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Managing diagnostic uncertainty in primary care: a systematic critical review.

ABSTRACT: Diagnostic 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.Sixteen 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.Ten 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.There 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.PROSPERO registration: CRD42015027555.

SUBMITTER: Alam R

PROVIDER: S-EPMC5545872 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Managing diagnostic uncertainty in primary care: a systematic critical review.

Alam Rahul R Cheraghi-Sohi Sudeh S Panagioti Maria M Esmail Aneez A Campbell Stephen S Panagopoulou Efharis E

BMC family practice 20170807 1

<h4>Background</h4>Diagnostic 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 impac ...[more]

PMID: 28784088

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Project description:BackgroundDiagnostic uncertainty is a pervasive issue in primary care where patients often present with non-specific symptoms early in the disease process. Knowledge about how clinicians communicate diagnostic uncertainty to patients is crucial to prevent associated diagnostic errors. Yet, in-depth research on the interpersonal communication of diagnostic uncertainty has been limited. We conducted an integrative systematic literature review (PROSPERO CRD42020197624, unfunded) to investigate how primary care doctors communicate diagnostic uncertainty in interactions with patients and how patients experience their care in the face of uncertainty.MethodsWe searched MEDLINE, PsycINFO, and Linguistics and Language Behaviour Abstracts (LLBA) from inception to December 2021 for MeSH and keywords related to 'communication', 'diagnosis', 'uncertainty' and 'primary care' environments and stakeholders (patients and doctors), and conducted additional handsearching. We included empirical primary care studies published in English on spoken communication of diagnostic uncertainty by doctors to patients. We assessed risk of bias with the QATSDD quality assessment tool and conducted thematic and content analysis to synthesise the results.ResultsInclusion criteria were met for 19 out of 1281 studies. Doctors used two main communication strategies to manage diagnostic uncertainty: (1) patient-centred communication strategies (e.g. use of empathy), and (2) diagnostic reasoning strategies (e.g. excluding serious diagnoses). Linguistically, diagnostic uncertainty was either disclosed explicitly or implicitly through diverse lexical and syntactical constructions, or not communicated (omission). Patients' experiences of care in response to the diverse communicative and linguistic strategies were mixed. Patient-centred approaches were generally regarded positively by patients.DiscussionDespite a small number of included studies, this is the first review to systematically catalogue the diverse communication and linguistic strategies to express diagnostic uncertainty in primary care. Health professionals should be aware of the diverse strategies used to express diagnostic uncertainty in practice and the value of combining patient-centred approaches with diagnostic reasoning strategies.

Project description:ObjectivesTo summarize the literature on prevalence, impact, and contributing factors related to diagnostic error in the PICU.Data sourcesSearch of PubMed, EMBASE, and the Cochrane Library up to December 2019.Study selectionStudies on diagnostic error and the diagnostic process in pediatric critical care were included. Non-English studies with no translation, case reports/series, studies providing no information on diagnostic error, studies focused on non-PICU populations, and studies focused on a single condition/disease or a single diagnostic test/tool were excluded.Data extractionData on research design, objectives, study sample, and results pertaining to the prevalence, impact, and factors associated with diagnostic error were abstracted from each study.Data synthesisUsing independent tiered review, 396 abstracts were screened, and 17 studies (14 full-text, 3 abstracts) were ultimately included. Fifteen of 17 studies (88%) had an observational research design. Autopsy studies (autopsy rates were 20-47%) showed a 10-23% rate of missed major diagnoses; 5-16% of autopsy-discovered diagnostic errors had a potential adverse impact on survival and would have changed management. Retrospective record reviews reported varying rates of diagnostic error from 8% in a general PICU population to 12% among unexpected critical admissions and 21-25% of patients discussed at PICU morbidity and mortality conferences. Cardiovascular, infectious, congenital, and neurologic conditions were most commonly misdiagnosed. Systems factors (40-67%), cognitive factors (20-3%), and both systems and cognitive factors (40%) were associated with diagnostic error. Limited information was available on the impact of misdiagnosis.ConclusionsKnowledge of diagnostic errors in the PICU is limited. Future work to understand diagnostic errors should involve a balanced focus between studying the diagnosis of individual diseases and uncovering common system- and process-related determinants of diagnostic error.

