Control charts to identify adverse outcomes in elective colon resection.

BACKGROUND: Control charts have been proposed for the measurement of quality in surgical care. METHODS: For each of 181 study hospitals in the 2005 National Inpatient Sample of the Healthcare Cost and Utilization Project database, an average moving range control chart for risk-adjusted postoperative length of stay (RApoLOS) was created for patients discharged alive after elective colectomy. RApoLOS outliers using upper control limits of 2.0σ, 2.5σ, and 3.0σ were correlated to coded complications (CCs). Hospital costs were correlated to RApoLOS outliers and CCs. RESULTS: Of 13,118 live discharges, 902 (6.9%) were outliers using a 3.0σ upper control limit, 1,350 (10.3%) were 2.5σ outliers, and 2,053 (15.7%) were 2.0σ outliers. CCs were identified in 92.7% of 3.0σ outliers, in 81.3% of 2.5σ outliers, and 70.6% of 2.0σ outliers. Increased costs were associated with RApoLOS outliers and poorly with CCs. CONCLUSIONS: Average moving range control charts for RApoLOS outliers are valid tools for measurement of surgical quality and costs.

Inefficiency as the major driver of excess costs in lung resection.

BACKGROUND: Risk-adjusted outcomes of surgical care are important for quality and cost assessments. Although cardiac surgery is commonly studied, risk-adjusted analysis of excess costs of lung resection has not been pursued. METHODS: We used 2002 to 2005 National Inpatient Sample of the Healthcare Cost and Utilization Project data to evaluate adverse outcomes and costs in elective lung resections in hospitals with more than 20 cases during that period. Adverse outcomes were inpatient death or excessive risk-adjusted postoperative stay. Logistic models were defined to predict adverse outcomes. Linear models were designed to predict costs. Hospital-specific adverse outcome rates and costs were measured to define performance outliers. Cost-effective reference hospitals were used to define total excess costs. RESULTS: Among 12,182 patients at 215 hospitals undergoing lung resection, there were 336 inpatient deaths (2.8%) and 880 live discharges with prolonged risk-adjusted postoperative stay (7.2%). Predictive models for mortality and risk-adjusted postoperative stay had C statistics of 0.773 and 0.643, respectively. There were 11 ineffective hospitals (5.1%) with excessive adverse outcomes (P < .005) and 34 inefficient hospitals (15.8%) meeting quality measures but with higher than predicted costs (P < .0005). Ineffective hospitals had costs $1020 per case lower than predicted. Inefficient hospitals had costs $9978 higher than predicted. CONCLUSIONS: Inefficiency is the major factor in excess inpatient costs associated with lung resection in this model. Although refinements in databases, including total physician costs and postdischarge adverse event costs, will alter models, excess costs of lung resection appear to be driven by inefficiency, not adverse outcomes.

The impact of ineffective and inefficient care on the excess costs of elective surgical procedures.

BACKGROUND: Ineffective and inefficient elective surgical care has been identified as a major factor accounting for excessive costs of elective surgical procedures. The identification of cost-effective hospitals permits objective measurement of excessive surgical costs and development of strategies to improve outcomes and efficiency. STUDY DESIGN: We used the 2002 to 2005 National Inpatient Sample from the Healthcare Cost and Utilization Project for colorectal resections, elective coronary bypass grafts, total hip replacement, and hysterectomy to assess hospitals' risk-adjusted adverse outcome rates and costs. Adverse outcomes were defined as inpatient deaths or prolonged risk-adjusted postoperative lengths of stay (RApoLOS). Risk-adjusted costs were determined for all patients, using hospital-specific cost-to-charge ratios to convert charges to costs. Effective, efficient hospitals were identified to serve as a reference standard. Outlier hospitals for ineffectiveness (p < 0.005) and inefficiency (p < 0.0005) were analyzed to measure excessive costs relative to reference hospitals. RESULTS: Hospital costs for the 4 operations combined were $325 million greater (8%) than predicted based on the reference standard. A total of 95% of excessive costs were due to inefficiency and only 5% were due to higher-than-predicted adverse outcomes rates. Elimination of predicted excess costs of all adverse outcomes for all 4 procedures at all hospitals studied would result in smaller savings than elimination of inefficiency-associated costs at inefficient hospitals alone. CONCLUSIONS: Inefficiency is substantially more important than suboptimal outcomes in accounting for the excessive hospital costs of elective surgical care in this study population.

Comparative effectiveness and efficiency in peripheral vascular surgery.

