The Impact of Under Coding of Cardiac Severity and Comorbid Diseases on the Accuracy of Hospital Report Cards

Impact of a specialty trained billing team on an academic otolaryngology practice

PURPOSE

To determine how the incorporation of specialty specific training for coders within a focused billing team affected revenue, efficiency, time to reimbursement, and physician satisfaction in an academic otolaryngology practice.

MATERIALS AND METHODS

Our academic otolaryngology department recently implemented a new billing system, which incorporated additional training in otolaryngology surgical procedures for medical coders. A mixed model analysis of variance was used to compare billing outcomes for the 6 months before and 6 months after this new approach was initiated. The following metrics were analyzed: Current Procedural Terminology codes, total charges, time between services rendered and billing submission, and time to reimbursement. A survey of department physicians assessing satisfaction with the system was reviewed.

RESULTS

There were 4087 Current Procedural Terminology codes included in the analysis. In comparing the periods before and after implementation of the new system, statistically significant decreases were found in the mean number of days to coding completion (19.3 to 12.0, respectively, p < 0.001), days to posting of charges (27.0 to 15.2, p < 0.001), days to final reimbursement (54.5 to 27.2, p < 0.001), and days to closure of form (179.2 to 76.6, p < 0.001). Physician satisfaction with communication and coder feedback increased from 36% to 64% after initiation of the new program.

CONCLUSIONS

The implementation of additional specialty training for medical coders in the otolaryngology department of a large medical system was associated with improved revenue cycle efficiency. Additionally, this model appears to improve physician satisfaction and confidence with the coding system.

Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities

Quantitative metrics are used to develop profiles of health care institutions, including hospitals, nursing homes, and dialysis clinics. These profiles serve as measures of quality of care, which are used to compare institutions and determine reimbursement, as a part of a national effort led by the Center for Medicare and Medicaid Services in the United States. However, there is some concern about how misclassification in case-mix factors, which are typically accounted for in profiling, impacts results. We evaluated the potential effect of misclassification on profiling results, using 20 744 patients from 2740 dialysis facilities in the US Renal Data System. In this case study, we compared 30-day readmission as the profiling outcome measure, using comorbidity data from either the Center for Medicare and Medicaid Services Medical Evidence Report (error-prone) or Medicare claims (more accurate). Although the regression coefficient of the error-prone covariate demonstrated notable bias in simulation, the outcome measure—standardized readmission ratio—and profiling results were quite robust; for example, correlation coefficient of 0.99 in standardized readmission ratio estimates. Thus, we conclude that misclassification on case-mix did not meaningfully impact overall profiling results. We also identified both extreme degree of case-mix factor misclassification and magnitude of between-provider variability as 2 factors that can potentially exert enough influence on profile status to move a clinic from one performance category to another (eg, normal to worse performer).

Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers

Background

The national Epithor database was initiated in 2003 in France. Fifteen years on, a quality assessment of the recorded data seemed necessary. This study examines the completeness of the data recorded in Epithor through a comparison with the French PMSI database, which is the national medico-administrative reference database. The aim of this study was to demonstrate the influence of data quality with respect to identifying 30-day mortality hospital outliers.

Methods

We used each hospital’s individual FINESS code to compare the number of pulmonary resections and deaths recorded in Epithor to the figures found in the PMSI. Centers were classified into either the good-quality data (GQD) group or the low-quality data (LQD) group. To demonstrate the influence of case-mix quality on the ranking of centers with low-quality data, we used 2 methods to estimate the standardized mortality rate (SMR). For the first (SMR1), the expected number of deaths per hospital was estimated with risk-adjustment models fitted with low-quality data. For the second (SMR2), the expected number of deaths per hospital was estimated with a linear predictor for the LQD group using the coefficients of a logistic regression model developed from the GQD group.

Results

Of the hospitals that use Epithor, 25 were classified in the GQD group and 75 in the LQD group. The 30-day mortality rate was 2.8% (n = 300) in the GQD group vs. 1.9% (n = 181) in the LQD group (P <0.0001). The between-hospital differences in SMR1 appeared substantial (interquartile range (IQR) 0–1.036), and they were even higher in SMR2 (IQR 0–1.19). SMR1 identified 7 hospitals as high-mortality outliers. SMR2 identified 4 hospitals as high-mortality outliers. Some hospitals went from non-outlier to high mortality and vice-versa. Kappa values were roughly 0.46 and indicated moderate agreement.

