Publications
68 results found
Pálfi B, Arora K, Prociuk D, et al., 2024, Risk prediction algorithms and clinical judgment: Impact of advice distance, social proof, and feature-importance explanations, Computers in Human Behavior, Vol: 153, ISSN: 0747-5632
Cancer risk algorithms are developed in ever-increasing numbers to support clinical decisions. However, their uptake in UK primary care remains low and there is little evidence of how they inform judgment. This study aimed to replicate and extend findings of a recent study, which found that family physicians integrated an unnamed risk algorithm in their risk estimates about hypothetical patients suspected to have colorectal cancer; consequently, their referral decisions improved. This study employed a similar methodology of presenting patient vignettes online but used a different cancer (upper gastrointestinal) and a larger physician sample (N = 215). Furthermore, it tested the impact of two interventions on algorithm uptake: a social proof nudge describing how previous study participants had found the algorithm useful, and a feature-importance explanation (graph depicting the relative contribution of symptoms and risk factors to the patients’ risk score). We provide further support that cancer risk algorithms have the potential to improve risk assessment and referral decisions, and evidence that the introduction of a simple and scalable social proof nudge can enhance algorithm uptake. Finally, we provide further support to the earlier finding that algorithms in tandem with clinical vignettes could be integrated into medical training programmes for risk assessment.
Sirota M, Habersaat KB, Betsch C, et al., 2024, We must harness the power of social and behavioural science against the growing pandemic of antimicrobial resistance, Nature Human Behaviour, Vol: 8, Pages: 11-13, ISSN: 2397-3374
Social and behavioural science offers a valuable toolkit for combating pandemics, but has not been broadly applied to tackle the rising pandemic of antimicrobial resistance.
Arora K, Kostopoulou O, Palfi B, 2023, Cancer risk algorithms in primary care: can they impact risk estimates and referral decisions?, BRITISH JOURNAL OF GENERAL PRACTICE, Vol: 73, ISSN: 0960-1643
Nurek M, Kostopoulou O, 2023, How the UK public views the use of diagnostic decision aids by physicians: a vignette-based experiment, Journal of the American Medical Informatics Association, Vol: 30, Pages: 888-898, ISSN: 1067-5027
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.
Nurek M, Hay AD, Kostopoulou O, 2023, Online experiment comparing GPs’ antibiotic prescribing decisions to a clinical prediction rule, British Journal of General Practice, Vol: 73, Pages: e176-e185, ISSN: 0960-1643
Background: The“STARWAVe” clinical prediction rule (CPR) uses seven factors toguide risk assessment and antibiotic prescribing in children with cough (Short illnessduration, 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 methodsof obtaining risk assessments and tested the impact of parental concern.Design and setting: Experiment comprising clinical vignettes administered to 188 UKGPs online.Method: GPs were randomly assigned to view 32 (of 64) vignettes depicting childrenwith cough. Vignettes varied the STARWAVe factors systematically. Per vignette, GPsassessed risk of deterioration in one of two ways (sliding scale vs. risk categoryselection) and indicated whether they would prescribe antibiotics. Finally, they saw anadditional vignette, suggesting that the parent was concerned. Using mixed-effectsregressions, we measured the influence of STARWAVe factors, risk elicitationmethod, 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 reducedthem (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) whilethe rest either reduced them (short duration, age, recession: ORs0.70, ps<0.001) orhad no significant impact (wheeze, asthma, vomiting: ps0.065). Parental concernincreased risk assessments (bsliding-scale=1.29, ORcategory-selection=2.82, ps0.003) butnot prescribing (p=0.378).Conclusion: GPs use some, but not all, STARWAVe factors when making unaidedrisk assessments and prescribing decisions. Such discrepancies must be consideredwhen introducing CPRs to clinical practice.
Kourtidis P, Nurek M, Delaney B, et al., 2022, Influences of early diagnostic suggestions on clinical reasoning, Cognitive Research: Principles and Implications, Vol: 7, ISSN: 2365-7464
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.
Palfi B, Arora K, Kostopoulou O, 2022, Algorithm-based advice taking and clinical judgement: impact of advice distance and algorithm information, Cognitive Research: Principles and Implications, Vol: 7, ISSN: 2365-7464
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.
