Missed Surgical Intensive Care Unit Billing: Potential Financial Impact of 24/7 Faculty Presence

Hendershot, Kimberly M.; Bollins, John P.; Armen, Scott B.; Thomas, Yalaunda M.; Steinberg, Steven M.; Cook, Charles H.

doi:10.1097/ta.0b013e3181a5e7fd

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In addition, being aware of the most common or most commonly associated diseases could prompt more accurate coding and ensure correct medical billing within a hospital system by suggesting missed common diagnoses to providers. Avoiding missed diagnoses to increase billing in ICU settings has the potential to substantially increase hospital income (Hendershot et al, 2009 ).…”

Section: Discussionmentioning

confidence: 99%

Comorbidity network analysis using graphical models for electronic health records

Zhao,

Huepenbecker,

Zhu

et al. 2023

Front. Big Data

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ImportanceThe comorbidity network represents multiple diseases and their relationships in a graph. Understanding comorbidity networks among critical care unit (CCU) patients can help doctors diagnose patients faster, minimize missed diagnoses, and potentially decrease morbidity and mortality.ObjectiveThe main objective of this study was to identify the comorbidity network among CCU patients using a novel application of a machine learning method (graphical modeling method). The second objective was to compare the machine learning method with a traditional pairwise method in simulation.MethodThis cross-sectional study used CCU patients' data from Medical Information Mart for the Intensive Care-3 (MIMIC-3) dataset, an electronic health record (EHR) of patients with CCU hospitalizations within Beth Israel Deaconess Hospital from 2001 to 2012. A machine learning method (graphical modeling method) was applied to identify the comorbidity network of 654 diagnosis categories among 46,511 patients.ResultsOut of the 654 diagnosis categories, the graphical modeling method identified a comorbidity network of 2,806 associations in 510 diagnosis categories. Two medical professionals reviewed the comorbidity network and confirmed that the associations were consistent with current medical understanding. Moreover, the strongest association in our network was between “poisoning by psychotropic agents” and “accidental poisoning by tranquilizers” (logOR 8.16), and the most connected diagnosis was “disorders of fluid, electrolyte, and acid–base balance” (63 associated diagnosis categories). Our method outperformed traditional pairwise comorbidity network methods in simulation studies. Some strongest associations between diagnosis categories were also identified, for example, “diagnoses of mitral and aortic valve” and “other rheumatic heart disease” (logOR: 5.15). Furthermore, our method identified diagnosis categories that were connected with most other diagnosis categories, for example, “disorders of fluid, electrolyte, and acid–base balance” was associated with 63 other diagnosis categories. Additionally, using a data-driven approach, our method partitioned the diagnosis categories into 14 modularity classes.Conclusion and relevanceOur graphical modeling method inferred a logical comorbidity network whose associations were consistent with current medical understanding and outperformed traditional network methods in simulation. Our comorbidity network method can potentially assist CCU doctors in diagnosing patients faster and minimizing missed diagnoses.

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Section: Discussionmentioning

confidence: 99%

Comorbidity network analysis using graphical models for electronic health records

Zhao,

Huepenbecker,

Zhu

et al. 2023

Front. Big Data

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“…Of note, their study did not include critical care or emergency department services. Hendershot et al 9 reported that missed surgical ICU billing could be identified with an electronic documentation and billing process. Although their study hypothesized that missed night and weekend billings might justify everyday, 24-hour in-hospital attending physician coverage of the ICU, their financial analysis demonstrated that capture of these charges would be insufficient to cover the cost of adding in-house intensivist coverage.…”

Section: Commentmentioning

confidence: 99%

Optimizing Advanced Practitioner Charge Capture in High-Acuity Surgical Intensive Care Units

Butler

Calabrese²,

Tandon³

et al. 2011

Arch Surg

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To determine the impact of standardized critical care documentation tools on charge capture by intensive care unit (ICU) advanced practitioners (APs). Design: Prospective charge capture analysis of AP critical care charges (Current Procedural Terminology codes 99291 or 99292). Setting: Neurosurgical, general surgical, and cardiothoracic ICUs in a level I, 800-bed hospital. The AP provider to patient ratio was 1:6, with 24-hour surgical intensivist oversight. Participants: Advanced practice registered nurses and physician assistants in the ICU. Interventions: Standardized templates were developed to simplify documentation and optimize billing of critical care. All APs participated in comprehensive educational sessions on billing compliance and documentation. Main Outcome Measures: Charge capture was collected for 3 years, and comparisons were made between the first quarter before (fiscal year [FY] 2008), during (FY 2009) and after (FY 2010) implementation. The number of ICU patient-days, length of stay, and of beds was collected. Results: During the implementation/education phase (FY 2009), there were no differences in charge capture compared with FY 2008. Each unit demonstrated an increase in charge capture after implementation, and an overall increase of 48% for all 3 ICUs was seen. The number of admissions and length of stay were not statistically different. The total number of ICU beds increased from 42 to 45 during the evaluation period. The salary offset for APs increased from 62% to 80%. Conclusions: Advanced practitioners represent an important component of the critical care services provided to patients in high-acuity surgical ICUs. Standardized critical care documentation and comprehensive education on evaluation and management guidelines significantly increased charge capture.

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Implementation of 24/7 intensivist presence in the SICU

Wilden

Schmidt

Chang

et al. 2013

Journal of Trauma and Acute Care Surgery

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Missed Surgical Intensive Care Unit Billing: Potential Financial Impact of 24/7 Faculty Presence

Cited by 3 publications

References 12 publications

Comorbidity network analysis using graphical models for electronic health records

Comorbidity network analysis using graphical models for electronic health records

Optimizing Advanced Practitioner Charge Capture in High-Acuity Surgical Intensive Care Units

Implementation of 24/7 intensivist presence in the SICU

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