More signals that overuse of healthcare is a pervasive problem contributing to health system waste
Bibliographic record
Abstract
The problem of the overuse of healthcare services and how to address it has gained increasing policy and research attention over the past decade.1-4 Overuse encompasses terms like ‘low-value care’, ‘unnecessary care’ and ‘too much medicine’ and refers to care that delivers no or very little benefit to patients, where risk of harm exceeds likely benefit or where benefit is disproportionately low compared with its cost.5 It is estimated that, on average, 20–30% of patients receive care that is unnecessary, ineffective or potentially harmful. As a consequence, patients are exposed to care that does little to help them yet poses an unnecessary risk of harm, while healthcare systems are exposed to unnecessary expenditure on services that could otherwise be directed towards effective and appropriate healthcare. The review by Scott, reported in this issue of the Internal Medicine Journal,6 describes the range and extent of the overuse of medical care in Australian hospital practice as documented in clinical audit studies published over the last decade. In two-thirds of included studies, estimates of overuse exceed 30%. Scott calls on Australian hospital leaders to audit their high-volume practices to ensure they align with current appropriateness standards. This review provides further evidence that rates of overuse of healthcare practices, including in Australia, are a widespread problem that vary substantially across clinical procedures. Rates of overuse of tests, reported in studies in Scott's review, range from 10% (for liver biopsies in patients with various types of liver disease) to 64% (for coagulation tests in patients with various clinical presentations), and rates of overuse of treatments range from 10% (for end-of-life care admissions featuring the administration of futile interventions) to 99% (for ondansetron prescription in patients with severe emesis). This wide variation in rates of overuse of individual healthcare practices is consistent with findings of previous reviews of audit studies of overuse conducted in other jurisdictions.2, 3 Rates of overuse reported in these reviews range from 1% (for carotid endarterectomy) to 89% (for antibiotics for upper respiratory tract infection) in studies from the United States3 and 0.3% (for magnetic resonance imaging in patients with mild traumatic brain injury) to 73% (for tumour-marking studies in patients with previous breast cancer) in studies from the United States, Canada, Australia and Sweden.2 The overuse of myocardial perfusion scans, echocardiographs and lumbar spine imaging highlighted in Scott's review is also generally consistent with signals of overuse of these tests reported in the Australian Atlas of Healthcare Variation.7, 8 The Atlas has reported that rates of echocardiographs, myocardial perfusion scanning and computed tomography of the lumbar spine vary substantially between local areas in Australia.7, 8 Importantly, the review by Scott also highlights how much we do not know about the extent of overuse of healthcare services. Only a small number of healthcare practices have been the focus of clinical audits in Australian hospitals to date. The 22 tests and treatments investigated in the audit studies included in Scott's review correspond to less than 20% of all Choosing Wisely Australia recommendations.9 This narrow focus on a small number of healthcare practices is consistent with previous reviews of audit studies.2, 3 In fact, Korenstein and colleagues report that only four practices (antibiotics for upper respiratory tract infections, coronary angiography, carotid endarterectomy and coronary artery bypass grafting) were the focus of investigation in over 50% of audit studies in their review of the overuse of healthcare services in the United States.3 While the overuse rates summarised by Scott provide a ‘gauge’ of the prevalence of overuse of medical care in Australian hospitals, he points out that the majority of studies were retrospective audits of individual healthcare practices in relatively small, non-representative samples conducted in single sites over 6 months or less. While these studies may have been designed to provide ‘locally relevant’ knowledge to inform local quality improvement initiatives, estimates of overuse from such studies should be interpreted with caution and may not be generalisable to other settings. We had some concerns with the review process. For example, the (somewhat limited) search of 16 Australasian journals using the search terms ‘appropriate’, ‘overuse’ and ‘audit’ applied to titles/abstracts may have failed to identify all eligible studies. Use of a single author to screen studies and extract and summarise data may have inadvertently introduced human error, while the quality and/or risk of bias of included studies was not appraised, so our confidence in the estimates of overuse of medical care is less certain. Notwithstanding these limitations, this is the first review to describe the range and extent of overuse of medical care in Australian hospitals as reported by clinical audit studies and, as such, is a useful addition to the literature. Identifying the extent of the overuse of healthcare services is a necessary but not sufficient step in reducing it. While measurement is essential for identifying and prioritising areas where quality improvement efforts are needed and for monitoring changes over time, further investigation to understand the drivers of overuse are required to inform the development of effective strategies for reducing overuse. A complex interplay of clinician-, patient-, societal- and system-level factors contribute to the overuse of healthcare services.10, 11 Reducing the overuse of healthcare services will require an understanding of these drivers and concerted effort by clinicians, policy makers, patients and consumers using multiple approaches to target these factors. It is likely that both ‘top–down’ and ‘bottom–up’ approaches will be needed to bring about change.11-13 Top–down approaches require governments, medical societies and private third-party payers to take the lead in efforts to improve care and minimise unnecessary expenditure, for example, by revising lists of publicly funded services to remove those that are of proven low value, establishing authorisation processes for use of overused services and creating performance measures or indicators to benchmark and then audit the quality of care. Bottom–up approaches require patients, clinicians and healthcare system leaders also to take a lead in effecting change, for example, by increasing the use of evidence-based shared decision-making between clinicians and patients and increasing public awareness about high-value care.11, 13 Various strategies designed to change clinician behaviour to address overuse, such as use of audit and feedback, are also promising approaches that could be implemented more widely to effect change.12, 14 Concerted legislative and other efforts will also be needed to address the problem of vested interests that often run counter to efforts to improve the quality of medical care.
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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 | 0.018 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.021 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".