↓ Skip to main content

What Drives Variation in Episode‐of‐care Payments for Primary TKA? An Analysis of Medicare Administrative Data

Overview of attention for article published in Clinical Orthopaedics & Related Research, August 2015
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users

Readers on

mendeley
84 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
What Drives Variation in Episode‐of‐care Payments for Primary TKA? An Analysis of Medicare Administrative Data
Published in
Clinical Orthopaedics & Related Research, August 2015
DOI 10.1007/s11999-015-4445-0
Pubmed ID
Authors
Abstract

Episode-of-care payments are defined as a single lump-sum payment for all services associated with a single medical event or surgery and are designed to incentivize efficiency and integration among providers and healthcare systems. A TKA is considered an exemplar for an episode-of-care payment model by many policymakers, but data describing variation payments between hospitals for TKA are extremely limited. We asked: (1) How much variation is there between hospitals in episode-of-care payments for primary TKA? (2) Is variation in payment explained by differences in hospital structural characteristics such as teaching status or geographic location, patient factors (age, sex, ethnicity, comorbidities), and discharge disposition during the postoperative period (home versus skilled nursing facility)? (3) After accounting for those factors, what proportion of the observed variation remains unexplained? We used Medicare administrative data to identify fee-for-service beneficiaries who underwent a primary elective TKA in 2009. After excluding low-volume hospitals, we created longitudinal records for all patients undergoing TKAs in eligible hospitals encompassing virtually all payments by Medicare for a 120-day window around the TKA (30 days before to 90 days after). We examined payments for the preoperative, perioperative, and postdischarge periods based on the hospital where the TKA was performed. Confounding variables were controlled for using multivariate analyses to determine whether differences in hospital payments could be explained by differences in patient demographics, comorbidity, or hospital structural factors. There was considerable variation in payments across hospitals. Median (interquartile range) hospital preoperative, perioperative, postdischarge, and 120-day payments for patients who did not experience a complication were USD 623 (USD 516-768), USD 13,119 (USD 12,165-14,668), USD 8020 (USD 6403-9933), and USD 21,870 (USD 19,736-25,041), respectively. Variation cannot be explained by differences in hospital structure. Median (interquartile range) episode payments were greater for hospitals in the Northeast (USD 26,291 [22,377-30,323]) compared with the Midwest, South, and West (USD 20,614, [USD 18,592-22.968]; USD 21,584, [USD 19,663-23,941]; USD 22,421, [USD 20,317-25,860]; p < 0.001) and for teaching compared with nonteaching hospitals (USD 23,152 [USD 20,426-27,127] versus USD 21,336 [USD 19,352-23,846]; p < 0.001). Patient characteristics explained approximately 15% of the variance in hospital payments, hospital characteristics (teaching status, geographic region) explained 30% of variance, and approximately 55% of variance was not explained by either factor. There is much unexplained variation in episode-of-care payments at the hospital-level, suggesting opportunities for enhanced efficiency. Further research is needed to ensure an appropriate balance between such efficiencies and access to care. Level II, economic analysis.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
Spain 1 1%
Canada 1 1%
Unknown 82 98%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 16 19%
Student > Ph. D. Student 8 10%
Student > Doctoral Student 7 8%
Student > Bachelor 6 7%
Student > Master 4 5%
Other 14 17%
Unknown 29 35%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 24 29%
Nursing and Health Professions 5 6%
Social Sciences 5 6%
Computer Science 3 4%
Economics, Econometrics and Finance 2 2%
Other 7 8%
Unknown 38 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 November 2016.
All research outputs
#22,745,795
of 34,410,315 outputs
Outputs from Clinical Orthopaedics & Related Research
#6,149
of 9,309 outputs
Outputs of similar age
#176,753
of 300,056 outputs
Outputs of similar age from Clinical Orthopaedics & Related Research
#72
of 127 outputs
Altmetric has tracked 34,410,315 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,309 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 300,056 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 127 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.