How do you know if your claims organization is producing a quality product? After all, there are numerous ways to identify quality, right? While no two carriers or claims processes are alike, there are often similarities. From FNOL, abandonment rate, and contact timeliness to cycle time, alternative parts utilization and average indemnity, there seems to be no shortage of usable metrics. But is this the optimal way to gauge performance?

During my years as a process and quality leader for a large multinational insurer, we grappled with a myriad of metrics in our quest to find the ideal formula to drive optimal results. Much like a football team, the claims organization was measured on statistical data points that were supposed to be indicative of outcomes. Just like a lot of points should win football games, prompt contact and inspections should win the claims race.

So what happens when all those metrics are surpassed, yet there is a rise in blown coverage, errant liability decisions, or litigation? While I am not minimizing the importance of statistical claims data points, I do like to put them into perspective. Just like football, there is only one statistic that truly matters as winning records are based upon accurate outcomes.

When designing a quality assurance process, emphasizing ultimate outcomes, or accuracy, takes into consideration all else. The actual investigation—including timely contacts and inspections, accurate coverage and liability decisions, effective negotiations and recovery opportunities—will ultimately drive accuracy. Metrics can then be used to predicatively model process improvement initiatives.

In many instances, far too much emphasis is spent on metrics as opposed to accuracy and investigation. In some organizations, so many metrics are measured that very little emphasis is placed on any singular data point resulting in everything being a low priority. For example, if subrogation identification is 2 percent of an annual evaluation, how much emphasis will be placed on identification and referral? To the contrary, a total quality model driven by a single quality score should result in improvements in all aspects of the claims process. If this cumulative total quality score is used to drive individual metrics, then the paradigm of the organization will change from chasing numbers to chasing results.

To emphasize accuracy, certain key milestones should be addressed during the evaluation process. At a minimum, this should include data compliance, coverage, liability, investigations, subrogation, salvage, timeliness and accuracy. Within each milestone, there should be a subset of data that is measured to determine if a file is worthy of replication, or reproduction. When this occurs, a file may be deemed to meet expectations.

It is also important to reiterate exactly what constitutes an acceptable work product and calibrate the organization so that everyone—from the executives to the rank-and-file employees—are on the same page. Doing what should be done in a file, consistently, timely and accurately, is precisely what should be defined as “meeting expectations.” Nothing more, nothing less.

To exceed expectations, one must go above and beyond the call of duty. Calling someone in seven hours versus the eight hour requirement hardly constitutes exceptional work. Rather, a person must take the initiative to think outside the box, dig deeper and farther, and turn up critical pieces of information that alter the outcome of what a standard claim investigation would have done. It can be done; it is the exception.

As I discuss in Re-Adjusted: 20 Essential Rules To Take Your Claims Organization From Ordinary to Extraordinary, using concepts to drive accuracy, investigation, metrics (AIM) can fundamentally transform any team. It will move from reactive to proactive an entire workflow that will expose inefficiencies that can be remediated, resulting in continual process improvement which will give adherents a significant competitive edge in the marketplace.

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Putting the &ldquo;AIM&rdquo; in the Claim

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