Dataset Information

How is diagnostic uncertainty communicated and managed in real world primary care settings?

ABSTRACT:

Background

Managing diagnostic uncertainty is a major challenge in primary care due to factors such as the absence of definitive tests, variable symptom presentations and disease evolution. Maintaining patient trust during a period of investigative uncertainty, whilst minimising scope for diagnostic error is a challenge. Mismanagement can lead to diagnostic errors, treatment delays, and suboptimal patient outcomes.

Objective

Our aim was to explore how UK primary care physicians (GPs) address and communicate diagnostic uncertainty in practice.

Design

This qualitative study used video and audio-recordings. Verbatim transcripts were coded with a modified, validated tool to capture GPs' actions and communication in primary care consultations that included diagnostic uncertainty. The tool includes items relating to advice regarding new symptoms or symptom deterioration (sometimes called 'safety netting'). Video data was analysed to identify GP and patient body postures during and after the delivery of the management plan.

Participants

All patient participants had a consultation with a GP, were over the age of 50 and had (1) at least one new presenting problem or (2) one persistent problem that was undiagnosed.

Approach

Data collection occurred in GP-patient consultations during 2017-2018 across 7 practices in UK during 2017-2018.

Key results

GPs used various management strategies to address diagnostic uncertainty, including (1) symptom monitoring without treatment, (2) prescribed treatment with symptom monitoring, and (3) addressing risks that could arise from administrative tasks. GPs did not make management plans for potential treatment side effects. Specificity of uncertainty management plans varied among GPs, with only some offering detailed actions and timescales. The transfer of responsibility for the management plan to patients was usually delivered rather than negotiated, with most patients confirming acceptance before concluding the discussion.

Conclusions

We offer guidance to healthcare professionals, improving awareness of using and communicating management plans for diagnostic uncertainty.

SUBMITTER: Russell J

PROVIDER: S-EPMC11318185 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

How is diagnostic uncertainty communicated and managed in real world primary care settings?

Russell Jessica J Boswell Laura L Ip Athena A Harris Jenny J Singh Hardeep H Meyer Ashley N D AND Giardina Traber D TD Bhuiya Afsana A Whitaker Katriina L KL Black Georgia B GB

BMC primary care 20240812 1

<h4>Background</h4>Managing diagnostic uncertainty is a major challenge in primary care due to factors such as the absence of definitive tests, variable symptom presentations and disease evolution. Maintaining patient trust during a period of investigative uncertainty, whilst minimising scope for diagnostic error is a challenge. Mismanagement can lead to diagnostic errors, treatment delays, and suboptimal patient outcomes.<h4>Objective</h4>Our aim was to explore how UK primary care physicians (G ...[more]

PMID: 39135159

Similar Datasets

Project description:BackgroundDiagnostic uncertainty is one of the largest contributory factors to the occurrence of diagnostic errors across most specialties in medicine and arguably uncertainty is greatest in primary care due to the undifferentiated symptoms primary care physicians are often presented with. Physicians can respond to diagnostic uncertainty in various ways through the interplay of a series of cognitive, emotional and ethical reactions. The consequences of such uncertainty however can impact negatively upon the primary care practitioner, their patients and the wider healthcare system. Understanding the nature of the existing empirical literature in relation to managing diagnostic uncertainty in primary medical care is a logical and necessary first step in order to understand what solutions are already available and/or to aid the development of any training or feedback aimed at better managing this uncertainty. This review is the first to characterize the existing empirical literature on managing diagnostic uncertainty in primary care.MethodsSixteen databases were systematically searched from inception to present with no restrictions. Hand searches of relevant websites and reference lists of included studies were also conducted. Two authors conducted abstract/article screening and data extraction. PRISMA guidelines were adhered to.ResultsTen studies met the inclusion criteria. A narrative and conceptual synthesis was undertaken under the premises of critical reviews. Results suggest that studies have focused on internal factors (traits, skills and strategies) associated with managing diagnostic uncertainty with only one external intervention identified. Cognitive factors ranged from the influences of epistemological viewpoints to practical approaches such as greater knowledge of the patient, utilizing resources to hand and using appropriate safety netting techniques. Emotional aspects of uncertainty management included clinicians embracing uncertainty and working with provisional diagnoses. Ethical aspects of uncertainty management centered on communicating diagnostic uncertainties with patients. Personality traits and characteristics influenced each of the three domains.ConclusionsThere is little empirical evidence on how uncertainty is managed in general practice. However we highlight how the extant literature can be conceptualised into cognitive, emotional and ethical aspects of uncertainty which may help clinicians be more aware of their own biases as well as provide a platform for future research.Trial registrationPROSPERO registration: CRD42015027555.

