Top 3 Workflows That Every Clinic Must Automate in 2025

#3 Medical Billing and Coding

The paradigm within which medical billing and coding operate, allocating codes to diagnoses, submitting claims, and tracking reimbursements, is way more intricate than it appears. It carries over to a dense web of payer rules, historical claim patterns, edit logic, and constantly evolving audit triggers. The challenges in this space rarely come from blatant errors, and significantly from subtle omissions, mismatched data, or deeply embedded rules that no human can feasibly monitor minute-by-minute. 

But automation can accurately fill in the blanks, identifying revenue opportunities, tagging compliance uncertainties, and refining claims to withstand layers of invisible scrutiny. Here’s how it plays out in complex medical and billing scenarios:

• In global periods and surgical bundling, when a procedure is performed, follow-up visits that happen within a set timeframe are generally included in the original payment, unless something completely unrelated happens. Automation reviews clinical notes, matches them to the procedure type, checks global period rules, and looks at anatomical modifiers to decide if the follow-up care should be billed separately.
• Medical necessity rules differ at the payer-ID level. One commercial payer may cover a colonoscopy based on certain indications, while another may deny it unless the wording is exact. Automation handles these background differences and labels or adjusts claims that may not align with current policies.
• Inpatient status often shifts after the fact. A patient may be under observation at first and then reclassified as inpatient after 24 hours. Without automation tracking those status timestamps, the billing system may submit the wrong claim type, which can lead to denials or repayment requests.
• Concurrent conditions influence payment levels. A patient with advanced dementia and a urinary tract infection could qualify for a higher Evaluation and Management level, but only if the chart supports decision complexity, risk, and caregiver involvement. Automation checks if all three are present or if the level might be downgraded during audit.
• Modifier order matters for payment. The way modifiers like 25, 59, and 91 are arranged can affect how a claim is handled. Automation applies payer-specific rules to make sure the modifier sequence fits the service.
• Device-based procedures are constantly denied when supply codes are missing. Implanting a cardiac loop recorder without listing the device code or linking it properly to the procedure CPT can cause rejection. Automation scans across related billing sections and fills in the required HCPCS or supply codes to prevent this.
• Claim history can trigger automatic audits. When a provider bills level 5 visits more frequently than others in their specialty, payers often move them into prepayment review without notice. Automation tracks these patterns and alerts coders when a chart is approaching a statistical red flag.

In a realm where accuracy depends as much on timing and terminology as on clinical truth, automation serves as strategic infrastructure, always checking, always adapting, and always one step ahead of hushed denials.

How do technologies endorse this automation?

Much like other domains, in the medical billing and coding setting too, the automation relies on a blend of deep learning models, billing rule engines, and NLP trained on millions of annotated medical notes.

Key capabilities include:

• Secured Integration with EHRs: Automated systems connect to clinical platforms using encrypted APIs. This gives the engine access to structured and unstructured chart data without slowing the documentation workflow.
• Natural Language Processing (NLP): Instead of relying on keyword triggers, the system reads clinical notes using NLP trained specifically on annotated medical texts. It extracts intent, context, and temporal sequences (e.g., onset of symptoms vs. treatment dates) to inform code generation.
• Custom Rule Engines: These engines store payer-specific logic, down to individual plan-level edits. When a claim is being built, the system instantly applies these rule sets, handling bundling logic, required modifiers, and medical necessity checks.
• Real-Time Evaluation Models: The engine includes decision trees and probabilistic models that assess coding appropriateness based on prior authorization policies, frequency limitations per CPT, comorbidity interactions (e.g., diabetes + neuropathy), and surgical global periods.
• Feedback Loop from Claim Outcomes: Denials, partial payments, and payer feedback are fed back into the model. Over time, the system ‘learns’ which language patterns or documentation gaps commonly lead to rejections, and highlights them proactively.

In advanced setups, these platforms also support:

• Predictive documentation prompts for providers (e.g., “Include time spent counseling to support 99215”)
• Intelligent upcoding prevention based on peer billing benchmarks
• Concurrent alerts when payer policies silently update (without public release)

What happens to the freed-up staff time?

As technology automates more of the code assignment and denial prevention work, the role of human coders and billing teams shift to oversight, strategy, and high-complexity case handling, areas where context and experience matter most. So:

• Coders are becoming case analysts. They no longer spend time selecting base codes. Instead, they audit escalated cases, verify complex combinations, and sign off on automation suggestions involving multiple specialties or overlapping services.
• Billers focus on upstream prevention. Rather than chasing denials, billers identify payer trends, frequent rejection reasons, and systemic bottlenecks in specific departments or with certain providers.
• Compliance becomes proactive. Teams now participate in provider training, helping clinicians phrase documentation in ways that support clean claim generation without overdocumenting.
• Data becomes part of daily work. Coders and billing managers rely on dashboards showing real-time denial causes, outlining billing behavior by specialty, missed revenue opportunities by service line, and risk of payer audits based on volume and code types.
• Collaboration expands. CDI staff, coders, and billing specialists work in cross-functional cycles, reviewing charts before submission, not after denial. The goal is preemptive correction.