Dataset Information

Correlation of clinical decision-making with probability of disease: A web-based study among general practitioners.

ABSTRACT:

Background

Medical decision-making relies partly on the probability of disease. Current recommendations for the management of common diseases are based increasingly on scores that use arbitrary probability thresholds.

Objective

To assess decision-making in pharyngitis and appendicitis using a set of clinical vignettes, and the extent to which management is congruent with the true probability of having the disease.

Design

We developed twenty-four clinical vignettes with clinical presentations corresponding to specific probabilities of having disease defined by McIsaac (pharyngitis) or Alvarado (appendicitis) scores. Each participant answered four randomly selected web-based vignettes.

Participants

General practitioners (GP) working in primary care structures in Switzerland and the USA.

Main measures

A comparison between the GP's management decision according to the true probability of having the disease and to the GP's estimated probability, investigating the GP's ability to estimate probability of disease.

Key results

The mean age of the GPs was 48 years (SD 12) and 66% were men. The correlation between the GP's clinical management decision based on the vignette and the recommendations was stronger for appendicitis than pharyngitis (kw = 0.74, 95% CI 0.70-0.78 vs. kw = 0.66, 95% CI 0.62-0.71). On the other hand, the association between the clinical management decision and the probability of disease estimated by GPs was more congruent with recommendations for pharyngitis than appendicitis (kw = 0.70, 95% CI 0.66-0.73 vs. 0.61, 95% CI 0.56-0.66). Only a minority of GPs correctly estimated the probability of disease (29% for appendicitis and 39% for pharyngitis).

Conclusions

Despite the fact that general practitioners often misestimate the probability of disease, their management decisions are usually in line with recommendations. This means that they use other approaches, perhaps more subjective, to make decisions, such as clinical judgment or reasoning that integrate factors other than just the risk of the disease.

SUBMITTER: De Alencastro L

PROVIDER: S-EPMC7595298 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Correlation of clinical decision-making with probability of disease: A web-based study among general practitioners.

De Alencastro Lionel L Locatelli Isabella I Clair Carole C Ebell Mark H MH Senn Nicolas N

PloS one 20201029 10

<h4>Background</h4>Medical decision-making relies partly on the probability of disease. Current recommendations for the management of common diseases are based increasingly on scores that use arbitrary probability thresholds.<h4>Objective</h4>To assess decision-making in pharyngitis and appendicitis using a set of clinical vignettes, and the extent to which management is congruent with the true probability of having the disease.<h4>Design</h4>We developed twenty-four clinical vignettes with clin ...[more]

PMID: 33119623

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Project description:BackgroundPsychosocial problems in children and youth are common and may negatively impact their lives and the lives of their families. Since general practitioners (GPs) play a crucial role in detecting and intervening in such problems, it is clinically necessary to improve our insight into their clinical decision-making (CDM). The objective of this study was to explore which mechanisms underlie GPs' everyday CDM and their options for management or referral.Material and methodsThis was a mixed methods study in which qualitative (interview substudy) and quantitative (online survey substudy) data were collected from GPs. Using a question framework and vignettes representative of clinical practice, GPs' CDM was explored. GPs were selected by means of an academic research network and purposive sampling. Data collection continued in constant comparison between both substudies. Using grounded theory, data from both substudies were triangulated into a flowchart consisting of mechanisms and management/referral options.ResultsCDM-mechanisms were divided into three groups. GP-related mechanisms were GPs' primary approach of the problem (somatically or psychosocially) and their self-assessed competence to solve the problem based on interest in and knowledge about youth mental health care. Mechanisms related to the child and its social context included GPs' assessment whether there was psychiatric (co)morbidity, their sense of self-limitedness of the problem and assessed complexity of the problem. Whether GPs' had existing collaboration agreements with youth care providers and how they experienced their collaboration were collaboration-related mechanisms.ConclusionThe current study contributes to a relatively unexplored research area by revealing GP's in-depth thought processes regarding their CDM. However, existing research in this area supports the identified CDM mechanisms. Future initiatives should focus on validating CDM mechanisms in a larger population. If confirmed, mechanisms could be integrated into GP training and may offer guidelines for regulating proper access to mental health care services.

Project description:BackgroundGeneral practitioners (GPs) provide the most antidepressant prescriptions and psychologist referrals in Australia, yet little is known about how they decide between treatments for depressive symptoms.AimsThis study examined the decision cues that GPs use when deciding how to treat depressive symptoms and the meaning they attribute to these associations.MethodsStructured interviews were conducted with 16 Australian GPs in a "think-aloud" verbal protocol analysis format. The transcripts were analyzed using content coding and thematic analysis, informed by the dual processes model of decision making.ResultsParticipants associated prescribing antidepressants with severe depressive symptoms, and psychologist referrals were the preferred initial treatment provided patients were willing to engage. Four main themes emerged from the thematic analysis: 1) psychologist as default, 2) the risk is just too high, 3) medication as supplement, and 4) drivers of antidepressants. Contrary to previous findings, participants identified a strong heuristic association between depressive symptoms and psychologist referral. Antidepressant prescription was associated with severe symptoms, higher risk, and a cluster of cues that lead them away from psychologist referral.ConclusionsParticipants demonstrated an oversensitivity to depressive symptom severity, associating psychologist referrals with subclinical depressive symptoms, and starting antidepressants for suicidal ideation and significant functional decline.HighlightsPsychologist referrals were the default preference for GPs when treating depressive symptoms, whereas antidepressants were reserved for patients with elevated risk from severe depressive symptoms and suicidal ideation.Participants' conceptualization of depression severity was oversensitive compared with treatment guidelines.Contrary to treatment guidelines, GPs in this study demonstrated their discomfort for referring patients with suicidal ideation to a psychologist without initiating antidepressants.GPs should consider whether patients within their mild to moderate assessment range can be treated exclusively with more cost-effective means than psychological therapy such as e-mental health programs, guided self-help, and lifestyle changes alone.

Project description:BackgroundPain medicines are widely prescribed by general practitioners (GPs) when managing people with low back pain (LBP), but little is known about what drives decisions to prescribe these medicines.ObjectivesThe aim of this study was to investigate what influences GPs' decision to prescribe pain medicines for LBP.DesignQualitative study with in-depth interviews.SettingAustralian primary care.ParticipantsWe interviewed 25 GPs practising in Australia experienced in managing LBP (mean (SD) age 53.4 (9.1) years, mean (SD) years of experience: 24.6 (9.3), 36% female). GPs were provided three vignettes describing common LBP presentations (acute exacerbation of chronic LBP, subacute sciatica and chronic LBP) and were asked to think aloud how they would manage the cases described in the vignettes.Data analysisWe summarised GP's choices of pain medicines for each vignette using content analysis and used framework analysis to investigate factors that affected GP's decision-making.ResultsGPs more commonly prescribed opioid analgesics. Anticonvulsants and antidepressants were also commonly prescribed depending on the presentation described in the vignette. GP participants made decisions about what pain medicines to prescribe for LBP largely based on previous experiences, including their own personal experiences of LBP, rather than guidelines. The choice of pain medicine was influenced by a range of clinical factors, more commonly the patient's pathoanatomical diagnosis. While many adhered to principles of judicious use of pain medicines, polypharmacy scenarios were also common. Concerns about drug-seeking behaviour, adverse effects, stigma around opioid analgesics and pressure from regulators also shaped their decision-making process.ConclusionsWe identified several aspects of decision-making that help explain the current profile of pain medicines prescribed for LBP by GPs. Themes identified by our study could inform future implementation strategies to improve the quality use of medicines for LBP.

Project description:IntroductionThe participation of general practitioners (GPs) in primary care research is variable and often poor. We aimed to develop a substantive and empirical theoretical framework to explain GPs' decision-making process to participate in research.MethodsWe used the grounded theory approach to construct a substantive theory to explain the decision-making process of GPs to participate in research activities. Five in-depth interviews and four focus group discussions were conducted among 21 GPs. Purposeful sampling followed by theoretical sampling were used to attempt saturation of the core category. Data were collected using semi-structured open-ended questions. Interviews were recorded, transcribed verbatim and checked prior to analysis. Open line-by-line coding followed by focus coding were used to arrive at a substantive theory. Memoing was used to help bring concepts to higher abstract levels.ResultsThe GPs' decision to participate in research was attributed to their inner drive and appreciation for primary care research and their confidence in managing their social and research environments. The drive and appreciation for research motivated the GPs to undergo research training to enhance their research knowledge, skills and confidence. However, the critical step in the GPs' decision to participate in research was their ability to align their research agenda with priorities in their social environment, which included personal life goals, clinical practice and organisational culture. Perceived support for research, such as funding and technical expertise, facilitated the GPs' participation in research. In addition, prior experiences participating in research also influenced the GPs' confidence in taking part in future research.ConclusionsThe key to GPs deciding to participate in research is whether the research agenda aligns with the priorities in their social environment. Therefore, research training is important, but should be included in further measures and should comply with GPs' social environments and research support.

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Project description:BackgroundAs the first point of contact for patients with health issues, general practitioners (GPs) are frequently confronted with patients presenting with non-specific symptoms of unclear origin. This can result in delayed, prolonged or false diagnoses. To accelerate and improve the diagnosis of diseases, clinical decision support systems would appear to be an appropriate tool. The objective of the project 'Smart physician portal for patients with unclear disease' (SATURN) is to employ a user-centered design process based on the requirements analysis presented in this paper to develop an artificial Intelligence (AI)-based diagnosis support system that specifically addresses the needs of German GPs.MethodsRequirements analysis for a GP-specific diagnosis support system was conducted in an iterative process with five GPs. First, interviews were conducted to analyze current workflows and the use of digital applications in cases of diagnostic uncertainty (as-is situation). Second, we focused on collecting and prioritizing tasks to be performed by an ideal smart physician portal (to-be situation) in a workshop. We then developed a task model with corresponding user requirements.ResultsNumerous GP-specific user requirements were identified concerning the tasks and subtasks: performing data entry (open system, enter patient data), reviewing results (receiving and evaluating results), discussing results (with patients and colleagues), scheduling further diagnostic procedures, referring to specialists (select, contact, make appointments), and case closure. Suggested features particularly concerned the process of screening and assessing results: e.g., the system should focus more on atypical patterns of common diseases than on rare diseases only, display probabilities of differential diagnoses, ensure sources and results are transparent, and mark diagnoses that have already been ruled out. Moreover, establishing a means of using the platform to communicate with colleagues and transferring patient data directly from electronic patient records to the system was strongly recommended.ConclusionsEssential user requirements to be considered in the development and design of a diagnosis system for primary care could be derived from the analysis. They form the basis for mockup-development and system engineering.

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Dataset Information

Correlation of clinical decision-making with probability of disease: A web-based study among general practitioners.

Background

Objective

Design

Participants

Main measures

Key results

Conclusions

Publications

Correlation of clinical decision-making with probability of disease: A web-based study among general practitioners.

Similar Datasets

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