Project description:IntroductionPneumonia is among the most prevalent complications of influenza. The purpose of this study is to quantify the burden of pneumonia among hospitalized patients with influenza.MethodsReal-world retrospective data from 01JAN2014-30JUN2019 (study period) were obtained from Optum's de-identified Clinformatics® Data Mart Database (2007-2020) for patients who had ≥ 1 diagnosis for influenza during the identification period and ≥ 1 all-cause inpatient visit within 1 day of diagnosis. Cases had ≥ 1 diagnosis claim for an influenza-related pneumonia within the 30 days after the initial influenza diagnosis date. Controls had no evidence of influenza-related pneumonia in the 30 days following the initial influenza diagnosis. Final 1:1 matching was determined using propensity score matching (PSM). Statistical significance between the cohorts was tested.ResultsAfter PSM, there were 4878 hospitalized patients with influenza in each of the case and control groups. During the index hospitalization, cases vs. controls had longer length of stay [Mean (standard deviation): 6.5 (8.3) vs. 1.9 (3.7)], greater intensive care unit (ICU) use (38.4 vs. 16.8%), and greater mechanical ventilation use (invasive: 11.4 vs. 2.3%; non-invasive: 6.8 vs. 2.6%) (all p < 0.001). Cases also had higher readmission rates than controls (12.3 vs. 3.5% within 30 days; 20.0 vs. 6.1% within 90 days; p < 0.001 for both). Post-index date direct all-cause healthcare costs were higher for cases than for controls (median total cost: $18,428 vs. $621 for 30 days; $21,774 vs. $3312 for 90 days; $25,960 vs. $8699 for 6 months; $35,875 vs. $21,619 for 1 year; all p < 0.001).ConclusionsPneumonia as a complication of influenza increases risk of mortality and leads to greater healthcare resource use and direct medical costs among patients hospitalized with influenza. These effects are seen early during the index hospitalization and within the first 30 days after diagnosis, but their impact continues throughout a year of follow-up.

Project description:BackgroundDepression is prevalent in primary care (PC) practices and poses a considerable public health burden in the United States. Despite nearly four decades of efforts to improve depression care quality in PC practices, a gap remains between desired treatment outcomes and the reality of how depression care is delivered.ObjectiveThis article presents a real-world PC practice model of depression care, elucidating the processes and their influencing conditions.DesignGrounded theory methodology was used for the data collection and analysis to develop a depression care model. Data were collected from 70 individual interviews (60 to 70 min each), three focus group interviews (n = 24, 2 h each), two surveys per clinician, and investigators' field notes on practice environments. Interviews were audiotaped and transcribed for analysis. Surveys and field notes complemented interview data.ParticipantsSeventy primary care clinicians from 52 PC offices in the Midwest: 28 general internists, 28 family physicians, and 14 nurse practitioners.Key resultsA depression care model was developed that illustrates how real-world conditions infuse complexity into each step of the depression care process. Depression care in PC settings is mediated through clinicians' interactions with patients, practice, and the local community. A clinician's interactional familiarity ("familiarity capital") was a powerful facilitator for depression care. For the recognition of depression, three previously reported processes and three conditions were confirmed. For the management of depression, 13 processes and 11 conditions were identified. Empowering the patient was a parallel process to the management of depression.ConclusionsThe clinician's ability to develop and utilize interactional relationships and resources needed to recognize and treat a person with depression is key to depression care in primary care settings. The interactional context of depression care makes empowering the patient central to depression care delivery.

