Variation in medical practice: getting the balance right
Bibliographic record
Abstract
Contemporary clinical practice is characterized by its complexity as the volume and diversity of medical interventions, whether they are drugs, procedures or diagnostic tests, are increasing and threaten to overwhelm our capacity to deliver patient-centred care. Consider some statistics: the average American citizen can expect to undergo seven operations in their lifetime, 10% will undergo an MRI scan annually (three times higher than the rate in neighbouring Canada) and 50% of Medicare beneficiaries are prescribed five or more medications. In Ireland, one-fifth of the whole population aged over 70 years are taking long-term Proton Pump Inhibitor (PPI) therapy.1–3 The consequences of this phenomenon for patients in terms of benefit (increase quantity and quality of life) versus harm (medicalization of a person, side effects of therapies and costs to the health service budget) give rise to questions concerning the epidemiology of health care utilization and how it differs between and within countries. Seminal work carried out by John Wennberg, a health services researcher and epidemiologist who developed the Dartmouth Atlas Health Project (www.dartmouthatlas.org), has produced an emerging science that examines variation in medical practice and raises important questions about what constitutes ‘appropriate’ health care. This editorial outlines the taxonomy of medical practice variation with clinical examples showing how it relates to family medicine. Medical practice variation may be grouped into three categories each with different implications for patients, clinicians and policy makers.4 Effective care is defined as interventions for which the benefits far outweigh the risks; in this case the ‘right’ rate of treatment is 100% of patients defined by evidence-based guidelines to be in need, and unwarranted variation is generally a matter of underuse or ineffective care delivery. An example of this form of practice includes the prescription of statins for the secondary prevention of cardiovascular disease.5 Preference sensitive care is when more than one generally accepted treatment option is available, such as watchful waiting, medical treatment or surgery. In this situation, the right rate should depend on informed patient choice, but treatment rates can vary because of differences in professional opinion and their subsequent impact on patient decision making. An example of preference sensitive care would be the management of menorrhagia, where informed patient preference may drive treatment rates.6 There can be an overlap between effective care and preference sensitive care, particularly in the area of preventive medicine. For instance in stroke prevention in patients with non-valvular atrial fibrillation, patients judged to have an intermediate risk, choosing between aspirin and warfarin is dependent on individual preference in terms of the risk and benefits of these alternative drug treatments.7 Supply sensitive care comprises the frequency of clinical activities such as doctor visits, diagnostic tests and hospital admissions, which relate to the capacity of the local health care system. Increased availability of a health care resource will be associated with increased utilization. Supply of more specialists will result in more visits to specialists, increased numbers of hospital beds per capita will result in higher rates of hospital admissions and greater availability of imaging will result in higher imaging rates.4 In the USA, supply sensitive care is thought to account for up to 50% of Medicare health care spending. Supply sensitive care, and how it varies, remains a major consideration in terms of the organization and delivery of cost-effective chronic disease management. This lack of evidence and consensus in terms of the most appropriate form of care delivery for chronic disease management results in practice variation in terms of the number and timing of return visits, frequency of diagnostic testing and disease monitoring in patients. In the USA, regions with high rates of chronic disease care utilization do not have better patient outcomes as measured by mortality and indicators of the quality of care.8 This variation suggests that the problem is overutilization of this category of care in some regions. Evidence about supply sensitive care is emerging from other forms of health care systems that have different models of funding, for example, the UK’s National Health Service Health Atlas (www.rightcare.nhs.uk/) and the social insurance system of the Netherlands (www.healthcareperformance.nl). A relatively recent development has seen health care organizations drawing attention to ineffective care—care which lacks an evidence base and should not be part of routine clinical practice. The process of highlighting this form of clinical practices is gaining momentum with the advent of the ‘Choosing wisely’ campaign in the USA (www.choosingwisely.org), which lists 45 tests and procedures that are overused in practice, such as imaging for uncomplicated headaches and indiscriminate use of antibiotics for sinus infections, and encourages informed decision making for patients and providers. The ‘Do not do’ recommendations from the National Institute for Health and Clinical Excellence (NICE) (http://www.nice.org.uk/usingguidance/donotdorecommendations/index.jsp) explicitly list therapies or tests that should not be prescribed or ordered. Examples include the prescription of glucosamine for osteoarthritis and the prescription of antibiotics in the management of patients with an exacerbation of COPD without purulent sputum unless there is clinical or radiological evidence of pneumonia. Antibiotic prescribing for lower respiratory tract infections is an important example of medical practice variation with significant cost implications and is characterized by considerable variation across countries. This is highlighted by a large-scale European primary care study, which found that patients presenting with acute cough were more than twice as likely to be prescribed antibiotics in certain countries compared to others.9 Patients’ belief systems regarding antibiotics or family practitioners’ perceptions of such beliefs can have a significant influence on prescribing rates. This appears to be an area where there can be an overlap between preference sensitive care and potentially ineffective care, which presents a challenge for the clinician in how best to communicate the potential risks and benefits to patients.10 Achieving optimal patient outcomes means a commitment to providing the right care to the right person at the right time. Allied to this process is careful consideration of resource allocation, which is essential to facilitate care delivery that is both effective, cost effective and takes account of patient preferences. Funding: The authors are members of the HRB Centre for Primary Care Research and are funded through grant Health Research Board award HRC/2007/1. Ethical approval: none. Conflict of interest: 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".