Variation in Payment Rates under Medicare's Inpatient Prospective Payment System

Diagnosis-Related Group-Based Payments for Adult Spine Deformity Surgery Significantly Vary across Centers: Results from a Multicenter Prospective Cohort Study

BACKGROUND

To investigate the variation in total episode-of-care (EOC) payment and quality-adjusted life-year (QALY) gain for complex adult spine deformity surgeries in the United States, adjusting for case type and surgeon preferences.

METHODS

Patients aged >18 years with adult spine deformity with Medicare Severity-Diagnosis-Related Groups (DRGs) 453-460 and a minimum of 2 years of follow-up from index surgery were included. Index and total payments were calculated using Medicare's Inpatient Prospective Payment System. All costs were adjusted for inflation to 2020 U.S. dollar values. QALYs gained were calculated using baseline, 1-year, and 2-year Short-Form 6D scores. Mixed-effect models were used to estimate the proportion of variation in total EOC payment and QALY gain.

RESULTS

A total of 330/543 patients from 6 sites were included. Mean age was 62.4 ± 11.9 years, 79% were women, and 92% were white. The mean index and total EOC payment were $77,302 and $93,182, respectively. Patients gained on average 0.15 QALY (P < 0.0001) 2 years after surgery. In unadjusted analysis, 39% of the variation in total EOC payment across the 6 centers was attributable to relative weight of DRG and base rate. Adjusting for patient and procedural factors increased the proportion of variation in total EOC payments across the centers to 56%. Less than 2% of the variation in QALY gain was observed across the 6 centers.

CONCLUSIONS

Medicare-based payments for complex spine deformity fusions are primarily driven by relative weight of the DRG and the hospital's base rate. Patient and procedural factors are unaccounted for in the DRG-based payments made to the providers.

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study

BACKGROUND

Sepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models.

OBJECTIVE

The aim of this study was to optimize the parameters of a sepsis prediction model within distinct patient groups to minimize the excess cost of sepsis care and analyze the potential effect of factors contributing to end-user response to sepsis alerts on overall model utility.

METHODS

We calculated the excess costs of sepsis to the Centers for Medicare and Medicaid Services (CMS) by comparing patients with and without a secondary sepsis diagnosis but with the same primary diagnosis and baseline comorbidities. We optimized the parameters of a sepsis prediction algorithm across different diagnostic categories to minimize these excess costs. At the optima, we evaluated diagnostic odds ratios and analyzed the impact of compliance factors such as noncompliance, treatment efficacy, and tolerance for false alarms on the net benefit of triggering sepsis alerts.

RESULTS

Compliance factors significantly contributed to the net benefit of triggering a sepsis alert. However, a customized deployment policy can achieve a significantly higher diagnostic odds ratio and reduced costs of sepsis care. Implementing our optimization routine with powerful predictive models could result in US $4.6 billion in excess cost savings for CMS.

CONCLUSIONS

We designed a framework for customizing sepsis alert protocols within different diagnostic categories to minimize excess costs and analyzed model performance as a function of false alarm tolerance and compliance with model recommendations. We provide a framework that CMS policymakers could use to recommend minimum adherence rates to the early recognition and appropriate care of sepsis that is sensitive to hospital department-level incidence rates and national excess costs. Customizing the implementation of clinical predictive models by accounting for various behavioral and economic factors may improve the practical benefit of predictive models.

Acute Myocardial Infarction Cohorts Defined by International Classification of Diseases, Tenth Revision Versus Diagnosis-Related Groups: Analysis of Diagnostic Agreement and Quality Measures in an Integrated Health System

BACKGROUND

Among Medicare value-based payment programs for acute myocardial infarction (AMI), the Hospital Readmissions Reduction Program uses International Classification of Diseases, Tenth Revision (ICD-10) codes to identify the program denominator, while the Bundled Payments for Care Improvement Advanced program uses diagnosis-related groups (DRGs). The extent to which these programs target similar patients, whether they target the intended population (type 1 myocardial infarction), and whether outcomes are comparable between cohorts is not known.

METHODS

In a retrospective study of 2176 patients hospitalized in an integrated health system, a cohort of patients assigned a principal ICD-10 diagnosis of AMI and a cohort of patients assigned an AMI DRG were compared according to patient-level agreement and outcomes such as mortality and readmission.