Conclusion

We found that most hospitals provided Epithor with high-quality data, but other hospitals needed to improve the quality of the information provided. Quality control is essential for this type of database and necessary for the unbiased adjustment of regression models.

Importance of coding co-morbidities for APR-DRG assignment: Focus on cardiovascular and respiratory diseases

BACKGROUND

The All Patient-Refined Diagnosis-Related Groups (APR-DRGs) system has adjusted the basic DRG structure by incorporating four severity of illness (SOI) levels, which are used for determining hospital payment. A comprehensive report of all relevant diagnoses, namely the patient's underlying co-morbidities, is a key factor for ensuring that SOI determination will be adequate.

OBJECTIVE

In this study, we aimed to characterise the individual impact of co-morbidities on APR-DRG classification and hospital funding in the context of respiratory and cardiovascular diseases.

METHODS

Using 6 years of coded clinical data from a nationwide Portuguese inpatient database and support vector machine (SVM) models, we simulated and explored the APR-DRG classification to understand its response to individual removal of Charlson and Elixhauser co-morbidities. We also estimated the amount of hospital payments that could have been lost when co-morbidities are under-reported.

RESULTS

In our scenario, most Charlson and Elixhauser co-morbidities did considerably influence SOI determination but had little impact on base APR-DRG assignment. The degree of influence of each co-morbidity on SOI was, however, quite specific to the base APR-DRG. Under-coding of all studied co-morbidities led to losses in hospital payments. Furthermore, our results based on the SVM models were consistent with overall APR-DRG grouping logics.

CONCLUSION AND IMPLICATIONS

Comprehensive reporting of pre-existing or newly acquired co-morbidities should be encouraged in hospitals as they have an important influence on SOI assignment and thus on hospital funding. Furthermore, we recommend that future guidelines to be used by medical coders should include specific rules concerning coding of co-morbidities.

Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture

BACKGROUND

The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records.

METHODS

We used PAS data from all Norwegian hospitals (2005-2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed.

RESULTS

The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes.

CONCLUSIONS

This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway.

Risk adjustment of ischemic stroke outcomes for comparing hospital performance: a statement for healthcare professionals from the American Heart Association/American Stroke Association

BACKGROUND AND PURPOSE

Stroke is the fourth-leading cause of death and a leading cause of long-term major disability in the United States. Measuring outcomes after stroke has important policy implications. The primary goals of this consensus statement are to (1) review statistical considerations when evaluating models that define hospital performance in providing stroke care; (2) discuss the benefits, limitations, and potential unintended consequences of using various outcome measures when evaluating the quality of ischemic stroke care at the hospital level; (3) summarize the evidence on the role of specific clinical and administrative variables, including patient preferences, in risk-adjusted models of ischemic stroke outcomes; (4) provide recommendations on the minimum list of variables that should be included in risk adjustment of ischemic stroke outcomes for comparisons of quality at the hospital level; and (5) provide recommendations for further research.

METHODS AND RESULTS

This statement gives an overview of statistical considerations for the evaluation of hospital-level outcomes after stroke and provides a systematic review of the literature for the following outcome measures for ischemic stroke at 30 days: functional outcomes, mortality, and readmissions. Data on outcomes after stroke have primarily involved studies conducted at an individual patient level rather than a hospital level. On the basis of the available information, the following factors should be included in all hospital-level risk-adjustment models: age, sex, stroke severity, comorbid conditions, and vascular risk factors. Because stroke severity is the most important prognostic factor for individual patients and appears to be a significant predictor of hospital-level performance for 30-day mortality, inclusion of a stroke severity measure in risk-adjustment models for 30-day outcome measures is recommended. Risk-adjustment models that do not include stroke severity or other recommended variables must provide comparable classification of hospital performance as models that include these variables. Stroke severity and other variables that are included in risk-adjustment models should be standardized across sites, so that their reliability and accuracy are equivalent. There is a pressing need for research in multiple areas to better identify methods and metrics to evaluate outcomes of stroke care.

CONCLUSIONS

There are a number of important methodological challenges in undertaking risk-adjusted outcome comparisons to assess the quality of stroke care in different hospitals. It is important for stakeholders to recognize these challenges and for there to be a concerted approach to improving the methods for quality assessment and improvement.

