RP Sanjiv Goenka Group

Identify inefficiencies with an MRI scan of your claims operations

Deepan Vashi
Deepan Vashi
EVP and Head of Solutions for Health Plans and Healthcare Services at Firstsource
Estimated reading time : 5 Minutes

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What you don’t know about your operations workflows is likely hurting your business. Process mining in healthcare reveals the friction due to workarounds, manual activities, dead ends, handoffs, and disconnects that cost time and money – and points the way toward truly effective solutions for delivering improved experiences at lower costs.

Healthcare claims processes are complicated. They require multiple systems—core claims platforms, provider contracts, financial systems, clinical editing, pricing, and customer relationship management platforms–to work together. Most payers know these systems don’t play well together. What isn’t obvious is where and how these systems are disconnected and the resulting impact on specific workflows and claims.

Payers feel the symptoms: higher interest costs, regulatory penalties, payment adjustments, lower auto-adjudication rates, reduced accuracy in financial forecasting, higher cost of operations, and reduced member, provider and employee satisfaction.

The difficulty is quickly getting to the root cause of the problem causing the pain. Just as an orthopedic surgeon orders an MRI to see the extent of damage in a hip, knee, or shoulder to decide on the best course of treatment, process mining is like an MRI for claims operations. It uses system logs to trace the digital footprints left by claims across every system and process they traverse. The result is an extremely detailed data model that captures the anatomy and physiology of real processes.  Process mining uncovers the reality of what’s happening every day in claims operations vs. assumptions and expectations. It also provides the data to show the results of these actions over weeks and months.

Process mining’s deep examination of complex processes reveals which systems, process steps, and connections among them are faltering and why. Payers can then effectively apply that critical data to clean up process flows, improve stakeholder experiences and generate new business value. That reality makes process mining a core element of operations design, maintenance, and continuous improvement.

Revealing hidden issues and root causes

Process mining results are data-driven, not anecdotal or perceptual. The data reveals the root causes of symptoms like pended claims and low auto adjudication rates. As we conduct processing mining, we’ve uncovered a variety of common hidden issues, including:

  • The time between department queries. Process mining showed one payer we worked with that some of its transactions moving between departments were sitting in exception queues for up to 20 days due to a lack of prioritization.
  • Loops and number of manual touches. Claims and other payer transactions often move unnecessarily among multiple departments based on outmoded business rules. Each touch equals greater risk, higher costs, and more dissatisfied stakeholders.
  • Surprise gaps in work schedules. For another payer, processing mining revealed that a type of claims incoming on Fridays was not batched and loaded for processing until Monday; the client hadn’t realized claims were not being loaded. The delay caused an average 1.5 days of additional turnaround time, leading to more calls from providers and members and increased regulatory risk because of missed turnaround time deadlines.
  • Provider data quality issues. Another client had up to 10% of claims falling out because of provider data mismatches. We also observed additional 10% of rework in fixing the provider records; the claims were simply recirculating vs. being corrected.
  • Adjustment issues. One client had claims worth approximately $8M taking more than 300 days to process. The issue was a system configuration error related to the contract with a group of providers and how members were enrolled under it.

Choose the best actions with digital twins and process simulations

The process mining data model helps payers effectively solve these issues by supporting the creation of a digital twin and process simulations. Using a process digital twin enables payers to test and evaluate the outcomes of different solutions based on data about their actual processes, flaws and all. Business owners can now predict the consequences of process changes, platform upgrades and regulatory updates with 100% objectivity.

Conditions payers can simulate include automation and AI-led improvements; the impact of lean/six sigma projects; configuration accuracy improvements; volume fluctuations; new software releases; and new business rules configurations. The simulations enable payers to assess the results of the changes and then prioritize those that will deliver the highest returns.

The visibility afforded into processes by using the process digital twin also supports daily actions that lead to better business outcomes. Process mining creates daily views of performance across a wide range of KPIs, including a number of claims, appeals, inquiries, billed amount, paid amount, cost per claim and many more. Operations managers can literally see gaps in capacity, from an aggregated view down to individual claims, and the next best actions to take on them.

Processing mining in action

A client asked us to help improve its automation rate to reduce cycle time and avoid interest payments on delayed claims. We mined a year’s worth of claim digital footprints and uncovered a variety of process paths. Inefficiencies identified included an average 11.5 manual touches per claim; 13% of claims accruing penalties and interest; 16% more effort and 25% longer cycle times due to member and provider data errors; and 6% of claims adjusted because of inaccurate processing, which also created greater call volumes. Process mining unlocked several simple solutions ranging from scheduling, prioritization, addressing training and audit requirements, identifying top automation opportunities, and reducing the rework.

We used the process mining data to baseline and benchmark key process metrics against industry standards. Then we created a digital twin of the claims operations to simulate the impact of various corrective measures, such as increasing automation by 10%, reducing provider mismatches and improving on-time performance. Implementing these measures helped the client save $2 million in the first year and increase member and provider satisfaction scores.

Another client needed to reduce its high adjustment rates and late payment penalties and interest for its government programs. Our process mining and execution management system apps showed that the current processes led to incorrectly adjudicated claims. The root cause was inaccuracies in benefits and provider contract configuration, provider data quality and late contract loading. The mining indicated areas where automating manual edits would improve accuracy and throughput.

The client is now saving $5.2 million annually with corrected automation scripts, improved benefits configuration, and member enrollment accuracy. The ML predictive model embedded in our solution continually monitors and identifies claims with a high likelihood of adjustment. Probing for the root causes of these issues also has resulted in a 20% reduction in the client’s adjustment rate.

Signaling a need for process mining

As payers deal with continuing regulatory mandates, having to operate multiple platforms, expand into new products and markets and respond to changing industry payment models, none will be immune to hidden system disconnects and workflow issues. The following signals especially indicate that process that mining would deliver value:

  • Operating a mix of older and newer systems. In this common scenario, added steps often contribute to inefficiencies. Process mining will create a model of actual processes vs. assumed ones.
  • Solutions not performing as expected. Process mining may show an upstream or downstream issue is the problem, not the claims function itself.
  • Low auto-adjudication rates. The issue may be in contracts, enrollment, and provider data quality. Process mining will accurately pinpoint the problem area.
  • High operating costs. Process mining, digital twins and simulations show precisely which efficiency levers to pull to bend the cost curve.
  • Digital transformation requirements to put member experience at their core. Process mining reveals the hidden issues that may directly impact quality and experience so these can be corrected.

Payers can’t afford inefficient processes that lead to higher costs and reduced member satisfaction. Process mining in healthcare uncovers issues and their root causes, then helps payers understand the most effective solutions.  Process mining can even identify issues that can be the impetus for improving existing services and developing new services, such as remote patient monitoring, clinical engagement, appeals and grievances.    Given their immediate and long-term value, process mining, execution management apps, and digital twin for simulations are valuable tools for every payer that wants to improve operations performance, enhance stakeholder experiences, and generate new business value.

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