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Insurers may deny a high percentage of claims, many of which are never resubmitted, resulting in losses of tens of millions of dollars. Most denials are recoverable and preventable, but only with proper root cause analysis of claims denials. Without robust analytics tools, the percentage of the organization’s denial rate exceeds best practice multiple times, and healthcare systems continue losing money. It’s necessary to develop advanced data capabilities to proactively identify issues early in the revenue cycle process. Challenges on the Way to Decreased Denial Rates Revenue cycles are complicated and variable for large networks with multiple facilities. Billing processes and requirements of hospitals and physician practices within a single healthcare system are peculiar. Different insurance companies offer various policies. All of this can lead to inconsistencies in handling claims and result in defects and waste.  The list of issues is often related to processes and technologies. Issues with Technology Lack of tools to proactively determine initial issues that appear early in the process of the revenue cycle to avoid repeating errors and improve cash flow  Inefficient utilization of the available technologies and absence of interaction between multiple IT systems, which results in difficulties with data analysis and unnecessary duplication of work Insufficient detailing of the available EHR reports, difficulties with generating them, and finding precise issues of denials  Process-Related Challenges Wide variation in the way the staff performs its functions   Unequal distribution of the workload and tiresome manual data entry Low speed of collecting detailed insurance and demographic information that is essential at the front end for preventing denials  The time-consuming process of selecting the right plan registration for complex cases due to lengthy documentation Missed insurance authorizations when patients stay in the hospital for a period longer than the initial authorization Organizational and Communication Gaps Lack of training among clinical and operational executives about the correlation between their performance and the general denial rate  Miscommunication between the financial teams and clinical and operational leaders, as the former struggle with preparing user-friendly reports with clear recommendations Strategic Weaknesses Ineffective embracing the whole problem at a time instead of concentrating on specific target areas and benefiting from incremental improvements  Absence of a holistic improvement plan, based on data, that would involve clinical, operational, and financial teams to address the problem.  Features of the Claims Analytics Solutions to Save Dollars To decrease the denial rate, healthcare organizations implement claims analytics solutions powered by modern data warehouses technologies, which consolidate data using ETL processes. It provides organizations with the following capabilities: Creating interactive visualizations for in-depth data analysis to generate more actionable insights compared to legacy Excel-based and embedded EHR reports Analyzing the denials, identifying their root causes, and exploring peculiar issues Visualizing and exploring the information regarding denial trends over certain periods of time, denial age, types, current procedural terminology (CPT) codes, as well as payers, divisions, and account classes Developing a standardized set of metrics to create a framework for understanding what exact performance improvements all the stakeholders expect and how to measure them. Organizational Measures to Implement Claims Analytics To achieve the best results with the new software, some organizational measures should be taken into consideration. Healthcare systems should pay attention to the following supporting activities: It is recommended for organization leaders to prepare the ground for commitment and advocate the idea of reducing denial claims The CEO, CFO, and COO should cooperate to make addressing claim denials a top priority, raise awareness about the problem in the organization, and assign a dedicated person to drive the initiatives forward An organization could also hire or train a dedicated denials analysis team that will learn denial trends, conduct root cause analysis, determine patterns, and measure opportunities for improvement over time It would be beneficial for executives to explain their vision and tasks to clinicians and demonstrate the impact of the revenue cycle on patient experience. Clinicians should realize that proper initial billing shows their caring attitude toward patients  Leaders should cease concentrating on one-time tasks. It is better to come up with a strategy for steady improvements with regular assessment and research and iteration of the Plan, Do, Check, and Act (PDCA) steps. What Benefits to Expect from Implementing Claims Analytics? By applying the claim analytics tool, as reported, organizations can gain the following results: Achieving a target that meets the best industry standard of denial rates Saving millions of dollars directly resulted from the reduction of denials  Gaining millions of dollars in annualized recurring benefit  Addressing inefficient workflows, processes, and manual entry through the PDCA approach that helps identify root causes and develop effective interventions  Identification of the root causes of claim denials in departments, where the leadership believed their processes had been effective Adoption of