RESULTS

One thousand nine hundred thirty-five patients were included in the ICD-10 cohort compared with 662 patients in the DRG cohort. Only 421 patients were included in both AMI cohorts (19.3% agreement). DRG cohort patients were older (70 versus 65 years, P<0.001), more often female (48% versus 30%, P<0.001), and had higher rates of heart failure (52% versus 33%, P<0.001) and kidney disease (42% versus 25%, P<0.001). Comparing outcomes, the DRG cohort had significantly higher unadjusted rates of 30-day mortality (6.6% versus 2.5%, P<0.001), 1-year mortality (21% versus 8%, P<0.001), and 90-day readmission (26% versus 19%, P=0.006) than the ICD-10 cohort. Two observations help explain these differences: 61% of ICD-10 cohort patients were assigned procedural DRGs for revascularization instead of an AMI DRG, and type 1 myocardial infarction patients made up a smaller proportion of the DRG cohort (34%) than the ICD-10 cohort (78%).

CONCLUSIONS

The method used to identify denominators for value-based payment programs has important implications for the patient characteristics and outcomes of the populations. As national and local quality initiatives mature, an emphasis on ICD-10 codes to define AMI cohorts would better represent type 1 myocardial infarction patients.

The likely economic impact of fewer elective surgical procedures on US hospitals during the COVID-19 pandemic

BACKGROUND

To help control the coronavirus disease 2019 pandemic, elective procedures have been cancelled in most US hospitals by government order. The purpose of this study is to estimate national hospital reimbursement and net income losses owing to elective surgical procedure cancellation during the coronavirus disease 2019 pandemic.

METHODS

The National Inpatient Sample and the Nationwide Ambulatory Surgery Sample were used to identify all elective surgical procedures performed in the inpatient setting and in hospital-owned outpatient surgery departments throughout the United States. Total cost, reimbursement, and net income was determined for all elective surgical procedures.

RESULTS

The estimated total annual cost of elective inpatient and outpatient surgical procedures in the United States was $147.2 billion, and estimated total hospital reimbursement was $195.4 to $212.2 billion. This resulted in a net income of $48.0 to $64.8 billion per year to the US hospital system. Cancellation of all elective procedures would result in estimated losses of $16.3 to $17.7 billion per month in revenue and $4 to $5.4 billion per month in net income to US hospitals.

CONCLUSION

Cancellation of elective procedures during the coronavirus disease 2019 pandemic has a substantial economic impact on the US hospital system.

Economic implications of decreased elective orthopaedic and musculoskeletal surgery volume during the coronavirus disease 2019 pandemic

PURPOSE

In order to reduce viral spread, elective surgery was cancelled in most US hospitals for an extended period during the COVID-19 pandemic. The purpose of this study was to estimate national hospital reimbursement and net income losses due to elective orthopaedic surgery cancellation during the COVID-19 pandemic.

METHODS

The National Inpatient Sample (NIS) and the Nationwide Ambulatory Surgery Sample (NASS) were used to identify all elective orthopaedic and musculoskeletal (MSK) surgery performed in the inpatient setting and in hospital owned outpatient surgery departments throughout the USA. Total cost, reimbursement, and net income were estimated for all elective orthopaedic surgery and were compared with elective operations from other specialties.

RESULTS

Elective MSK surgery accounted for $65.6-$71.1 billion in reimbursement and $15.6-$21.1 billion in net income per year to the US hospital system, equivalent to $5.5-$5.9 billion in reimbursement and $1.3-$1.8 billion in net income per month. When compared with elective surgery from all other specialties, elective MSK surgery accounted for 39% of hospital reimbursement and 35% of hospital net income. Compared with all hospital encounters for all specialties, elective MSK surgery accounted for 13% of reimbursement and 23% of net income. Estimated hospital losses from cancellation of elective MSK surgery during 8 weeks of the COVID-19 pandemic were $10.9-$11.9 billion in reimbursement and $2.6-3.5 billion in net income.

CONCLUSION

Cancellation of elective MSK surgery for 8 weeks during the COVID-19 pandemic has substantial economic implications on the US hospital system.

A Comparison of Estimated Cost Savings from Potential Reductions in Hospital-Acquired Conditions to Levied Penalties Under the CMS Hospital-Acquired Condition Reduction Program

BACKGROUND

The Hospital-Acquired Condition Reduction Program (HACRP) from the Centers for Medicare & Medicaid Services (CMS) reduces Medicare payments to hospitals with high rates of hospital-acquired conditions (HACs) by 1% each year. It is not known how the savings accruing to CMS from such penalties compare to savings resulting from a reduction in HACs driven by this program. This study compares the reported savings to CMS from financial penalties levied under the HACRP with savings resulting from potential reductions in HACs.

