Medicare Severity Diagnosis-Related Group Research Articles

Early Experiences With Bundled Payments for Care Improvement for Major Bowel Surgery.

Bundled Payment (BP) models are becoming more common in surgery. We share our early experiences with Bundled Payments for Care Improvement for major bowel surgery. Patients undergoing major bowel surgery between January and October 2021 were identified using Medicare Severity-Diagnosis Related Group (MS-DRG) codes. Major drivers of cost in a BP model are reported and compared to the Fee-For-Service (FFS) payment model. A total of 202 cases (173 FFS vs 29 BP) were analyzed. The mean BP cost per Clinical Episode was $28,340. Eleven patients (38%) in the BP model had costs greater than the Target Price. The drivers of cost in the BP model were 59% acute care facility, 17% physician services, 9% post-acute care facilities, 8% other, and 7% readmissions. Clinical Episode of care costs varied considerably by MS-DRG case complexity. Robotic surgery increased costs by 35% (mean increase $3724, P < .01). The 90-day readmission rate was 17% for a mean cost of $11,332 per readmission. Three patients (10%) were discharged to a skilled nursing facility at an average cost of $11,009, while fifteen patients (52%) received home health services at a mean cost of $2947. Acute care facility costs were similar in the BP vs FFS groups (mean difference $1333, P = .22). Patients undergoing major bowel surgery are a heterogeneous population. Physicians are ideally positioned to deliver high-value, patient-centered care and are crucial to the success of a BP model. The post-acute care setting is a key component of improving efficiency and quality of care.

Requesting That Delirium Achieve Parity With Acute Encephalopathy in the MS-DRG System

Since 2007, the Medicare Severity Diagnosis Related Groups (MS-DRG) classification system has favored billing codes for acute encephalopathy over delirium codes in determining hospital reimbursement and several quality-of-care value metrics, despite broad overlap between these sets of diagnostic codes. Toxic and metabolic encephalopathy codes are designated as major complication or comorbidity (MCC) whereas causally specified delirium codes are designated as complication or comorbidity (CC) and thus associated with lower reimbursement and lesser impact on value metrics.

Sepsis Inpatient Diagnostic Testing at Mid-Size Academic and Non-academic Health Systems

Abstract Introduction/Objective Comparison of clinical laboratory test and radiology imaging utilization for sepsis inpatients across multiple academic and non-academic health systems would be useful for healthcare resource utilization benchmarking. Methods/Case Report We compared laboratory and imaging utilization for adult patients hospitalized for sepsis procedures at mid-size (250-450 hospital beds), academic (N=12; 31,353 hospitalizations) and non-academic (N=18; 54,192 hospitalizations), hospitals using the Vizient Clinical Data Base™ over 3 years (2019-2021). We used Medicare Severity Diagnosis Related Groups (MSDRG), used by the US Centers for Medicare and Medicaid Services (CMS), to identify hospitalizations for sepsis (MSDRG triplet 870/871/872). We stratified patients by severity comorbid conditions and complications into high, moderate, and low severity groups. We compared the mean number of clinical laboratory (CPT codes 80000-89999) and imaging studies (CPT codes 70000-79999) per hospitalization and per hospital day. We also measured aggregate clinical Resource Intensity Weight (RIW) per hospitalization to quantify diagnostic resource consumption for each encounter. The RIW is based on the US Centers for Medicare and Medicaid Services (CMS) Ambulatory Payment Classification (APC) weights. Results (if a Case Study enter NA) Mean laboratory tests were significantly greater (p&amp;lt;.01) at academic hospitals per encounter (152.2 vs. 95.4 tests, 59.5% higher) and per hospital day (14.5 vs. 9.9 tests, 46.5% higher). Laboratory RIW was also higher per hospitalization (21.0 vs. 13.5 RIW, 55.6% higher) and per hospital day (2.0 vs. 1.4 RIW, 42.9% higher) at academic hospitals. Mean imaging studies at academic hospitals were higher per case (7.8 vs. 7.2 studies, 8.3% higher) but not different per day (0.80 for both hospital groups). Imaging RIW was not significantly different between academic and non-academic hospitals either by encounter (11.1 vs. 9.9, 12.1% higher) or per day (1.2 for both hospital groups). Imaging comparisons lacked statistical significance. Analysis of utilization after stratification for severity levels revealed a similar pattern with greater differences in the high severity group. Conclusion Mean laboratory tests and laboratory RIW per hospitalization are greater at academic hospitals. These differences persist after adjustment for hospital length of stay and severity level. Differences in imaging studies and imaging RIW were not significant.

