment selection).
Payers can use this information to provide services to meet members’ needs, such as targeted outreach encouraging patients to get regular screenings for a condition they are at risk of developing. Knowing where to prioritize spending in gap closure helps payers save money, reduces expensive hospital readmissions and emergency care, and improves member health outcomes.
Case Study: Regional Health Insurer and Veradigm Risk Adjustment Analytics
When providers misreport or underreport patient conditions, health plans cannot accurately predict the cost of care. Alerting providers to report patient conditions accurately can remedy this problem.
Regional Health Insurer, a large not-for-profit insurer in the Northeast US, originally aimed to complete 10% more provider alerts than the year before. Using Veradigm Payer Analytics, the organization completed 25% more provider alerts. Increasing provider alerts helped Regional Health Insurer ensure that the right providers used the right interventions at the right time.
Predictive Analytics
Predictive analytics examine patterns in large datasets to forecast trends. These solutions help payers predict member behavior, healthcare utilization, and potential health crises. Insights from predictive analytics solutions help payers know how to allocate funds and prepare for possible disruptions. Wherever it is used, predictive analytics should drive decision-making, not replace decision-makers.
Incorporating predictive analytics into decision-making can help health plans better serve members. For example, predictive analytics can integrate lifestyle, symptom, and treatment data to understand more holistic treatment plans for members. Health plans can use this intel to implement new programs that fit members’ needs, improving member satisfaction and health outcomes.
Financial Analytics
Analytics solutions can also provide insights into health plans’ financial performance. Financial analytics can comb through large amounts of data for patterns in costs, expenditures, and pricing. These insights can help health plans create accurate budgets, manage financial risks, and optimize provider payment models.
A wealth of healthcare financial data is already available—the software enables health plans to make sense of it all. Many analytics solutions, such as Veradigm Revenue Cycle Analytics, have reporting features that simplify understanding and applying the insights generated by the software. Users can configure dashboards to provide overviews or detailed insights.
Quality and Outcomes Analytics
Quality and outcomes analytics help payers improve their plan’s quality rating and align with quality measures reporting. The analytics solutions provide insights into gaps in care, including which patients will be included in the denominator of each quality metric—the patients eligible for a service. Closing care gaps improves member health outcomes and health plan metrics, which can attract new members and increase revenue.
Veradigm Quality Analytics helps payers optimize performance in quality measures reporting, including reports for the Healthcare Effectiveness Data and Information Set (HEDIS®), Quality Rating Systems, Star Ratings, Pharmacy Quality Alliance, and state-specific programs. Through Veradigm Quality Analytics, payers gain insights into their current performance and find the ideal interventions to close gaps.
Utilization Analytics
Utilization analytics track how members use providers and services. Payers can use this information to identify where to monitor provider networks, manage cost of care, and stop revenue leakage. For example, revenue leakage often results from providers referring members to out-of-network services or members accessing emergency care for non-urgent healthcare needs. Understanding exactly where this happens allows health plans to address problems effectively.
Health Equity Analytics
Healthcare data often includes information about a population’s social determinants of health (SDOH), such as housing, transportation, food, education, employment, and economic status. Analytics solutions can use SDOH to address the root causes of health problems in ways previously harder to view outside of traditional medical claim data.
Veradigm Health Equity Analytics, part of Veradigm Risk Adjustment Analytics, uses SDOH to help health plans understand the needs of at-risk members. This tool may also be used to help payers determine which benefits to include in their Medicare Advantage Bids and develop supportive care plans.
Customized services help prevent complications, reduce hospitalizations and emergency care visits, provide timely and effective care, and decrease long-term healthcare costs—improving revenue and member health outcomes. Through health equity analytics solutions, payers can provide the personalized, holistic care members need. This increases member satisfaction and loyalty, which, in turn, improves member retention.
Tips for Choosing the Right Data Analytics Solution
Analytics solutions can transform how health plans operate and perform. However, which healthcare payer data model you choose depends on various factors. The following six points and related questions will help you find the right solution for your organization.
- Identify your needs. What specific challenges does your organization face? What outcomes do you want to achieve with data analytics?
Evaluate vendor capabilities. What is a vendor’s track record in the healthcare payer sector? Do they offer scalable solutions? Are their solutions customizable, or do they only provide off-the-shelf services? - Ensure compliance and security. Does the vendor adhere to regulatory requirements, such as HIPAA? What data security measures do they offer?
- Consider integration and interoperability. Can the analytics platform integrate with your existing systems? Can it use available data sources?
- Focus on user-friendly interfaces. How easy is the platform to use? How much training does it require? Managing complex tools can frustrate employees and take time away from other tasks.
- Look for ongoing support and updates. Does the vendor offer support once the analytics solution is in place? Do they offer training for your employees? How often does the vendor update the tools to ensure they remain effective?
Healthcare analytics vendors must provide subject matter expertise and a willingness to work alongside customers in addition to high-performing software platforms. Veradigm brings a collaborative spirit to support customers using our analytics solutions. Our healthcare payer solutions include analytics for risk adjustment, cost management, and quality care improvement—all available to help payers reach financial goals and boost member services.
Enhance your payer operations with the right data analytics tools from Veradigm.