METHODS

Using a random sample of 20% of Medicare claims data (January 1, 2009-September 30, 2014), the research team evaluated the association between HACs and 90-day episode spending (adjusted to 2015 dollars), then estimated potential annual savings to CMS if there was a relative decrease in incidence of all HACs by 1%-20%. These savings were then compared to the actual collected HACRP penalties reported by CMS in 2015.

RESULTS

All HACs were associated with significant increases in total 90-day episode spending, ranging from $3,183 for iatrogenic pneumothorax to $21,654 for postoperative hip fracture. The total estimated savings to Medicare from potential reduction in all HACs ranged from $2.2 million to $44 million per year, an amount much lower than the $361 million in penalties levied on hospitals per year for HACs.

CONCLUSION

The penalties levied under the HACRP far exceed the potential cost savings accruing from a 1%-20% reduction in HACs that might result from hospitals' efforts in response to the program.

Comparing Outcomes and Costs of Surgical Patients Treated at Major Teaching and Nonteaching Hospitals: A National Matched Analysis

OBJECTIVE

To compare outcomes and costs between major teaching and nonteaching hospitals on a national scale by closely matching on patient procedures and characteristics.

BACKGROUND

Teaching hospitals have been shown to often have better quality than nonteaching hospitals, but cost and value associated with teaching hospitals remains unclear.

METHODS

A study of Medicare patients at 340 teaching hospitals (resident-to-bed ratios ≥ 0.25) and matched patient controls from 2444 nonteaching hospitals (resident-to-bed ratios < 0.05).We studied 86,751 pairs admitted for general surgery (GS), 214,302 pairs of patients admitted for orthopedic surgery, and 52,025 pairs of patients admitted for vascular surgery.

RESULTS

In GS, mortality was 4.62% in teaching hospitals versus 5.57%, (a difference of -0.95%, <0.0001), and overall paired cost difference = $915 (P < 0.0001). For the GS quintile of pairs with highest risk on admission, mortality differences were larger (15.94% versus 18.18%, difference = -2.24%, P < 0.0001), and paired cost difference = $3773 (P < 0.0001), yielding $1682 per 1% mortality improvement at 30 days. Patterns for vascular surgery outcomes resembled general surgery; however, orthopedics outcomes did not show significant differences in mortality across teaching and nonteaching environments, though costs were higher at teaching hospitals.

CONCLUSIONS

Among Medicare patients, as admission risk of mortality increased, the absolute mortality benefit of treatment at teaching hospitals also increased, though accompanied by marginally higher cost. Major teaching hospitals appear to return good value for the extra resources used in general surgery, and to some extent vascular surgery, but this was not apparent in orthopedic surgery.

Comparing Outcomes and Costs of Medical Patients Treated at Major Teaching and Non-teaching Hospitals: A National Matched Analysis

BACKGROUND

Teaching hospitals typically pioneer investment in new technology and cultivate workforce characteristics generally associated with better quality, but the value of this extra investment is unclear.

OBJECTIVE

Compare outcomes and costs between major teaching and non-teaching hospitals by closely matching on patient characteristics.

DESIGN

Medicare patients at 339 major teaching hospitals (resident-to-bed (RTB) ratios ≥ 0.25); matched patient controls from 2439 non-teaching hospitals (RTB ratios < 0.05).

PARTICIPANTS

Forty-three thousand nine hundred ninety pairs of patients (one from a major teaching hospital and one from a non-teaching hospital) admitted for acute myocardial infarction (AMI), 84,985 pairs admitted for heart failure (HF), and 74,947 pairs admitted for pneumonia (PNA).

EXPOSURE

Treatment at major teaching hospitals versus non-teaching hospitals.

MAIN MEASURES

Thirty-day all-cause mortality, readmissions, ICU utilization, costs, payments, and value expressed as extra cost for a 1% improvement in survival.

KEY RESULTS

Thirty-day mortality was lower in teaching than non-teaching hospitals (10.7% versus 12.0%, difference = - 1.3%, P < 0.0001). The paired cost difference (teaching - non-teaching) was $273 (P < 0.0001), yielding $211 per 1% mortality improvement. For the quintile of pairs with highest risk on admission, mortality differences were larger (24.6% versus 27.6%, difference = - 3.0%, P < 0.0001), and paired cost difference = $1289 (P < 0.0001), yielding $427 per 1% mortality improvement at 30 days. Readmissions and ICU utilization were lower in teaching hospitals (both P < 0.0001), but length of stay was longer (5.5 versus 5.1 days, P < 0.0001). Finally, individual results for AMI, HF, and PNA showed similar findings as in the combined results.

