In the complex world of health insurance claims, the pursuit of operational efficiency is paramount. Technology offers powerful tools to streamline processes, reduce errors, and accelerate turnaround times. However, the promise of “Achieving operational efficiency using technology” is not a destination reached on day one of an implementation. Instead, it’s a journey of continuous improvement, fueled by the ability to make previously invisible inefficiencies, visible.
Why Operational Efficiency Isn’t a “Day One” Deliverable?
Many organizations invest in new technologies expecting an immediate, transformative impact. This is even more difficult when it comes to a problem like claims processing. While initial improvements are often seen, true, deep-seated operational efficiency is rarely an instant outcome. Here’s why:
Baseline Establishment: New systems and technologies first need to operate within the existing environment to gather data and establish a performance baseline. This initial phase is crucial for understanding the current state, not the optimized state.
Gathering data is a time-consuming activity, not years but definitely weeks and in some cases months. It is defined by the amount of data that gets generated every day for machine learning systems to hone themselves. The primary outcome is to ensure every day activities are not affected and the new system is able to outperform the previous benchmarks consistently.
Uncovering Hidden Bottlenecks: Legacy processes and ingrained habits often mask underlying inefficiencies. It’s only when new technology starts to collect and present data in new ways that these “invisible” chokepoints, manual workarounds, or redundant steps truly come to light. These aren’t always apparent during initial design or rollout.
Right from Document Classification, to digitization, to adjudication of claims and automated quality control to ensure high accuracy of digitization. These activities when moved into automation reveal gaps in the process that need to be addressed. Most of these issues are usually data quality challenges and increased human involvement to address them.
Iterative Nature of Optimization:
Claims Processing in Health Insurance by nature is complex. The primary reason for this this complexity is the process that involves digitization, number of line items to be reviewed in hospital bills, complex medical terminologies, and handling fraud.
The first pass with a new solutions reveals the most obvious opportunities. Deeper, more nuanced efficiencies are typically identified through ongoing analysis and experience with the new system. What seems optimal on paper might need adjustments based on real-world claims volume and complexity.
The Blueprint for Lasting Efficiency:
Most solutions will help reduce claims processing time by half and increase Tariff coverage by more than 60%. However, this does not imply reduction of human capital by the same number. To achieve this, companies will need consistent Monitoring and Process Tweaking.
The key to unlocking sustained operational efficiency lies in a commitment to an iterative cycle of monitoring, analysis, and refinement. This is where technology truly “makes the invisible, visible”.
Consistent Monitoring with Technology: Modern claims processing technologies provide dashboards, analytics, and reporting capabilities that offer unprecedented insight into every stage of the claims lifecycle. Companies must leverage these tools to: Track Key Performance Indicators (KPIs) in real-time (e.g., claim processing time, error rates, first-pass resolution rates, specific task durations).
Zscore has been helping companies automate claims processing and also identify patterns and trends that highlight emerging issues or areas for improvement. Segment data to understand variations in efficiency across different claim types, teams, or individual processors.
How are some companies achieving this?
Some insurers have started to enjoy substantial savings and a great ROI and are reducing overpayments and saving money every day. In India, a few health insurers save an average of Rs. 4,000 per claim and enjoy an ROI of more than 10X.
The main ingredient to achieve this is by using a Data + AI approach to solving this problem. For an AI solution to make meaningful impact it is important to solve the business problem and the underlying data problem as well.
Digitisation of bills and other hospital documents can be tricky. It’s always challenging to ensure accuracy of the digitized output. Accuracy validation of data requires tools and solutions to check various aspects of the data.
It is important to acknowledge that in this whole approach, data resides in the center around which all other solutions revolve. Hence it is imperative that companies understand data quality is sacrosanct.
The need for a human-in-the-loop to validate and remediate digitization is critical. It serves as a great way to validate data and ensure quality for upstream applications to consume. Without human involvement, we risk the authenticity of the data and thus reduced trust.
Over a period of time patterns and clarity start to emerge on cost reductions. This helps in making bold and informed decisions on cost reductions after achieving the optimization at satisfactory levels.
Consistently monitoring performance and courageously tweaking processes based on data-driven insights is the true path to making the invisible visible and achieving sustainable operational efficiency in claims processing.