Will AI Supersede Medical Coding Careers? Debunking 9 Misconceptions through an In-depth Case Analysis
Tags: naturallanguageprocessin, nlp, machinelearning
Published: 2023-09-14 09:46:44


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Is the Reign of Artificial Intelligence Over Medical Coders Imminent?
In the labyrinthine corridors of the healthcare sector, where an astronomical number of medical documents circulate, the medical coders stand as the linchpin, ensuring meticulous documentation and coding. Yet, the burgeoning advancements in Artificial Intelligence (AI) technology have sown seeds of apprehension, fostering debates on the potential eclipse of medical coding vocations by AI. This discourse seeks to unravel the enigma, delving deep to ascertain if AI stands as a contender or a collaborator augmenting the prowess of seasoned medical coders.

Before we embark on this exploration, let us elucidate:

The essence of Computer-Assisted Coding (CAC)


**The modus operandi of CAC**
**Unveiling the Essence of Computer-Assisted Coding**
CAC, a brainchild of AI and Natural Language Processing (NLP), emerges as a potent tool automating the intricate process of medical coding. Within the healthcare cosmos, coding translates to the metamorphosis of clinical narratives, including physician annotations and medical reports, into a standardized lexicon for billing and reporting undertakings. These codes encapsulate diagnoses, procedures, and pertinent data. Below, we present a prototype of a medical record delineating a patient's multifaceted condition:

CAC systems stand as the vanguard, enhancing the precision, alacrity, and uniformity of the coding trajectory. By relegating the coding task to automation, CAC mitigates human discrepancies, fine-tunes the billing cycle, and guarantees rightful reimbursement for healthcare facilitators.

**Decoding the Modus Operandi of Computer-Assisted Coding
CAC operates on the bedrock of algorithms and NLP, scrutinizing clinical manuscripts to autonomously allocate fitting codes derived from the document's content. Here, we dissect the procedure into its fundamental phases:

**Input:** The genesis involves feeding clinical documents, like Electronic Health Records (EHRs), into the CAC ecosystem.
Analysis: Leveraging NLP, the system delves into the textual elements, extracting salient details while discerning medical terminologies and their contextual usage.
Code Allocation: Post-analysis, the system autonomously earmarks appropriate codes to the discerned diagnoses, procedures, and pertinent details.

**Review:** Subsequent to initial code allocation, a human coder undertakes the mantle to validate the system-generated codes, fine-tuning them to uphold the coding process's integrity.
Integration: The authenticated codes find their way into the healthcare provider's billing infrastructure, facilitating bill and claim generation for reimbursement.

**Feedback Loop:** A significant number of CAC systems harbor a feedback mechanism, evolving through the corrections imparted by human coders, thereby refining its proficiency over time.
By commandeering the initial code allocation to automation, CAC systems curtail the time and labor invested in coding clinical documents, simultaneously elevating the accuracy and consistency of the process.

To furnish a holistic analysis, we shall scrutinize the adeptness of our avant-garde NLP instrument, Emedlogix, in fulfilling the exigencies of medical coding.

1. **Proficient Management of PDF Medical Records**
Medical archives predominantly reside in PDF format, necessitating coders to navigate through diverse sections meticulously. The Coding Productivity Benchmarks accentuate the imperative of gauging recurrent activities such as chart scrutiny, claim coding, claim submission, and denial appeals.

Coders in medical practice exhibit divergent productivity rates across specialties. For instance, orthopedic and pain management coders pinnacle with a daily average of 94 and 93 claims coded respectively, while counterparts in otolaryngology, urology, and gastroenterology record lower averages.

**Can AI Decipher Scanned Medical Records?
**On average, a medical dossier encompasses 20 to 100 pages, albeit some extend to a staggering thousand pages. Manual coding demands coders to scrutinize records for:

Legitimate medical record encompassing various facets including History of Present Illness (HPI), reason for visit, familial and social history, LAB, system review, physical examination, provider's authentic signature, and Date of Service (DOS).
Medical record frameworks - SOAP (Subjective, Objective, Assessment, Plan), CHEDDAR format.

This monumental task beckons a Herculean effort from medical coders. Our tool, wielding AI, ML, and NLP, extracts conditions with unerring accuracy. Collaborating with the Emedlogix NLP tool, medical coders can process up to 250 charts in an 8-hour shift, alleviating the burden on them.

2. **Augmented Precision in Seizing ICD-10 Codes**
Erroneous coding of familial conditions, like overlooking an ICD-10 code for diabetes mellitus (Z833) or colon cancer (Z800), can detrimentally affect prospective risk adjustment and Medicare reimbursement.

As depicted below, omitting certain conditions in medical records culminates in a diminished RAF. Emedlogix NLP, fortified through extensive training, guarantees the precise identification and population of pertinent ICD codes, empowering coders to cross-verify charts, thereby minimizing errors and maximizing reimbursements.

3. **BMI Computation for Exact Risk Adjustment**
BMI (Body Mass Index) holds a pivotal role in risk adjustment, where deviations from established thresholds can influence the RAF (Risk Adjustment Factor) score. Emedlogix NLP excels in extracting BMI data, even from low-resolution PDF charts, ensuring meticulous risk adjustment calculations.

4. **Swift Extraction of Social History Data**
Extracting social history data, such as substance dependency, from medical records is a labor-intensive task, especially when sifting through voluminous documents. [Emedlogix NLP](https://www.emedlogix.com/) facilitates the rapid analysis of a 60-page medical record, presenting relevant social history codes within a span of 60 to 90 seconds, enhancing efficiency and reducing the workload on coders.

5. **Precise Documentation of Previous Surgical Histories**
Overlooking past surgical histories can result in diminished provider premiums. Emedlogix leverages its rules engine to swiftly identify and extract surgical history codes from charts, ensuring accurate documentation and coding.

6. **Family History Retrieval**
Family history extraction is quintessential in healthcare settings, aiding in precise risk assessment and fostering preventive care strategies. Emedlogix NLP tool facilitates instantaneous retrieval of family history data, even from extensive charts, enhancing diagnostic accuracy and facilitating proactive healthcare.

7. **Abreast with Code Updates**
Medical coding necessitates up-to-date code sets. [Emedlogix NLP](https://www.emedlogix.com/emedlogixnlp) streamlines the process of staying updated with code alterations, allowing coders to concentrate on their primary duties.

8. **Integrated Search Utility**

Recognizing the reliance of coders on online coding tools, [Emedlogix](https://www.emedlogix.com/why-emedlogix) provides its own search tool. Coders can easily search for codes or descriptions within the tool itself, eliminating the need to browse external resources. 

9. **Addressing Diverse Chart Quality**

The quality of Electronic Health Record (EHR) medical charts can vary, and handwritten charts pose additional challenges for coders. 

Emedlogix NLP is equipped with trained models and Optical Character Recognition (OCR) capabilities that can accurately process charts of different quality levels, including handwritten notes.