rohan modak

Let’s answer this question taking example of healthcare claims adjudication process. HealthCare BPO Associate receives the claims from the Payer where decision has to be made to accept or reject the claim
In Claims adjudication process, AI can easily handle 90% of work – most of which is repetitive. Tasks include scan the claim form, basic validation, duplicate checks, COB checks, verifying CPT/ ICD codes, checking if any exception code has been triggered, and flagging any rule violations using configuration logic defined by Payer
However, the final decision to release the claim (approve or deny) should always rest with human examiner, especially in case if complex claims, where medical necessity is questionable and patient circumstances don’t always fit the rulebook
Such cases demand empathy and ethical judgement -reading between the lines and understanding doctors’ intent. The decision should always reflect balancing compliance with compassion. Since algorithms are trained purely on data patterns, such nuances cannot be expected to be executed by AI accurately
A practical AI Precision Assistant – Human collaboration model can be designed in this example where AI can do the heavy lifting
1.      Claim intake and basic validation can be done by AI agent using OCR+NLP models to extract and validate member, provider and service fields
2.      Rule based checks and duplicate checks which involved applying payer policies, checking if claims fall between designated coverage limits, and assessing historical claims data – all these can be handled by AI agent
3.      Predictive scoring: AI agent can rank the claims by likelihood of approval/denial and can assign confidence score to it
4.      If confidence score assigned by AI model is <85% or if patient context is not clear, then these claims will get routed to human examiner who will evaluate intent, context and fairness before making the final call
This clear demarcation allows AI to work as Precision assistant while human examiner will retain the ethical and strategic accountability.
This clear demarcation between AI Agent and Human examiner is required and human should make final decision because:
·        We are not just processing claims, but impacting person’s health and financial well being
·        Only human examiner can recognize when a rule should not override compassion
·        Only human can bring in unique blend of empathy, sound judgement and ethical awareness

I will explain with provider credentialing example in healthcare domain. In my domain,  process SLA (namely TAT and FTQ%), are single most important metrics that we cannot afford miss. Here, we face two major challenges
a.      Sudden Volume Surges: due to new launches, seasonal hiring by big provider group
b.      Policy shifts:  on account of Payer specific primary source verification changes, NCQA reverification windows  
We have to be very wary of our SLA, and cannot risk penalties so advance scenario planning is critical
 
My solution design would comprise of creating Credentialing Digital Twin - a safe sandbox to ask “what-if” and see operations react. Below are the features of this tool
1.      Demand model and mix driver: Basis past submissions, go live calendars; create time series by payer, state, specialty. Then add a classifier to simulate denial mix shifts
2.      Policy as a code engine: Encode each NCQA/payer rule as logic (for e.g.: FL licenses require PRN verification step + Address proof). We can further flip a toggle to model new requirements to see extra verification steps and expected deficiencies
3.      NLP Deficiency predictor: A small NLP model will scan attestation text along with any attachments to predict missing/invalid items (DEA, Board cert, etc.)
4.      Capacity and Skill routing: Models skill mix, learning curves and shift patterns
5.      Monte Carlo Simulation – will give outputs (Distributions for TAT, FTQ%; and FTE gap) that Operations can action on
6.      Generate Panel Readiness Risk Score
This approach is one step beyond basic forecasting, as
1.      We are simulating flow of work as policies, skills, deficiencies interact
2.      Using Policy as code feature, we can quick toggle changes proposed by payer/ NCQA which will prevent tribal debates
3.      This will prevent back and forth outreach as we now have capability of front load fixes on day 0
4.      Backlog trajectory can be defined and notified to leadership beforehand
5.      FTE delta and recommendations to redeploy or cross train can be notified to leadership
So, AI is not replacing judgement, but rather gives clear actionable plan to tackle the upcoming volume surges or policy shifts. This can turn into huge value addition for Healthcare Credentialing Shop as it can prevent costly escalations.