Healthcare payer analytics assesses data about payers’ operations, health plans, and claims to derive insights into prevailing healthcare patterns. The insights derived from analyzing payer data help insurance companies adapt existing health plans, allowing provider organizations to modify their approach to offering medical services. In other words, using healthcare payer analytics solutions helps senior healthcare administrators make informed business decisions that increase revenues and benefit all stakeholders without any extra hassle.
Healthcare professionals acknowledge that collaboration between different care teams is essential, but lack of time for meetings and unreliable communication solutions restrict interprofessional collaboration.
Through collaborative analytics, clinics, payers, and patients can achieve better interoperability and improve patient experiences. Healthcare payer analytics solutions provide a collaborative framework that promotes information sharing and analysis distribution for the collection and management of patient data across providers, shifts, and locations efficiently.
Another useful feature of healthcare payer data analytics is economic analytics. Economic analytical systems analyze the historical economic and financial data of the payer organization. Data analytics solutions provide valuable insights into the costs involved, the quality areas, and populations at-risk, through pattern identification.
Healthcare payers can reduce the risk of fraud and revenue loss by utilizing accurate, timely, and comprehensive economic analysis that follows easy-to-use standardized theories.
Healthcare payer data analytics embedded with reporting analytics allow payer companies to collect, report, and intelligently visualize data.
Reporting analytics can help healthcare payers improve organizational process efficiency through easy-to-follow performance metrics and reports.
Traditional reports generally display data in a tabular or columnar format. Reporting analytics, on the other hand, present information graphically and through multiple interactive reports. So, the user can create different views and discover meaningful patterns in critical healthcare data.
Organizations can lessen patient wait-times by using healthcare payer data analytics to monitor and leverage scheduling and staffing procedures with minimal errors and enhance employee satisfaction.
As the healthcare industry transitions from volume-based care to value-based care, a robust provider network is imperative for better patient outcomes. Provider analytical tools offer compelling visualizations that aid in identifying and fulfilling future business opportunities, improving process and cost inefficiencies, and increasing productivity and revenue.
Healthcare payer data analytics helps in profiling and monitoring provider practice patterns and assessing bundled payments and risk-based arrangements. So healthcare organizations can partner with high-value providers aligned with their cost and quality initiatives and enhance quality care.
Population Analytics gathers and analyzes population health data that helps healthcare payers recognize at-risk populations and their care needs. These solutions also assess the quality of care provided and aid in the delivery of appropriate care.
This element of healthcare payer data analytics provides comprehensive, reliable, and timely metrics, reports, trends, and graphs. These are designed by data obtained from multiple sources inside and outside the organization on patients and populations.
Population analytics systems can considerably improve payer strategies and promote holistic caregiving by addressing the specific needs of relevant populations.
Among the various healthcare payer data analytics solutions, progress tracking is critical toward the smooth execution of the organization’s payer-related plans and activities. Progress analytics backed by data-related research offers holistic and valuable feedback that helps the organization track current tools, services, and approaches.
Progress analytics systems provide formative modeling methods, advanced data analysis, and reliable transportation planning modeling and traffic analysis. Through progress analytics systems, payers can review their plan design for claims regularly and make changes based on the findings to derive transformative results.
Payers and providers often have disagreements over claims and reimbursements. One of the reasons for that is when payers disagree with doctors' decisions regarding the necessity of specific treatments or tests. As a result, payers may end up paying more than is medically necessary, or providers might recommend treatments not covered by payers. OSP can develop payer analytics solutions to help prevent these problems and indirectly improve revenues for payers.
A lot of Americans are reported to be underinsured. Many health plans lack coverage for people who are then denied necessary treatment. But OSP can build healthcare payer software to enable insurance companies to assess existing and historical data from payer operations and better develop health plans that cater to patient needs. In doing so, patients can avail themselves of the necessary treatments, and the providers can be reimbursed for the care they provide.
There is a wide range of reasons why members might drop out of plans. These include lack of coverage for some services, lack of covered pharmacies nearby, delays in care, etc. But OSP's custom solutions for healthcare payer analytics would enable insurance companies to assess all the operational data from health plans and obtain insights into what the patients need. This enables them to serve their members better.
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We reached out to OSP to provide an estimate on a technology solution we were interested in developing. From the initial conversation, the team was professional, courteous, and thorough. We were able to make a quick decision to move forward with OSP because we were confident that our requirements were accurately captured and the development deliverables and associated costs were clear.
The OSP development team stayed on schedule and within budget throughout the build phase and provided weekly communications to keep our team informed along the way. If we require application development in the future, OSP will be the first call we make.
