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). We aimed to determine whether general-purpose open-source natural language programs can make decisions regarding a patient's suitability for a total knee replacement (TKR) or a UKR and how confident AI NLP programs are in surgical decision making. METHODS: We conducted a case-based cohort study using data from a separate study, where participants (73 surgeons and AI NLP programs) were presented with 32 fictitious clinical case scenarios that simulated patients with predominantly medial knee OA who would require surgery. Using the overall UKR/TKR judgments of the 73 experienced knee surgeons as the gold standard reference, we calculated the sensitivity, specificity, and positive predictive value of AI NLP programs to identify whether a patient should undergo UKR. RESULTS: There was disagreement between the surgeons and ChatGPT in only five scenarios (15.6%). With the 73 surgeons' decision as the gold standard, the sensitivity of ChatGPT in determining whether a patient should undergo UKR was 0.91 (95% confidence interval (CI): 0.71 to 0.98). The positive predictive value for ChatGPT was 0.87 (95% CI: 0.72 to 0.94). ChatGPT was more confident in its UKR decision making (surgeon mean confidence = 1.7, ChatGPT mean confidence = 2.4). CONCLUSIONS: It has been demonstrated that ChatGPT can make surgical decisions, and exceeded the confidence of experienced knee surgeons with substantial inter-rater agreement when deciding whether a patient was most appropriate for a UKR.

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. In addition, we investigated the potential benefit of such an approach in combination with a decision support system (DSS). METHODS: We simulated virtual groups using data sets from 2 previously published studies. In study 1, 260 GPs independently diagnosed 9 patient cases in a vignette-based study. In study 2, 30 GPs independently diagnosed 12 patient actors in a patient-facing study. In both data sets, GPs provided diagnoses in a control condition and/or DSS condition(s). Each GP's diagnosis, confidence rating, and years of experience were entered into a computer simulation. Virtual groups of varying sizes (range: 3-9) were created, and different collective intelligence rules (plurality, confidence, and seniority) were applied to determine each group's final diagnosis. Diagnostic accuracy was used as the performance measure. RESULTS: Aggregating independent diagnoses by weighing them equally (i.e., the plurality rule) substantially outperformed average individual accuracy, and this effect increased with increasing group size. Selecting diagnoses based on confidence only led to marginal improvements, while selecting based on seniority reduced accuracy. Combining the plurality rule with a DSS further boosted performance. DISCUSSION: Combining independent diagnoses may substantially improve a GP's diagnostic accuracy and subsequent patient outcomes. This approach did, however, not improve accuracy in all patient cases. Therefore, future work should focus on uncovering the conditions under which collective intelligence is most beneficial in general practice. HIGHLIGHTS: We examined whether aggregating independent diagnoses of GPs can improve diagnostic accuracy.Using data sets of 2 previously published studies, we composed virtual groups of GPs and combined their independent diagnoses using 3 collective intelligence rules (plurality, confidence, and seniority).Aggregating independent diagnoses by weighing them equally substantially outperformed average individual GP accuracy, and this effect increased with increasing group size.Combining independent diagnoses may substantially improve GP's diagnostic accuracy and subsequent patient outcomes.

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. The DDA recommended a test to rule out serious disease. We varied the test's invasiveness, the doctor's adherence to DDA advice, and the severity of the patient's disease. Before disease severity was revealed, respondents indicated how worried they felt. Both before [t1] and after [t2] severity was revealed, we measured satisfaction with the consultation, likelihood of recommending the doctor, and suggested frequency of DDA use. RESULTS: At both timepoints, satisfaction and likelihood of recommending the doctor increased when the doctor adhered to DDA advice (P ≤ .01), and when the DDA suggested an invasive versus noninvasive test (P ≤ .05). The effect of adherence to DDA advice was stronger when participants were worried (P ≤ .05), and the disease turned out to be serious (P ≤ .01). Most respondents felt that DDAs should be used by doctors "sparingly" (34%[t1]/29%[t2]), "frequently," (43%[t1]/43%[t2]) or "always" (17%[t1]/21%[t2]). DISCUSSION: People are more satisfied when doctors adhere to DDA advice, especially when worried, and when it helps to spot serious disease. Having to undergo an invasive test does not appear to dampen satisfaction. CONCLUSION: Positive attitudes regarding DDA use and satisfaction with doctors adhering to DDA advice could encourage greater use of DDAs in consultations.

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. METHOD: GPs were randomly assigned to view 32 (out of a possible 64) vignettes online depicting children with cough. The vignettes comprised the seven STARWAVe factors, which were varied systematically. For each vignette, GPs assessed risk of deterioration in one of two ways (sliding-scale versus risk-category selection) and indicated whether they would prescribe antibiotics. Finally, GPs saw an additional vignette, suggesting that the parent was concerned. Mixed-effects regressions were used to measure the influence of STARWAVe factors, risk-elicitation method, and parental concern on GPs' assessments and decisions. RESULTS: Six STARWAVe risk factors correctly increased GPs' risk assessments (bssliding-scale≥0.66, odds ratios [ORs]category-selection≥1.75, Ps≤0.001), whereas one incorrectly reduced them (short illness duration: b sliding-scale -0.30, ORcategory-selection 0.80, P≤0.039). Conversely, one STARWAVe factor increased prescribing odds (temperature: OR 5.22, P<0.001), whereas the rest either reduced them (short illness duration, age, and recession: ORs≤0.70, Ps<0.001) or had no significant impact (wheeze, asthma, and vomiting: Ps≥0.065). Parental concern increased risk assessments (b sliding-scale 1.29, ORcategory-selection 2.82, P≤0.003) but not prescribing odds (P = 0.378). CONCLUSION: GPs use some, but not all, STARWAVe factors when making unaided risk assessments and prescribing decisions. Such discrepancies must be considered when introducing CPRs to clinical practice.

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. It is possible that the provision of diagnostic suggestions disrupts physicians' intuitive thinking and reduces their certainty in their initial diagnostic hypotheses. This may encourage them to seek more information before reaching a diagnostic conclusion, evaluate this information more objectively, and be more open to changing their initial hypotheses. Three online experiments explored the effects of early diagnostic suggestions, provided by a hypothetical decision aid, on different aspects of the diagnostic reasoning process. Family physicians assessed up to two patient scenarios with and without suggestions. We measured effects on certainty about the initial diagnosis, information search and evaluation, and frequency of diagnostic changes. We did not find a clear and consistent effect of suggestions and detected mainly non-significant trends, some in the expected direction. We also detected a potential biasing effect: when the most likely diagnosis was included in the list of suggestions (vs. not included), physicians who gave that diagnosis initially, tended to request less information, evaluate it as more supportive of their diagnosis, become more certain about it, and change it less frequently when encountering new but ambiguous information; in other words, they seemed to validate rather than question their initial hypothesis. We conclude that further research using different methodologies and more realistic experimental situations is required to uncover both the beneficial and biasing effects of early diagnostic suggestions.

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.g. general knowledge questions) and students as participants. Using the judge-advisor framework, we tested whether the principles of advice discounting apply in the clinical domain. We used realistic patient scenarios, algorithmic advice from a validated cancer risk calculator, and general practitioners (GPs) as participants. GPs could update their risk estimates after receiving algorithmic advice. Half of them received information about the algorithm's derivation, validation, and accuracy. We measured weight of advice and found that, on average, GPs weighed their estimates and the algorithm equally-but not always: they retained their initial estimates 29% of the time, and fully updated them 27% of the time. Updating did not depend on whether GPs were informed about the algorithm. We found a weak negative quadratic relationship between estimate updating and advice distance: although GPs integrate algorithmic advice on average, they may somewhat discount it, if it is very different from their own estimate. These results present a more complex picture than simple egocentric discounting of advice. They cast a more optimistic view of advice taking, where experts weigh algorithmic advice and their own judgement equally and move towards the advice even when it contradicts their own initial estimates.

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. GPs gave their risk estimates and inclination to refer. They then saw the risk score of an unnamed algorithm and could update their responses. Half of the sample was given information about the algorithm's derivation, validation, and accuracy. At the end, we measured their algorithm disposition. We analysed the data using multilevel regressions with random intercepts by GP and vignette. Results: We find that, after receiving the algorithm's estimate, GPs' inclination to refer changes 26% of the time and their decisions switch entirely 3% of the time. Decisions become more consistent with the NICE 3% referral threshold (OR 1.45 [1.27, 1.65], p < .001). The algorithm's impact is greatest when GPs have underestimated risk. Information about the algorithm does not have a discernible effect on decisions but it results in a more positive GP disposition towards the algorithm. GPs' risk estimates become better calibrated over time, i.e., move closer to the algorithm. Conclusions: Cancer risk algorithms have the potential to improve cancer referral decisions. Their use as learning tools to improve risk estimates is promising and should be further investigated.

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.K. and U.S. public between April 24 and May 12, 2020. Participants (N = 2087) rated 14 COVID-related symptoms in terms of perceived severity and induced worry. They were then asked about their intention to practise social distancing in response to three public health messages: the standard U.K. government message: "Most people will experience only mild symptoms"; the standard message "unpacked" by listing six of those symptoms as examples; and "Most people will not require hospitalisation." The unpacked message resulted in the highest intention to comply with social distancing (b = .22 [.04, .40], p = .02) and there was no interaction with country. Worry about symptoms was an independent predictor of intention to comply (b = .02 [.01, .03], p < .001). In the days before lockdown amidst a raging pandemic, the U.K. and U.S. governments sought to reassure the public. Had their messaging been more detailed, it might have been less reassuring but more effective in promoting social distancing. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician's final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding. RESULTS: Supported documentation contained significantly more codes (incidence rate ratio [IRR] = 5.76 [4.31, 7.70] P < .001) and less free text (IRR = 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b = -0.08 [-0.11, -0.05] P < .001) in the supported consultations, and this was the case for both codes and free text. CONCLUSIONS: We provide evidence that data entry in the EHR is incomplete and reflects physicians' cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.

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

Background. 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. Here, we test the generalizability of our findings to a different cancer. Methods. A total of 252 GPs responded to 48 vignettes describing patients with possible colorectal cancer. For each vignette, respondents decided whether urgent referral to a specialist was needed. They then completed the 8-item Stress from Uncertainty scale. We measured GPs' discrimination (d') and response bias (criterion; c) and their associations with organizational performance and GP demographics. We also measured correlations of d' and c between the 2 studies for the 165 GPs who participated in both. Results. As in the lung study, organizational PPV was associated with response bias: in practices with higher PPV, GPs had higher criterion (b = 0.05 [0.03 to 0.07]; P < 0.001), that is, they were less inclined to refer. As in the lung study, female GPs were more inclined to refer than males (b = -0.17 [-0.30 to -0.105]; P = 0.005). In a mediation model, stress from uncertainty did not explain the gender difference. Only response bias correlated between the 2 studies (r = 0.39, P < 0.001). Conclusions. This study confirms our previous findings regarding the relationship between provider and organizational performance and strengthens the finding of gender differences in referral decision making. It also provides evidence that response bias is a relatively stable feature of GP referral decision making.

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. PARTICIPANTS: 252 GPs, currently practising in the UK. DESIGN: GPs were randomly assigned to view four (of a possible eight) clinical vignettes online. Each vignette depicted a child presenting with cough, who was described in terms of the seven STARWAVe variables. Systematically, we manipulated patient age (20 months vs 5 years), illness duration (3 vs 6 days), vomiting (present vs absent) and wheeze (present vs absent), holding the remaining STARWAVe variables constant. OUTCOME MEASURES: Per vignette, GPs assessed risk of hospitalisation and indicated whether they would prescribe antibiotics or not. RESULTS: GPs overestimated risk of hospitalisation in 9% of vignette presentations (88/1008) and underestimated it in 46% (459/1008). Despite underestimating risk, they overprescribed: 78% of prescriptions were unnecessary relative to GPs' own risk assessments (121/156), while 83% were unnecessary relative to STARWAVe risk assessments (130/156). All four of the manipulated variables influenced risk assessments, but only three influenced prescribing decisions: a shorter illness duration reduced prescribing odds (OR 0.14, 95% CI 0.08 to 0.27, p<0.001), while vomiting and wheeze increased them (ORvomit 2.17, 95% CI 1.32 to 3.57, p=0.002; ORwheeze 8.98, 95% CI 4.99 to 16.15, p<0.001). CONCLUSIONS: Relative to STARWAVe, GPs underestimated risk of hospitalisation, overprescribed and appeared to misinterpret illness duration (prescribing for longer rather than shorter illnesses). It is important to ascertain discrepancies between CPRs and current clinical practice. This has implications for the integration of CPRs into the electronic health record and the provision of intelligible explanations to decision-makers.

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"). This is important because respondents may produce the same accuracy rate for different reasons. We employed SDT to measure the referral decision making of general practitioners (GPs) in cases of possible lung cancer. METHODS: We constructed 44 vignettes of patients for whom lung cancer could be considered and estimated their 1-year risk. Under UK risk-based guidelines, half of the vignettes required urgent referral. We recruited 216 GPs from practices across England. Practices differed in the positive predictive value (PPV) of their urgent referrals (chance of referrals identifying cancer) and the sensitivity (chance of cancer patients being picked up via urgent referral from their practice). Participants saw the vignettes online and indicated whether they would refer each patient urgently or not. We calculated each GP's discrimination ( d ') and criterion ( c) and regressed these on practice PPV and sensitivity, as well as on GP experience and gender. RESULTS: Criterion was associated with practice PPV: as PPV increased, GPs' c also increased, indicating lower inclination to refer ( b = 0.06 [0.02-0.09]; P = 0.001). Female GPs were more inclined to refer than male GPs ( b = -0.20 [-0.40 to -0.001]; P = 0.049). Average discrimination was modest ( d' = 0.77), highly variable (range, -0.28 to 1.91), and not associated with practice referral performance. CONCLUSIONS: High referral PPV at the organizational level indicates GPs' inclination to avoid false positives, not better discrimination. Rather than bluntly mandating increases in practice PPV via more referrals, it is necessary to increase discrimination by improving the evidence base for cancer referral decisions.

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. AIM: To provide an evidence-based analysis of symptoms observed in lung cancer patients prior to diagnosis. DESIGN AND SETTING: Systematic review and meta-analysis of primary and secondary care data. METHOD: Seven databases were searched (MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, Health Management Information Consortium, Web of Science, British Nursing Index and Cochrane Library). Thirteen studies were selected based on predetermined eligibility and quality criteria for diagnostic assessment to establish the value of symptom-based diagnosis using diagnosistic odds ratio (DOR) and summary receiver operating characteristic (SROC) curve. In addition, routinely collated real-time data from primary care electronic health records (EHR), TransHis, was analysed to compare with our findings. RESULTS: Haemoptysis was found to have the greatest diagnostic value for lung cancer, diagnostic odds ratio (DOR) 6.39 (3.32-12.28), followed by dyspnoea 2.73 (1.54-4.85) then cough 2.64 (1.24-5.64) and lastly chest pain 2.02 (0.88-4.60). The use of symptom-based diagnosis to accurately diagnose lung cancer cases from non-cases was determined using the summary receiver operating characteristic (SROC) curve, the area under the curve (AUC) was consistently above 0.6 for each of the symptoms described, indicating reasonable discriminatory power. The positive predictive value (PPV) of diagnostic symptoms depends on an individual's prior risk of lung cancer, as well as their presenting symptom pattern. For at risk individuals we calculated prior risk using validated epidemiological models for risk factors such as age and smoking history, then combined with the calculated likelihood ratios for each symptom to establish posterior risk or positive predictive value (PPV). CONCLUSION: Our findings show that there is diagnostic value in the clinical symptoms associated with lung cancer and the potential benefit of characterising these symptoms using routine data studies to identify high-risk patients.

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). We investigated whether physicians adapt their risk communication to accommodate the needs of patients with low numeracy, and how physicians' own numeracy influences their understanding and communication of screening statistics. METHODS: UK family physicians ( N = 151) read a description of a patient seeking advice on cancer screening. We manipulated the level of numeracy of the patient (low v. high v. unspecified) and measured physicians' risk communication, recommendation to the patient, understanding of screening statistics, and numeracy. RESULTS: Consistent with best practices, family physicians generally preferred to use visual aids rather than numbers when communicating information to a patient with low (v. high) numeracy. A substantial proportion of physicians (44%) offered high quality (i.e., complete and meaningful) risk communication to the patient. This was more often the case for physicians with higher (v. lower) numeracy who were more likely to mention mortality rates, OR=1.43 [1.10, 1.86], and harms from overdiagnosis, OR=1.44 [1.05, 1.98]. Physicians with higher numeracy were also more likely to understand that increased detection or survival rates do not demonstrate screening effectiveness, OR=1.61 [1.26, 2.06]. CONCLUSIONS: Most physicians know how to appropriately tailor risk communication for patients with low numeracy (i.e., with visual aids). However, physicians who themselves have low numeracy are likely to misunderstand the risks and unintentionally mislead patients by communicating incomplete information. High-quality risk communication and shared decision making can depend critically on factors that improve the risk literacy of physicians.

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. METHODS: Thirty-four General Practitioners (GPs) consulted with 6 standardised patients (SPs) using only their EHR system (baseline session); on another day, they consulted with 6 different but matched for difficulty SPs, using the EHR with the integrated DSS prototype (DSS session). GPs were interviewed twice (at the end of each session), and completed the Post-Study System Usability Questionnaire at the end of the DSS session. The SPs completed the Consultation Satisfaction Questionnaire after each consultation. RESULTS: The majority of GPs (74%) found the DSS useful: it helped them consider more diagnoses and ask more targeted questions. They considered three user interface features to be the most useful: (1) integration with the EHR; (2) suggested diagnoses to consider at the start of the consultation and; (3) the checklist of symptoms and signs in relation to each suggested diagnosis. There were also criticisms: half of the GPs felt that the DSS changed their consultation style, by requiring them to code symptoms and signs while interacting with the patient. SPs sometimes commented that GPs were looking at their computer more than at them; this comment was made more often in the DSS session (15%) than in the baseline session (3%). Nevertheless, SP ratings on the satisfaction questionnaire did not differ between the two sessions. CONCLUSIONS: To use the DSS effectively, GPs would need to adapt their consultation style, so that they code more information during rather than at the end of the consultation. This presents a potential barrier to adoption. Training GPs to use the system in a patient-centred way, as well as improvement of the DSS interface itself, could facilitate coding. To enhance patient acceptability, patients should be informed about the potential of the DSS to improve diagnostic accuracy.

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. Response order of prescribing/probability was manipulated in Experiment 1. RESULTS: Overall, the expectations for antibiotics increased intention to prescribe (Experiment 1, F(1, 301) = 25.32, p < .001, ηp² = .08, regardless of the response order; Experiment 2, odds ratio [OR] = 2.31, and OR = 0.75, Vignettes 1 and 2, respectively). Expectations for antibiotics did not change the perceived probability of a bacterial infection (Experiment 1, F(1, 301) = 1.86, p = .173, ηp² = .01, regardless of the response order; Experiment 2, d = -0.03, and d = +0.25, Vignettes 1 and 2, respectively). Physicians' experience was positively associated with prescribing, but it did not moderate the expectations effect on prescribing. CONCLUSIONS: Patients' and their parents' expectations increase antibiotics prescribing, but their effect is localized-it does not leak into the perceived probability of a bacterial infection. Interventions reducing the overprescribing of antibiotics should target also psychological factors. (PsycINFO Database Record

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. DESIGN AND SETTING: Within-participant design: 34 GPs consulted with six standardised patients (actors) using their usual EHR. On a different day, GPs used the EHR with the integrated DSS to consult with six other patients, matched for difficulty and counterbalanced. METHOD: Entering the reason for encounter triggered the DSS, which provided a patient-specific list of potential diagnoses, and supported coding of symptoms during the consultation. At each consultation, GPs recorded their diagnosis and management. At the end, they completed a usability questionnaire. The actors completed a satisfaction questionnaire after each consultation. RESULTS: There was an 8-9% absolute improvement in diagnostic accuracy when the DSS was used. This improvement was significant (odds ratio [OR] 1.41, 95% confidence interval [CI] = 1.13 to 1.77, P<0.01). There was no associated increase of investigations ordered or consultation length. GPs coded significantly more data when using the DSS (mean 12.35 with the DSS versus 1.64 without), and were generally satisfied with its usability. Patient satisfaction ratings were the same for consultations with and without the DSS. CONCLUSION: The DSS prototype was successfully employed in simulated consultations of high fidelity, with no measurable influences on patient satisfaction. The substantially increased data coding can operate as motivation for future DSS adoption.