Current Procedural Terminology Codes is a numerical coding system used to bill for medical procedures and services and crucially, represents a major reimbursement pathway. Given that pathology services represent a consequential source of hospital revenue, understanding instances where codes may have been misassigned or underbilled is critical. Several algorithms have been proposed that can identify improperly billed CPT codes in existing datasets of pathology reports. Estimation of the fiscal impacts of these reports requires a coder (i.e., billing staff) to review the original reports and manually code them again. As the re-assignment of codes using machine learning algorithms can be done quickly, the bottleneck in validating these reassignments is in this manual re-coding process, which can prove cumbersome. This work documents the development of a rapidly deployable dashboard for examination of reports that the original coder may have misbilled. Our dashboard features the following main components: (1) a bar plot to show the predicted probabilities for each CPT code, (2) an interpretation plot showing how each word in the report combines to form the overall prediction, and (3) a place for the user to input the CPT code they have chosen to assign. This dashboard utilizes the algorithms developed to accurately identify CPT codes to highlight the codes missed by the original coders. In order to demonstrate the function of this web application, we recruited pathologists to utilize it to highlight reports that had codes incorrectly assigned. We expect this application to accelerate the validation of re-assigned codes through facilitating rapid review of false-positive pathology reports. In the future, we will use this technology to review thousands of past cases in order to estimate the impact of underbilling has on departmental revenue.

Revision total hip arthroplasties (THA) are time-consuming, expensive, and technically challenging. Today\'s Current Procedural Terminology (CPT) codes and relative value units (RVU) may in fact disincentivize surgeons to perform revision THAs. Our study reviewed labor and time investments for each component-specific revision THA and analyzed the gap between procedural value billed and final reimbursement.
A retrospective review of 165 primary and revision THAs were validated using operative notes and billing records. We stratified revision THAs by standard CPT coding (with modifiers) as single acetabular component, single femoral component, femoral head plus polyethylene liner (head/liner) exchange, all-components, and spacer placement for infection. Operative time, RVUs, total charges, deductions, and final reimbursement data was collected. Mann-Whitney U tests studied final reimbursement per minute vs per RVU in revision and primary THAs.
Our cohort consisted of 27 primary THAs, 26 acetabular component revisions, 32 head/liner exchanges, 26 femoral component revisions, 27 all-component revisions, and 27 spacer placements. Compared to primary THAs, every revision subgroup except for head/liner exchanges were found to reimburse less per minute and all revision subgroups reimbursed less per RVU (P < .05).
Physicians face less reimbursement per minute and per RVU for revision THAs. With cuts in reimbursement set forth by Centers for Medicare and Medicaid Services (CMS) and insurers, revisions may be financially unfavorable. This ultimately will lead to an impending access to care problem in the future. Our study supports the need to re-examine the RVU allocation amongst revision THAs and evaluate changes to the Current Procedural Terminology (CPT) coding system.

BACKGROUND: Pathology reports serve as an auditable trial of a patient\'s clinical narrative, containing text pertaining to diagnosis, prognosis, and specimen processing. Recent works have utilized natural language processing (NLP) pipelines, which include rule-based or machine-learning analytics, to uncover textual patterns that inform clinical endpoints and biomarker information. Although deep learning methods have come to the forefront of NLP, there have been limited comparisons with the performance of other machine-learning methods in extracting key insights for the prediction of medical procedure information, which is used to inform reimbursement for pathology departments. In addition, the utility of combining and ranking information from multiple report subfields as compared with exclusively using the diagnostic field for the prediction of Current Procedural Terminology (CPT) codes and signing pathologists remains unclear.
METHODS: After preprocessing pathology reports, we utilized advanced topic modeling to identify topics that characterize a cohort of 93,039 pathology reports at the Dartmouth-Hitchcock Department of Pathology and Laboratory Medicine (DPLM). We separately compared XGBoost, SVM, and BERT (Bidirectional Encoder Representation from Transformers) methodologies for the prediction of primary CPT codes (CPT 88302, 88304, 88305, 88307, 88309) as well as 38 ancillary CPT codes, using both the diagnostic text alone and text from all subfields. We performed similar analyses for characterizing text from a group of the 20 pathologists with the most pathology report sign-outs. Finally, we uncovered important report subcomponents by using model explanation techniques.
RESULTS: We identified 20 topics that pertained to diagnostic and procedural information. Operating on diagnostic text alone, BERT outperformed XGBoost for the prediction of primary CPT codes. When utilizing all report subfields, XGBoost outperformed BERT for the prediction of primary CPT codes. Utilizing additional subfields of the pathology report increased prediction accuracy across ancillary CPT codes, and performance gains for using additional report subfields were high for the XGBoost model for primary CPT codes. Misclassifications of CPT codes were between codes of a similar complexity, and misclassifications between pathologists were subspecialty related.
CONCLUSIONS: Our approach generated CPT code predictions with an accuracy that was higher than previously reported. Although diagnostic text is an important source of information, additional insights may be extracted from other report subfields. Although BERT approaches performed comparably to the XGBoost approaches, they may lend valuable information to pipelines that combine image, text, and -omics information. Future resource-saving opportunities exist to help hospitals detect mis-billing, standardize report text, and estimate productivity metrics that pertain to pathologist compensation (RVUs).

Methods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT-RVU combined. Performance of each model was assessed by inference time, MAE, and RMSE compared to the actual case length on a test set. Models were compared to each other and to the manual scheduler method that currently exists. RMSE for the RVU model (60.8 min) was similar to the CPT model (61.9 min), both of which were lower than scheduler (90.2 min). 65.2% of our RVU model\'s predictions (compared to 43.2% from the current human scheduler method) fell within 20% of actual case time. Using RVUs reduced model prediction time by ninefold and reduced the number of training features from 485 to 44. Replacing pre-operative CPT codes with RVUs maintains model performance while decreasing overall model complexity in the prediction of surgical case length.

The purpose of this study is to determine whether orthopedic resident and fellow case logs accurately reflect trainee case volume.
For each orthopedic case performed at our institution between 7/1/14 and 10/31/14, the names of trainees who participated were obtained from the chart. The trainee Accreditation Council for Graduate Medical Education case logs were queried to determine if the procedure in question was logged and, if so, which current procedural terminology (CPT) codes were reported. The CPT codes reported by the trainees were compared to those reported by the attendings in the billing database. To ascertain the opinions of trainees regarding coding, a survey was conducted.
University of Maryland Medical Center (Baltimore, MD), a tertiary and quaternary care center which features a state-wide trauma referral center as well as orthopedic residency and fellowship training programs.
All orthopedic surgery residents and fellows present at the institution during the study period.
Trainees failed to log their cases 24% of the time (465/1925), including 25% (283/1117) for residents and 23% (182/808) for fellows (p = 0.16). Among cases that were logged, CPT codes were missed 46% of the time (673/1460) and extra codes were added 28% of the time (412/1460) compared to the attendings. In the survey, most trainees stated that it was \"extremely\" or \"very\" important for them to be able to code correctly (83%; 29/35).
In this study of orthopedic trainee case logging practices, cases were not logged 24% of the time. Caution should be taken with activities which rely on trainee case logs given the potential for inaccuracy.

BACKGROUND: The Accreditation Council for Graduate Medical Education (ACGME) case log captures resident operative experience based on Current Procedural Terminology (CPT) codes and is used to track operative experience during residency. With increasing emphasis on resident operative experiences, coding is more important than ever. It has been shown in other surgical specialties at similar institutions that the residents\' ACGME case log may not accurately reflect their operative experience. What barriers may influence this remains unclear. As the only objective measure of resident operative experience, an accurate case log is paramount in representing one\'s operative experience. This study aims to determine the accuracy of procedural coding by general surgical residents at a single institution.
METHODS: Data were collected from 2 consecutive graduating classes of surgical residents\' ACGME case logs from 2008 to 2014. A total of 5799 entries from 7 residents were collected. The CPT codes entered by residents were compared to departmental billing records submitted by the attending surgeon for each procedure. Assigned CPT codes by institutional American Academy of Professional Coders certified abstract coders were considered the \"gold standard.\" A total of 4356 (75.12%) of 5799 entries were identified in billing records. Excel 2010 and SAS 9.3 were used for analysis. In the event of multiple codes for the same patient, any match between resident codes and billing record codes was considered a \"correct\" entry. A 4-question survey was distributed to all current general surgical residents at our institution for feedback on coding habits, limitations to accurate coding, and opinions on ACGME case log representation of their operative experience.
RESULTS: All 7 residents had a low percentage of correctly entered CPT codes. The overall accuracy proportion for all residents was 52.82% (range: 43.32%-60.07%). Only 1 resident showed significant improvement in accuracy during his/her training (p = 0.0043). The survey response rate was 100%. Survey results indicated that inability to find the precise code within the ACGME search interface and unfamiliarity with available CPT codes were by far the most common perceived barriers to accuracy. Survey results also indicated that most residents (74%) believe that they code accurately most of the time and agree that their case log would accurately represent their operative experience (66.6%).
CONCLUSIONS: This is the first study to evaluate correctness of residents\' ACGME case logs in general surgery. The degree of inaccuracy found here necessitates further investigation into the etiology of these discrepancies. Instruction on coding practices should also benefit the residents after graduation. Optimizing communication among attendings and residents, improving ACGME coding search interface, and implementing consistent coding practices could improve accuracy giving a more realistic view of residents\' operative experience.

We evaluated the operative notes for justification on the use of the 22-modifier in ankle fracture cases and compared the differences in physician billing and reimbursement. A total of 265 patients who had undergone operative management of isolated ankle fractures across a 10-year period were identified at a level I trauma center through a retrospective chart review. Of the 265 patients, 61 (23.0%) had been billed with the 22-modifier. The radiographs were reviewed by 3 surgeons to determine the complexity of the case. The amount of the professional fees and payments was obtained from the financial services department. Operative reports were reviewed for inclusion of eight 22-modifier criteria and word count. Mann-Whitney U tests of means were used to compare cases with and without the 22-modifier. From our analysis of preoperative radiographs, 37 (60%) showed evidence of a significantly complex fracture that justified the use of the 22-modifier. A review of the operative reports showed that 42 (68%) did not identify 2 or more reasons for requesting the 22-modifier in the report. Overall, the 22-modifier cases were not always reimbursed significantly greater amounts than the nonmodifier cases. No significant difference in the average word count of the operative notes was found. We have concluded that orthopedic trauma surgeons do not appropriately justify the use of the 22-modifier within their operative report. Further education on modifiers and the use of the operative report as billing documentation is required to ensure surgeons are adequately reimbursed for difficult trauma cases.

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OBJECTIVE: To investigate contemporary trends in the use of midurethral sling procedures for the surgical correction of female stress urinary incontinence over the past decade.
METHODS: Annualized case log data for female incontinence surgeries from certifying and recertifying urologists were obtained from the American Board of Urology. Descriptive analysis of the number and type of cases per year was performed. Associations between surgeon characteristics and the use of female incontinence procedures were evaluated.
RESULTS: A total of 6355 nonpediatric urologists applied for certification or recertification between 2003 and 2012. Two-thirds (4185) reported performing any procedures for female incontinence. Procedures sharply increased from 4632 in 2003 to 7548 in 2004, then remained relatively stable between 2005 and 2012 (range, 8014-10,238 cases). Traditional procedures decreased from 17% of female incontinence procedures in 2003 to 5% in 2004 to <1% since 2010 (P <.0005). Midurethral sling procedures have risen sharply from 3210 procedures in 2003 to 7200 in 2012 (P <.0005). Endoscopic injection treatments have remained stable.
CONCLUSIONS: Midurethral slings have been widely adopted by urologists over the last decade. Increase in sling usage coincided with a drastic decline in traditional repairs, implying that the newer midurethral slings were replacing these traditional procedures for the treatment of female incontinence. In addition, the fact that the use of periurethral injections did not change significantly during this time period indicates that increased sling usage is responsible for most of the decline in traditional repairs.

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