Determining and verifying eligibility for social benefits, public services, and professional certifications can be a manually intensive and document heavy process. Multiple manual touch points results in delays in processing requests, variances in determination outcomes, inefficient utilization of staff, and frustrated applicants. Manual effort is often expended to:
· Access, navigate and update disparate business systems,
· View and locate critical document data, and
· Determine compliance with program policies and business rules.
The U.S. Centers for Medicare & Medicaid Services (CMS) faced this challenge with the implementation and administration of the 2010 Affordable Care Act (ACA). The Federally Facilitated Marketplace (FFM) and State-based exchanges (SBE) were required to operate mailrooms and online portals to intake applications, review documentation to ensure compliance, identify any potential issues with applications, and resolve eligibility issues.
A critical component of the ACA eligibility determination process is the verification of application data submitted by consumers. The majority of consumer applications are verified and approved by referencing demographic data from a Data Services Hub. The Data Services Hub combines data on income and employment from IRS records, health and entitlements from HHS records, identity from Social Security, citizenship from Department of Homeland Security records, criminality from Department of Justice records, and residency/income from state records. In many instances, consumers are determined to be conditionally eligible for healthcare insurance coverage when application data doesn’t match data from government data bases. This outcome is called a “data matching issue” or DMI. Conditionally eligible consumers are mailed a notice asking them to submit additional documentation to address the DMI.
Processing of consumer documents and DMI business rules was manually intensive during the initial years of the ACA roll-out. It required staff to execute multi-step workflows, access disparate systems, and manually adjudicate DMI exceptions. Document processing entailed the use of image file viewers to manually scroll through collections of digital pages, locating key documents (W-2, Passport, Form 1040, etc.), and manually transcribing document data into a case management system “notes or comment” fields. Staff often were required to manually search system databases to validate that documents were associated with the correct applicant. Verification of income levels involved manual entry of document data in online calculators or the use desktop calculators to compute projected annual income (PAI) for comparison with the attested application income levels.
The ACA eligibility determination process was an ideal candidate for an Intelligent Process Automation (IPA) solution strategy in 2016. IPA combines emerging new technologies such as Robotic Process Automation (RPA) and Artificial Intelligence (AI) with established process automation technologies to relieve staff of mundane manual tasks. In forthcoming blog posts, InfoCap will highlight a number of lessons learned over the past four years in developing and deploying IPA and innovations to streamline the ACA process. Below are some of the areas we will explore for you.
· What’s involved in Eligibility Determination
· Applicant Names and the Social Reality
· BPM meets Document Intensive
· Business Rules, Tribal Knowledge, and Intelligent Automation
· The Curse of Smart Devices, when Beautiful Granite Counters are not Good
· Systems, Systems, and More Systems
· The New Work Force, having Robots and Humans Tackle the Work
We look forward to hearing about your challenges and hope our lessons learned can assist with your eligibility determination workflow processes.