BACKGROUND: Elective peripheral vascular surgery provides quality outcomes that are of great benefit to patients, but have complications that result in severe morbidity and excessive costs. METHODS: We studied elective carotid endarterectomy, aortofemoral bypass/aortic aneurysm repairs, and femoral-distal bypass surgeries among hospitals(N ≥ 20 cases) from 2002 to 2005 from the national Inpatient Sample of the Healthcare Cost and Utilization project. Adverse outcomes, mortality, and cost models were developed. Outlier hospitals were defined for excessive adverse outcomes (P < .005) and excess cost (P < .0005). RESULTS: There were 43,700 carotid endarterectomy patients from 447 hospitals, 9,090 aortofemoral bypass/aortic aneurysm patients from 187 hospitals, and 14,453 femoral-distal bypass patients from 243 hospitals. Approximately 3% of hospitals were quality outliers, and 8% to 24% of hospitals were efficiency outliers by procedure. CONCLUSIONS: Comparative effectiveness and efficiency modeling at the hospital level shows inefficiency and is responsible for 90% of excess costs. Overall reduced complication rates will further enhance cost reductions.

Controlling costs without compromising quality: paying hospitals for total knee replacement.

BACKGROUND: Unit costs of health services are substantially higher in the United States than in any other developed country in the world, without a correspondingly healthier population. An alternative payment structure, especially for high volume, high cost episodes of care (eg, total knee replacement), is needed to reward high quality care and reduce costs. METHODS: The National Inpatient Sample of administrative claims data was used to measure risk-adjusted mortality, postoperative length-of-stay, costs of routine care, adverse outcome rates, and excess costs of adverse outcomes for total knee replacements performed between 2002 and 2005. Empirically identified inefficient and ineffective hospitals were then removed to create a reference group of high-performance hospitals. Predictive models for outcomes and costs were recalibrated to the reference hospitals and used to compute risk-adjusted outcomes and costs for all hospitals. Per case predicted costs were computed and compared with observed costs. RESULTS: Of the 688 hospitals with acceptable data, 62 failed to meet effectiveness criteria and 210 were identified as inefficient. The remaining 416 high-performance hospitals had 13.4% fewer risk-adjusted adverse outcomes (4.56%-3.95%; P < 0.001; χ) and 9.9% lower risk-adjusted total costs ($12,773-$11,512; P < 0.001; t test) than all study hospitals. Inefficiency accounted for 96% of excess costs. CONCLUSIONS: A payment system based on the demonstrated performance of effective, efficient hospitals can produce sizable cost savings without jeopardizing quality. In this study, 96% of total excess hospital costs resulted from higher routine costs at inefficient hospitals, whereas only 4% was associated with ineffective care.

Surgical warranties to improve quality and efficiency in elective colon surgery.

BACKGROUND: Uncomplicated surgical care has highly variable costs. High costs of complications have led payers to deny additional payments even for predictable complications. HYPOTHESIS: A payment warranty indexed to effective and efficient hospitals can promote quality and economic stewardship in surgical care. DESIGN: Analysis of hospital costs for elective colon surgery in the Healthcare Cost and Utilization Project's National Inpatient Sample from 2002 through 2005. SETTING: A 20% sample of acute care hospitals in the United States. PATIENTS AND METHODS: Data for elective colon resections were used to create predictive models for adverse outcomes (AOs) and costs. Total hospital costs were determined using cost-to-charge ratios. Costs of AOs were computed as total costs minus predicted costs of uncomplicated care. Surgical warranties were computed as the probability of AOs times per-case predicted costs of AOs. Final predictive models were calibrated using data only from effective and efficient hospitals. RESULTS: We studied 51 602 cases from 632 hospitals. There were 4048 (7.8%) AOs with 505 deaths (1.0%); 19 hospitals had excessive AOs and 95 hospitals had excessive costs. For 518 effective and efficient hospitals, total per-case costs for routine care were $9843 with an average warranty of $1294 and a $276 stop-loss allocation. This cost model would reduce national expenditures for colon surgery by 6%. CONCLUSIONS: Complications and costs of care can be indexed to quality performing hospitals. Warranties for surgical care can reward effective and efficient care and preclude the need for additional payments for complications.

Patient characteristics and the occurrence of never events.

