Predecisional information distortion in physicians’ diagnostic judgments: Strengthening a leading hypothesis or weakening its competitor?

© 2014.Decision makers have been found to bias their interpretation of incoming information to support an emerging judgment (predecisional information distortion). This is a robust finding in human judgment, and was recently also established and measured in physicians’ diagnostic judgments (Kostopoulou et al. 2012). The two studies reported here extend this work by addressing the constituent modes of distortion in physicians. Specifically, we studied whether and to what extent physicians distort information to strengthen their leading diagnosis and/or to weaken a competing diagnosis. We used the “stepwise evolution of preference” method with three clinical scenarios, and measured distortion on separate rating scales, one for each of the two competing diagnoses per scenario.In Study 1, distortion in an experimental group was measured against the responses of a separate control group. In Study 2, distortion in a new experimental group was measured against participants’ own, personal responses provided under control conditions, with the two response conditions separated by amonth. The two studies produced consistent results. On average, we found considerable distortion of information to weaken the trailing diagnosis but little distortion to strengthen the leading diagnosis. We also found individual differences in the tendency to engage in either mode of distortion. Given that two recent studies found both modes of distortion in lay preference (Blanchard, Carlson & Meloy, 2014; DeKay, Miller, Schley & Erford, 2014), we suggest that predecisional information distortion is affected by participant and task characteristics. Our findings contribute to the growing research on the different modes of predecisional distortion and their stability to methodological variation

Information search and information distortion in the diagnosis of an ambiguous presentation

Physicians often encounter diagnostic problems with ambiguous and conflicting features. What are they likely to do in such situations? We presented a diagnostic scenario to 84 family physicians and traced their information gathering, diagnoses and management. The scenario contained an ambiguous feature, while the other features supported either a cardiac or a musculoskeletal diagnosis. Due to the risk of death, the cardiac diagnosis should be considered and managed appropriately. Forty-seven participants (56%) gave only a musculoskeletal diagnosis and 45 of them managed the patient inappropriately (sent him home with painkillers). They elicited less information and spent less time on the scenario than those who diagnosed a cardiac cause. No feedback was provided to participants. Stimulated recall with 52 of the physicians revealed differences in the way that the same information was interpreted as a function of the final diagnosis. The musculoskeletal group denigrated important cues, making them coherent with their representation of a pulled muscle, whilst the cardiac group saw them as evidence for a cardiac problem. Most physicians indicated that they were fairly or very certain about their diagnosis. The observed behaviours can be described as coherencebased reasoning, whereby an emerging judgment influences the evaluation of incoming information, so that confident judgments can be achieved even with ambiguous, uncertain and conflicting information. The role of coherence-based reasoning in medical diagnosis and diagnostic error needs to be systematically examined

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

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

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. They saw 6 patient cases, 3 of which could be cancers. Each cancer case included 2 consultations, whereby each patient consulted again with nonimproving and some new symptoms. After reading an introduction (patient description and presenting problem), physicians could request more information, which the researcher displayed online. In 2 of the possible cancers, physicians thought aloud. Two raters coded independently the physicians’ first utterances (after reading the introduction but before requesting more information) as either acknowledging the possibility of cancer or not. We measured the association of these first impressions with the final diagnoses and management decisions. Results. The raters coded 297 verbalizations with high interrater agreement (Kappa = 0.89). When the possibility of cancer was initially verbalized, the odds of subsequently diagnosing it were on average 5 times higher (odds ratio 4.90 [95% CI 2.72 to 8.84], P &lt; 0.001), while the odds of appropriate referral doubled (OR 1.98 [1.10 to 3.57], P = 0.002). The number of cancer-related questions physicians asked mediated the relationship between first impressions and subsequent diagnosis, explaining 29% of the total effect. Conclusion. We measured a strong association between family physicians’ first diagnostic impressions and subsequent diagnoses and decisions. We suggest that interventions to influence and support the diagnostic process should target its early stage of hypothesis generation. </jats:p

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.This study was partly funded by the National Awareness and Early Diagnosis Initiative, grant number C33754/A1787

Online experiment comparing GPs’ antibiotic prescribing decisions to a clinical prediction rule

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 General Practitioners’ (GPs) unaided risk assessments and prescribing decisions. We also explored two methods of obtaining risk assessments and tested the impact of parental concern. Design and setting: Experiment comprising clinical vignettes administered to 188 UK GPs online. Method: GPs were randomly assigned to view 32 (of 64) vignettes depicting children with cough. Vignettes varied the STARWAVe factors systematically. Per vignette, GPs assessed risk of deterioration in one of two ways (sliding scale vs. risk category selection) and indicated whether they would prescribe antibiotics. Finally, they saw an additional vignette, suggesting that the parent was concerned. Using mixed-effects regressions, we measured 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-scale0.66, ORscategory-selection1.61, ps0.001) while one incorrectly reduced them (short duration: bsliding-scale=-0.31, ORcategory-selection=0.75, ps0.039). Conversely, one STARWAVe factor increased prescribing odds (fever: OR=5.22, p<0.001) while the rest either reduced them (short duration, age, recession: ORs0.70, ps<0.001) or had no significant impact (wheeze, asthma, vomiting: ps0.065). Parental concern increased risk assessments (bsliding-scale=1.29, ORcategory-selection=2.82, ps0.003) but not prescribing (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

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. The learning health system (LHS) provides a suitable infrastructure for development of a new breed of learning decision support tools. These tools exploit the potential for appropriate use of the growing volumes of aggregated sources of electronic health records. Methods We describe the experiences of the TRANSFoRm project developing a diagnostic decision support infrastructure consistent with the wider goals of the LHS. We describe an architecture that is model driven, service oriented, constructed using open standards, and supports evidence derived from electronic sources of patient data. We describe the architecture and implementation of 2 critical aspects for a successful LHS: the model representation and translation of clinical evidence into effective practice and the generation of curated clinical evidence that can be used to populate those models, thus closing the LHS loop. Results/Conclusions Six core design requirements for implementing a diagnostic LHS are identified and successfully implemented as part of this research work. A number of significant technical and policy challenges are identified for the LHS community to consider, and these are discussed in the context of evaluating this work: medico-legal responsibility for generated diagnostic evidence, developing trust in the LHS (particularly important from the perspective of decision support), and constraints imposed by clinical terminologies on evidence generation

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). To address this, recent research introduced new response scales to SEP. We explore whether and how these new response scales might influence the very proleader and antitrailer processes that they were designed to capture (“reactivity”). We used the SEP method with concurrent verbal reporting: fifty family physicians verbalized their thoughts as they evaluated patient symptoms and signs (“cues”) in relation to two competing diagnostic hypotheses. Twenty-five physicians evaluated each cue using the response scale traditional to SEP (a single response scale, returning a single measure of distortion); the other twenty-five did so using the response scales introduced in recent studies (two separate response scales, returning two separate measures of distortion: proleader and antitrailer). We measured proleader and antitrailer processes in verbalizations, and compared verbalizations in the single-scale and separate-scales groups. Response scales did not appear to affect proleader processes: the two groups of physicians were equally likely to bolster their leading diagnosis verbally. Response scales did, however, appear to affect antitrailer processes: the two groups denigrated their trailing diagnosis verbally to differing degrees. Our findings suggest that the response scales used to measure information distortion might influence its constituent processes, limiting their generalizability across and beyond experimental studies

Determination of the effect of collars containing 10% w/w imidacloprid and 4.5% w/w flumethrin (Seresto®) on the incidence of Leishmania and other canine vector-borne pathogen infections in Greece

Background: The objective of this field study was to assess the effect of treating a considerable portion of a dog population naturally exposed to canine vector-borne pathogens (CVBPs) in endemic areas with a 10% w/w imidacloprid/4.5% w/w flumethrin collar (Seresto®) on the transmission of CVBPs and the resulting incidence of infection. Methods: A total of 479 dogs from two sites were enrolled in the study. Collars were placed on all dogs continuously for 21 months, with replacement of the collar every 7 months. All dogs were examined, including body weight and blood/conjunctival swab collections, every 7 months. Serum samples were analysed for the presence of antibodies against Leishmania infantum, Ehrlichia canis and Anaplasma phagocytophilum. PCR assays were also performed on blood samples and conjunctival swab collected from the dogs for the presence of L. infantum, and on blood samples only for the presence of Ehrlichia spp. and Anaplasma spp. Sand flies were collected, identified to species level and molecularly tested for L. infantum throughout two vector activity seasons. Results: The results showed that the Seresto collar was safe with continuous use. At study inclusion, 419, 370 and 453 dogs tested negative for L. infantum, Ehrlichia spp. and Anaplasma spp., respectively (353 dogs tested negative for any pathogen). Overall, 90.2% of the dogs were protected from L. infantum infection on both sites combined. The entomological survey confirmed the presence of competent vectors of L. infantum at all monitored locations, namely the sand flies Phlebotomus neglectus and Phlebotomus tobbi, both of which are regarded as the most important competent vectors in the Mediterranean basin. All captured sand flies tested negative for L. infantum. Protection against ticks and fleas was high, with only two dogs showing a low number of ticks and seven dogs having low numbers of fleas at single evaluation time points. Across the entire study population, a number of dogs became infected with tick-transmitted pathogens, but prevention of transmission was 93% for E. canis and 87.2% for Anaplasma spp. when all cases from both sites were combined. Conclusions: The Seresto® (10% w/w imidacloprid/4.5% w/w flumethrin) collar significantly reduced the risk of CVBP transmission when compared to previously observed incidences of CVBP infections in two highly endemic areas under field conditions