Correlation Between Large-Scale Health System Operational Parameters and Laboratory Test Utilization

Abstract Introduction/Objective The extent of correlation between clinical laboratory test utilization and large-scale operational parameters would be helpful to adjust for these parameters when benchmarking utilization. Methods/Case Report We compared clinical laboratory and imaging utilization over 3 years (2019-2021) for adult patients hospitalized for specific surgical, medical, and obstetrics diagnoses at multiple academic hospitals (N=58; 625,436 hospitalizations) ranging in size from 250-1300 hospital beds. We queried the Vizient Clinical Data Base with permission from Vizient, Inc.® employing Medicare Severity Diagnosis Related Groups (MSDRG), used by the US Centers for Medicare and Medicaid Services (CMS), to identify hospitalizations for gastrointestinal (GI) surgery (MSDRG triplet 329/330/331), sepsis (MSDRG triplet 870/871/872), and vaginal delivery (MSDGR triplet 805/806/807). We stratified patients within each diagnostic group for comorbid conditions and complications into high, moderate, and low severity groups. We compared mean laboratory tests and Resource Intensity Weight (RIW) per hospitalization and hospital day. The RIW is based on the CMS Ambulatory Payment Classification (APC) weights. We measured correlation of laboratory utilization with hospital bed size as well as annual discharges (sepsis group), surgical procedures (GI surgery group), births (vaginal delivery group), and diagnosis-specific discharges. Results (if a Case Study enter NA) For sepsis hospitalizations, there were small significant (p&amp;lt;.01) negative correlations (r2 0.05-0.16) between laboratory utilization, measured as test numbers or RIW, and diagnosis-specific discharges for low and moderate severity groups; there was no significant correlation with either bed size or overall annual discharges. There were no consistent correlations of laboratory utilization with bed size, annual surgical procedures, or diagnosis-specific discharges for GI surgery patients. Hospitalizations for vaginal delivery demonstrated small negative correlations between laboratory utilization and bed size, annual births, and diagnosis- specific discharges. Conclusion For specific diagnostic groups, laboratory utilization (tests as well as laboratory RIW) exhibited negative correlations with large-scale hospital operational parameters. These correlations were typically weak with varying statistical significance. The findings suggest that laboratory test utilization efficiency may require evaluation of individual health systems on a case-by-case basis.

Elective THA for Indications Other Than Osteoarthritis Is Associated With Increased Cost and Resource Use: A Medicare Database Study of 135,194 Claims.

Under Medicare's fee-for-service and bundled payment models, the basic unit of hospital payment for inpatient hospitalizations is determined by the Medicare Severity Diagnosis Related Group (MS-DRG) coding system. Primary total joint arthroplasties (hip and knee) are coded under MS-DRG code 469 for hospitalizations with a major complication or comorbidity and MS-DRG code 470 for those without a major complication or comorbidity. However, these codes do not account for the indication for surgery, which may influence the cost of care.Questions/purposes We sought to (1) quantify the differences in hospital costs associated with six of the most common diagnostic indications for THA (osteoarthritis, rheumatoid arthritis, avascular necrosis, hip dysplasia, posttraumatic arthritis, and conversion arthroplasty), (2) assess the primary drivers of cost variation using comparisons of hospital charge data for the diagnostic indications of interest, and (3) analyze the median length of stay, discharge destination, and intensive care unit use associated with these indications. This study used the 2019 Medicare Provider Analysis and Review Limited Data Set. Patients undergoing primary elective THA were identified using MS-DRG codes and International Classification of Diseases, Tenth Revision, Procedure Coding System codes. Exclusion criteria included non-fee-for-service hospitalizations, nonelective procedures, patients with missing data, and THAs performed for indications other than the six indications of interest. A total of 713,535 primary THAs and TKAs were identified in the dataset. After exclusions were applied, a total of 135,194 elective THAs were available for analysis. Hospital costs were estimated using cost-to-charge ratios calculated by the Centers for Medicare and Medicaid Services. The primary benefit of using cost-to-charge ratios was that it allowed us to analyze a large national dataset and to mitigate the random cost variation resulting from unique hospitals' practices and patient populations. As an investigation into matters of health policy, we believe that assessing the surgical cost borne by the "average" hospital was most appropriate. To analyze estimated hospital costs, we performed a multivariable generalized linear model controlling for patient demographics (gender, age, and race), preoperative health status, and hospital characteristics (hospital setting [urban versus rural], geography, size, resident-to-bed ratio, and wage index). We assessed the principal drivers of cost variation by analyzing the median hospital charges arising from 30 different hospital revenue centers using descriptive statistics. Length of stay, intensive care use, and discharge to a nonhome location were analyzed using multivariable binomial logistic regression. The cost of THA for avascular necrosis was 1.050 times (95% confidence interval 1.042 to 1.069; p < 0.001), or 5% greater than, the cost of THA for osteoarthritis; the cost of hip dysplasia was 1.132 times (95% CI 1.113 to 1.152; p < 0.001), or 13% greater; the cost of posttraumatic arthritis was 1.220 times (95% CI 1.193 to 1.246; p < 0.001), or 22% greater; and the cost of conversion arthroplasty was 1.403 times (95% CI 1.386 to 1.419; p < 0.001), or 40% greater. Importantly, none of these CIs overlap, indicating a discernable hierarchy of cost associated with these diagnostic indications for surgery. Rheumatoid arthritis was not associated with an increase in cost. Medical or surgical supplies and operating room charges represented the greatest increase in charges for each of the surgical indications examined, suggesting that increased use of medical and surgical supplies and operating room resources were the primary drivers of increased cost. All of the orthopaedic conditions we investigated demonstrated increased odds that a patient would experience a prolonged length of stay and be discharged to a nonhome location compared with patients undergoing THA for osteoarthritis. Avascular necrosis, posttraumatic arthritis, and conversion arthroplasty were also associated with increased intensive care unit use. Posttraumatic arthritis and conversion arthroplasty demonstrated the largest increase in resource use among all the orthopaedic conditions analyzed. Compared with THA for osteoarthritis, THA for avascular necrosis, hip dysplasia, posttraumatic arthritis, and conversion arthroplasty is independently associated with stepwise increases in resource use. These cost increases are predominantly driven by greater requirements for medical and surgical supplies and operating room resources. Posttraumatic arthritis and conversion arthroplasty demonstrated substantially increased costs, which can result in financial losses in the setting of fixed prospective payments. These findings underscore the inability of MS-DRG coding to adequately reflect the wide range of surgical complexity and resource use of primary THAs. Hospitals performing a high volume of THAs for indications other than osteoarthritis should budget for an anticipated increase in costs, and orthopaedic surgeons should advocate for improved MS-DRG coding to appropriately reimburse hospitals for the financial and clinical risk of these surgeries. Level IV, economic and decision analysis.

Conversion THA With Concomitant Removal of Orthopaedic Hardware Should Be Reclassified as a Revision Surgery in the Medicare Severity Diagnosis-Related Group Coding Scheme: An Analysis of Cost and Resource Use.

Conversion THA, which we defined for this study as THA with concomitant removal of preexisting orthopaedic hardware, has been associated with increased hospital costs and perioperative complications compared with primary THA. Yet, conversion THA is classified as a primary procedure under the Medicare Severity Diagnosis-Related Group coding scheme, and hospitals are reimbursed based on the resource use expected for a routine primary surgery. Prior authors have argued for conversion THA to be reclassified as a revision procedure. Although prior research has focused on comparisons between conversion THAs and primary arthroplasties, little is known about the resource use of conversion THA compared with that of revision THA. (1) Do inpatient hospital costs, estimated using cost-to-charge ratios, differ between conversion THA and revision THA? (2) Do the median length of stay, intensive care unit use, and likelihood of discharge to home differ between conversion and revision THA? This was a retrospective study of the Medicare Provider Analysis and Review Limited Data Set for 2019. A total of 713,535 primary and 74,791 revision THAs and TKAs were identified initially. Exclusion criteria then were applied; these included non-fee-for-service hospitalizations, nonelective admissions, and patients with missing data. Approximately 37% (263,545 of 713,535) of primary and 34% (25,530 of 74,791) of revision arthroplasties were excluded as non-fee-for-service hospitalizations. Two percent (13,159 of 713,535) of primaries and 11% (8159 of 74,791) of revisions were excluded because they were nonelective procedures. Among the remaining 436,831 primary and 41,102 revision procedures, 31% (136,748 of 436,831) were primary THAs and 36% (14,774 of 41,102) were revision THAs. Two percent (2761 of 136,748) of primary THAs involved intraoperative removal of hardware and were classified as conversion THAs. After claims with missing data were excluded, there were 2759 conversion THAs and 14,764 revision THAs available for analysis. Propensity scores were generated using a multivariate logistic regression model using the following variables as covariates: gender, age, race, van Walraven index, hospital setting, geography, hospital size, resident-to-bed ratio, and wage index. After matching, 2734 conversion THAs and 5294 revision THAs were available for analysis. The van Walraven index, which is a weighted score of patient preoperative comorbidities, was used to measure patient health status. Hospital costs were estimated by multiplying cost-to-charge ratios obtained from the 2019 Impact File by total hospital charges. This methodology enabled the use of a large national database to mitigate the random effects of individual hospitals' unique practices and patient populations. Multivariable regression was performed after matching to determine the independent effects of surgery type (that is, conversion versus revision THA) on hospital cost, length of stay greater than 2 days, intensive care unit use, and discharge to home. There was no difference in the estimated hospital cost between conversion THA and revision THA (β = 0.96 [95% confidence interval 0.90 to 1.01]; p = 0.13). Patients undergoing conversion THA had increased odds of staying in the hospital for more than 2 days (odds ratio 1.12 [95% CI 1.03 to 1.23]; p = 0.01), increased odds of using the intensive care unit (OR 1.24 [95% CI 1.03 to 1.48]; p = 0.02), and decreased odds of being discharged to home (OR 0.74 [95% CI 0.67 to 0.80]; p < 0.001). The inpatient hospital cost of conversion THA is no different from that of revision THA, although patients undergoing conversion surgery have modestly increased odds of prolonged length of stay, intensive care unit use, and discharge to a nonhome location. These findings support the conclusion that reclassification of conversion THA is warranted. Orthopaedic surgeons must advocate for the reclassification of conversion THA using data-backed evidence or run the risk that orthopaedic procedures will be given decreased reimbursement. Level III, economic and decision analysis.

Variation in Ischemic Stroke Payments in the USA: A Medicare Beneficiary Study

Introduction: The growing cost of stroke care has created the need for outcome-oriented and cost-saving payment models. Identifying imbalances in the current reimbursement model is an essential step toward designing impactful value-based reimbursement strategies. This study describes the variation in reimbursement fees for ischemic stroke management across the USA. Methods: This Medicare Fee-For-Service claims study examines USA beneficiaries who suffered an ischemic stroke from 2021Q1 to 2022Q2 identified using the Medicare-Severity Diagnosis-Related Groups (MS-DRGs). Demographic national and regional US data were extracted from the Census Bureau. The MS-DRG codes were grouped into four categories according to treatment modality and clinical complexity. Our primary outcome of interest was payments made across individual USA and US geographic regions, assessed by computing the mean incremental payment in cases of comparable complexity. Differences between states for each MS-DRG were statistically evaluated using a linear regression model of the logarithmic transformed payments. Results: 227,273 ischemic stroke cases were included in our analysis. Significant variations were observed among all DRGs defined by medical complexity, treatment modality, and states (p < 0.001). Differences in mean payment per case with the same MS-DRG vary by as high as 500% among individual states. Although higher payment rates were observed in MS-DRG codes with major comorbidities or complexity (MCC), the variation was more expressive for codes without MCC. It was not possible to identify a standard mean incremental fee at a state level. At a regional level, the Northeast registered the highest fees, followed by the West, Midwest, and South, which correlate with poverty rates and median household income in the regions. Conclusions: The payment variability observed across USA suggests that the current reimbursement system needs to be aligned with stroke treatment costs. Future studies may go one step further to evaluate accurate stroke management costs to guide policymakers in introducing health policies that promote better care for stroke patients.

An assessment of completeness and medical coding of Medicare Advantage hospitalizations in two national data sets.

To compare the Encounter Data System (EDS) and Medicare Provider Analysis and Review (MedPAR) completeness and medical coding of Medicare Advantage hospitalizations. FY 2016-FY 2019 data limited to hospitals paid under Medicare's Inpatient Prospective Payment System. Secondary data analysis. Completeness of EDS and MedPAR data was estimated using the total number of unique hospitalizations in both data sources as denominator. Deriving this denominator involved matching cases in the EDS and MedPAR by MA enrollee, discharge date, and hospital. The higher the match rate, the more informative the comparison of EDS and MedPAR medical coding of the same hospitalization. EDS and MedPAR codes were assessed for similarity on six measures of Medicare Severity Diagnosis-Related Group (MS-DRG) assignment and identical diagnosis and procedure codes. EDS hospitalizations' completeness increased steadily each year from 90% to 93%, driven by the 23 largest Medicare Advantage Organizations, which account for 83% of total cases. MedPAR completeness was relatively stable (89%) and benefited from 91% completeness among the largest hospitals, which are often teaching hospitals and account for 63% of MedPAR cases. By 2019, 97% of medical cases were assigned the same MS-DRG, indicating the high consistency of the severity level coding, since 98% were assigned the same base MS-DRGs, which include all severity levels for the same condition. Without chart reviews, medical cases with identical diagnosis codes increased from 87% to 92%. The EDS has a completeness advantage over MedPAR for studies of non-teaching disproportionate share (DSH) hospitals and individual hospitals generally. MedPAR is only slightly less complete for hospitalizations of teaching DSH hospitals and large hospitals in general. A highly consistent EDS and MedPAR medical coding of matched cases is an important finding since the matched cases are 88% of EDS and 90% of MedPAR cases.

Patients Undergoing Elective Inpatient Major Therapeutic Procedures in Florida Had No Significant Change in Hospital Mortality or Mortality-Related Comorbidities Between 2007 and 2019.

In a recent study, rapid response team implementation at 1 hospital was associated with only a 0.1% reduction in inpatient mortality from 2005 to 2018, characterized in the accompanying editorial as a "tepid" improvement. The editorialist postulated that an increase in the degree of illness of hospitalized patients might have masked a larger reduction that otherwise might have occurred. Impressions of greater patient acuity during the studied period might have been an artifact of efforts to document more comorbidities and complications, possibly facilitated by the change in diagnosis coding from the International Classification of Diseases , Ninth Revision ( ICD-9 ) to the Tenth Revision ( ICD-10 ). We used inpatient data from every nonfederal hospital in Florida from the last quarter of 2007 through 2019. We studied hospitalizations for major therapeutic surgical procedures with lengths of stay ≥2 days. Using logistic regression with clustering by the Clinical Classification Software (CCS) code of the primary surgical procedure, we evaluated the trends for decreased mortality, changes in the prevalence of Medicare Severity Diagnosis Related Groups (MS-DRG) with complications or comorbidities (CC) or major complications or major comorbidities (MCC), and changes in the van Walraven index (vWI), a measure of patient comorbidities associated with increased inpatient mortality. Also incorporated in the modeling was the change from ICD-9 to ICD-10 . There were 3,151,107 hospitalizations comprising 130 distinct CCS codes and 453 MS-DRG groups among 213 hospitals. Despite a progressive increase of 4.1% per year in the odds of a CC or MCC ( P = .001), there were no significant changes in the marginal estimates of in-house mortality over time (net estimated decrease, 0.036%; 99% confidence interval [CI], -0.168% to 0.097%; P = .49). There was also absence of a significantly greater fraction of discharges with vWI >0 attributable to the year of the study (odds ratio, 1.017 per year; 99% CI, 0.995-1.041). The changes in MS-DRG to those with CC or MCC were not increased significantly from either the ICD-10 coding change or the number of years after the change. Consistent with the previous study, there was at most a small decrease in the mortality rate over a 12-year period. We found no reliable evidence that patients undergoing elective inpatient surgical procedures were any sicker in 2019 than in 2007. There were substantively more comorbidities and complications documented over time, but this was unrelated to the change to ICD-10 coding.

Impact of COVID on surgical case volume at military treatment facilities with surgical residencies

IntroductionThe Military Health System (MHS) is tasked with the dual mission of providing medical care to beneficiaries while ensuring medical readiness. MHS provides care through a combination of military treatment facilities (MTF) (“direct care”; DC) & off-base civilian facilities (“purchased care”; PC). Given recent concerns regarding low surgical volume at MTFs, we sought to evaluate COVID's impact on elective and non-elective case volume at MTFs with surgical residencies. MethodsRetrospective review of 2017–2021 M2 database was performed on Tricare beneficiaries who underwent bariatric surgery or major colorectal surgery in the DC or PC market at, or, surrounding MTFs with surgical residencies. Procedures were identified using ICD-10 procedure codes and Medicare severity-diagnosis related groups. A detailed analysis was then performed on changes in case volume in the DC and PC markets. Results5,698 bariatric and 5,517 major colorectal procedures were performed during the study period. There was an 84% vs 20% quarterly decrease in elective bariatric surgeries completed in the DC and PC markets from Q1 to Q2 2020. Pre to post-COVID (Q1 2017 – Q1 2020 vs Q3 2020 – Q4 2021) there was a decrease in the percentage of bariatric surgeries completed in the DC market (74.1% vs 55.0%, p = 0.001). Meanwhile, major colorectal surgery quarterly case volume remained unchanged in the DC (137 vs 125, p = 0.18) and PC (146 v 137, p = 0.13) markets, pre- and post-COVID. DiscussionBariatric surgical case volume at MTFs disproportionately decreased during COVID when compared to the PC market and major colorectal cases. Bariatric case volume has rebounded in PC markets surpassing pre-COVID levels while DC case volume remains depressed. Further attention is warranted regarding decreased elective surgical case volume at MTFs.

Laboratory Testing for Obstetrics Patients at Academic and Non-Academic Health Systems

Abstract Introduction/Objective Evaluation of obstetric clinical laboratory test utilization differences between academic and non-academic health systems would provide important benchmarking information for healthcare leaders and policy makers. Methods/Case Report Over a 3-year period (2019-2021), using the Vizient® Clinical Data Base with permission from Vizient, Inc. (All rights reserved.), we compared clinical laboratory utilization for adult obstetrics inpatients at mid-size (250-450 hospital beds) academic (N=9; 27,036 hospitalizations) and non-academic (N=17; 50,462 hospitalizations) hospitals across the US. We used Medicare Severity Diagnosis Related Groups (MSDRG), employed by the US Centers for Medicare and Medicaid Services (CMS), to identify Cesarean section (MSDRG triplet 786-788) and vaginal delivery (MSDGR triplet 805-807) patient groups. For both groups, we stratified by comorbid conditions and complications into high, moderate, and low severity subgroups. We compared the mean number of laboratory tests (CPT codes 80000-89999) per hospitalization and per hospital day. We measured aggregate Clinical Resource Intensity Weight (RIW) per hospitalization to quantify laboratory resource consumption for each encounter. The RIW is based on the CMS Ambulatory Payment Classification (APC) weights. Results (if a Case Study enter NA) Academic hospitals had a significantly (p&amp;lt;.01) higher mean number of lab tests per Cesarean section hospitalization (20.3 vs. 10.3 tests, 97.1% higher) and hospital day (4.7 vs. 3.3 tests, 42.4% higher). Laboratory RIW per case was also significantly (p&amp;lt;.01) higher at academic hospitals (4.8 vs. 3.6 RIW, 33.3% higher); however, RIW per day did not differ between academic and non-academic hospitals. Significant differences (p&amp;lt;.01) persisted after adjusting for severity level. Although differences were smaller, academic hospitals had greater lab tests per case (12.1 vs. 8.1 tests, 49.4% higher) and day (4.3 vs. 3.6 tests, 19.4% higher) for vaginal delivery hospitalizations. Laboratory RIW per case was also significantly (p&amp;lt;.01) higher at academic hospitals (3.4 vs. 2.8 RIW, 21.4% higher). Laboratory RIW per day did not differ significantly. Significant differences remained for the high and moderate severity vaginal delivery groups. Conclusion Academic hospitals have greater laboratory test utilization for obstetrics patients. This difference appears to be largely due to a longer mean length of stay as well as use of tests with higher RIW.

Retrospective Identification of Barriers to On-Time Discharge in Common General Surgery Operations

Introduction: Increased durations of hospital stays are associated with increased nosocomial infections, higher mortality rates, lower patient satisfaction, and increased cost. This study aimed to identify barriers to safe and on-time discharge. Methods: Data was collected from electronic health records for all elective surgeries performed at an academic medical center over the last 5 years. The top 4 operations were selected: coronary artery bypass graft surgery (CABG) (n = 342), video-assisted thoracoscopic surgery (VATS) (n = 216), cholecystectomy (n = 350), and ventral hernia repair (n = 153). The primary outcome is length of stay (LOS), specifically extended LOS. Extended LOS was defined by a longer LOS compared with the average LOS provided by the Medicare Severity Diagnosis Related Groups (MS-DRG). Results: There were 6,473 patients and 10,468 operations analyzed. Extended LOS represented 23% of cases. There was no association between extended LOS and demographics, payor type, or employment status. Disposition to short-term nursing facility was associated with extended LOS in CABG and VATS. Unemployed and retired patients were associated with extended LOS among cholecystectomy patients. Conclusion: We identified predictive factors (disposition to a skilled nursing facility, and employment status) contributing to extended LOS postoperatively. We suspect that referral processes, number of facilities, and selection biases associated with short-term nursing facilities are major contributors to extended LOS and improvements in these processes can improve outcomes while decreasing identified risks associated with longer hospital stays.

Early trends in ECMO mortality during the first quarters of 2019 and 2020: Could we have predicted the onset of the pandemic?

ObjectiveTo compare mortality trends in patients requiring Extracorporeal Membrane Oxygenation (ECMO) support between the first quarters of 2019 and 2020 and determine whether these trends might have predicted the severe acute respiratory syndrome coronavirus-2 (SARS)-Cov-2 pandemic in the United States.MethodsWe analyzed 5% Medicare claims data at aggregate, state, hospital, and encounter levels using MS-DRG (Medicare Severity-Diagnosis Related Group) codes for ECMO, combining state-level data with national census data. Necessity and sufficiency relations associated with change in mortality between the 2 years were modeled using qualitative comparative analysis (QCA). Multilevel, generalized linear modeling was used to evaluate mortality trends.ResultsBased on state-level data, there was a 3.36% increase in mortality between 2019 and 2020. Necessity and sufficiency evaluation of aggregate data at state and institutional levels did not identify any association or combinations of risk factors associated with this increase in mortality. However, multilevel and generalized linear models using disaggregated patient-level data to evaluate institution mortality and patient death, identified statistically significant differences between the first (p = .019) and second (p = .02) months of the 2 years, the first and second quarters (p < .001 and p = .042, respectively), and the first 6 months (p < .001) of 2019 and 2020.ConclusionMortality in ECMO patients increased significantly during the first quarter of 2020 and may have served as an early warning of the SARS-Cov-2 pandemic. Granular data shared in real-time may be used to better predict public health threats.

MedCDI: A technology-based documentation and quality improvement initiative in neurosurgery.

Background: Clinical documentation of patient care alters coding accuracy of Medicare Severity Diagnosis-Related Groups (MS-DRGs), expected mortality, and expected length of stay (LOS) which impact quality metrics. We aimed to determine if neurosurgical quality metrics could be improved by facilitating accurate documentation and subsequently developed a mobile application and educational video to target areas of opportunity.Methods: Vizient software was used to analyze MS-DRGs and expected LOS for sample of patients requiring surgery for spinal pathology, brain tumors, and subarachnoid hemorrhage (SAH) between January 2019 and August 2021. Chart reviews were conducted to discover variables missed by documenting provider and/or coder.Results: Review of 114 spinal surgeries, 20 brain tumors, and 53 SAH patients revealed at least one additional variable impacting LOS in 43% of spine, 75% of brain tumor, and 92% of SAH patients, with an average of 1 (1.25), 2 (1.75), and 3 (2.89) new variables, respectively. Recalculated expected LOS increased by an average of 0.86 days for spine, 3.08 for brain tumor, and 6.46 for SAH cases.Conclusion: Efforts to accurately document patient care can improve quality metrics such as expected LOS, mortality, and cost estimates. We determined several missing variables which impact quality metrics, showing opportunity exists in neurosurgical documentation improvement. Subsequently, we developed an educational video and mobile-supported application to specifically target these variables. To the best of our knowledge, this represents the first initiative to utilize the proven powers of mobile phones in health care toward the novel application of specifically improving neurosurgical quality metrics.

Diagnostic Coding Intensity among a Pneumonia Inpatient Cohort Using a Risk-Adjustment Model and Claims Data: A U.S. Population-Based Study.

Hospital payments depend on the Medicare Severity Diagnosis-Related Group’s estimated cost and the set of diagnoses identified during inpatient stays. However, over-coding and under-coding diagnoses can occur for different reasons, leading to financial and clinical consequences. We provide a novel approach to measure diagnostic coding intensity, built on commonly available administrative claims data, and demonstrated through a 2019 pneumonia acute inpatient cohort (N = 182,666). A Poisson additive model (PAM) is proposed to model risk-adjusted additional coded diagnoses. Excess coding intensity per patient visit was estimated as the difference between the observed and PAM-based expected counts of secondary diagnoses upon risk adjustment by patient-level characteristics. Incidence rate ratios were extracted for patient-level characteristics and further adjustments were explored by facility-level characteristics to account for facility and geographical differences. Facility-level factors contribute substantially to explain the remaining variability in excess diagnostic coding, even upon adjusting for patient-level risk factors. This approach can provide hospitals and stakeholders with a tool to identify outlying facilities that may experience substantial differences in processes and procedures compared to peers or general industry standards. The approach does not rely on the availability of clinical information or disease-specific markers, is generalizable to other patient cohorts, and can be expanded to use other sources of information, when available.

Implications of Payment for Acute Myocardial Infarctions as a 90-Day Bundled Single Episode of Care: A Cost of Illness Analysis.

ObjectivesEvaluate the cost of illness associated with the 90-day period following acute myocardial infarction (AMI) and the implication of care pathway (percutaneous coronary intervention [PCI] vs medical management [MM]), in order to assess the potential financial risk incurred by providers for AMI as an episode of care.PerspectiveReimbursement payment systems for acute care episodes are shifting from 30-day to 90-day bundled payment models. Since follow-up care and readmissions beyond the early days/weeks post-AMI are common, financial risk may be transferred to providers.SettingAMI hospitalization Centers for Medicare & Medicaid Services (CMS) standard analytical files between 10/1/2015 and 9/30/2016 were reviewed.MethodsIncluded patients were Medicare beneficiaries with a primary diagnosis of AMI subsequently treated with either PCI or MM. Payments were standardized to remove geographic variation and separated into reimbursements for services during the hospitalization and from discharge to 90 days post-discharge. Results were stratified by Medicare Severity Diagnosis Related Groups (MS-DRGs) individually and grouped between patients treated with MM and PCI. Risk-adjusted likelihood of utilization of post-acute nursing care and all-cause readmission was assessed by logistic regression.ResultsA total of 96,546 patients were included in the analysis. The highest total mean payment (US$32,714) was for MS-DRG 248 (PCI with non-drug-eluting stent with major complication or comorbidity). Total payments were similar between MM and PCI patients, but MM patients incurred the majority of costs in the post-acute period after discharge, with the converse true for PCI patients. MM without catheterization was associated with a twofold increase in risk of requiring post-acute nursing care and 90-day readmission versus PCI (odds ratio [95% confidence interval]: 2.01 [1.92–2.11] and 2.17 [2.08–2.27]). Smaller hospital size, diabetes, peripheral arterial disease, prior AMI, and multivessel disease were predictors of higher healthcare utilization.ConclusionsMS-DRGs associated with the lowest reimbursements (and presumably, lowest costs of inpatient care) incur the highest post-discharge expenditures. As the CMS Bundled Payment for Care Improvement and similar programs are implemented, there will be a need to account for heterogeneous post-discharge care costs. Video abstract (MP4 274659 KB)Supplementary InformationThe online version contains supplementary material available at 10.1007/s41669-022-00328-4.

Publicly Available Pricing Information for Orthopaedic Upper Extremity Procedures in the United States Lacks Transparency and Consistency Between Major Hospitals

PurposeThe purpose of this study was to assess the price variation of various Medicare severity diagnosis-related group codes for orthopaedic upper extremity procedures for the top 20 orthopaedic hospitals using chargemaster price listings from each hospital.MethodsThe top 20 orthopaedic hospitals in the United States were determined by querying the U.S. News and World Report’s 2020 orthopaedic hospital ranking. This report ranks orthopaedic hospitals according to 4 major domains: outcomes, structure, process/expert opinion, and patient experience. Chargemaster data for the top 20 orthopaedic hospitals was compiled from their websites. Five DRG codes that represented orthopaedic upper extremity procedures were selected, and the pricing information for each was extracted from hospital chargemasters. The median income and cost-of-living index were also compiled for the county that each hospital is located in so that pricing data could be compared to economic measures through regression analysis.ResultsOf the top 20 orthopaedic hospitals, 18 had publicly available pricing information in DRG format on their websites. The DRG code with the highest pricing variability was Hand Injury Procedures (DRG 906; range, $12,832-$253,633). The procedure with the least pricing variability was Hand or Wrist Procedures (DRG 514; range, $24,533-$128,403). Additionally, only the cost of living index was a statistically significant predictor of procedure pricing with a weak correlation.ConclusionHospital chargemaster listings are lacking in 2 major areas: true price transparency and standardization/consistency between hospitals. Chargemaster data are often difficult to find, confusing to patients, and inaccurate. Additionally, the price range for a single DRG code can also vary substantially depending on the hospital. It is possible that hospitals located in areas with high costs of living and median incomes would charge higher prices, but these factors were not found to support this hypothesis.

Trends in Medicare Physician and Facility Fee Reimbursement in Orthopaedic Foot and Ankle

Category:Ankle; Ankle Arthritis; Arthroscopy; Bunion; Diabetes; Hindfoot; Lesser Toes; Midfoot/Forefoot; Sports; TraumaIntroduction/Purpose:The purpose of the study was to examine the trends in physician and facility reimbursement for the orthopaedic foot and ankle subspecialty utilizing the 23 most common foot and ankle surgeries based on national payment amounts.Methods:The Current Procedural Terminology (CPT) codes and corresponding Medicare Severity - Diagnosis Related Group (MS-DRG) codes for the 23 most common orthopaedic foot and ankle surgeries were identified. With the CPT codes, physician reimbursement was obtained through querying the Medicare Physician Fee Schedule (MPFS) for the work, malpractice, and practice expense relative value units (RVU) and then multiplying the total RVUs by the yearly conversion factor. CPT codes were also utilized to gather hospital outpatient reimbursement data from the Hospital Outpatient Prospective Payment System (OPPS) database. Hospital inpatient reimbursement rates were obtained from the Hospital Inpatient Prospective Payment System (IPPS) database utilizing the MS-DRG codes. All amounts were then adjusted for inflation using the Consumer Price Index (CPI).Results:From 2008 to 2021, physician reimbursement for the 23 most common orthopaedic foot and ankle surgeries decreased by 20%, with a mean Compound Annual Growth Rate (CAGR) of -1.7%. Hospital inpatient reimbursement increased by 34.7% with a mean CAGR of 2.3%; while outpatient hospital reimbursement increased by 97.2% with a mean CAGR of 5.1%.Conclusion:Over the past 13 years, physician reimbursement for common orthopaedic foot and ankle surgeries has declined while hospital reimbursement has increased. The larger increase in outpatient reimbursement compared to inpatient reimbursement highlights how CMS aims to transition surgeries from the inpatient setting to the more efficient and less expensive outpatient setting. This downward pressure on physician reimbursement while incentivizing outpatient services could have many unintended consequences on the landscape of foot and ankle practice in the US.

Multi-Health System Comparison of Diagnostic Testing in Hospitalized Sepsis Patients

Abstract Introduction/Objective Comparisons of diagnostic testing across multiple health systems over multiple years are uncommon. Such comparisons would quantify variations in test use between health systems. Methods/Case Report Using the Vizient Clinical Database, we compared clinical laboratory and imaging utilization for hospitalized adult sepsis patients (N = 69,035) over three years (2017-2019) at 19 large academic-affiliated health systems across the United States. We used Medicare Severity Diagnosis Related Groups (MSDRG), employed by the US Centers for Medicare and Medicaid Services (CMS), and identified sepsis patients (MSDRG triplet 870/871/872). We stratified hospitalized sepsis patients by severity of illness (SOI) into high severity (MSDRG 870), moderate severity (MSDRG 871), and low severity (MSDRG 872) groups. SOI further categorizes patients within a diagnostic group, quantifying the extent of comorbid conditions and complications. We measured hospital length of stay (LOS), number of laboratory tests (CPT codes 80000-89999), and number of imaging studies (CPT codes 70000-79999). We divided the number of laboratory tests and radiology studies by mean hospital LOS (in days) to calculate laboratory tests and imaging studies per hospital day. Results (if a Case Study enter NA) Between health systems, lowest and highest values for laboratory and imaging utilization ranged from 50.1 to 141.3 tests per hospitalization and 1.4 to 7.2 studies per hospitalization, respectively. Differences in laboratory tests between health systems persisted after adjusting for SOI with low to high laboratory tests per hospitalization ranging from 127.8 to 405.7, 51.0 to 144.7, and 31.9 to 78.2, for high, moderate, and low SOI groups. Utilization ranges were smaller for imaging studies. After adjusting for hospital LOS, laboratory testing differences between health systems were more pronounced and with low and high laboratory test per day utilization of 6.5 to 24.3, 6.1 to 18.5, and 6.0 to 17.1, for high, moderate, and low SOI groups. Differences in radiology studies were not as pronounced after adjusting for LOS. Conclusion There is considerable variation among health systems in laboratory and radiology resource utilization for hospitalized sepsis patients. This variation persists, especially for laboratory testing, after adjusting for SOI and LOS.

Laboratory Testing Patterns by Day of Hospital Stay for Medical and Surgical Hospitalizations

Abstract Introduction/Objective Studies of laboratory test utilization and costs by specific hospital day of stay (DOS) have yet to be widely published. Evaluation of laboratory test use by DOS would be helpful to better predict laboratory test reduction as hospital length of stay (LOS) is shortened, since testing on the final day of hospitalization is likely to differ from the average daily figures. Methods/Case Report Using an internal cost accounting database, we evaluated laboratory tests and costs by hospital DOS over one year (2017) at a large health system (N=133,139 hospital days). To evaluate changes over the first days of hospitalization, we set day 1 of hospitalization as a baseline and determined subsequent days as a percentage of day 1 figures. We also calculated laboratory variable cost as a percent of aggregate variable costs per DOS. We limited our analysis to the first week of hospitalization. We employed Medicare Severity Diagnosis Related Groups (MSDRG), used by the US Centers for Medicare and Medicaid Services (CMS), to aggregate hospital encounters into medical or surgical hospitalizations using MSDRG grouping methods. Results (if a Case Study enter NA) For medical inpatient stays, average laboratory tests (variable costs) were 10.8 ($74.11) on day 1, 7.7 ($38.53) on day 2, and 5.8 ($23.75) on day 3, with little change over the next four hospital DOS. Laboratory testing, as a percent of day 1 testing, for days 2-7 was: 70.7%, 53.4%, 54.3%, 54.5%, 55.1%, and 54.0%. Laboratory variable costs represented 7.8% of aggregate variable costs on hospital day 1 and declined sequentially over days 2-7: 5.6%, 4.3%, 3.9%, 3.8%, 3.8%, and 3.5%. For surgical hospitalizations, average laboratory tests (variable costs) were 18.2 ($130.02) on day 1, 11.9 ($57.38) on day 2, and 8.4 ($35.32) on day 3. As with medical stays, there was little change over the next four hospital DOS. Laboratory testing, as a percent of day 1 testing, for days 2-7 was: 65.6%, 46.1%, 44.6%, 46.3%, 45.9%, and 44.9%. Laboratory variable costs represented 3.2% of aggregate variable costs on hospital day 1 and remained essentially unchanged over the following days (range 3.3%-3.7%). Conclusion Laboratory variable costs are highest on the first day of hospitalization and decline over subsequent days to flatten by day 3.