OBJECTIVE: To determine whether the occurrence of "never events" after major surgical procedures is affected by patient and disease characteristics and by the type of operation performed. DESIGN: Epidemiological analysis. INTERVENTIONS: Derivation and assessment of predictive equations for postoperative infectious events and decubitus ulcers using Healthcare Cost and Utilization Project Nationwide Inpatient Sample administrative claims data for patients hospitalized between 2002 and 2005. MAIN OUTCOME MEASURES: C statistics for each predictive equation with and without hospital dummy variables. RESULTS: Predictive equations for 6 of 8 complications had C statistics greater than 0.65 without hospital variables, while 2 had C statistics of less than 0.55. All equations had C statistics greater than 0.75 when hospital dummy variables were included. CONCLUSIONS: Patient characteristics and type of operative procedure are important predictors of complications of surgical care evaluated in this study, undermining the rationale for their current classification as "never events." Variations in risk-adjusted complication rates among hospitals support the influence of quality of care on their occurrence. Development and use of warranties to cover costs associated with caring for the unavoidable components of potentially avoidable complications is proposed as a means of rewarding high-quality providers without creating unrealistic expectations or perverse financial incentives.

Screening algorithms to assess the accuracy of present-on-admission coding.

Present-on-admission modifiers for International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes are rapidly becoming a standard coding requirement. Inaccurate coding of these modifiers can distort analyses of risk-adjusted outcomes and determinations of hospital reimbursement. A set of 12 screens for the plausibility of present-on-admission designations was developed and tested using New York State claims data for 2003, 2004, and 2005. Application of these screens uncovered numerous potential problems in coding with 39 percent of hospitals achieving a composite score higher than 90 percent and 36 percent of hospitals scoring 80 percent or less. Whether data quality control personnel adopt the screens employed in this study or develop similar sets of their own, the analytic approach used in this study provides a cost-effective method of assessing the quality of coding and the integrity of clinical performance reports based on enhanced claims data.

Adverse outcomes in surgery: redefinition of postoperative complications.

BACKGROUND: We propose that excess risk-adjusted, postoperative length of stay (poLOS) is a valid indicator of an adverse outcome. METHODS: Hospital administrative claims data for elective colon resection, coronary bypass graft surgery, and total hip replacement were used from the 100 largest-volume hospitals in the Health Care Cost and Utilization Project for 2005. Risk-adjusted poLOS linear models were designed and outliers were determined using control charts. Costs of hospital care were examined by the presence of coded complications (CCs) and/or being a poLOS outlier. RESULTS: Patterns of CCs and risk-adjusted poLOS outliers were significantly different (P < .0001, chi-square test). For all procedures, costs of care were similar with or without CCs if the patients were not poLOS outliers. For patients who were poLOS outliers, costs were significantly different (Tukey-Kramer test) independent of whether CCs were present or not. CONCLUSIONS: Adverse surgical outcomes are better defined by risk-adjusted poLOS and cost criteria rather than coded or surveillance observations.

Modifying ICD-9-CM coding of secondary diagnoses to improve risk-adjustment of inpatient mortality rates.

OBJECTIVE: To assess the effect on risk-adjustment of inpatient mortality rates of progressively enhancing administrative claims data with clinical data that are increasingly expensive to obtain. Data Sources. Claims and abstracted clinical data on patients hospitalized for 5 medical conditions and 3 surgical procedures at 188 Pennsylvania hospitals from July 2000 through June 2003. METHODS: Risk-adjustment models for inpatient mortality were derived using claims data with secondary diagnoses limited to conditions unlikely to be hospital-acquired complications. Models were enhanced with one or more of 1) secondary diagnoses inferred from clinical data to have been present-on-admission (POA), 2) secondary diagnoses not coded on claims but documented in medical records as POA, 3) numerical laboratory results from the first hospital day, and 4) all available clinical data from the first hospital day. Alternative models were compared using c-statistics, the magnitude of errors in prediction for individual cases, and the percentage of hospitals with aggregate errors in prediction exceeding specified thresholds. RESULTS: More complete coding of a few under-reported secondary diagnoses and adding numerical laboratory results to claims data substantially improved predictions of inpatient mortality. Little improvement resulted from increasing the maximum number of available secondary diagnoses or adding additional clinical data. CONCLUSIONS: Increasing the completeness and consistency of reporting a few secondary diagnosis codes for findings POA and merging claims data with numerical laboratory values improved risk adjustment of inpatient mortality rates. Expensive abstraction of additional clinical information from medical records resulted in little further improvement.

Combining administrative and clinical data to stratify surgical risk.