Kostopoulou O, Arora K, Palfi B, 2022, Using cancer risk algorithms to improve risk estimates and referral decisions, Communications Medicine, Vol: 2, ISSN: 2730-664X
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.
Kostopoulou O, Schwartz A, 2021, To unpack or not? Testing public health messaging about COVID-19, Journal of Experimental Psychology: Applied, Vol: 27, Pages: 751-761, ISSN: 1076-898X
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.
Nurek M, Delaney B, Kostopoulou O, 2021, GENERAL PRACTITIONERS' RISK ASSESSMENTS AND ANTIBIOTIC PRESCRIBING DECISIONS IN CHILDREN WITH COUGH: A VIGNETTE STUDY, Publisher: SAGE PUBLICATIONS INC, Pages: E51-E52, ISSN: 0272-989X
Kourtidis P, Nurek M, Delaney B, et al., 2021, INFLUENCES OF DIAGNOSTIC SUGGESTIONS ON CLINICAL REASONING, Publisher: SAGE PUBLICATIONS INC, Pages: E262-E264, ISSN: 0272-989X
Kostopoulou O, Tracey C, Delaney B, 2021, Can decision support combat incompleteness and bias in routine primary care data?, Journal of the American Medical Informatics Association, ISSN: 1067-5027
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 di- agnoses, the DSS facilitates data coding. We compared the documentation from consultations with the elec- tronic 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 re- lated to their diagnosis, while in supported consultations, they would also document other observations as a re- sult of exploring more diagnoses and/or ease of coding.Results: Supported documentation contained significantly more codes (incidence rate ratio [IRR] 1⁄4 5.76 [4.31, 7.70] P < .001) and less free text (IRR 1⁄4 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b 1⁄4 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.
Kostopoulou O, Nurek M, Delaney B, 2020, Disentangling the relationship between physician and organizational performance: a signal detection approach, Medical Decision Making, Vol: 40, Pages: 746-755, ISSN: 0272-989X
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.
Nurek M, Delaney BC, Kostopoulou O, 2020, Risk assessment and antibiotic prescribing decisions in children presenting to UK primary care with cough: a vignette study, BMJ Open, Vol: 10, ISSN: 2044-6055
Objectives: The validated “STARWAVe” clinical prediction rule (CPR) uses seven variables to guide risk assessment and antimicrobial stewardship in children presenting with cough(Short illness duration, Temperature, Age, Recession, Wheeze, Asthma,Vomiting). 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 hospitalisationin 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 relativeto 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 0.08-0.27, p<0.001), while vomiting and wheeze increased them (ORvomit2.17, 95% CI 1.32-3.57, p=0.002; ORwheeze8.98, 95% CI 4.99-16.15, p<0.001). Conclusions: Relative to STARWAVe, GPs underestimated riskof hospitalisation, overprescribed, and appeared to
Ramtale S, Delaney B, Kostopoulou O, 2020, USING A DIAGNOSTIC AID CHANGES PHYSICIAN BEHAVIOR IN THE CONSULTATION, Publisher: SAGE PUBLICATIONS INC, Pages: E270-E271, ISSN: 0272-989X
Tracey C, Delaney B, Kostopoulou O, 2020, THE USE OF DIAGNOSTIC DECISION SUPPORT CAN REDUCE BIAS IN CLINICAL DOCUMENTATION, Publisher: SAGE PUBLICATIONS INC, Pages: E270-E270, ISSN: 0272-989X
Tracey C, Delaney B, Kostopoulou O, 2019, Impact of a diagnostic decision support system on GP clinical documentation, Publisher: ROYAL COLL GENERAL PRACTITIONERS, ISSN: 0960-1643
Kostopoulou O, Nurek M, Cantarella S, et al., 2019, Referral decision making of General Practitioners: a signal detection study, Medical Decision Making, Vol: 39, Pages: 21-31, ISSN: 0272-989X
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 discrimina
Okoli GN, Kostopoulou O, Delaney BC, 2018, Correction: 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, PLoS One, Vol: 13, Pages: 1-1, ISSN: 1932-6203
Okoli GN, Kostopoulou O, Delaney BC, 2018, 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, PLoS ONE, Vol: 13, ISSN: 1932-6203
BackgroundLung 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.AimTo provide an evidence-based analysis of symptoms observed in lung cancer patients prior to diagnosis.Design and settingSystematic review and meta-analysis of primary and secondary care data.MethodSeven 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.ResultsHaemoptysis 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 t
Petrova D, Kostopoulou O, Delaney BD, et al., 2018, Strengths and gaps in physicians’ risk communication: a scenario study of the influence of numeracy on cancer screening communication, Medical Decision Making, Vol: 38, Pages: 355-365, ISSN: 0272-989X
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.
