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Record W2986967600 · doi:10.1111/acem.13885

Hot Off the Press: The Effect of Financial Incentives on Patient Decisions to Undergo Low‐value Head CT Scans

2019· article· en· W2986967600 on OpenAlexaffabout
Justin Morgenstern, Corey Heitz, Christopher Bond, William K. Milne

Bibliographic record

VenueAcademic Emergency Medicine · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsWestern UniversityUniversity of Calgary
Fundersnot available
KeywordsMedicineValue (mathematics)IncentiveHead (geology)Nuclear medicineRadiologyMedical physicsMicroeconomicsStatisticsEconomics

Abstract

fetched live from OpenAlex

The CT scan is arguably the most important piece of diagnostic technology that we use in emergency medicine. It allows for incredibly rapid identification of a myriad of life-threatening conditions. However, likely because it is such a valuable tool, there seems to be little doubt that we overuse it. For example, one study that looked retrospectively at all head CTs ordered for trauma concluded that more than one-third were unnecessary based on the Canadian CT Head Rule.1 Not only does unnecessary testing reduce efficiency and add costs, it also directly harms patients with unnecessary radiation.2 Many imaging decisions are obvious—the patient either clearly requires or clearly does not require imaging. However, there is a great deal of uncertainty in emergency medicine, which leaves a sizeable number of patients in a gray zone—where harms and benefits are closely matched, qualitatively different, or just unknown. For these patients, shared decision making is probably the best route forward.3, 4 Furthermore, even when it seems clear to the physician that imaging is not required, we are often met with resistance from our patients. Thus, it is important to know what factors influence patients’ decisions to undergo CT. This study by Iyengar et al.5 examines the impacts of financial incentives, as well as varying levels of risk and benefit, on patient preference for CT imaging in the setting of low-risk head injury. This is a cross-sectional survey in which participants were presented with a hypothetical low-risk head injury scenario to assess their desire for diagnostic imaging. Participants were randomized to receive different estimates of risk and benefit (1% or 0.1%). They were also randomized to receive a hypothetical offer of either $0 or $100 to forgo imaging. They recruited 913 patients and overall 54% wanted imaging. Desire for CT decreased with lower benefit and higher risk and when money was offered to forgo the CT. This is a clever study examining an interesting question. However, using hypothetical scenarios probably limits external validity, as decisions made while healthy will not necessarily mirror those made when stressed and unwell, especially after a head injury. That being said, it is not clear which represent better decisions: those made while healthy and stress-free or those made in the heat of the moment when facing a high-stress emergency. External validity is further limited in this single-center study by the unique study population, with the majority of the participants being Caucasian and having at least some college education. Furthermore, it is interesting that nearly 25% of the participants worked in health care, which could significantly influence the results of this study. Finally, from the perspective of a physician who has only worked in Canada and New Zealand, the fact that more than half of patients wanted a CT despite the very-low-risk scenario seemed incredibly high, and we wonder whether these results would be replicated in other countries. Finally, it should be noted that the numbers used for harms and benefit in this study were hypothetical and designed to be easy to understand rather than to accurately represent the true harms and benefits of CT. Therefore, although the trends are likely true, the exact numbers would vary in real clinical scenarios. They enrolled a convenience sample of 913 patients. Overall, despite the low-risk scenario (in which the Canadian CT Head Rule would advise against imaging), 54% of patients stated that they would want a CT scan. A higher benefit of CT resulted in a greater desire for imaging, whereas a higher risk and the offer of a financial incentive decreased desire for imaging. Specifically, if the benefit was reported as 0.1% then 49.6% of people wanted a CT, whereas if it was 1% then 58.9% wanted a CT (odds ratio [OR] = 1.48, 95% confidence interval [CI] = 1.13 to 1.92). If the risk was reported as 0.1% then 59.3% of people wanted a CT, whereas if it was 1% then 49.1% wanted a CT (OR = 0.66, 95% CI = 0.51 to 0.86). Finally, if no cash incentive was offered then 60% of people wanted a CT, whereas if $100 was offered to forgo the CT then 48.3% of people wanted a CT (OR = 0.64, 95% CI = 0.49 to 0.83). One number really jumped out in this study. In the group of participants who were told that there was only a 0.1% benefit from CT but a 1% harm, half of people still wanted a CT. In other words, despite being explicitly told that the harms of CT were 10 times higher than the benefits, half still opted for the imaging. That is a shocking finding. It may be explained by qualitative differences in the harms and benefits (the harms are delayed, but the benefits are immediate). Or perhaps, despite the excellent efforts of the authors to display information in multiple ways, harms and benefits were simply misunderstood. Or perhaps the number represents informational bias, in which people assume more information is always better, and therefore will always prefer more tests. Whatever the reason, the desire for imaging even when the harms are known to outweigh the benefits is a fascinating finding in a study designed to examine excessive, unnecessary diagnostic imaging. Minh Le Cong ( @ketaminh ) I don’t agree with paying financial incentive to influence health care decisions. Ethically it’s similar to drug companies giving gifts to influence doctors decisions. Ken Milne ( @theSGEM ) responds We felt similar. Adding in $$$ incentives could create more health inequities and we had concerns about social justice aspect. Medicine is hard enough without having to consider these $$$ which could bias our management. Listen to the podcast. Pik Mukherji ( @ercowboy ) I work in NYC. My experience in a busy ED, with multiple referring services and urgent cares sending people for CT- is NOT that 50% still want one after we chat. 10-15% is generous. “It only takes a 1wk ICU stay (or a 3 hr head CT) to avoid a 15 min. convo.” Tim Montrief ( @EMinMiami ) Big thing that stood out to me (having lived in Ann Arbor for the first 20 some odd years of my life) The vast majority of these pts are highly educated and white. There was also a very high percentage (24%) that worked in healthcare. How might that affect external validity? Youri Yordanov ( @YordaYou ) This is so weird from our side of the atlantic …. Ken Milne ( @theSGEM ) responds Also on this side of the Atlantic but north of the US border. Michael Schweitzer Would there be a limit, like when the supermarket will only let you buy so many units of something on sale? Because I'd go twice a day to the ER to demand a CT head if this came true. Easy money. Imagine the hordes of folks who show up just to say “Oh well, if you don't think I need a CT for this large pimple on my forehead, I'll just take the cheque and go now.” Will Meurer responds Even in very-low-risk scenarios, patients demonstrate a desire for advanced diagnostic imaging. When the harms clearly outweigh the risks, we have a responsibility to protect our patients. However, when decisions are not clear cut, it is important to understand the various factors that influence patients’ decisions, so that we are able to guide our patients through an effective shared decision-making process.

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)0.006
metaresearch head score (Gemma)0.113
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.113
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0190.001

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.

Opus teacher head0.361
GPT teacher head0.545
Teacher spread0.184 · 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 designObservational
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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Citations0
Published2019
Admission routes2
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