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Why is missing coverage so detrimental? When patients are unable to pay, providers see an increase in self-pay receivables – 97% of hospitals reported an increase in self-pay receivables compared with the prior fiscal year. When providers can’t collect, the result is uncompensated care $35.7 billion – The amount of uncompensated care hospitals provided in 2015 (AHA, Dec 2016, http://www.aha.org/content/15/uncompensatedcarefactsheet.pdf) Registration errors and frequently changing insurance coverage contribute to an increase in self-pay receivables and bad debt: 20% error rate: Wastes an estimated $15.5B in claims that are never corrected and re-processed. Find previously unidentified coverage to reduce bad debt Experian Health’s Coverage Discovery® solution provides an efficient and trusted means for healthcare providers to reduce misclassified self-pay accounts. Our advanced self-pay engine maximizes the actionable coverage found for our customers across government and commercial payers, previously missed due to frequently changing insurances, data entry mistakes or patient misrepresentation to avoid high deductible health plans. The benefits to you: Make coverage identification more efficient Reduce number of accounts sent to collections & charity Maximize insurance reimbursement revenue Identify primary, secondary, & tertiary coverage Automate the self-pay scrubbing process What are our clients are saying about Coverage Discovery: [Video] Murry Ford, Grady Health System: “Coverage Discovery has brought in quite a bit of revenue for us—revenue that we would not have identified otherwise.” [Video] Mike Simms, Cone Health: “Since we’ve been using Coverage Discovery, we’ve received over $3 million in payments—more than a 300% ROI. It’s well worth every dollar spent.” Here’s why Coverage Discovery is different: Flexibility: Multiple delivery options easily fit Coverage Discovery into your revenue cycle Time matters: Find coverage on-demand Powered by Experian: Billions of data assets improve account specific intelligence and maximize found coverage Relevant: Confidence Scoring reduces data noise and removes potential false-positives to ensure staff time is focused on actionable accounts Uncover previously unidentified coverage with Coverage Discovery.
Challenge: a disconnected healthcare ecosystem Exchanging information across the healthcare ecosystem and achieving interoperability is a goal and challenge all healthcare organizations share. While regulations such as the Affordable Care Act introduced incentives and requirements to drive adoption of electronic medical records, they also highlighted a critical gap in healthcare – a universal patient identifier. Impact: Felt downstream across your enterprise The lack of a universal patient identifier, compounded by data integration challenges and the increasing fluidity of patient data, has created significant issues downstream – billing errors, redundant treatments and testing, HIPAA breaches, incorrect administration of treatments and prescriptions, and more. These issues contribute to the pool of preventable medical errors, which is currently the number three leading cause of death in the United States. Solution: Universal Identity Manager Experian Health’s Universal Identity Manager (UIM) accurately identifies patients and matches records within and across disparate healthcare entities, creating a universal patient identifier to facilitate information exchange. Drawing on more than 40 years of experience managing universal identifiers across various industries and leveraging Experian’s consumer demographic information, the UIM achieves higher match rates than traditional industry solutions. Integration flexibility The UIM is integrated within eCare NEXT®, but also supports various API options. It can also be leveraged in conjunction with biometric and traditional Enterprise Master Patient Index solutions. Batch: Receives, processes, and loads patient demographic information via patient data file. Starting with an initial historical patient data file in batch is recommended. The UIM algorithm is applied to accurately identify duplicate records and assign a Universal Patient Identifier (UPI) that can be used to facilitate information exchange across different healthcare entities. The UIM Batch is currently being offered at no charge to the entire healthcare industry. Duplicate Merge Tool: Provides a workflow tool to address duplicate patient records identified through the UIM batch. The Duplicate Merge Tool encompasses robust access and issue assignment management, configurable merge strategies, customizable search and filter capabilities, comprehensive audit trails, and postback or file output options. Search & Duplicate Medical Record Number (MRN) Alert: Leverages in process scripting to perform a real-time search the UIM as patient demographic information is being entered into an HIS during registration. The optimized patient record for each potential patient match is returned. If the search is circumvented, a Duplicate MRN alert will be created within eCare NEXT and prompt manual user review. Real-time search can also be facilitated through a direct API integration. Experian Single Best Record: Leverages an algorithm to look across all linked patient accounts that exist within a client’s enterprise database and aliases and addresses from Experian’s demographic information and returns back a patient’s single best record by demographic field. Data Analysis: Offers standard, premium, and custom reporting options with detailed data analysis across your patient data file. Learn more about Universal Identity Manager here, or contact your account representative for more information.
Sanford Health vetted four revenue cycle vendors to meet their challenges of manual collection processes, bad debt and low employee collection rates. They selected Experian Health largely based on ease of implementation with the Epic platform, products that required little intervention from Sanford’s IT department, a strong team of consulting resources and previous success with other Experian Health products. According to Mike Beyer, Director Patient Accounts, Sanford Health, “Collections Optimization Manager really allows me to segment out and find those patients that have the ability to pay; and to avoid chasing those who instead qualify for our Presumptive Financial Assistance/Charity program.” With Collections Optimization Manager, Sanford Health realized a total in-house patient collection lift of $40+ million since they first implemented in October 2014; with an average monthly lift is $2.3 million. Contributing drivers of this success: leveraging segmentation and Federal Poverty Level (FPL) within Collections Optimization Manager to determine Presumptive Financial Assistance and account routing to our new cloud-based dialer solution, PatientDial. Download the full case study Watch the video
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