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We have worked closely with OSP for two years, meeting twice a week to work through development requirements, strategy, design, progress, and support. OSP has become an integral part of our business, and our mutual teams work together as one team. OSP tackles problems that arise with integrity and operate with respect for budgeting.
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We built a tailored RPM solution with telehealth features.
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Healthcare payer analytics assesses the operational data about healthcare payer companies. This assessment considers insurance premiums, health plans, claims, reimbursements, and payments to identify useful patterns and obtain insights. These insights help payer companies to adjust their services accordingly. This could include modifying health plans to match patient needs, adjusting coverage according to provider services, optimizing adjudication processes to boost operational productivity, and so forth. In other words, healthcare payer analytics helps insurance companies serve their stakeholder better.
Payer healthcare analytics analyzes medical insurance companies’ operational data to reveal actionable insights. The data analyzed results from claims, reimbursements, premiums, denials, rejections, approvals, etc. Assessing this data reveals patterns that highlight the behavior of doctors and patients, which in turn can be used to make informed decisions about premiums and health plans.
The use of payer analytics solutions in healthcare involves data from several aspects of everyday operations at insurance payer companies. This can include claims adjudication, provider enrolment, credentialing workflows, out-of-pocket payments, and health plans. By assessing these processes, payers can identify the pain points and the causes of inefficiencies. Knowing this insight helps them to address the problems and boost the company’s efficiency and productivity.
Healthcare payer analytics also helps to minimize the approval of fraudulent claims and curb losses. Additionally, it enables payers to analyze providers’ services and alter their coverage of health plans accordingly. This would enable patients to get adequate care and help providers serve their patients.
Some examples of payers can include Medicare, Medicaid, and private health plan providers.
When patients see doctors at hospitals or clinics, they are provided consultations. The consultations may be followed by medication prescriptions, tests or scans, or all of them. These are called healthcare services, and the entities that provide them (doctors, hospitals, etc.) are called providers. A payer is an entity that pays for the services provided by providers.
Providers offer medical services to patients and send the bill over to payers through claims. The payers assess these claims to ensure that the services provided by providers are valid and necessary for the patient. If found to be valid, the provider claims are approved, and the providers are reimbursed. If there are problems with the claims, then they are rejected.
Providers are professionals who provide medical services like consultations, tests, treatments, procedures, scans, etc. Payers are entities that pay for the medical services provided by providers.
The spending on healthcare in the United States has reached a staggering $4 trillion. That is more than the economies of all but four countries. A very large portion of Americans has incurred sizeable healthcare debt. The spiraling costs of medical care are a major concern that policymakers have struggled to address.
Payers negotiate and set fixed prices for providers’ services with all the providers in the network. This means that when a person with a health plan visits a provider organization included in that health plan, he or she will be charged a fixed amount of money for medical services. Without health plans or coverage, the provider organization (hospital, clinic, practice) can charge an excessive amount for the same services. This is why payers are important in the United States healthcare system.
Some of the trends that are expected to shape the healthcare payer industry include the following –
Telehealth
Telehealth is one of the biggest trends in healthcare to watch out for. It aims to disseminate medical services remotely through digital and telecommunication technologies. However, some providers are skeptical about this as some payers don’t reimburse telehealth services in the same way as they do for in-person patient visits.
Artificial Intelligence
Artificial intelligence (AI) has been the hot buzzword in every industry. For healthcare payers, AI holds enormous promise in automating parts of processes like claims adjudication. Additionally, AI-powered algorithms can also be used to analyze large quantities of data to provide useful insights for boosting the productivity and efficiency of everyday operations.
Affordable Care
The growing costs of care in the United States have prompted many policymakers to prioritize affordable care. In light of this, value-based care models have been growing in popularity as they enable more people to access the care they need. Entities like Accountable care organizations (ACOs) and Health Maintenance Organizations (HMOs) are gaining popularity. Payment methods like bundled payments are known to reduce the cost of a single episode of care.
Data analytics often uses historical data to forecast what will happen shortly. But with rapid changes afoot, payers would increasingly have to turn to real-time data to fuel their analytics solutions.
A good example of the importance of real-time data is the shift in memberships of health plans. As the world is bracing for a recession following the already crippling pandemic, there is a migration from employer-sponsored health plans to Medicare, Medicaid, and Affordable Care Act entities.
Payers can derive useful insights into health plans and reimbursements by assessing real-time employment data and subsequent coverage. This is perhaps one of the most prominent examples of using real-time data analytics for forecasting by payers.
