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Record W1473950003 · doi:10.1177/229255031302100306

The cost of NOT running a business: Shifting the responsibility, incentives and penalties for surgical care delivery back toward the surgeon

2013· article· en· W1473950003 on OpenAlexaffvenueabout
Edward W. Buchel

Bibliographic record

VenueCanadian Journal of Plastic Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineIncentiveOperations managementSurgeryEngineering

Abstract

fetched live from OpenAlex

The rising cost of health care continues to dominate the political and economic landscape of our country. Comprehensiveness, universality, portability, public administration and accessibility strived for, and apparently guaranteed under the Canada Health Act, continue to be weakened by limited funds and increasing demands for an improved quality of life care. All physicians, especially surgeons, utilize a significant proportion of health care dollars in delivering their defined area of care. However, few, if any, practice surgery as a private business within the publicly funded health care system. Nationally, the proportion of funds spent on physician remuneration continues to decrease, with the greatest proportion of funds continuing to go to global hospital budgets. Increasingly, we hear that the budgets, wait lists, scope of practice and quality of care are the responsibility of this ‘system’. Personal responsibility of the providing surgeon for optimizing the delivery of surgical care provided by each surgeon has been degraded due the funding and administrative complexity of hospitals and the effort it takes to initiate change within that system. Surgeons use resources but have limited accountability for the costs or quality of the care we provide with these resources. The costs of the used resources are not understood, optimized or, at a minimum, managed by the people who use them. Not only is this a nonsustainable business model from a system standpoint, but the lack of input from the surgeons in directly managing these resources results in significant potential efficiencies and cost reductions never being realized. This cost directly hurts each patient and, eventually, each surgeon. Every region struggles with fixed budgets and increasing care demands. Each system deals with this challenge in different ways. Typically, hospital budgets are under central control within a health care region. Global operating budgets are transferred to the hospitals based on historical values. Hospital administration allocates global budgets to specific programs within its control, rarely with deliverables attached. Even less common would be a situation in which a health care provider, such as a surgeon, is held accountable for the budget they use within a hospital system. Instead, the budget (resources) for an entire operating room group is assigned by the hospital administrations and the surgeon gets to use resources without any knowledge of specific costs or the expectation to manage the costs per care provided. Management of the budgets appears to be only punitive in nature AND only if the assigned historical budgets are overspent. Changes to this paradigm need to happen for the users of the resources to become actively involved in the management of the global resources. Presently, massive disincentives exist for physicians to become cost efficient. As it exists now, the physicians who become cost effective and save money are ‘rewarded’ by having their cost savings transferred to other areas that remain inefficient and over spend their budgets. While altruistic, this does not lead to a sustainable business plan for efficient care delivery. Incentives must exist for physicians and surgeons to work hard and invest the time needed to develop ‘system’ efficiencies. If portions of realized savings are directly returned to the physician or group involved in developing and delivering the savings, the involvement to deliver improved and more cost-effective care will increase. When there is no benefit directly to the physician or group developing the improved care plan, the effort to create these simply will not happen. Creative solutions are difficult to implement from within the administrative offices removed for direct patient care. Incentivizing frontline care delivery professionals should result in many potential ideas. Plastic surgeons have a unique opportunity to become involved in establishing a model that holds the surgeon responsible and accountable for care delivery budgets within a hospital setting. Many plastic surgeons operate private surgical facilities and deliver care with budgets that they are directly responsible for. Incentives are aligned in the private system to deliver the best care at the best cost per case. In many situations, the same care delivered at the private facilities is done so at a fraction of the cost to that within the public facilities within the same geographical region. Attempts should be made for surgeons to actively manage the resources they use within public hospitals and be incentivized to do so. The best of both systems could evolve. Saving will decrease deficits and could be directed back to increased surgical care delivery, additional surgical equipment or other initiative for improved care. Ultimately, the efficiencies will result in improved and more patient care (benefiting the patients), increase resources for the surgeon (benefiting the surgeon) and cost per cast savings for the system (benefiting the taxpayer).

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)0.015
metaresearch head score (Gemma)0.084
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.084
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0110.016
Scholarly communication0.0200.017
Open science0.0020.008
Research integrity0.0080.015
Insufficient payload (model declined to judge)0.0150.004

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 head0.031
GPT teacher head0.249
Teacher spread0.218 · 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
GenreCommentary

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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Citations0
Published2013
Admission routes3
Has abstractyes

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