Impact of improved documentation on an academic neurosurgical practice

OBJECT

Accuracy in documenting clinical care is becoming increasingly important; it can greatly affect the success of a neurosurgery department. As patient outcomes are being more rigorously monitored, inaccurate documentation of patient variables may present a distorted picture of the severity of illness (SOI) of the patients and adversely affect observed versus expected mortality ratios and hospital reimbursement. Just as accuracy of coding is important for generating professional revenue, accuracy of documentation is important for generating technical revenue. The aim of this study was to evaluate the impact of an educational intervention on the documentation of patient comorbidities as well as its impact on quality metrics and hospital margin per case.

METHODS

All patients who were discharged from the Department of Neurosurgery of the Penn State Milton S. Hershey Medical Center between November 2009 and June 2012 were evaluated. An educational intervention to improve documentation was implemented and evaluated, and the next 16 months, starting in March 2011, were used for comparison with the previous 16 months in regard to All Patient Refined Diagnosis-Related Group (APR-DRG) weight, SOI, risk of mortality (ROM), case mix index (CMI), and margin per discharge.

RESULTS

The APR-DRG weight was corrected from 2.123 ± 0.140 to 2.514 ± 0.224; the SOI was corrected from 1.8638 ± 0.0855 to 2.154 ± 0.130; the ROM was corrected from 1.5106 ± 0.0884 to 1.801 ± 0.117; and the CMI was corrected from 2.429 ± 0.153 to 2.825 ± 0.232, and as a result the average margin per discharge improved by 42.2%. The mean values are expressed ± SD throughout.

CONCLUSIONS

A simple educational intervention can have a significant impact on documentation accuracy, quality metrics, and revenue generation in an academic neurosurgery department.

The problem of controlling for imperfectly measured confounders on dissimilar populations: a database simulation study

OBJECTIVE(S)

Observational database research frequently relies on imperfect administrative markers to determine comorbid status, and it is difficult to infer to what extent the associated misclassification impacts validity in multivariable analyses. The effect that imperfect markers of disease will have on validity in situations in which researchers attempt to match populations that have strong baseline health differences is underemphasized as a limitation in some otherwise high-quality observational studies. The present simulations were designed as a quantitative demonstration of the importance of this common and underappreciated issue.

DESIGN

Two groups of Monte Carlo simulations were performed. The first demonstrated the degree to which controlling for a series of imperfect markers of disease between different populations taking 2 hypothetically harmless drugs would lead to spurious associations between drug assignment and mortality. The second Monte Carlo simulation applied this principle to a recent study in the field of anesthesiology that purported to show increased perioperative mortality in patients taking metoprolol versus atenolol.

SETTING/PARTICIPANTS/INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Simulation 1: High type-1 error (ie, false positive findings of an independent association between drug assignment and mortality) was observed as sensitivity and specificity declined and as systematic differences in disease prevalence increased. Simulation 2: Propensity score matching across several imperfect markers was unlikely to eliminate important baseline health disparities in the referenced study.

CONCLUSIONS

In situations in which large baseline health disparities exist between populations, matching on imperfect markers of disease may result in strong bias away from the null hypothesis.

The relationship between the C-statistic of a risk-adjustment model and the accuracy of hospital report cards: a Monte Carlo Study

BACKGROUND

Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is risk-adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for risk adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards.

OBJECTIVES

To determine the relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards.

RESEARCH DESIGN

Monte Carlo simulations were used to examine this issue. We examined the influence of 3 factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for risk adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada.

RESULTS

The c-statistic of the risk-adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact.

CONCLUSIONS

The c-statistic of a risk-adjustment model should not be used to assess the accuracy of a hospital report card.

Does hospital performance on process measures directly measure high quality care or is it a marker of unmeasured care?

OBJECTIVE

Quality measures may be associated with improved outcomes for two reasons. First, measured activities may directly improve care. Second, success on these measures may be a marker for other unmeasured aspects of high quality care. Our objective is to test the contribution of both possible effects.

DATA SOURCES

2004 Medicare data on hospital performance from Hospital Compare and risk-adjusted mortality rates from Medicare Part A claims.

STUDY DESIGN

We studied 3,657 acute care U.S. hospitals and compared observed differences in condition-specific hospital mortality rates based on hospital performance with expected differences in mortality from the clinical studies underlying the measures.

PRINCIPAL FINDINGS

Differences in observed mortality rates across U.S. hospitals are larger than what would be expected if these differences were due only to the direct effects of delivering measured care.

CONCLUSIONS

Performance measures reflect care processes that both improve care directly and are also markers of elements of health care quality that are otherwise unmeasured. This finding suggests that process measures capture important information about care that is not directly measured, and that these unmeasured effects are in general larger than the measured effects.

