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Gaming hospital-level pneumonia 30-day mortality and readmission measures by legitimate changes to diagnostic coding.

ABSTRACT: Risk-standardized 30-day mortality and hospital readmission rates for pneumonia are increasingly being tied to hospital reimbursement to incentivize the delivery of high-quality care. Such measures may be susceptible to gaming by recoding patients with pneumonia to a primary diagnosis of sepsis or respiratory failure. We sought to determine the degree to which hospitals can game mortality or readmission measures and change their rankings by recoding patients with pneumonia.Simulated experimental study of 2,906 U.S. acute care hospitals with at least 25 admissions for pneumonia using 2009 Medicare data.Elderly (age ? 65 yr) Medicare fee-for-service beneficiaries hospitalized with pneumonia. Patients eligible for recoding to sepsis or respiratory failure were those with a principal International Classification of Diseases, 9th Edition, Clinical Modification, discharge code for pneumonia and secondary codes for respiratory failure or acute organ dysfunction.None.We measured the number of hospitals that improved their pneumonia mortality or readmission rates after recoding eligible patients. When a sample of 100 hospitals with pneumonia mortality rates above the 50th percentile recoded all eligible patients to sepsis or respiratory failure, 90 hospitals (95% CI, 84-95) improved their mortality rate (mean improvement, 1.09%; 95% CI, 0.94-1.28%) and 41 hospitals dropped below the 50th percentile (95% CI, 33-52). When a sample of 100 hospitals with pneumonia readmission rates above the 50th percentile recoded all eligible patients, 66 hospitals (95% CI, 54-75) improved their readmission rate (mean improvement, 0.34%; 95% CI, 0.19-0.45%) and 15 hospitals (95% CI, 9-22) dropped below the 50th percentile.Hospitals can improve apparent pneumonia mortality and readmission rates by recoding pneumonia patients. Centers for Medicare and Medicaid Services should consider changes to their methods used to calculate hospital-level pneumonia outcome measures to make them less susceptible to gaming.

SUBMITTER: Sjoding MW

PROVIDER: S-EPMC4617210 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Gaming hospital-level pneumonia 30-day mortality and readmission measures by legitimate changes to diagnostic coding.

Sjoding Michael W MW Iwashyna Theodore J TJ Dimick Justin B JB Cooke Colin R CR

Critical care medicine 20150501 5

<h4>Objective</h4>Risk-standardized 30-day mortality and hospital readmission rates for pneumonia are increasingly being tied to hospital reimbursement to incentivize the delivery of high-quality care. Such measures may be susceptible to gaming by recoding patients with pneumonia to a primary diagnosis of sepsis or respiratory failure. We sought to determine the degree to which hospitals can game mortality or readmission measures and change their rankings by recoding patients with pneumonia.<h4> ...[more]

PMID: 25746747

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Project description:BackgroundLowering hospital readmission rates has become a primary target for the Centers for Medicare & Medicaid Services, but studies of the relationship between adherence to the recommended hospital care processes and readmission rates have provided inconsistent and inconclusive results.ObjectiveTo examine the association between hospital performance on Medicare's Hospital Compare process quality measures and 30-day readmission rates for patients with acute myocardial infarction (AMI), heart failure and pneumonia, and for those undergoing major surgery.Design, setting and participantsWe assessed hospital performance on process measures using the 2007 Hospital Inpatient Quality Reporting Program. The process measures for each condition were aggregated in two separate measures: Overall Measure (OM) and Appropriate Care Measure (ACM) scores. Readmission rates were calculated using Medicare claims.Main outcome measureRisk-standardized 30-day all-cause readmission rate was calculated as the ratio of predicted to expected rate standardized by the overall mean readmission rate. We calculated predicted readmission rate using hierarchical generalized linear models and adjusting for patient-level factors.ResultsAmong patients aged ≥ 66 years, the median OM score ranged from 79.4 % for abdominal surgery to 95.7 % for AMI, and the median ACM scores ranged from 45.8 % for abdominal surgery to 87.9 % for AMI. We observed a statistically significant, but weak, correlation between performance scores and readmission rates for pneumonia (correlation coefficient R = 0.07), AMI (R = 0.10), and orthopedic surgery (R = 0.06). The difference in the mean readmission rate between hospitals in the 1st and 4th quartiles of process measure performance was statistically significant only for AMI (0.25 percentage points) and pneumonia (0.31 percentage points). Performance on process measures explained less than 1 % of hospital-level variation in readmission rates.ConclusionsHospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.

Project description:BackgroundHospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance.MethodsThis population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance.ResultsOf the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance.ConclusionThe performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.

Project description:ImportancePneumonia often leads to functional decline during and after hospitalization and is a leading cause of hospital readmissions. Physical and occupational therapists help improve functional mobility and may be of help in this population.ObjectiveTo evaluate whether use of physical and occupational therapy in the acute care hospital is associated with 30-day hospital readmission risk or death.Design, setting, and participantsThis cohort study included the electronic health records and administrative claims data of 30 746 adults discharged alive with a primary or secondary diagnosis of pneumonia or influenza-related conditions from January 1, 2016, to March 30, 2018. Patients were treated at 12 acute care hospitals in a large health care system in western Pennsylvania. Data for this study were analyzed from September 2019 through March 2020.ExposuresNumber of physical and occupational therapy visits during the acute care stay categorized as none, low (1-3), medium (4-6), or high (>6).Main outcomes and measuresOutcomes were 30-day hospital readmission or death. Generalized linear mixed models were estimated to examine the association of therapy use and outcomes, controlling for patient demographic and clinical characteristics. Subgroup analyses were conducted for patients older than 65 years, for patients with low functional mobility scores, for patients discharged to the community, and for patients discharged to a post-acute care facility (ie, skilled nursing or inpatient rehabilitation facility).ResultsOf 30 746 patients, 15 507 (50.4%) were men, 26 198 (85.2%) were White individuals, and the mean (SD) age was 67.1 (17.4) years. The 30-day readmission rate was 18.4% (5645 patients), the 30-day death rate was 3.7% (1146 patients), and the rate of either outcome was 19.7% (6066 patients). Relative to no therapy visits, the risk of 30-day readmission or death decreased as therapy visits increased (1-3 visits: odds ratio, 0.98; 95% CI, 0.89-1.08; 4-6 visits: odds ratio, 0.89; 95% CI, 0.79-1.01; >6 visits: odds ratio, 0.86; 95% CI, 0.75-0.98). The association was stronger in the subgroup with low functional mobility and in individuals discharged to a community setting.Conclusions and relevanceIn this study, the number of therapy visits received was inversely associated with the risk of readmission or death. The association was stronger in the subgroups of patients with greater mobility limitations and those discharged to the community.

Dataset Information

Gaming hospital-level pneumonia 30-day mortality and readmission measures by legitimate changes to diagnostic coding.

Publications

Gaming hospital-level pneumonia 30-day mortality and readmission measures by legitimate changes to diagnostic coding.

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