the initiatives across the organization with coherence and engagement thanks to visible and measurable analytics data Encouraging sustained improvements due to engaging operational leaders. How Belitsoft Can Help Belitsoft is a full-cycle software development and analytics consulting company that specializes in healthcare software development. We help top healthcare data analytics companies build robust data analytics platforms. For integrated data platforms developed to collect, store, process, and analyze large volumes of data from various sources (Electronic Medical Records, clinic management systems, laboratory systems, financial systems, etc.), we: Automate data processing workflows (cleansing, standardization, and normalization). Configure scalable data warehouses. Set up and implement analytical tools for creating dashboards, reports, and data visualizations. Ensure a high level of data security and compliance with healthcare regulations such as HIPAA. Integrate machine learning and AI into analytics. We also help build specialized analytical applications like Claims Analytics for:  Applying custom date filters to analyze specific timeframes Visualizing denial trends, including initial and current rates, and aging buckets in graphs and diagrams Aggregating and analysing denial reasons Highlighting key denial reports. If you're looking for expertise in data analytics, data integration, data infrastructure, data platforms, HL7 interfaces, workflow engineering, and development within cloud (AWS, Azure, Google Cloud), hybrid, or on-premises environments, we are ready to serve your needs. Contact us today to discuss your project requirements.

Converting Business Problems into an Analytics Solution Organizations have goals like making more money, getting new customers, selling more, or cutting down on fraud. In a data analytics project, it's really important to first understand the problem the organization wants to solve. Then, figure out how a predictive analytics model, built using machine learning, can provide insights to help solve this problem. This step is all about creating the right analytics solution and is the key part of the Business Understanding phase in the project. Fraudulent Claim Prediction A predictive analytics model predicts the likelihood of fraud in insurance claims. It analyzes patterns in past insurance claims data, including both fraudulent and non-fraudulent claims, to identify indicators of fraud. To train the model, it would require a large dataset of insurance claims that have been classified as fraudulent or non-fraudulent.  The model would use the data to learn patterns and correlations that are often seen in fraudulent claims. For example, it might find that claims filed immediately after a policy change or claims for certain types of incidents are more likely to be fraudulent. Once the model is trained, it can be applied to new claims. Each claim would be given a score representing the likelihood of it being fraudulent. This is typically done on a scale, where a higher score indicates a higher likelihood of fraud. Claims that receive a high fraud likelihood score would be flagged by the system. This doesn't mean they are certainly fraudulent, but they have characteristics that warrant closer inspection. By using the model to prioritize which claims are investigated, the company can focus on the most suspicious cases. This targeted approach is more efficient than random checks or trying to investigate a large number of claims. This approach will increase the detection of fraudulent claims, thereby saving the company money and protecting resources. This could also deter fraud over time, as potential fraudsters realize that the chance of being caught is higher. The feasibility The key requirement for successfully implementing a claim prediction analytics solution in an insurance company is the business's capacity to provide database of historical claims marked as fraudulent and non-fraudulent, with the details of each claim, the related policy, and the related claimant. The prioritization mechanism should  identify and flag certain claims as high priority and operate within the existing timeframe for handling claims.  If the insurance company already has a claims investigation team, the feasibility study would assess how the team currently operates and how they would adapt to using a new system. High Risk Policyholders Prediction The primary goal is to predict the likelihood of a member (policyholder) committing fraud in the near future. This preemptive strategy aims to identify potential fraud before it occurs, rather than reacting to it after the fact. Running the model, for example, quarterly allows for regular updates on the risk profiles of members.  The model would likely use historical data, including past claims, behavioral patterns, policy changes, payment history, and other relevant data points. Advanced analytics and machine learning algorithms would analyze this data to identify patterns or behaviors that have historically been indicative of fraud. The model assigns a risk score to each member, indicating their propensity to commit fraud. Members with higher scores would be flagged as high risk. Based on this risk assessment, the company might contact the policyholder with a warning to with some kind of canceling their policies. By identifying and addressing potential fraud proactively, the insurance company could save significant amounts by preventing fraudulent claims. This approach could also deter potential fraudsters if they are aware of the company's proactive measures. The feasibility The feasibility of the proposed analytics solution for detecting potential fraud risks among members depends on several key conditions being met. Here are scenarios where the solution would be considered feasible. The organization has: the ability to link every claim and policy to a specific member and maintain historical records of policy changes. the operational capacity to conduct detailed analyses of customer behavior every quarter. a skilled team adept at maintaining positive customer relations, even when discussing sensitive issues like fraud. The organization should be well-versed in relevant legal and regulatory standards, such as privacy laws, and has mechanisms in place to ensure compliance. Fraudulent Intent of an Applicant Prediction This is a strategy aimed at identifying potential fraudulent activity at the earliest stage – when a policy application is submitted.  The primary goal of the model is to assess the likelihood of a new insurance application resulting in a fraudulent claim in the future. This preemptive measure is aimed at fraud prevention rather than detection after the fact. To make accurate predictions, the model would analyze a variety of data points. This could include information provided in the application, historical data of similar policies, patterns identified in past fraudulent claims, and possibly external data sources (like credit scores or public records). Each application would be screened by the model, assigning a risk score indicating the likelihood of a future fraudulent claim. Applications that score above a certain risk threshold could be flagged for further review or potentially rejected. The feasibility Here are scenarios where this solution would be considered feasible. The organization: has access to a collection of claims data, classified as either fraudulent or non-fraudulent, spanning many years, given the potential long interval between policy applications and claim submissions. have the capability to link each claim to the original application details. must have the capacity to integrate the automated application assessment process seamlessly with the existing application approval processes. Exaggerated Insurance Claim Prediction A common problem in insurance is claims where the requested payout is higher than what is justifiable. When an insurance company suspects a claim is over-exaggerated, they conduct an investigation. This process is resource-intensive and costly. The idea is to develop a machine learning model that predicts the likely payout amount based on historical data of similar claims and their outcomes. The model would use historical claim data, including the nature of the claim, the amount initially claimed, the results of any investigations, and the final settled amount. When a new claim is filed, this model can be run to estimate the likely legitimate payout amount.  Instead of going through the full investigation process, the insurer could offer the claimant the amount predicted by the model. This would be a faster, less costly process than a full investigation. The feasibility The solution will be feasible in scenarios where the following conditions are met. The organization: have access to information on the original amount specified in a claim and the final amount paid out.  needs the operational capacity to act on the insights provided by the model. This includes making offers to claimants, which assumes the existence of a customer contact center or a similar mechanism for direct communication with claimants. In this article, we are working under the assumption that following a review of its feasibility, the decision was made to move forward with the claim prediction solution. This involves developing a model capable of predicting the likelihood of fraud in insurance claims. Designing the Analytics Base Table The core of the model's design involves the creation of an Analytics Base Table. This table will compile historical claims data, focusing on specific features that are likely indicators of fraud (descriptive features) and the outcome of whether a claim was ultimately deemed fraudulent (target feature). The design of the Analytics Base Table is driven by the domain concepts. Domain concepts are the fundamental ideas or categories that are essential to understand a particular domain or industry.  Each domain concept translates into one or more features in the Analytics Base Table. For instance, the domain concept of "Policy Details" might be represented in the table through features like policy age, policy type, coverage amount, etc. The identification of relevant domain concepts is a collaborative effort involving analytics practitioners and domain experts within the business. The general domain concepts here are:  Policy Details. Information about the claimant’s policy, including the policy's age and type. Claim Details. Specifics of the claim, such as the incident type and the claimed amount.  Claimant History. Historical data on the claimant's previous claims, including the types and frequency of past claims. Claimant Links. Connections between the current claim and other claims, particularly focusing on repeated involvement of the same individuals in multiple claims, which can be a red flag for fraud. Claimant Demographics. Demographic information of the claimant, like age, gender, and occupation. Fraud Outcome. The target feature, which is derived from various raw data sources, indicating whether a claim was fraudulent.

When applying for life insurance coverage, there is a wide range of information that the underwriter will ask. Each question relates directly to the amount of premium you’ll pay. In addition to your name, age, and gender, the company also needs to know about your health status and history. Thus, data on whether you have diabetes or are there cases in your family, as well as any other potential risks, allows insurers to determine the exact amount of a claim. Where do life insurance carriers get client information? The primary source of information for life insurers is the application for coverage. In addition to the medical history, an applicant provides, an underwriter may request reports from their healthcare provider or from other insurance companies to which they have applied for insurance. They may also resort to knowledge bases to obtain additional details on customer background. One such source is the Medical Information Bureau or MIB Inc. If insurers do so, they use the authorization form an applicant signed with their health statement. What is MIB Inc.? MIB Group, Inc. is a non-stock corporation owned by nearly 500 insurance companies throughout the US and Canada. The organization was created in 1902 and is America’s oldest and longest continuously operating credit reporting agency accessing 100 million records and growing weekly. Source: mib.com/facts_about_mib.html MIB Inc. currently owns North America’s largest database of medical conditions on insurance applicants. Collected medicine-related records may include medical conditions, tobacco usage, alcoholism, drug addiction, and personal or family genetic history. The information in MIB’s database is encrypted and may be accessible only to authorized personnel of the member company. They contribute underwriting files to the MIB database that may be helpful to other members. Applicants can also provide the MIB with personal medical records from their healthcare provider that are relevant to the disputing conditions. When customers apply for insurance, they should authorize the use of MIB as an information resource. According to the Federal Trade Commission, MIB is obliged to provide a copy of a consumer’s medical record, if requested, to verify that all information is correct. EHR - a tool to gather data from life insurance applicants The United Services Automobile Association (USAA) is among the first, who let applicants use EHR to simplify the life insurance purchase progress. The Association worked with Cerner to test and implement EHR retrieval technology named HealtheHistory. Source: twitter.com/Cerner/status/847814228694237184 The solution is available to applicants at the Department of Veterans Affairs and Department of Defense. Thus, they are able to deliver their health data directly to the insurer via the patient portal. ‘We can’t emphasize enough how important life insurance is to a financial plan, but we also understand that the process of obtaining a policy continues to introduce challenges industrywide. By using portal retrieval technology and existing EHR platforms, we can provide our members a more secure, easy way to supply records to get a policy decision as soon as possible.’ Dr. Steven Dunlap, medical director at USAA HealtheHistory supports health data collection, encrypts data transmission and limits access to approved members. The tool connects to any accessible patient portal, facilitating the delivery of an applicant’s health history up to 30 days faster than manual retrieval options. Cerner calls this a longitudinal record. The program gathers various sources of raw data, organizes into groups by commonalities, standardizes to match industry terminology, and, as a result, formes a 360-degree view of the applicant. USAA data shows that one of five members do not carry life insurance, with many citing cost and the application process as the top concerns. Recent stats from the marketing research organization LIMRA indicate the number is much higher for the general population, with over 40% of Americans having no life insurance. By deploying EHR technology, USAA is able to speed up the application and underwriting process, without increasing cost for members. MIB Inc.: steps towards EHR integration In April 2018 MIB Inc. released a platform created to automate the acquisition of applicant-authorized EHR for accelerated delivery to the member insurers. The MIB EHR Data Platform have to replace paper-based APS (Attending Physician Statement) retrieval process by offering a unified method through which MIB’s 400 American insurers can securely access health records. The corporation is in current negotiations with leading EHR vendors to facilitate the acquisition and delivery of EHR data for the new EHR Data Platform. This platform will allow the insurance industry to drive process optimization, reduce costs and enhance data-driven decision making. ‘The EHR Data Platform naturally aligns with MIB's competencies in technology, data security, user experience expertise and our large-scale network capabilities. Our mission, and the mandate we have to serve our members, empowers us to deliver an industry-wide data solution that helps all our members drive more rapid issue of life insurance to meet market demands. MIB is owned by the industry we serve—we are the obvious choice for EHR delivery to the life insurance industry.’ Lee B. Oliphant, MIB President and Chief Executive Officer, said Weaknesses of the current system that EHR may resolve Statistics show that 20% of credit histories from the major credit reporting agencies contain errors. However, the percentage of errors in records from the nationwide specialty credit reporting agencies for insurance (e.g. MIB Inc., Ingenix, and Milliman) is unknown. Even though no official data is available, it is estimated that about 5% - 10% of medical report files are inaccurate or contain errors. EHR systems, in turn, are a substantial collection of codified data that appears more credible, since physicians add the info manually. Here insurers can find vital records of allergies, medications, surgical procedures, lab results, as well as social determinants of health. Source: play.google.com/store/apps/details?id=com.epic.haiku.android&hl=en_US EHRs and coded data they contain can drive life insurance underwriting. However, is it a silver bullet for the industry? ‘On top of that, EHRs still don’t do the necessary job of making patient records easily available to providers and patients. EHRs were originally designed as a tool to help with billing, and they are falling short in their ability to provide data in a portable and accessible format. So in many ways, EHRs have merely replaced paper silos with electronic ones, while providers, and the patients they serve, still have difficulty obtaining health records.’ From the speech of CMS Administrator Seema Verma in HIMSS18 Conference, March 3, 2018 Improve EHR system to meet life insurers needs According to Health IT Dashboard 2017, 85.9% of doctors and 96% of hospitals use EHR systems. Patients and physicians have widespread access to the Internet and nearly everyone has a mobile device. All of these benefits provide many access points for viewing and upgrading healthcare data. For the lucky few who get their records, the information is often incomplete, and not always digital or understandable. Customers might be able to get some info in their provider’s portal but if they are consulting different specialists, they might be checking a bunch of portals. As a result, the data is scattered and unstandardized. The existence of multiple technical and terminology standards that serve similar functions is one of the key issues. Thus, the looming shadow of EHRs interoperability will be settled more quickly. Plus, communities join into a single data sharing network, in which each participant makes one connection to the web and then can access records from all parties. This is the way Carequality have created a standardized, national-level interoperability framework to link all data sharing networks. Human API released a new version of their medical data platform for life insurance carriers. The solution leverages the company's nationwide network of EHR, pharmacy, and lab integrations to deliver electronic health data for more efficient underwriting. ‘We started with two simple questions: "why can't consumers access their health data?" and "why do enterprises struggle to connect this data? These questions inspired our mission to create data liquidity throughout the healthcare ecosystem.’ from Human API website The platform includes the Enterprise Portal, where insurers can request real-time access to medical records, view the results in a longitudinal timeline, and leverage a robust clinical data flow to automate underwriting decisions. With no IT integration required, the solution enables insurance carriers to incorporate Human API into their current underwriting programs. The adoption of electronic health records has increased rapidly in recent years, opening the door for new approaches to medical record retrieval and direct consumer engagement. ‘Attending physician statements (APS) have always been the gold standard for underwriting, but they take too long and cost too much. The ability to directly access EHR data will be the biggest game changer for underwriting in my now very long career. Companies who figure out how to access digital EHR data first will find a distinct competitive edge in the marketplace’ Jennifer Richards, the Head of Life Insurance New Business and Underwriting at Mass Mutual Conclusion Despite the comparatively large hundreds-years-old databases, EHR information has the potential to essentially shatter and improve the life insurance risk assessment process. More and more companies made the investment to incorporate EHRs into their automated underwriting programs. The benefits are clear - EHR can reduce application time while improving customer experience. Hire Belitsoft - a top offshore software development company!