Project description:ObjectiveTo determine the effectiveness of interventions designed to improve outcomes in patients with multimorbidity in primary care and community settings.DesignSystematic review.Data sourcesMedline, Embase, CINAHL, CAB Health, Cochrane central register of controlled trials, the database of abstracts of reviews of effectiveness, and the Cochrane EPOC (effective practice and organisation of care) register (searches updated in April 2011).Eligibility criteriaRandomised controlled trials, controlled clinical trials, controlled before and after studies, and interrupted time series analyses reporting on interventions to improve outcomes for people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual. Outcomes included any validated measure of physical or mental health and psychosocial status, including quality of life outcomes, wellbeing, and measures of disability or functional status. Also included were measures of patient and provider behaviour, including drug adherence, utilisation of health services, acceptability of services, and costs.Data selectionTwo reviewers independently assessed studies for eligibility, extracted data, and assessed study quality. As meta-analysis of results was not possible owing to heterogeneity in participants and interventions, a narrative synthesis of the results from the included studies was carried out.Results10 studies examining a range of complex interventions totalling 3407 patients with multimorbidity were identified. All were randomised controlled trials with a low risk of bias. Two studies described interventions for patients with specific comorbidities. The remaining eight studies focused on multimorbidity, generally in older patients. Consideration of the impact of socioeconomic deprivation was minimal. All studies involved complex interventions with multiple components. In six of the 10 studies the predominant component was a change to the organisation of care delivery, usually through case management or enhanced multidisciplinary team work. In the remaining four studies, intervention components were predominantly patient oriented. Overall the results were mixed, with a trend towards improved prescribing and drug adherence. The results indicated that it is difficult to improve outcomes in this population but that interventions focusing on particular risk factors in comorbid conditions or functional difficulties in multimorbidity may be more effective. No economic analyses were included, although the improvements in prescribing and risk factor management in some studies could provide potentially important cost savings.ConclusionsEvidence on the care of patients with multimorbidity is limited, despite the prevalence of multimorbidity and its impact on patients and healthcare systems. Interventions to date have had mixed effects, although are likely to be more effective if targeted at risk factors or specific functional difficulties. A need exists to clearly identify patients with multimorbidity and to develop cost effective and specifically targeted interventions that can improve health outcomes.

Project description:BackgroundPhysicians routinely encounter diagnostic uncertainty in practice. Despite its impact on health care utilization, costs and error, measurement of diagnostic uncertainty is poorly understood. We conducted a systematic review to describe how diagnostic uncertainty is defined and measured in medical practice.MethodsWe searched OVID Medline and PsycINFO databases from inception until May 2017 using a combination of keywords and Medical Subject Headings (MeSH). Additional search strategies included manual review of references identified in the primary search, use of a topic-specific database (AHRQ-PSNet) and expert input. We specifically focused on articles that (1) defined diagnostic uncertainty; (2) conceptualized diagnostic uncertainty in terms of its sources, complexity of its attributes or strategies for managing it; or (3) attempted to measure diagnostic uncertainty.Key resultsWe identified 123 articles for full review, none of which defined diagnostic uncertainty. Three attributes of diagnostic uncertainty were relevant for measurement: (1) it is a subjective perception experienced by the clinician; (2) it has the potential to impact diagnostic evaluation-for example, when inappropriately managed, it can lead to diagnostic delays; and (3) it is dynamic in nature, changing with time. Current methods for measuring diagnostic uncertainty in medical practice include: (1) asking clinicians about their perception of uncertainty (surveys and qualitative interviews), (2) evaluating the patient-clinician encounter (such as by reviews of medical records, transcripts of patient-clinician communication and observation), and (3) experimental techniques (patient vignette studies).ConclusionsThe term "diagnostic uncertainty" lacks a clear definition, and there is no comprehensive framework for its measurement in medical practice. Based on review findings, we propose that diagnostic uncertainty be defined as a "subjective perception of an inability to provide an accurate explanation of the patient's health problem." Methodological advancements in measuring diagnostic uncertainty can improve our understanding of diagnostic decision-making and inform interventions to reduce diagnostic errors and overuse of health care resources.

Project description:BackgroundManaging 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.ObjectiveOur aim was to explore how UK primary care physicians (GPs) address and communicate diagnostic uncertainty in practice.DesignThis 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.ParticipantsAll 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.ApproachData collection occurred in GP-patient consultations during 2017-2018 across 7 practices in UK during 2017-2018.Key resultsGPs 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.ConclusionsWe offer guidance to healthcare professionals, improving awareness of using and communicating management plans for diagnostic uncertainty.

Project description:BackgroundResearch on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC.ObjectiveWe intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings.MethodsWe conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data.ResultsWe retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%).ConclusionsWe observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

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Managing diagnostic uncertainty in primary care: a systematic critical review.

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Managing diagnostic uncertainty in primary care: a systematic critical review.

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