There are roughly 1,200 healthcare payer companies in the United States. All these payers face the same challenges – increasing their customer base, surviving in a competitive space, incorporating new health plans, and reducing organizational costs. With the help of data analytics, healthcare payers can not only resolve these problems but also increase revenue and improve efficiency.
Through customized healthcare payer data analytics, healthcare payers can determine areas of improvement in patient care and implement innovative strategies to engage high-risk patients. Advanced data visualization techniques enable healthcare administrators to make intelligent business decisions that significantly impact the organization’s performance and revenue.
The common bottlenecks which might slow down your business growth
Stringent processes of reporting and systems that are not measuring up to the evolving reforms in the healthcare industry.
Low quality measuring systems and tracking puts a strain on the revenue stream due to the increasing number of frauds.
Scattered and disorganized data proves to be meaningless and a burden on payer organizations, without offering any analytics.
The existent analytics technology is not able to cope with the huge volumes of data and therefore proves to be useless.
Old systems lack the potential to offer interoperability between physicians, clinics, administrative staff and patients for holistic analysis.
Constantly changing reforms and regulations lay an added burden on healthcare payer organizations, as systems struggle to keep up.
Process unification of clinical and financial data through effective measuring and reporting analytics
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.
OSP has worked with Stephen to create a mobile health application offering 'Doctor on Demand'. This mhealth solution is based on the Uber model to enhance the availability of health access in the US.
Through the deployment of real-time information sharing across physicians, clinics, patients and payer organization, pattern identification is undertaken to detect potential frauds towards claims. Missing or incorrect information is highlighted, gaps are identified, unrequired services are avoided, separate billing procedures are reduced and miscoding is realized. This form of payer analytics uses interoperability data analysis to track and mitigate potential frauds before realization.
How we do it
One of the prime reasons for revenue leakage in healthcare payer companies is the high rate of claim denials. Though available claim analytics systems can spot routine flags in the claims adjudication process, they are ineffective in complex catastrophic claims.
A US-based client was looking for healthcare payer data analytics to simplify the claims adjudication process. The requirement was an intelligent system that can process claims data from several clearinghouses using a set library of rules.
OSP developed a robust payer rule engine with comprehensive dashboards that allow the client to predict, evaluate, and manage high dollar healthcare claims. The highly efficient system segregates large amounts of data quickly and accurately predict if an insurance claim is payable or not.
How we do it
Insurers often adopt speedy processes to reduce costs and settle claims quickly. However, sometimes this results in tremendous losses for carriers due to overpayment on insurance settlement claims.
OSP’s customized healthcare payer data analytics system assists in the optimization of payouts to curtail the time involved in claim cycles and reduce insurer costs through limited instant payouts and save revenue.
How we do it
Claim Management is a painstakingly complicated process that takes up a lot of time, and any errors in claim submissions result in loss of revenue through claim rejections and denials.
OSP helped a California-based healthcare claim management company automate its complete claim process from submission with CMS codes to reimbursements. The claim analytics solution also manages claim denials with electronic remittance advice, EOB, and appeal letters that helped to reduce the unpaid bill ratio.
How we do it
Estimates show that healthcare fraud costs the nation about $68 billion of the annual health care expenditure. Fraudulent provider billing, upcoding for expensive services, duplicate billing, and billing for services not performed are some common frauds observed by healthcare payers.
Real-time information sharing across physicians, clinics, patients, and payer organizations helps in pattern identification to detect potential fraud claims. Payer Analytics uses interoperability data analysis to mitigate potential frauds by highlighting incorrect information, identifying gaps, reducing separate billing procedures, and recognizing miscoding.
How we do it
At OSP Labs, we deploy predictive analysis and care quality intervention strategies to put together data and information to offer insights that can render substantial quality improvement. This strategy toward healthcare payer data analytics software identifies critical factors and measures from the data to provide feedback towards quality improvement towards claims data and clinical components.
We undertake the implementation of solutions that come with the capability to monitor structured and unstructured forms of data to analyse historical and current healthcare information to identify and predict areas that require intervention. Through data mining embedding in our healthcare payer data analytics software costs are substantially reduced and outcomes are improved substantially with actuarial risk adjustment and fraud detection and vulnerability tracking.
As regulatory processes and compliance procedures evolve, our healthcare payer analytics software is geared to automatically adapt and comply with the changing regulatory requirement. We further provide consulting support to orient the entire payer organization with the changing structures and help your organization to adjust to the modifications and reap the benefits of the same.
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