OBJECTIVE: To evaluate whether administrative claims data (ADM) from hospital discharges can be transformed by present-on-admission (POA) codes and readily available clinical data into a refined database that can support valid risk stratification (RS) of surgical outcomes. SUMMARY BACKGROUND DATA: ADM from hospital discharges have been used for RS of medical and surgical outcomes, but results generally have been viewed with skepticism because of limited clinical information and questionable predictive accuracy. METHODS: We used logistic regression analysis to choose predictor variables for RS of mortality in abdominal aortic aneurysm repair, coronary artery bypass graft surgery, and craniotomy, and for RS of 4 postoperative complications (ie, physiologic/metabolic derangement, respiratory failure, pulmonary embolism/deep vein thrombosis, and sepsis) after selected operations. RS models were developed for age only (Age model), ADM only (ADM model), ADM enhanced with POA codes for secondary diagnoses (POA-ADM model), POA-ADM supplemented with admission laboratory data (Laboratory model), Laboratory model supplemented with admission vital signs and additional laboratory data (VS model), VS model supplemented with key clinical findings abstracted from medical records (KCF model), and KCF model supplemented with composite clinical scores (Full model). Models were evaluated using c-statistics, case-based errors in predictions, and measures of hospital-based systematic bias. RESULTS: The addition of POA codes and numerical laboratory results to ADM was associated with substantial improvements in all measures of analytic performance. In contrast, the addition of difficult-to-obtain key clinical findings resulted in only small improvements in predictions. CONCLUSIONS: Enhancement of ADM with POA codes and readily available laboratory data can efficiently support accurate risk-stratified measurements of clinical outcomes in surgical patients.

Enhancement of claims data to improve risk adjustment of hospital mortality.

CONTEXT: Comparisons of risk-adjusted hospital performance often are important components of public reports, pay-for-performance programs, and quality improvement initiatives. Risk-adjustment equations used in these analyses must contain sufficient clinical detail to ensure accurate measurements of hospital quality. OBJECTIVE: To assess the effect on risk-adjusted hospital mortality rates of adding present on admission codes and numerical laboratory data to administrative claims data. DESIGN, SETTING, AND PATIENTS: Comparison of risk-adjustment equations for inpatient mortality from July 2000 through June 2003 derived by sequentially adding increasingly difficult-to-obtain clinical data to an administrative database of 188 Pennsylvania hospitals. Patients were hospitalized for acute myocardial infarction, congestive heart failure, cerebrovascular accident, gastrointestinal tract hemorrhage, or pneumonia or underwent an abdominal aortic aneurysm repair, coronary artery bypass graft surgery, or craniotomy. MAIN OUTCOME MEASURES: C statistics as a measure of the discriminatory power of alternative risk-adjustment models (administrative, present on admission, laboratory, and clinical for each of the 5 conditions and 3 procedures). RESULTS: The mean (SD) c statistic for the administrative model was 0.79 (0.02). Adding present on admission codes and numerical laboratory data collected at the time of admission resulted in substantially improved risk-adjustment equations (mean [SD] c statistic of 0.84 [0.01] and 0.86 [0.01], respectively). Modest additional improvements were obtained by adding more complex and expensive to collect clinical data such as vital signs, blood culture results, key clinical findings, and composite scores abstracted from patients' medical records (mean [SD] c statistic of 0.88 [0.01]). CONCLUSIONS: This study supports the value of adding present on admission codes and numerical laboratory values to administrative databases. Secondary abstraction of difficult-to-obtain key clinical findings adds little to the predictive power of risk-adjustment equations.

The hazards of using administrative data to measure surgical quality.

Administrative claims data have been used to measure risk-adjusted clinical outcomes of hospitalized patients. These data have been criticized because they cannot differentiate risk factors present at the time of admission from complications that occur during hospitalization. This paper illustrates how valid risk-adjustment can be achieved by enhancing administrative data with a present-on-admission code, admission laboratory data, and admission vital signs. Examples are presented for inpatient mortality rates following craniotomy and rates of postoperative sepsis after elective surgical procedures. Administrative claims data alone yielded a risk-adjustment model with 10 variables and a C-statistic of 0.891 for mortality after craniotomy, and a model with 18 variables and a C-statistic of 0.827 for postoperative sepsis. In contrast, the combination of administrative data and clinical data abstracted from medical records increased the number of variables in the craniotomy model to 21 with a C-statistic of 0.923, and the number of variables in the postoperative sepsis model to 29 with a C-statistic of 0.858. Use of only administrative data resulted in unacceptable amounts of systematic bias in 24 per cent of hospitals for craniotomy and 19 per cent of hospitals for postoperative sepsis. Addition of a present-on-admission code, laboratory data, and vital signs reduced the percentage of hospitals with unacceptable bias to two percent both for craniotomy and for postoperative sepsis. These illustrations demonstrate suboptimal risk stratification with administrative claims data only, but show that present-on-admission coding combined with readily available laboratory data and vital signs can support accurate risk-adjustment for the assessment of surgical outcomes.