Robotic assisted Total Knee Arthroplasty (rTKA), provides surgeons with preoperative planning and real-time data allowing for continuous assessment of ligamentous tension and range-of-motion. Using this technology, soft tissue protection, reduced early post-operative pain and improved patient satisfaction have been shown. These advances have the potential to enhance surgical outcomes and may also reduce episode-of-care (EOC) costs for patients, payers, and hospitals. The purpose of this study was to compare robotic assisted vs. manual total knee arthroplasty: 1) 90-day episode-of-care (EOC) costs; 2) index costs; 3) lengths-of-stay (LOS); 4) discharge disposition; and 5) readmission rates. TKA procedures were identified using the Medicare 100% Standard Analytic Files including; Inpatient, Outpatient, Skilled Nursing (SNF) and Home Health. Members included patients with rTKA or manual TKA (mTKA) between 1/1/2016-3/31/2017. To account for potential baseline differences, propensity score matching (PSM) was performed in a 1-to-5 ratio, robotic to manual based on age, sex, race, geographic division, and comorbidities. After PSM, 519 rTKA and 2,595 mTKA were identified and included for analysis. Ninety-day episode-of-care cost, index cost, LOS, discharge disposition and readmission rates were assessed.Introduction
Methods
‘Big data’ is a term for data sets that are so
large or complex that traditional data processing applications are
inadequate. Billions of dollars have been spent on attempts to build predictive
tools from large sets of poorly controlled healthcare metadata.
Companies often sell reports at a physician or facility level based
on various flawed data sources, and comparative websites of ‘publicly
reported data’ purport to educate the public. Physicians should
be aware of concerns and pitfalls seen in such data definitions,
data clarity, data relevance, data sources and data cleaning when
evaluating analytic reports from metadata in health care. Cite this article:
Modern healthcare contracting is shifting the
responsibility for improving quality, enhancing community health
and controlling the total cost of care for patient populations from
payers to providers. Population-based contracting involves capitated
risk taken across an entire population, such that any included services
within the contract are paid for by the risk-bearing entity throughout
the term of the agreement. Under such contracts, a risk-bearing entity,
which may be a provider group, a hospital or another payer, administers
the contract and assumes risk for contractually defined services.
These contracts can be structured in various ways, from professional
fee capitation to full global per member per month diagnosis-based
risk. The entity contracting with the payer must have downstream
network contracts to provide the care and facilities that it has
agreed to provide. Population health is a very powerful model to
reduce waste and costs. It requires a deep understanding of the nuances
of such contracting and the appropriate infrastructure to manage
both networks and risk. Cite this article:
Episodic, or bundled payments, is a concept now
familiar to most in the healthcare arena, but the models are often
misunderstood. Under a traditional fee-for-service model, each provider
bills separately for their services which creates financial incentives
to maximise volumes. Under a bundled payment, a single entity, often
referred to as a convener (maybe the hospital, the physician group,
or a third party) assumes the risk through a payer contract for
all services provided within a defined episode of care, and receives
a single (bundled) payment for all services provided for that episode.
The time frame around the intervention is variable, but defined
in advance, as are included and excluded costs. Timing of the actual payment
in a bundle may either be before the episode occurs (prospective
payment model), or after the end of the episode through a reconciliation
(retrospective payment model). In either case, the defined costs
over the defined time frame are borne by the convener. Cite this article: