Can ChatGPT make surgical decisions with confidence similar to experienced knee surgeons?

Background: Unicompartmental knee replacements (UKRs) have become an increasingly attractive option for end-stage single-compartment knee osteoarthritis (OA). However, there remains controversy in patient selection. Natural language processing (NLP) is a form of artificial intelligence (AI).

Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting.

Background: General practitioners (GPs) work in an ill-defined environment where diagnostic errors are prevalent. Previous research indicates that aggregating independent diagnoses can improve diagnostic accuracy in a range of settings. We examined whether aggregating independent diagnoses can also improve diagnostic accuracy for GP decision making.

Comparing GPs' antibiotic prescribing decisions to a clinical prediction rule: an online vignette study.

Background: The 'STARWAVe' clinical prediction rule (CPR) uses seven factors to guide risk assessment and antibiotic prescribing in children with cough (Short illness duration, Temperature, Age, Recession, Wheeze, Asthma, Vomiting).

Aim: To assess the influence of STARWAVe factors on GPs' unaided risk assessments and prescribing decisions.

Design And Setting: Clinical vignettes administered to 188 UK GPs online.

How the UK public views the use of diagnostic decision aids by physicians: a vignette-based experiment.

Objective: Physicians' low adoption of diagnostic decision aids (DDAs) may be partially due to concerns about patient/public perceptions. We investigated how the UK public views DDA use and factors affecting perceptions.

Materials And Methods: In this online experiment, 730 UK adults were asked to imagine attending a medical appointment where the doctor used a computerized DDA.

Influences of early diagnostic suggestions on clinical reasoning.

Previous research has highlighted the importance of physicians' early hypotheses for their subsequent diagnostic decisions. It has also been shown that diagnostic accuracy improves when physicians are presented with a list of diagnostic suggestions to consider at the start of the clinical encounter. The psychological mechanisms underlying this improvement in accuracy are hypothesised.

Algorithm-based advice taking and clinical judgement: impact of advice distance and algorithm information.

Evidence-based algorithms can improve both lay and professional judgements and decisions, yet they remain underutilised. Research on advice taking established that humans tend to discount advice-especially when it contradicts their own judgement ("egocentric advice discounting")-but this can be mitigated by knowledge about the advisor's past performance. Advice discounting has typically been investigated using tasks with outcomes of low importance (e.

Using cancer risk algorithms to improve risk estimates and referral decisions.

Background: Cancer risk algorithms were introduced to clinical practice in the last decade, but they remain underused. We investigated whether General Practitioners (GPs) change their referral decisions in response to an unnamed algorithm, if decisions improve, and if changing decisions depends on having information about the algorithm and on whether GPs overestimated or underestimated risk.

Methods: 157 UK GPs were presented with 20 vignettes describing patients with possible colorectal cancer symptoms.

To unpack or not? Testing public health messaging about COVID-19.

Support theory suggests that the judged probability of events depends on the explicitness of their description. We tested whether risk communication messages that specify risks involved are associated with increased intentions to comply with public health advice during a pandemic. We conducted an anonymous online survey of the U.

Can decision support combat incompleteness and bias in routine primary care data?

Objective: Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.

Materials And Methods: We used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS.

Risk assessment and antibiotic prescribing decisions in children presenting to UK primary care with cough: a vignette study.

Objectives: The validated 'STARWAVe' (Short illness duration, Temperature, Age, Recession, Wheeze, Asthma, Vomiting) clinical prediction rule (CPR) uses seven variables to guide risk assessment and antimicrobial stewardship in children presenting with cough. We aimed to compare general practitioners' (GPs) risk assessments and prescribing decisions to those of STARWAVe and assess the influence of the CPR's clinical variables.

Setting: Primary care.

Disentangling the Relationship between Physician and Organizational Performance: A Signal Detection Approach.

In previous research, we employed a signal detection approach to measure the performance of general practitioners (GPs) when deciding about urgent referral for suspected lung cancer. We also explored associations between provider and organizational performance. We found that GPs from practices with higher referral positive predictive value (PPV; chance of referrals identifying cancer) were more reluctant to refer than those from practices with lower PPV.

Referral Decision Making of General Practitioners: A Signal Detection Study.

Background: Signal detection theory (SDT) describes how respondents categorize ambiguous stimuli over repeated trials. It measures separately "discrimination" (ability to recognize a signal amid noise) and "criterion" (inclination to respond "signal" v. "noise").

Is symptom-based diagnosis of lung cancer possible? A systematic review and meta-analysis of symptomatic lung cancer prior to diagnosis for comparison with real-time data from routine general practice.

Background: Lung cancer is a good example of the potential benefit of symptom-based diagnosis, as it is the commonest cancer worldwide, with the highest mortality from late diagnosis and poor symptom recognition. The diagnosis and risk assessment tools currently available have been shown to require further validation. In this study, we determine the symptoms associated with lung cancer prior to diagnosis and demonstrate that by separating prior risk based on factors such as smoking history and age, from presenting symptoms and combining them at the individual patient level, we can make greater use of this knowledge to create a practical framework for the symptomatic diagnosis of individual patients presenting in primary care.

Strengths and Gaps in Physicians' Risk Communication: A Scenario Study of the Influence of Numeracy on Cancer Screening Communication.

Objective: Many patients have low numeracy, which impedes their understanding of important information about health (e.g., benefits and harms of screening).

The impact of a diagnostic decision support system on the consultation: perceptions of GPs and patients.

Background: Clinical decision support systems (DSS) aimed at supporting diagnosis are not widely used. This is mainly due to usability issues and lack of integration into clinical work and the electronic health record (EHR). In this study we examined the usability and acceptability of a diagnostic DSS prototype integrated with the EHR and in comparison with the EHR alone.

Requirements and validation of a prototype learning health system for clinical diagnosis.

Introduction: Diagnostic error is a major threat to patient safety in the context of family practice. The patient safety implications are severe for both patient and clinician. Traditional approaches to diagnostic decision support have lacked broad acceptance for a number of well-documented reasons: poor integration with electronic health records and clinician workflow, static evidence that lacks transparency and trust, and use of proprietary technical standards hindering wider interoperability.

Prevalence and alternative explanations influence cancer diagnosis: An experimental study with physicians.

Objective: Cancer causes death to millions of people worldwide. Early detection of cancer in primary care may enhance patients' chances of survival. However, physicians often miss early cancers, which tend to present with undifferentiated symptoms.

Expectations for antibiotics increase their prescribing: Causal evidence about localized impact.

Objective: Clinically irrelevant but psychologically important factors such as patients' expectations for antibiotics encourage overprescribing. We aimed to (a) provide missing causal evidence of this effect, (b) identify whether the expectations distort the perceived probability of a bacterial infection either in a pre- or postdecisional distortions pathway, and (c) detect possible moderators of this effect.

Method: Family physicians expressed their willingness to prescribe antibiotics (Experiment 1, n₁ = 305) or their decision to prescribe (Experiment 2, n₂ = 131) and assessed the probability of a bacterial infection in hypothetical patients with infections either with low or high expectations for antibiotics.

Diagnostic accuracy of GPs when using an early-intervention decision support system: a high-fidelity simulation.

Background: Observational and experimental studies of the diagnostic task have demonstrated the importance of the first hypotheses that come to mind for accurate diagnosis. A prototype decision support system (DSS) designed to support GPs' first impressions has been integrated with a commercial electronic health record (EHR) system.

Aim: To evaluate the prototype DSS in a high-fidelity simulation.

What You Find Depends on How You Measure It: Reactivity of Response Scales Measuring Predecisional Information Distortion in Medical Diagnosis.

"Predecisional information distortion" occurs when decision makers evaluate new information in a way that is biased towards their leading option. The phenomenon is well established, as is the method typically used to measure it, termed "stepwise evolution of preference" (SEP). An inadequacy of this method has recently come to the fore: it measures distortion as the total advantage afforded a leading option over its competitor, and therefore it cannot differentiate between distortion to strengthen a leading option ("proleader" distortion) and distortion to weaken a trailing option ("antitrailer" distortion).

The Role of Physicians' First Impressions in the Diagnosis of Possible Cancers without Alarm Symptoms.

Background: First impressions are thought to exert a disproportionate influence on subsequent judgments; however, their role in medical diagnosis has not been systematically studied. We aimed to elicit and measure the association between first impressions and subsequent diagnoses in common presentations with subtle indications of cancer.

Methods: Ninety UK family physicians conducted interactive simulated consultations online, while on the phone with a researcher.

Translational Medicine and Patient Safety in Europe: TRANSFoRm--Architecture for the Learning Health System in Europe.

Unlabelled: The Learning Health System (LHS) describes linking routine healthcare systems directly with both research translation and knowledge translation as an extension of the evidence-based medicine paradigm, taking advantage of the ubiquitous use of electronic health record (EHR) systems. TRANSFoRm is an EU FP7 project that seeks to develop an infrastructure for the LHS in European primary care.

Methods: The project is based on three clinical use cases, a genotype-phenotype study in diabetes, a randomised controlled trial with gastroesophageal reflux disease, and a diagnostic decision support system for chest pain, abdominal pain, and shortness of breath.