Given the growing prevalence of obesity around
the world and its association with osteoarthritis of the knee, orthopaedic
surgeons need to be familiar with the management of the obese patient
with degenerative knee pain. The precise mechanism by which obesity
leads to osteoarthritis remains unknown, but is likely to be due
to a combination of mechanical, humoral and genetic factors. . Weight loss has clear medical benefits for the obese patient
and seems to be a logical way of relieving joint pain associated
with degenerative arthritis. There are a variety of ways in which
this may be done including diet and exercise, and treatment with
drugs and bariatric surgery. Whether substantial weight loss can
delay or even reverse the symptoms associated with osteoarthritis
remains to be seen. . Surgery for osteoarthritis in the obese patient can be technically
more challenging and carries a risk of additional complications.
Substantial weight loss before undertaking total knee replacement
is advisable. More prospective studies that evaluate the effect
of significant weight loss on the
In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. 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:
