Coding in surgery: impact of a specialized coding proforma in hepato-pancreato-biliary surgery

Optimising vitrectomy operation note coding with machine learning

BACKGROUND

The accurate encoding of operation notes is essential for activity-based funding and workforce planning. The aim of this project was to evaluate the procedural coding accuracy of vitrectomy and to develop machine learning, natural language processing (NLP) models that may assist with this task.

METHODS

This retrospective cohort study involved vitrectomy operation notes between a 21-month period at the Royal Adelaide Hospital. Coding of procedures were based on the Medicare Benefits Schedule (MBS)-the Australian equivalent to the Current Procedural Terminology (CPT®) codes used in the United States. Manual encoding was conducted for all procedures and reviewed by two vitreoretinal consultants. XGBoost, random forest and logistic regression models were developed for classification experiments. A cost-based analysis was subsequently conducted.

RESULTS

There were a total of 1724 procedures with individual codes performed within 617 vitrectomy operation notes totalling $1 528 086.60 after manual review. A total of 1147 (66.5%) codes were missed in the original coding that amounted to $736 539.20 (48.2%). Our XGBoost model had the highest classification accuracy (94.6%) in the multi-label classification for the five most common procedures. The XGBoost model was the most successful model in identifying operation notes with two or more missing codes with an AUC of 0.87 (95% CI 0.80-0.92).

CONCLUSIONS

Machine learning has been successful in the classification of vitrectomy operation note encoding. We recommend a combined human and machine learning approach to clinical coding as automation may facilitate more accurate reimbursement and enable surgeons to prioritise higher quality clinical care.

Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics

Variations in procedure coding intensity, defined as excess coding of procedures versus industry (instead of clinical) standards, can result in differentials in quality of care for patients and have additional implications for facilities and payors. The literature regarding coding intensity of procedures is limited, with a need for risk-adjusted methods that help identify over- and under-coding using commonly available data, such as administrative claims. Risk-adjusted metrics are needed for quality control and enhancement. We propose a two-step approach to risk adjustment, using a zero-inflated Poisson model, applied to a hip-knee arthroplasty cohort discharged during 2019 (n = 313,477) for patient-level risk adjustment, and a potential additional layer for adjustment based on facility-level characteristics, when desired. A 21.41% reduction in root-mean-square error was achieved upon risk adjustment for patient-level factors alone. Furthermore, we identified facilities that over- and under-code versus industry coding expectations, adjusting for both patient-level and facility-level factors. Excess coding intensity was found to vary across multiple levels: (1) geographically across U.S. Census regional divisions; (2) temporally with marked seasonal components; (3) by facility, with some facilities largely departing from industry standards, even after adjusting for both patient- and facility-level characteristics. Our proposed method is simple to implement, generalizable, it can be used across cohorts with different sets of information available, and it is not limited by the accessibility and sparsity of electronic health records. By identifying potential over- and under-coding of procedures, quality control personnel can explore and assess internal needs for enhancements in their health delivery services and monitor subsequent quality improvements.

Surgeon involvement in clinical coding to improve data accuracy and remuneration in a shoulder and elbow unit

BACKGROUND

Clinical coders are dependent on clear data regarding diagnoses and procedures to generate an accurate representation of clinical activity and ensure appropriate remuneration is received. The accuracy of this process may potentially be improved by collaboration with the surgical team.

METHODS

Between November 2017 and November 2019, 19 meetings took place between the Senior Clinical Fellow of our tertiary Shoulder & Elbow Unit and the coding validation lead of our Trust. At each meeting, the Clinical Fellow assessed the operative note of cases in which uncertainty existed as to the most suitable clinical codes to apply and selected the codes which most accurately represented the operative intervention performed.

RESULTS

Over a 24-month period, clinical coding was reviewed in 153 cases (range 3-14 per meeting, mean 8). Following review, the clinical coding was amended in 102 (67%) of these cases. A total of £115,160 additional income was generated as a result of this process (range £1677-£15,796 per meeting, mean £6061). Only 6 out of 28 (21%) cases initially coded as arthroscopic sub-acromial decompressions were correctly coded as such.

DISCUSSION

Surgeon input into clinical coding greatly improves data quality and increases remuneration received for operative interventions performed.

Real-Time Operative Coding for Endoscopic Sinonasal Procedures: Quality Improvement in Practice

Objective: investigate the impact of an intraoperative coding sticker (ICS) on the accuracy of coding in endoscopic sinonasal procedures. Methods: this was a two-cycle audit evaluating the accuracy (and financial impact) of intraoperative coding of sinonasal procedures at a single tertiary centre. An ICS was introduced following consultation with the coding department. The accuracy of coding was measured before (cycle 1) and after (cycle 2) the ICS was introduced to a pilot firm and compared to a control firm. The ICS was used in 35% of the pilot firm cases. Results: the accuracy of clinical coding for endoscopic sinus surgery was 60% in the first cycle. Switching to the ICS has improved the accuracy in that firm from 50% in first cycle to 70% in the second cycle (p = 0.936; Chi-squared test). The median reimbursement for endoscopic sinus surgery was equal in both cycles of £1493.00 per patient. However, inaccurate coding resulted in £109.92 excess tariff payment in first cycle and £130.96 deficiency in the second cycle. Users of ICS reported it to be easy to use for clinicians, staff and clinical coders, whilst minimizing human error. Conclusions: The integration of the ICS improves the coding in sinonasal procedures and offers low-fidelity option alternative to live coding on the computer. The accuracy was not statistically significant in the study possibly due to the low number of observations. This can allow a precise coding standard with reliable service remuneration.

Clinical coding and data quality in oculoplastic procedures

INTRODUCTION

Hospitals in England are reimbursed via national tariffs set out by NHS England. The tariffs payable to hospitals are determined by the activity coded for each patient's hospital visit. There are no national standards or publications within oculoplastics for coding accuracy. Our audit aimed to determine the accuracy of coding oculoplastic procedures carried out in theatres and to assess the financial implications of any discrepancies.

METHODS

We carried out a prospective audit of consecutive oculoplastic procedures performed at one hospital site over a 6-week period. We subsequently created a coding proforma and performed a re-audit using the same methods.

RESULTS

In the first cycle, clinical coding was 'correct' in 30.7% of cases, 'incomplete' for 12.9% and 'incorrect' for 56.5%. Of the 'incorrect' codes, 54.3% were coded as non-oculoplastic procedures (e.g. extraocular muscle surgery). We discussed our findings with the coding team in order to address the sources of error. We also created a 'tick box' coding proforma, for completion by surgeons. Our re-audit results showed an improvement of 'correct' coding to 85.7%.

CONCLUSION

Clinical coding is complex and vulnerable to inaccuracy. Our audit showed a high rate of coding error, which improved following collaboration with our coding team to address the sources of error and by creating a coding proforma to improve accuracy. Accurate clinical coding has financial implications for hospital trusts and consequently Clinical Commissioning Groups. In times of severe financial pressures, this could be a valuable tool, if rolled out over all specialities, to make much needed savings.