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.

Utility of the Infantile Hemangioma Referral Score (IHReS) as a decision-making tool for referral to treatment

BackgroundThe general paediatricians and primary care physicians sometimes face immense difficulty in referral judgements regarding which infantile hemangiomas (IHs) require referrals and when is the appropriate time to refer IHs for treatment. This resulted in the treatment being delayed beyond IHs’ critical timeframe. The Infantile Hemangioma Referral Score (IHReS) has been recently developed, with the aim to solve this problem.ObjectivesThe objective of the present study is to evaluate the reliability of IHReS and to assess the possibility of using this instrument in our country where a similar problem of delaying treatment of IHs is currently existing.MethodsThe present study was a prospective, cross-sectional study. Thirteen selected clinical cases were used to assess the reliability of IHReS among physicians who may have had the chance to deal with patients with IHs. The target physicians across the country were asked to participate in the study via an online platform (Google Forms) to decide whether to refer patients with IHs for treatment or observe. There were 3 steps of assessment: step 1, usual practice evaluation; step 2, using IHReS; step 3, retesting by using IHReS.ResultsSubstantial agreement was observed after using IHReS (step 2) for interrater reliability, with Fleiss’ Kappa values of 0.80 and 0.78 among IH experts and non-expert physicians, respectively. Regarding repeatability, in the test–retest assessments, Cohen’s Kappa coefficient values revealed almost perfect agreement in intrarater repeatability for both experts and non-expert physicians (1.00).ConclusionIHReS is a simple, easy-to-assess tool for non-expert physicians. The benefit in the increase of interrater agreement was found in both IH experts and non-expert physicians. It has had the reliability to be used in making referral decisions regarding patients with IH for treatment among Thai physicians. Using IHReS can improve clinical outcomes by identifying which patient needs early intervention to minimise the possible complications.

Beyond Efficacy and Effectiveness: Clinical Efficiency Is Necessary for Dissemination

Nearly all patients interact with critical gatekeepers—insurance companies or centralized healthcare systems. For mental health dissemination efforts to be successful, these gatekeepers must refer patients to evidence-based care. To make these referral decisions, they require evidence about the amount of resources expended to achieve therapeutic gains. Without this information, a bottleneck to widespread dissemination of evidence-based care will remain. To address this need for information, we introduce a new perspective, clinical efficiency. This approach directly ties resource usage to clinical outcomes. We highlight how cost-effectiveness approaches and other strategies can address clinical efficiency, and we also introduce a related new metric, the incremental time efficiency ratio (ITER). The ITER is particularly useful for quantifying the benefits of low-intensity and concentrated interventions, as well as stepped-care approaches. Given that stakeholders are increasingly requiring information on resource utilization, the ITER is a metric that can be estimated for past and future clinical trials. As a result, the ITER can allow researchers to better communicate desirable aspects of treatment, and an increased focus on clinical efficiency can improve our ability to deliver high-quality treatment to more patients in need.

Cancer risk algorithms in primary care: can they improve risk estimates and referral decisions?

Background: Cancer risk calculators were introduced to clinical practice in the last decade, but they remain underused. We aimed to test their potential to improve risk assessment and 2-week-wait referral decisions. Methods: 157 GPs were presented with 23 vignettes describing patients with possible colorectal cancer symptoms. GPs gave their intuitive risk estimate and inclination to refer. They then saw the risk score of an algorithm (QCancer was not named) 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. Results: GPs changed their inclination to refer 26% of the time and switched decisions entirely 3% of the time. Post-algorithm decisions improved significantly vis-a-vis the 3% NICE threshold (OR 1.45 [1.27, 1.65], p<.001). The algorithm's impact was greater where GPs had underestimated risk. GPs who received information about the algorithm had more positive disposition towards it. A learning effect was observed: GPs' intuitive risk estimates became better calibrated over time, i.e., moved closer to QCancer. Conclusions: Cancer risk calculators have the potential to improve 2-week-wait referral decisions. Their use as learning tools to improve intuitive risk estimates is promising and should be further investigated.

Reward Design for Customer Referral Programs: Reward–Product Congruence Effect and Gender Difference

Referral reward design is the core component of customer referral programs, which are often applied to recruit new customers. This research investigates the effectiveness of utilitarian vs. hedonic rewards in terms of referral generation. Through one field study and two laboratory studies, we demonstrate a reward–product congruency effect; that is, utilitarian rewards, compared with hedonic rewards, yield a higher referral likelihood for utilitarian products, while the opposite holds true for hedonic products. However, such a congruency effect would be crippled by gender segmentation. When males make referral decisions toward hedonic products, the effectiveness of utilitarian rewards is at least equal to that of hedonic rewards. When females make referral decisions toward utilitarian products, there is no difference in effectiveness between utilitarian and hedonic rewards. These findings provide novel insights into referral reward design.

The establishment of a multi-source dataset of images on common oral lesions

Purpose To establish an oral lesion image database that could accelerate the development of artificial intelligence systems for lesion recognition and referral decision. Materials and Methods We describe the establishment of a multi-sourced image dataset through the development of a platform for the collection and annotation of images. Further, we developed a used-friendly tool (MeMoSA® ANNOTATE) for systematic annotation to collect a rich dataset associated with the images. We evaluated the sensitivities comparing referral decisions through the annotation process with the clinical diagnosis of the lesions to identify lesions that are challenging to identify through images alone. Results The image repository hosts 2474 images of oral lesions consisting of oral cancer, oral potentially malignant disorders, benign lesions, normal anatomical variants and normal mucosa that were collected through our platform, MeMoSA® UPLOAD. Over 800 images were annotated by seven oral medicine specialists on MeMoSA®ANNOTATE, to mark the lesion and to collect clinical labels. The sensitivity in referral decision for all lesions that required a referral for cancer management/surveillance was moderate to high depending on the type of lesion (64.3–100%). Conclusion This is the first description of a database with well-annotated oral lesions. This database has already been used for the development of AI algorithm for classifying oral lesions. Further expansion of this database could accelerate the improvement in AI algorithms that can facilitate the early detection of oral potentially malignant disorders and oral cancer.

Patient and GP experiences of pathways to diagnosis of a second primary cancer: a qualitative study

Background More people are surviving a first primary cancer and experiencing a second, different cancer. However, little is known about the diagnostic journeys of patients with second primary cancer (SPC). This study explores the views of patients and general practitioners (GPs) on their experiences of pathways to diagnosis of SPC, including the influence of a previous diagnosis of cancer on symptom appraisal, help-seeking and referral decisions. Methods Qualitative interviews with patients with a SPC diagnosis and case-linked GP interviews in a Scottish primary care setting. In-depth face to face or telephone interviews were conducted, underpinned by a social constructionist approach. Interviews were transcribed and Braun and Clarke’s thematic analysis undertaken. Three analysts from the research team read transcripts and developed the coding framework using QSR NVivo version 10, with input from a fourth researcher. Themes were developed from refined codes and interpreted in the context of existing literature and theory. Results Interviews were conducted with 23 patients (aged 43–84 years) with a SPC diagnosis, and 7 GPs. Five patient themes were identified: Awareness of SPC, symptom appraisal and help-seeking, pathways to diagnosis, navigating the healthcare system, and impact of SPC. GPs interviews identified: experience and knowledge of SPC and referrals and decision-making. Conclusions Insights into the pathway to diagnosis of SPC highlights the need for increased awareness of and vigilance for SPC among patients and healthcare providers (HCPs), and emotional support to manage the psychosocial burden.

Validation of a measure of parental responsiveness: Comparison of the brief Parental Responsiveness Rating Scale with a detailed measure of responsive parental behaviours

Parental responsiveness is vital for child language development. Its accurate measurement in clinical settings could identify families who may benefit from preventative interventions; however, coding of responsiveness is time-consuming and expensive. This study investigates in a clinical context the validity of the Parental Responsiveness Rating Scale (PaRRiS): a time- and cost-effective global rating scale of parental responsiveness. Child health nurse (CHN) PaRRiS ratings are compared to a detailed coding of parental responsiveness. Thirty parent–child dyads completed an 8-min free-play session at their 27-month health review. CHNs rated the interaction live using PaRRiS. Videos of these interactions were then blindly coded using the more detailed coding system. PaRRiS ratings and detailed codings were compared using correlational analysis and the Bland–Altman method. PaRRiS and the detailed coding showed a moderate-strong correlation ( rs (28) = 0.57, 95% CI [0.26, 0.77]) and high agreement (Bland–Altman). CHNs using PaRRiS can capture parental responsiveness as effectively as trained clinicians using detailed coding. This may allow (1) increased accuracy and efficiency in identifying toddlers at risk for long-term language difficulties; (2) more accurate allocation to speech and language therapy (SLT) services; (3) decreased burden on SLT resources by empowering CHNs to make more informed referral decisions.