Project description:BackgroundThe past few decades saw considerable advances in research and dissemination of evidence-based psychotherapies, yet available treatment resources are not able to meet the high need for care for individuals suffering from depression or anxiety. Blended care psychotherapy, which combines the strengths of therapist-led and internet interventions, can narrow this gap and be clinically effective and efficient, but has rarely been evaluated outside of controlled research settings.ObjectiveThis study evaluated the effectiveness of a blended care intervention (video-based cognitive behavior therapy and internet intervention) under real-world conditions.MethodsThis is a pragmatic retrospective cohort analysis of 385 participants with clinical range depression and/or anxiety symptoms at baseline, measured using Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7), who enrolled in blended care psychotherapy treatment. Participants resided in the United States and had access to the blended care intervention as a mental health benefit offered through their employers. Levels of depression and anxiety were tracked throughout treatment. Hierarchical linear modeling was used to examine the change in symptoms over time. The effects of age, gender, and providers on participants' symptom change trajectories were also evaluated. Paired sample t-tests were also conducted, and rates of positive clinical change and clinically significant improvement were calculated.ResultsThe average depression and anxiety symptoms at 6 weeks after the start of treatment were 5.94 and 6.57, respectively. There were significant linear effects of time on both symptoms of depression and anxiety (β=-.49, P<.001 and β=-.64, P<.001). The quadratic effect was also significant for both symptoms of depression and anxiety (β=.04, P<.001 for both), suggesting a decelerated decrease in symptoms over time. Approximately 73% (n=283) of all 385 participants demonstrated reliable improvement, and 83% (n=319) recovered on either the PHQ-9 or GAD-7 measures. Large effect sizes were observed on both symptoms of depression (Cohen d=1.08) and of anxiety (d=1.33).ConclusionsVideo blended care cognitive behavioral therapy interventions can be effective and efficient in treating symptoms of depression and anxiety in real-world conditions. Future research should investigate the differential and interactive contribution of the therapist-led and digital components of care to patient outcomes to optimize care.

Project description:ImportanceCommunication of information has emerged as a critical component of diagnostic quality. Communication of diagnostic uncertainty represents a key but inadequately examined element of diagnosis.ObjectiveTo identify key elements facilitating understanding and managing diagnostic uncertainty, examine optimal ways to convey uncertainty to patients, and develop and test a novel tool to communicate diagnostic uncertainty in actual clinical encounters.Design, setting, and participantsA 5-stage qualitative study was performed between July 2018 and April 2020, at an academic primary care clinic in Boston, Massachusetts, with a convenience sample of 24 primary care physicians (PCPs), 40 patients, and 5 informatics and quality/safety experts. First, a literature review and panel discussion with PCPs were conducted and 4 clinical vignettes of typical diagnostic uncertainty scenarios were developed. Second, these scenarios were tested during think-aloud simulated encounters with expert PCPs to iteratively draft a patient leaflet and a clinician guide. Third, the leaflet content was evaluated with 3 patient focus groups. Fourth, additional feedback was obtained from PCPs and informatics experts to iteratively redesign the leaflet content and workflow. Fifth, the refined leaflet was integrated into an electronic health record voice-enabled dictation template that was tested by 2 PCPs during 15 patient encounters for new diagnostic problems. Data were thematically analyzed using qualitative analysis software.Main outcomes and measuresPerceptions and testing of content, feasibility, usability, and satisfaction with a prototype tool for communicating diagnostic uncertainty to patients.ResultsOverall, 69 participants were interviewed. A clinician guide and a diagnostic uncertainty communication tool were developed based on the PCP interviews and patient feedback. The optimal tool requirements included 6 key domains: most likely diagnosis, follow-up plan, test limitations, expected improvement, contact information, and space for patient input. Patient feedback on the leaflet was iteratively incorporated into 4 successive versions, culminating in a successfully piloted prototype tool as an end-of-visit voice recognition dictation template with high levels of patient satisfaction for 15 patients with whom the tool was tested.Conclusions and relevanceIn this qualitative study, a diagnostic uncertainty communication tool was successfully designed and implemented during clinical encounters. The tool demonstrated good workflow integration and patient satisfaction.

