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Value-based Healthcare: Increasing Value by Reducing Implant-related Health Care Costs

2018· article· en· W2803677835 on OpenAlexaff
Virginia H. Waldrop, David Laverty, Kevin J. Bozic

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2018
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsCARE Canada
Fundersnot available
KeywordsMedicineImplantHealth careReduction (mathematics)Orthopedic surgeryInternal fixationPhysical therapyAnkleArthroplastyImplant failureSurgeryOrthodontics

Abstract

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Value in healthcare can be defined as health outcomes that matter to patients divided by the cost incurred to achieve those outcomes [8]. Value is increased by improving health outcomes, reducing costs, or both. One way to increase value is to reduce implant-related costs, provided that doing so does not compromise pain reduction, functional gains, or other improvements in endpoints that matter to patients. In a 2014 paper, Egol and colleagues [3] outlined six strategies for reducing trauma implant-related healthcare costs. Of those strategies, the use of generic fracture implants stands out for its relative simplicity, provided that generic implants are functionally equivalent to conventional implants. Indeed, if researched and implemented correctly, the use of generic implants has the potential to improve value and have wide application across orthopaedic subspecialties. Surprisingly, clinical research comparing conventional and generic orthopaedic implants is relatively limited. One study [1] found that generic sacroiliac screws could result in cost savings of USD 327 per procedure over conventional screws in patients who were otherwise similar in terms of age, sex, fracture pattern complication rate, and radiographic outcomes. The authors reported similar findings for screws used for the internal fixation of femoral neck fractures. Another study [7] reported savings of USD 1197 per procedure (a 56% reduction) when generic locking plates were compared to conventional ones for fractures of the proximal humerus, distal radius, proximal tibia, tibial pilon, and ankle, with no increase in complications or surgical time. Findings in arthroplasty are a bit more complicated, if still quite preliminary. While Waddell and colleagues [9] found that generic total hip implants were comparable to more-expensive prostheses at 2-year followup with respect to clinical endpoints, Hothi and colleagues [5] identified potentially important design differences between branded and generic stems in terms of trunnion surface roughness and mass, but not volume. Greater trunnion roughness (as was found in the generic stem) has been associated with greater corrosion at the head-stem junction [5, 11]. The mass and volume findings suggest different material densities, manufacturing processes, and possibly mechanical properties between the two stem designs of uncertain clinical significance. These findings led Haddad [4] to caution against rushing to adopt generic implants and to urge clinicians instead to conduct a “full and thorough stepwise evaluation” before considering them for routine care. We agree that for joint replacements, the adoption of generic implants should be more cautious. Yet this caution should be tempered by the need to provide value-based care, which incorporates consideration of cost. The use of generic implants remains limited despite the evidence that simple generic implants such as plates and screws are functionally equivalent to (and less expensive than) conventional implants. A 2016 survey of active Orthopaedic Trauma Association (OTA) members [10] found that most surgeons (72%) knew of their availability, but few used them in practice (26%). Of those who used them, a majority used generic implants in less than 10% of their procedures. Why the reluctance? The barriers to adoption of generic implants are generally institution-, surgeon-, and patient-related. At the institutional level, many hospitals lack financial incentives to use them. Hospitals often pass the cost of implants on to payors through contracts that include carve-outs for implants. If the reimbursement rate is favorable, the hospitals have no incentive to adopt lower-cost implants. Even in the case of uninsured patients, hospitals have little cost incentive, since many have compliance agreements with major implant vendors whereby they receive discounted prices on medical devices if they reach a threshold market share percentage with that vendor. If hospitals were to begin purchasing generic orthopaedic trauma implants for these patients, they might lose their discounted prices on other devices. Many hospitals also operate on a consignment model with branded implant vendors that complicates the switch to generic implants. In this model, implants are stored at the hospital but not purchased until they are used. Branded implant vendors also provide representatives to support the operating room staff and surgeons (whether the support is needed or not), restock unused product, and manage inventory. Even though hospitals are not charged a separate fee for these services, they pay for these services through higher implant costs. Generic implant vendors generally operate on a representative-free model, which helps them sell their product at a lower price. Hospitals would have to restructure the way they purchase and manage inventory and provide technical support in the operating room if they switched to generic implants. Bringing this job in-house would likely result in further cost-savings—the hospital could hire and train service technicians at a lower cost to provide the same services as implant vendor representatives. Again, reimbursement rates and compliance agreements with branded implant vendors likely disincentivize making this structural change. Surgeons themselves can serve as barriers to the use of generic implants. Many surgeons have relationships with branded implant vendors, such as consulting agreements, or royalty agreements. Although the latter are less common, they certainly can result in large payments and potentially a deeper connection between the surgeon and the branded implant vendor. Even noncontractual relationships—in the form of relationships with sales representatives—can result in loyalty to branded implant manufacturers. Finally, the fact that hospital-physician gainsharing is not widespread, and therefore few physicians benefit from gainsharing or comanagement agreements, means that most surgeons are not incentivized to choose generic over conventional implants and find it easier to stick with the branded implant that they are most familiar with due to the structural barriers previously noted. Interestingly, in the OTA survey mentioned above [10], the most commonly cited reasons for not adopting generic implants were satisfaction with current implants (47%) and lack of financial incentive to adopt generic implants (34%). The ethical responsibility to protect patients from unnecessarily high costs should motivate surgeons to choose generic implants (though insured patients are partially shielded from these costs). But surgeons may lack confidence in generic devices, given the relative dearth of published clinical data on their use and the perceived limitations of the FDA 510K approval process and postmarket surveillance [2]. These limitations apply to new branded implants as well as generics, yet new branded implants are more frequently adopted. Surgeons may also be concerned about patients’ perceptions of generics as cheap or unsafe. This concern, which was not an option on the OTA survey analyzing barriers to adoption of generics, remains a theoretical barrier and warrants further investigation. Underserved populations with historical mistrust of the medical system might negatively perceive the use of generic implants, if informed of their use. Overcoming this mistrust would require open and empathetic communication between surgeons and their patients, and clear public health messaging about the safety and value of generic implants. Finally, patients may lack the incentive or understanding to request generic implants. If given the choice, an insured patient who has already reached his or her deductible would not have a financial incentive to choose a generic implant, and instead may prefer the branded implant. An uninsured patient in that situation who has the financial incentive to choose the generic implant may lack the system knowledge or self-efficacy to ask about surgical options (including implant options) or later challenge an egregiously high medical bill. The barriers above are substantial but not insurmountable. Surgeons need to work with communication and public health experts to engage patients and policymakers on the issue and incorporate patient feedback into future messaging. At the same time, more clinical research needs to be done. Retrospective cohort studies [1, 7] comparing generic and branded simple trauma implants like plates and screws have demonstrated equivalent clinical outcomes. A natural next step that would help overcome barriers at each of the levels discussed for generic trauma implants would be a prospective randomized trial comparing the two, ideally funded by grant support, but also potentially by the relevant generic. Increasing the use of generic arthroplasty implants poses greater challenges given the timescale involved in evaluating outcomes. It should also be approached more cautiously, given the findings in Hothi and colleagues [5] and past failed technologies introduced in a haphazard manner. Malchau and colleagues [6] have proposed a stepwise algorithm for the introduction of new orthopaedic implants. In keeping with this algorithm, generic arthroplasty implants should first be subjected to rigorous preclinical testing in joint simulators, and data on wear rates should be disseminated to surgeons, hospital administrators, and other supply-chain decision makers. These data, if supportive, will help in the recruitment of funding for prospective randomized studies to determine in vivo wear. At the same time, surgeons at high-volume medical centers enrolled in the American Joint Replacement Registry and with a strong track record of monitoring outcomes that matter to patients, including patient reported outcomes, might begin introducing them in a controlled manner and in conversation with patients. Clinical outcomes could then be monitored in real-time and disseminated more rapidly than those of randomized trials. In short, different levels of evidence gathered over time will be necessary to overcome the barriers to adopting generic arthroplasty implants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0130.008
Open science0.0020.008
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0130.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.438
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2018
Admission routes1
Has abstractyes

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