Delaney BC, Kostopoulou O, 2017, Decision support for diagnosis should become routine in 21st century primary care, British Journal of General Practice, ISSN: 0960-1643
Porat T, Delaney BC, Kostopoulou, 2017, The impact of a diagnostic decision support system on the consultation: perceptions of GPs and patients, BMC Medical Informatics and Decision Making, Vol: 17, ISSN: 1472-6947
BackgroundClinical 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.MethodsThirty-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.ResultsThe 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.ConclusionsTo 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
Corrigan D, Munelley G, Kazienko P, et al., 2017, Requirements and validation of a prototype learning health system for clinical diagnosis, Learning Health Systems, Vol: 1, ISSN: 2379-6146
IntroductionDiagnostic 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.MethodsWe 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/ConclusionsSix 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.
Sirota M, Kostopoulou O, Round T, et al., 2017, Prevalence and alternative explanations influence cancer diagnosis: an experimental study with physicians, Health Psychology, Vol: 36, Pages: 477-485, ISSN: 1930-7810
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. Within a theoretical framework of the hypothesis generation (HyGene) model, together with psychological literature, we studied how 2 factors—cancer prevalence and an alternative explanation for the patient’s symptoms—impede early cancer detection, as well as prompt patient management. Method: Three hundred family physicians diagnosed and managed 2 patient cases, where cancer was a possible diagnosis (one colorectal cancer, the other lung cancer). We employed a 2 (cancer prevalence: low vs. high) × 2 (alternative explanation: present vs. absent) between-subjects design. Cancer prevalence was manipulated by changing either patient age or sex; the alternative explanation for the symptoms was manipulated by adding or removing a relevant clinical history. Each patient consulted twice. Results: In a series of random-intercept logistic models, both higher prevalence (OR = 1.92, 95% confidence interval [CI 1.27, 2.92]) and absence of an alternative explanation (OR = 1.70, 95% CI [1.11, 2.59]) increased the likelihood of a cancer diagnosis, which, in turn, increased the likelihood of prompt referral (OR = 22.84, 95% CI [16.14, 32.32]). Conclusions: These findings confirm the probabilistic nature of the diagnosis generation process and validate the application of the HyGene model to early cancer detection. Increasing the salience of cancer—such as listing cancer as a diagnostic possibility—during the initial hypothesis generation phase may improve early cancer detection. (PsycINFO Database Record).
Sirota M, Round T, Samaranayaka S, et al., 2017, Expectations for antibiotics increase their prescribing: causal evidence about localized impact, Health Psychology, Vol: 36, Pages: 402-409, ISSN: 1930-7810
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, η p² = .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 (c) 2017 APA, all rights reserved)
Kostopoulou O, Porat T, Corrigan D, et al., 2017, Diagnostic accuracy of GPs when using an early-intervention decision support system: a high-fidelity simulation, British Journal of General Practice, Vol: 67, Pages: e201-e208, ISSN: 1478-5242
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.
Nurek M, Kostopoulou O, 2016, What You Find Depends on How You Measure It: Reactivity of Response Scales Measuring Predecisional Information Distortion in Medical Diagnosis, PLOS One, Vol: 11, ISSN: 1932-6203
“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 infor
Kostopoulou O, 2016, The transient nature of utilities and health preferences, MEDICAL DECISION MAKING, Vol: 26, Pages: 304-306, ISSN: 0272-989X
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Kostopoulou O, Sirota M, Round T, et al., 2016, The role of physicians’ first impressions in the diagnosis of possible cancers without alarm symptoms, Medical Decision Making, Vol: 37, Pages: 9-16, ISSN: 1552-681X
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 < 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.
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