Project description:We aim to assess the real-world accuracy (primary outcome), feasibility and acceptability (secondary outcomes) of an automated retinal photography and artificial intelligence (AI)-based cardiovascular disease (CVD) risk assessment system (rpCVD) in Australian primary care settings. Participants aged 45-70 years who had recently undergone all or part of a CVD risk assessment were recruited from two general practice clinics in Victoria, Australia. After consenting, participants underwent retinal imaging using an automated fundus camera, and an rpCVD risk score was generated by a deep learning algorithm. This score was compared against the World Health Organisation (WHO) CVD risk score, which incorporates age, sex, and other clinical risk factors. The predictive accuracy of the rpCVD and WHO CVD risk scores for 10-year incident CVD events was evaluated using data from the UK Biobank, with the accuracy of each system assessed through the area under the receiver operating characteristic curve (AUC). Participant satisfaction was assessed through a survey, and the imaging success rate was determined by the percentage of individuals with images of sufficient quality to produce an rpCVD risk score. Of the 361 participants, 339 received an rpCVD risk score, resulting in a 93.9% imaging success rate. The rpCVD risk scores showed a moderate correlation with the WHO CVD risk scores (Pearson correlation coefficient [PCC] = 0.526, 95% CI: 0.444-0.599). Despite this, the rpCVD system, which relies solely on retinal images, demonstrated a similar level of accuracy in predicting 10-year incident CVD (AUC = 0.672, 95% CI: 0.658-0.686) compared to the WHO CVD risk score (AUC = 0.693, 95% CI: 0.680-0.707). High satisfaction rates were reported, with 92.5% of participants and 87.5% of general practitioners (GPs) expressing satisfaction with the system. The automated rpCVD system, using only retinal photographs, demonstrated predictive accuracy comparable to the WHO CVD risk score, which incorporates multiple clinical factors including age, the most heavily weighted factor for CVD prediction. This underscores the potential of the rpCVD approach as a faster, easier, and non-invasive alternative for CVD risk assessment in primary care settings, avoiding the need for more complex clinical procedures.

Project description:ObjectivesTo assess the diagnostic accuracy of self-diagnosis compared with a clinical diagnosis for common conditions in primary care.DesignSystematic review.Meta-analysisData sourcesMedline, Embase, Cochrane CENTRAL, Cochrane Database of Systematic Reviews and CINAHL from inception to 25 January 2021.Study selectionEligible studies were prospective or retrospective studies comparing the results of self-diagnosis of common conditions in primary care to a relevant clinical diagnosis or laboratory reference standard test performed by a healthcare service provider. Studies that considered self-testing only were excluded.Data extractionTwo authors independently extracted data using a predefined data extraction form and assessed risk of bias using Quality Assessment of Diagnostic Accuracy Studies-2.Methods and results5047 records identified 18 studies for inclusion covering the self-diagnosis of three common conditions: vaginal infection (five studies), common skin conditions (four studies) and HIV (nine studies). No studies were found for any other condition. For self-diagnosis of vaginal infection and common skin conditions, meta-analysis was not appropriate and data were reported narratively. Nine studies, using point-of-care oral fluid tests, reported on the accuracy of self-diagnosis of HIV and data were pooled using bivariate meta-analysis methods. For these nine studies, the pooled sensitivity was 92.8% (95% CI, 86% to 96.5%) and specificity was 99.8% (95% CI, 99.1% to 99.9%). Post hoc, the robustness of the pooled findings was tested in a sensitivity analysis only including four studies using laboratory testing as the reference standard. The pooled sensitivity reduced to 87.7% (95% CI, 81.4% to 92.2%) and the specificity remained the same. The quality of all 18 included studies was assessed as mixed and overall study methodology was not always well described.Conclusions and implications of key findingsOverall, there was a paucity of evidence. The current evidence does not support routine self-diagnosis for vaginal infections, common skin conditions and HIV in primary care.Prospero registration numberCRD42018110288.

Dataset Information

How is diagnostic uncertainty communicated and managed in real world primary care settings?

Background

Objective

Design

Participants

Approach

Key results

Conclusions

Publications

How is diagnostic uncertainty communicated and managed in real world primary care settings?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets