Define a single, concrete business problem to solve, such as cutting denials for a specific payer segment or reducing days in AR for molecular claims. Write the objective so it is clear, financially meaningful, and time bound, and get leadership agreement before moving on.
Document how work actually happens from order to payment, not how you think it happens. Capture handoffs, systems, edits, and decision points across intake, LIS, billing, clearinghouse, and payer touchpoints. Pull baseline metrics for denial categories, first pass clean claim rate, days in AR, and write-off patterns so you can quantify gaps later.
Translate the objective into measurable targets that reflect financial impact. Set explicit thresholds for metrics such as first pass clean claim rate, denial rate by root cause, or AR aging distribution by payer. Align targets with budget, staffing, and technology constraints so they are aggressive yet achievable.
Drill into the data to isolate the few issues that create most of the loss. Segment by payer, CPT, ICD-10, test type, site, and submitter to see where defects cluster. Validate hypotheses with chart reviews, workflow observations, and claim tracing so you separate symptoms from true causes.
Convert each root cause into a specific corrective action with an owner, start date, and deadline. Define how success will be measured, how data will be captured, and what policy, form, or system change is required. Include change management items such as training, quick reference guides, and audit criteria so adoption sticks.
Pilot changes in a limited scope first, then scale once results are proven. Track leading indicators weekly, such as eligibility pass rates or clean claim yield, and track lagging indicators monthly, such as denial reductions and cash acceleration. Review progress with leadership on a fixed cadence and remove roadblocks quickly.
Visualize the problem and its drivers so teams align priorities fast. Use a fishbone diagram to group causes under categories like People, Process, Technology, Data, and Payer Policy, then link each branch to a corrective action. Refresh the graphic as you learn and keep it visible in meetings, so focus does not drift.
Identifying and addressing the root causes of denials reduces unnecessary write-offs and preserves margins.
Cleaner claims and streamlined workflows shorten days in accounts receivable and reduce reliance on credit lines.
A detailed review of coding, documentation, and billing practices helps labs avoid penalties and payer recoupments.
Reliable data supports stronger payer negotiations and informed investments in automation and staffing.
