Healthcare expenditure in the United States of America in the last year of life: where ethics, medicine and economics collide?
Bibliographic record
Abstract
To the Editor: A just published article in the Lancet from the Harvard School of Public Health in the United States of America reported on a retrospective cohort study of elderly beneficiaries of fee-for-service Medicare in the USA, aged 65 years or older, who died in 2008 (1). Of 1,802,029 elderly beneficiaries of fee-for-service Medicare who died in 2008, 31.9% (95% CI 31.9–32.0; 575,596 of 1,802,029) underwent an inpatient surgical procedure during the year before death, 18.3% (95% CI 18.2–18.4; 329,771 of 1,802,029) underwent a procedure in their last month of life and 8.0% (95% CI 8.0–8.1; 144,162 of 1,802,029) underwent a procedure during their last week of life. The authors of this report concluded that many elderly people in the USA undergo surgery in the year before their death (1). Similarly, there has been established very clearly over the years, the fact that medical expenses in the last year of life in general compared with total lifetime healthcare expenses here in the United States of America is very much skewed in favour of the former (2-7). In 2006, treatment for patients in the United States of America in their last year of life accounted for more than one-quarter of Medicare spending (2). Moreover, marked geographical variation in Medicare end-of-life spending is well documented (3). Furthermore, this geographical variation in end-of-life healthcare related expenditures is believed to be driven by physician practice styles rather than by differences in patients’ preferences for aggressiveness of treatment at the end of life (4). There is increasing concern in certain quarters that this expensive care may have limited clinical effectiveness and may be contrary to what the receiving patients actually wanted (5). Surveys report that many patients do not wish to receive aggressive treatment at the end of their lives; however, these preferences are often undocumented (6, 7). This brings up the question of advanced directives which even when properly documented as patients’ wishes, can get turned around by family members ‘when the chips are down’. The ethical implications of such practices resonate around the intensive care units and other acute care settings around the country, every single day. From the foregoing, we submit that healthcare delivery is at the crossroads of ethics and marketing. We in the United States of America, and other similarly affected countries, must as a society have to confront these apparent inequities and inefficiencies and potentially unethical practices in our healthcare system regarding medical/surgical treatment of the elderly. This is one place where ethics, medicine and economics collide. We have dubbed this the syndrome of ‘Ethicomedicinomics’. None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".