CONCLUSIONS AND RELEVANCE

Among Medicare patients admitted for common medical conditions, as admission risk of mortality increased, the absolute mortality benefit of treatment at teaching hospitals also increased, though accompanied by marginally higher cost. Major teaching hospitals appear to return good value for the extra resources used.

Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms

Objectives

The study aimed to develop and compare predictive models based on supervised machine learning algorithms for predicting the prolonged length of stay (LOS) of hospitalized patients diagnosed with five different chronic conditions.

Methods

An administrative claim dataset (2008-2012) of a regional network of nine hospitals in the Tampa Bay area, Florida, USA, was used to develop the prediction models. Features were extracted from the dataset using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes. Five learning algorithms, namely, decision tree C5.0, linear support vector machine (LSVM), k-nearest neighbors, random forest, and multi-layered artificial neural networks, were used to build the model with semi-supervised anomaly detection and two feature selection methods. Issues with the unbalanced nature of the dataset were resolved using the Synthetic Minority Over-sampling Technique (SMOTE).

Results

LSVM with wrapper feature selection performed moderately well for all patient cohorts. Using SMOTE to counter data imbalances triggered a tradeoff between the model's sensitivity and specificity, which can be masked under a similar area under the curve. The proposed aggregate rank selection approach resulted in a balanced performing model compared to other criteria. Finally, factors such as comorbidity conditions, source of admission, and payer types were associated with the increased risk of a prolonged LOS.

Conclusions

Prolonged LOS is mostly associated with pre-intraoperative clinical and patient socioeconomic factors. Accurate patient identification with the risk of prolonged LOS using the selected model can provide hospitals a better tool for planning early discharge and resource allocation, thus reducing avoidable hospitalization costs.

Reimbursement, Utilization, and 1-Year Survival Post-Allogeneic Transplantation for Medicare Beneficiaries With Acute Myeloid Leukemia

Background

The economics of allogeneic hematopoietic cell transplantation (alloHCT) for older patients with acute myeloid leukemia (AML) affects clinical practice and public policy. To assess reimbursement, utilization, and overall survival (OS) up to 1 year post-alloHCT for Medicare beneficiaries aged 65 years or older with AML, a unique merged dataset of Medicare claims and national alloHCT registry data was analyzed.

Methods

Patients diagnosed with AML undergoing alloHCT from 2010 to 2011 were included for a retrospective cohort analysis with generalized linear model adjustment. One-year post-alloHCT reimbursement included Medicare, secondary payer, and beneficiary copayments (no coinsurance) (inflation adjusted to 2017 dollars). Cost-to-charge ratios were applied to estimate department-specific inpatient costs. Cox proportional hazards regression models were utilized to identify risk factors of 1-year OS post-alloHCT.

Results

A total of 250 patients met inclusion criteria. Mean total reimbursement was $230 815 (95% confidence interval [CI] = $214 381 to $247 249) 1 year after alloHCT. Pharmacy was the most- costly inpatient service category. Adjusted mean total reimbursement was statistically higher for patients who received cord blood grafts (P = .01), myeloablative conditioning (P < .0001), and alloHCT in the Northeast and West (P = .03). Mortality increased with age (hazard ratio [HR] = 1.08, 95% CI = 1.0 to 1.17), poorer Karnofsky performance score (<90% vs ≥90%, HR = 1.60, 95% CI = 1.08 to 2.35), and receipt of myeloablative conditioning (HR = 1.88, 95% CI = 1.21 to 2.92).

Conclusions

This merged dataset allowed adjustment for a richer set of patient- and HCT-related characteristics than claims data alone. The finding that nonmyeloablative conditioning was associated with lower reimbursement and improved OS 1 year post-alloHCT warrants further investigation.

Managing imperfect competition by pay for performance and reference pricing

I study a managed health service market where differentiated providers compete for consumers by choosing multiple service qualities, and where copayments that consumers pay and payments that providers receive for services are set by a payer. The optimal regulation scheme is two-sided. On the demand side, it justifies and clarifies value-based reference pricing. On the supply side, it prescribes pay for performance when consumers misperceive service benefits or providers have intrinsic quality incentives. The optimal bonuses are expressed in terms of demand elasticities, service technology, and provider characteristics. However, pay for performance may not outperform prospective payment when consumers are rational and providers are profit maximizing, or when one of the service qualities is not contractible.