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Abstract
Precise medical billing is essential for decreasing hospital liability, upholding environmental stewardship and ensuring fair costs for patients. We instituted a multifaceted approach to improve the billing accuracy of our robotic-assisted thoracic surgery programme by including an educational component, updating procedure cards and removing the auto-populating function of our electronic medical record. Overall, we saw significant improvements in both the number of inaccurate billing cases and, specifically, the number of cases that overcharged patients.
- Quality improvement
- Surgery
- Data Accuracy
- Electronic Health Records
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Background
Accurate billing for procedures is necessary as inaccuracies can lead to unnecessary financial hardship to patients and potential legal consequences for the hospital.1–3 The use of robot-assisted surgery (RAS) in thoracic surgery has become increasingly prevalent due to its benefits; however, the complexity of RAS and the associated instrumentation makes it challenging to accurately document these procedures.4 5 Nevertheless, with the widespread adoption of electronic medical records (EMR), there are abundant opportunities to enhance the precise cataloguing of instruments used.
When reviewing thoracic cases, several surgeons and team members observed instances where specific instruments were logged as used in procedures where they would not typically be employed. Frequently, these instruments were unpacked in preparation for cases but were never used, resulting in unnecessary waste. In response to this issue, we initiated a comprehensive team approach.
Methods
Baseline data collection
The study focused on RAS in thoracic cases (lung, benign oesophageal and mediastinal) at a single academic hospital. Retrospective data collection occurred during the pre-intervention interval of six consecutive months (January to June) in 2021 to ensure an adequate number of cases. At least 20 cases of each subgroup were selected to be included in the pre-intervention. The documented instrument use was collected from the operative section of the EMR.
Pre-intervention
At pre-intervention, billing for RAS in thoracic procedures was managed through a hybrid approach involving both the EMR’s auto-population feature and manual entry by the operating room (OR) staff. The EMR was programmed to pull up a list of instruments based on a predetermined preference list, aiming to simplify the documentation and billing processes. During the preparation of the OR for surgery, staff members referred to this preference list to ensure the necessary instruments were opened and ready on the sterile field before the surgery was started. As instruments were unpacked, the barcodes on their packaging were scanned for billing purposes. Any additional instruments required and opened intraoperatively were scanned into the EMR. To prepare the medical bill, the list of supplies that were scanned into the EMR were used to calculate the cost of the surgery.
Intervention
After gathering pre-intervention data, the documented instrument use was compared with the internal data collected by the da Vinci surgical system (Intuitive Surgical, Sunnyvale, California) that showed what instruments were used by the da Vinci robot. Upon insertion of an instrument into a robotic arm, data are captured and stored within the manufacturer’s system. A root cause analysis determined that unnecessary instruments were often added when the EMR auto-populated the instruments/charges from the preference list. The project team developed and implemented updated procedure cards for RAS procedures, which included procedure-specific instrument lists and removed the EMR’s auto-populating of robotic instruments into the medical record (figure 1). An educational service about accurately charting instrument use was conducted for the OR staff. Additionally, the educational component introduced a new emphasis on using procedure cards to ensure that instruments were available in the room for surgery, instead of preemptively unpacking them. This approach ensured that the instruments were both accessible and not unnecessarily used. The interventions were implemented in June 2022.
Process map for creating accurate procedure cards.
Post-intervention assessments measured the effectiveness of the intervention. Data collection occurred 3 months post-intervention was implemented to allow for an adequate adjustment period. The interval occurred over eight consecutive months (September 2022 to April 2023) to ensure that a comparable number of cases were identified. The assessments included a review of EMR and manufacturer data to identify billing inaccuracies to assess the effectiveness of the procedure cards.
Billing inaccuracies were identified when the list of instruments used on the operative side of the EMR did not match the data from the da Vinci Surgical System. An overcharge was noted if the EMR listed an instrument as being charged, despite the manufacturer data not showing its use during the procedure. Conversely, an undercharge was noted if the manufacturer data recorded an instrument as being used, despite it not being logged in the EMR. Medical billing at our institution is issued using the information entered in the EMR.
Statistical analysis
Statistical analysis of the whole and subgroup was performed. Welch’s t-test was performed to compare costs between the two groups. The study size was determined using a power analysis, which indicated that a sample size of at least 100 participants would be needed to detect a significant difference in billing accuracy with 80% power at a significance level of 0.05. Therefore, a minimum of 120 cases were targeted for inclusion to accommodate potential missing data. Fischer’s exact test was used to compare categorical variables and was selected due to the small study sample size. A p value of <0.05 was considered statistically significant.
Results
A total of 144 cases were reviewed with a similar number and type of surgeries in the pre-intervention (n=74) and post-intervention (n=70) groups (p=0.95) (table 1). The intervention resulted in a significant reduction in billing inaccuracies, with a decrease from 67.6% (50/74) pre-intervention to 37.1% (26/70) post-intervention (p<0.001). There was a higher rate of overcharges in the pre-intervention group as compared with the post-intervention group (54.1% vs 20.0%; p<0.001). In the inaccurate billing cases, the average mischarge by case was examined. The intervention decreased the average mischarges with a pre-intervention overcharge of $224.30 and a post-intervention undercharge of -$85.92 per case (p=0.009). There were two notable outliers in recent cases where no instruments were charged at all, both of which involved the same nurse who was relatively new to the practice.
Data gathered regarding billing accuracy for all cases and subgroups
Discussion
Our intervention reduced billing inaccuracies, improved documentation accuracy and lowered medical waste by preventing unnecessary instrument openings. Although the intervention reduced the rate of billing inaccuracies, it did not eliminate it as there was still a 37.1% rate of inaccurate billing post-intervention, which remains high. Observing a decrease in documentation errors just 3 months after the intervention demonstrates that the intervention will continue to have a positive impact. Long-term follow-up could provide better insight to the full impact of the intervention. The widespread use of the EMR system, accounting for up to 47% of the market, indicates that our intervention could benefit numerous hospitals and clinics using a similar system.6 Moreover, hospitals and clinics employing procedure cards and auto-populating functions may find value in implementing a similar intervention to ours.
In cases of inaccurate billing, the average mischarges per case went from overcharging to undercharging. Decreasing the rate of overcharges is particularly important as it decreases the hospital liability and practice patient-centred care.7 Overcharging can have legal and regulatory implications, exposing healthcare providers to penalties, fines and legal action.8 9 Additionally, overcharging can erode patient trust in the healthcare system and damage the patient-provider relationship.10 Patients may feel exploited or deceived, leading to dissatisfaction and a negative perception of the healthcare facility. The changes from overcharges to undercharges suggest that an active auto-populating function in the EMR increases the use of unnecessary instruments and cost for the patient as it relies on staff to recognise and remove those instruments. Removing the auto-populating function increases the likelihood of undercharging patients as it relies on the OR staff to input charges into the EMR. Staff education is a particularly important adjuvant as these post-intervention errors came from newer staff who may not have had the experience of logging the instruments used. It’s worth noting that even after our intervention, there was still a 20% overcharge rate. These cases primarily involved newer staff who had not yet undergone the educational sessions emphasising the importance of keeping supplies in the room unopened until needed, rather than preemptively unpacking them. Continued education is crucial for newly hired staff to ensure adherence to proper procedures.
Beyond removing the auto-populating function of the procedure cards, procedure cards with surgeon-specific preferences were updated. Updating procedure cards represents a simple and effective solution that could be easily replicated at other institutions and can familiarise travel nurses and newer staff members with instrument use in specific procedures and surgeon preferences.11 12 It is important, however, to update cards regularly as outdated ones may contribute towards errors during charge entry.
Healthcare institutions have an obligation to upload principles of fiscal responsibility and environmental sustainability by minimising medical waste.13 14 Our intervention reduced medical waste by limiting the opening of unnecessary instruments that were ultimately unused and discarded. While robotic instruments can be reused, unnecessarily unpacking them still incurs costs for the hospital as well. Opening an instrument without using it wastes resources and necessitates ordering replacements sooner, ultimately driving up costs for hospitals and increasing medical waste.
The limitations of this study include its single-centre design and small sample size. A larger study with multiple centres could provide more generalisable results. Another limitation is the lack of a formal continuous improvement process during our intervention, specifically in the absence of a structured plan for ongoing modifications. Thus, any implementations of these changes should include a process monitoring plan to ensure that any benchmark effectiveness can be maintained over time. Future projects like this could benefit from incorporating continuous refinement to cultivate a culture of ongoing learning and improvement in healthcare practices. Another limitation of the analysis is the absence of a control group, which could have helped account for confounding factors. The observed improvements in billing accuracy could be influenced by factors such as varying procedure rates over time or the transition from the late COVID-19 pandemic to post-pandemic conditions. It is important to acknowledge that the improvements observed in this study may have been influenced by a general trend of process improvement over time, representing a potential limitation. However, considering the challenges posed by staff turnover and the influx of new personnel, relying solely on natural improvement for enhancing billing accuracy may not be sufficient. Therefore, there is a compelling argument for implementing the intervention as a proactive measure to address these challenges and enhance efficiency and accuracy in the OR. Future studies should incorporate a control group to adjust for these potential confounders. Our study demonstrates short-term benefits that are worth exploring further.
Integrating the da Vinci system’s internal data with the EMR for billing purposes could improve accuracy and efficiency, but this integration is hindered by various technical, regulatory and practical challenges. Data privacy and security concerns prevent the da Vinci system from directly interfacing with the EMR. Additionally, compatibility issues and differing data formats between the two systems complicate direct integration. Moreover, due to manufacturer policies, accessing the data is not easily available and is only possible after the initiation of our quality improvement project. Therefore, the direct use of the da Vinci system for billing purposes may not be feasible in most hospitals and clinics, underscoring the potential value of our intervention as a more general solution.
Overall, this quality improvement project demonstrates the importance of continuously updating standardised documentation to ensure efficient healthcare delivery and proper reimbursement. The use of updated procedure cards in RAS can significantly reduce billing inaccuracies and improve the accuracy of documentation, ultimately leading to better patient care and outcomes. The implementation of these changes should incorporate a continuous improvement process, as our study demonstrated that a single intervention did not eliminate all billing inaccuracies. Despite this, we observed a 30% reduction in inaccuracies during the implementation of our quality improvement project, indicating its value and warranting further pursuit. As previously mentioned, establishing a process monitoring plan is crucial to maintaining any improvements in effectiveness over time.
Ethics statements
Patient consent for publication
Footnotes
Presented at A poster presentation of this study was given at the 2022 Duke University Annual Quality Improvement Scholars Program Symposium, May 11, Durham, North Carolina, USA, and at the 2023 Joint Annual Meeting of the North Carolina and South Carolina Chapters of the American College of Surgeons, August 4-6, Asheville, North Carolina, USA.
Contributors The improvement effort was jointly designed by all authors and carried out by KB and DB. KW performed the data analysis and manuscript writing, while KB performed the data collection and contributed to editing. The figures and tables included in the manuscript were crafted by KW. Supervision of the project was done by MH and JK. All authors gave approval of the final manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.