A sharp question to start
If you could cut coding turnaround, trim denials, and free up your front desk without adding headcount, what would stop you from doing it this quarter?
Medical coding sits between care and cash flow. When coding cycles drag, visits stack up in accounts receivable, phones ring longer, and clinicians wait for authorizations instead of seeing patients. Automation changes that equation. By assigning routine codes consistently and moving clean data into billing, teams spend less time reworking claims and more time opening access for the next patient. Leaders tell me the practical win is simple, fewer bottlenecks and fewer handoffs.
Solum’s stance aligns with this operational reality. A unified inbox paired with AI intake automation keeps communications, registration, and pre visit tasks in one place, integrated with EHR and PM systems, which is exactly where clinics lose time. For a concise overview, see Solum Health, review How it works, and scan the solutions pages that support outpatient workflows.
Medical coding automation is the use of artificial intelligence that includes natural language processing and machine learning to translate clinical documentation into standardized codes, such as ICD 10 CM and CPT, and in many cases HCPCS as well. The software reads encounter notes, recognizes clinical terms and context, applies rules, and proposes or assigns codes. Humans keep control, they review edge cases and confirm final selections. The promise is consistency at scale with a log of every decision.
For clinics that already centralize messages, intake, and authorizations, the extension to coding is logical. Related concepts show the same pattern, clean connections, not extra steps. See EHR inbox integration, patient intake, Fax to EHR integration, and HIPAA compliant texting.
Most platforms follow a similar flow. The steps below mirror the core capabilities and the oversight that administrators expect.
Accuracy depends on documentation quality. If a note is vague, the output will be tentative. The best results come from strong intake and clinician prompts, which is one reason a clinic that tightens pre visit workflows often sees coding improve as a byproduct. For core privacy concepts that inform this pipeline, see PHI and confirm the Minimum Necessary Standard.
You do not need a big rollout. Start with a single, well defined scope that your team can validate.
To understand how intake and authorizations set the table for this work, review the sequencing in How it works and confirm related steps inside solutions.
Four patterns show up often. First, weak documentation, if note templates are inconsistent, the model spends more time guessing and reviewers spend more time fixing. Second, unclear review rules, when people do not know who approves what, queues stall and staff blame the tool. Third, poor integration discipline, if data mapping is loose, codes can land in the wrong fields and auditors will find it later. Fourth, unrealistic expectations, automation handles routine work, people still manage nuance, policy changes, and clinical context. Recognize the division of labor early and adoption will go smoother.
No, it supports coders. Routine charts move faster, complex or ambiguous charts still require human judgment. Oversight remains a core control.
With strong documentation and regular review, tools can achieve high accuracy and consistent application of rules. Accuracy improves when teams validate edge cases and feed those edits back into the system.
Modern platforms integrate with electronic health records and practice management systems. The benefit is elimination of duplicate entry and preservation of context across systems.
Reputable vendors design for HIPAA compliance, which includes encryption in transit and at rest, access controls, and audit logs. Your policies must still enforce the Minimum Necessary Standard.
ROI depends on volume and payer mix. Leaders track fewer denials, faster coding cycles, and reduced manual hours. Savings often fund other improvements, such as expanding intake automation.
If you are ready to test the waters, choose a narrow visit type, connect read only data, and run a two week pilot with human review. Document acceptance rules, measure edit rates and claim speed, and adjust clinician prompts where documentation falls short. When the edit rate is low and stable, expand in careful increments. Keep a simple dashboard that counts throughput, denial reasons, and reviewer time, and revisit it every month. If your operation also needs cleaner communications and pre visit flow, align the work with your unified inbox and AI intake plan using How it works and confirm scope in solutions.
