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Clinicians' perspectives on incidentally discovered silent brain infarcts - A qualitative study.

ABSTRACT: BACKGROUND:While silent brain infarcts (SBIs) in screened cohorts are associated with risk of symptomatic stroke and dementia, the clinical significance of incidentally discovered SBIs (id-SBIs) is unknown. Detection may offer an opportunity to initiate prevention measures, but uncertainties about id-SBIs may impede clinicians from addressing them and complicate further study of this condition. METHODS AND RESULTS:This study used semi-structured interviews of practicing clinicians. Interviews were audio recorded, transcribed, and analyzed using a grounded theory approach. A constant comparative method was used to organize emergent themes and examine new themes. Purposeful sampling was employed to achieve participant diversity. Fifteen clinicians were interviewed. Emergent themes centered on uncertainty about id-SBIs, clinical decision making in response to uncertainty, and evidence needed to resolve uncertainty. All clinicians reported uncertainty about id-SBIs: diagnostic, prognostic, or therapeutic. Differential responses to uncertainties resulted in practice variation within and between specialties. Diagnostic and prognostic uncertainty discouraged disclosure of imaging findings to patients. Vascular neurologists viewed the prognostic significance of id-SBIs as similar to symptomatic stroke. Therapeutic uncertainty was common, but most participants endorsed using stroke secondary prevention strategies. Regarding future research, all internists indicated they would consider changing practices in response to observational studies, whereas half of the neurologists expressed reluctance to modify practices based on non-randomized data. Several expressed concerns about clinical trial feasibility and lack of equipoise. CONCLUSIONS:id-SBIs are a focus of uncertainty for clinicians, leading to practice variation. Future studies must address diagnostic and prognostic uncertainty to facilitate implementation of prevention strategies.

SUBMITTER: Leung LY

PROVIDER: S-EPMC5875806 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Clinicians' perspectives on incidentally discovered silent brain infarcts - A qualitative study.

Leung Lester Y LY Han Paul K J PKJ Lundquist Christine C Weinstein Gene G Thaler David E DE Kent David M DM

PloS one 20180329 3

<h4>Background</h4>While silent brain infarcts (SBIs) in screened cohorts are associated with risk of symptomatic stroke and dementia, the clinical significance of incidentally discovered SBIs (id-SBIs) is unknown. Detection may offer an opportunity to initiate prevention measures, but uncertainties about id-SBIs may impede clinicians from addressing them and complicate further study of this condition.<h4>Methods and results</h4>This study used semi-structured interviews of practicing clinicians ...[more]

PMID: 29596459

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Project description:BackgroundSilent brain infarction (SBI) is defined as the presence of 1 or more brain lesions, presumed to be because of vascular occlusion, found by neuroimaging (magnetic resonance imaging or computed tomography) in patients without clinical manifestations of stroke. It is more common than stroke and can be detected in 20% of healthy elderly people. Early detection of SBI may mitigate the risk of stroke by offering preventative treatment plans. Natural language processing (NLP) techniques offer an opportunity to systematically identify SBI cases from electronic health records (EHRs) by extracting, normalizing, and classifying SBI-related incidental findings interpreted by radiologists from neuroimaging reports.ObjectiveThis study aimed to develop NLP systems to determine individuals with incidentally discovered SBIs from neuroimaging reports at 2 sites: Mayo Clinic and Tufts Medical Center.MethodsBoth rule-based and machine learning approaches were adopted in developing the NLP system. The rule-based system was implemented using the open source NLP pipeline MedTagger, developed by Mayo Clinic. Features for rule-based systems, including significant words and patterns related to SBI, were generated using pointwise mutual information. The machine learning models adopted convolutional neural network (CNN), random forest, support vector machine, and logistic regression. The performance of the NLP algorithm was compared with a manually created gold standard. The gold standard dataset includes 1000 radiology reports randomly retrieved from the 2 study sites (Mayo and Tufts) corresponding to patients with no prior or current diagnosis of stroke or dementia. 400 out of the 1000 reports were randomly sampled and double read to determine interannotator agreements. The gold standard dataset was equally split to 3 subsets for training, developing, and testing.ResultsAmong the 400 reports selected to determine interannotator agreement, 5 reports were removed due to invalid scan types. The interannotator agreements across Mayo and Tufts neuroimaging reports were 0.87 and 0.91, respectively. The rule-based system yielded the best performance of predicting SBI with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.991, 0.925, 1.000, 1.000, and 0.990, respectively. The CNN achieved the best score on predicting white matter disease (WMD) with an accuracy, sensitivity, specificity, PPV, and NPV of 0.994, 0.994, 0.994, 0.994, and 0.994, respectively.ConclusionsWe adopted a standardized data abstraction and modeling process to developed NLP techniques (rule-based and machine learning) to detect incidental SBIs and WMDs from annotated neuroimaging reports. Validation statistics suggested a high feasibility of detecting SBIs and WMDs from EHRs using NLP.

Project description:ImportanceDiscussions about goals of care with patients who are seriously ill typically occur infrequently and late in the illness trajectory, are of low quality, and focus narrowly on the patient's resuscitation preferences (ie, code status), risking provision of care that is inconsistent with patients' values. The Serious Illness Care Program (SICP) is a multifaceted communication intervention that builds capacity for clinicians to have earlier, more frequent, and more person-centered conversations.ObjectiveTo explore clinicians' experiences with the SICP 1 year after implementation.Design, setting, and participantsThis qualitative study was conducted at 2 tertiary care hospitals in Canada. The SICP was implemented at Hamilton General Hospital (Hamilton, Ontario) from March 1, 2017, to January 19, 2018, and at Foothills Medical Centre (Calgary, Alberta) from March 1, 2018, to December 31, 2020. A total of 45 clinicians were invited to participate in the study, and 23 clinicians (51.1%) were enrolled and interviewed. Semistructured interviews of clinicians were conducted between August 2018 and May 2019. Content analysis was used to evaluate information obtained from these interviews between May 2019 and May 2020.ExposuresThe SICP includes clinician training, communication tools, and processes for system change.Main outcomes and measuresClinicians' experiences with and perceptions of the SICP.ResultsAmong 23 clinicians interviewed, 15 (65.2%) were women. The mean (SD) number of years in practice was 14.6 (9.1) at the Hamilton site and 12.0 (6.9) at the Calgary site. Participants included 19 general internists, 3 nurse practitioners, and 1 social worker. The 3 main themes were the ways in which the SICP (1) supported changes in clinician behavior, (2) shifted the focus of goals-of-care conversations beyond discussion of code status, and (3) influenced clinicians personally and professionally. Changes in clinician behavior were supported by having a unit champion, interprofessional engagement, access to copies of the Serious Illness Conversation Guide, and documentation in the electronic medical record. Elements of the program, especially the Serious Illness Conversation Guide, shifted the focus of goals-of-care conversations beyond discussion of code status and influenced clinicians on personal and professional levels. Concerns with the program included finding time to have conversations, building transient relationships, and limiting conversation fluidity.Conclusions and relevanceIn this qualitative study, hospital clinicians described components of the SICP as supporting changes in their behavior and facilitating meaningful patient interactions that shifted the focus of goals-of-care conversations beyond discussion of code status. The perceived benefits of SICP implementation stimulated uptake within the medical units. These findings suggest that the SICP may prompt hospital culture changes in goals-of-care dialogue with patients and the care of hospitalized patients with serious illness.

Dataset Information

Clinicians' perspectives on incidentally discovered silent brain infarcts - A qualitative study.

Publications

Clinicians' perspectives on incidentally discovered silent brain infarcts - A qualitative study.

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