Impact of hospital nursing care on 30-day mortality for acute medical patients

AIM

This paper reports on structures and processes of hospital care influencing 30-day mortality for acute medical patients.

BACKGROUND

Wide variation in risk-adjusted 30-day hospital mortality rates for acute medical patients indicates that hospital structures and processes of care affect patient death. Because nurses provide the majority of care to hospitalized patients, we propose that structures and processes of nursing care have an impact on patient death or survival.

METHOD

A model hypothesizing the impact of nursing-related hospital care structures and processes on 30-day mortality was tested. Patient data from the Ontario, Canada Discharge Abstract Database 2002-2003, nurse data from the Ontario Nurse Survey 2003, and hospital staffing data from the Ontario Hospital Reporting System 2002-2003 files were used to develop indicators for variables hypothesized to impact 30-day mortality. Two multiple regression models were implemented to test the model. First, all variables were forced to enter the model simultaneously. Second, backward regression was implemented.

FINDINGS

Using backward regression, 45% of variance in risk-adjusted 30-day mortality rates was explained by eight predictors. Lower 30-day mortality rates were associated with hospitals that had a higher percentage of Registered Nurse staff, a higher percentage of baccalaureate-prepared nurses, a lower dose or amount of all categories of nursing staff per weighted patient case, higher nurse-reported adequacy of staffing and resources, higher use of care maps or protocols to guide patient care, higher nurse-reported care quality, lower nurse-reported adequacy of manager ability and support, and higher nurse burnout.

CONCLUSION

Just as hospitals and clinicians caring for patients focus carefully on completing accurate diagnosis and appropriate and effective interventions, so too should hospitals carefully plan and manage structures and processes of care such as the proportion of Registered Nurses in the staff mix, percentage of baccalaureate-prepared nurses, and routine use of care maps to minimize unnecessary patient death.

The impact of unmeasured clinical variables on the accuracy of hospital report cards: a Monte Carlo study

PURPOSE

Hospital report cards are commonly produced using administrative data. The objective of this study was to determine the impact of unmeasured clinical data on the accuracy of hospitals' report cards.

METHODS

Monte Carlo simulations were based on both administrative and detailed clinical data for patients hospitalized with an acute myocardial infarction in Ontario, Canada. Data were simulated such that the true performance of each hospital was known. Both clinical and administrative risk scores were randomly generated for each patient. The ability of hospital report cards to correctly identify hospitals that truly had higher than acceptable mortality was compared when both clinical and administrative data were used and when only administrative data were used. By using Monte Carlo simulations, we were able to incrementally increase the divergence between the 2 risk scores.

RESULTS

In a wide range of settings, sensitivity and specificity of hospital report cards was only negligibly greater when both administrative and clinical data were used compared to when only administrative data were used.

CONCLUSIONS

Unmeasured clinical data have at most a minor impact on the accuracy of cardiac hospital report cards.

Quality of diagnosis and procedure coding in ICD-10 administrative data

OBJECTIVES

The International Classification of Disease, 10th Revision (ICD-10) was introduced worldwide beginning in the late 1990s. Because there have been no published data on the quality of coding using ICD-10, the aim of our analysis is to assess the quality of ICD-10 coding in routinely collected hospital discharge data from Australia, which began using ICD-10 in 1998.

METHODS

Audit data from the years 1998-1999 (n = 7004) and 2000-2001 (n = 7631), excluding same-day chemotherapy and dialysis cases, were used in data analysis. Quality measures included prevalence comparisons, sensitivity, positive predictive value (PPV), and the kappa statistic.

RESULTS

Comparison of the audit sample to public hospital discharges showed little difference in age and gender, with audited cases more likely to be overnight stays. There was no difference in the median number of hospital assigned diagnosis and procedure codes per discharge. Agreement of the principal diagnosis code was 85% at the 3-digit level and 79% at the 4-digit level in 1998-1999; this rate had improved to 87% and 81% in 2000-2001. Principal procedure code agreement was 85% in 1998-1999 and 83% in 2000-2001 at the 5-digit level, and 81% and 80% at the 7-digit level, respectively. Specific major diagnoses, comorbid diagnoses, major procedures, and minor procedures showed good-to-excellent coding quality.

CONCLUSIONS

The transition to ICD-10 has occurred with no loss of data quality, with data showing a high level of reliability and adherence to coding standards. When consideration is given to the nature of the analysis, administrative data can provide highly reliable population-based estimates of hospitalization rates.