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There is growing evidence that long-term outcomes after traumatic brain injury (TBI) are more dynamic than stable. People continue to change, both improving and declining, many years post-injury. Research, practice and medical education have not yet fully embraced the implications of TBI as a chronic, dynamic condition. In 2020, the National Institute on Disability Independent Living and Rehabilitation Research funded the BeHEALTHY project to develop a model for long-term support of persons with chronic brain injury. Based on initial reviews of the available evidence and expert input from researchers, clinicians, and persons with lived experience, the BeHEALTHY model was proposed. Among existing chronic disease treatment models, Wagner's Chronic Care Model was selected as a starting point, with several critical refinements. The BeHEALTHY model endorses a person-centered approach, recognizing the individual with brain injury, and their social support system, as the primary source of goals in care planning. The model also acknowledges the critical importance of self-management and the need for healthcare programs to actively promote self-direction by the person using their social supports. The model also recognizes that outcomes are not determined solely by the person and the injury incurred, but also by the environment in which a person lives and recovers. The importance of integrating healthcare with community resources is underscored by embracing environmental influences as a shared responsibility of the community and the healthcare system. This article recounts the impetus for developing the BeHEALTHY model, and describes those it is intended to serve, structural features, and core components.
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The 3-item pain intensity (P), interference with the enjoyment of life (E), and interference with general activity (G), or PEG, has become one of the most widely used measures of pain severity and interference. The minimally important differences (MID) and responsiveness of the PEG are essential metrics for solidifying its role in research and clinical care. The current study aims to establish the MID and responsiveness of the PEG by synthesizing data from 1,710 participants across 6 controlled trials. MIDs were estimated using absolute score changes among individuals reporting their pain was "a little better" on a retrospective global change anchor as well as distribution-based estimates using standard deviation thresholds and 1 and 2 standard errors of measurement. Responsiveness was assessed using standardized response means, area under the curve, and treatment effect sizes. MID estimates for the PEG ranged from 0.60 to 1.1 when using 0.35 SD, and 0.78 to 1.22 using 1 standard error of measurement. MID estimates using the global anchor had somewhat more variability but most estimates ranged from 1.0 to 1.75. Responsiveness effect sizes were generally large (>.80) for standardized response means and moderate (>.50) for treatment effect. Similarly, the most area under the curve values demonstrated an acceptable level of scale responsiveness (≥.70). Importantly, MID estimates and responsiveness of the PEG and BPI scales were largely comparable when aggregating data across trials. Our synthesis indicates that 1 point is a reasonable MID estimate on these 0- to 10-point pain scales, with 2 points being an upper bound. PERSPECTIVE: This article synthesizes data from 6 clinical trials to establish the minimally important difference (MID) and responsiveness of the 3-item PEG pain scale. The PEG demonstrated good responsiveness, and 1 to 2 points proved to be reasonable estimates for the lower and upper bounds of the MID.
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Dor , Qualidade de Vida , Humanos , Medição da Dor , Estudos Retrospectivos , Dor/diagnóstico , Dor/tratamento farmacológico , Inquéritos e QuestionáriosRESUMO
Social stereotypes are more likely to influence decision-making under conditions of high cognitive load (ie, mental workload), such as in medical settings. We examined how patient race, patient socioeconomic status (SES), physician cognitive load, and physician implicit beliefs about race and SES differences in pain tolerance impacted physicians' pain treatment decisions. Physician residents and fellows (N = 120) made treatment decisions for 12 computer-simulated patients with back pain that varied by race (Black/White) and SES (low/high). Half of the physicians were randomized to be interrupted during the decision task to make hypertension medication conversion calculations (high cognitive load group), while the other half completed the task without interruptions (low cognitive load group). Both groups were given equal time to make pain care decisions (2.5 minutes/patient). Results of multilevel ordinal logistic regression analyses indicated that physicians prescribed weaker analgesics to patients with high vs. low SES (odds ratio = .68, 95% confidence interval [.48, .97], P = .03). There was also a patient SES-by-cognitive load interaction (odds ratio = .56, 95% confidence interval [.31, 1.01], P = .05) that is theoretically and potentially practically meaningful but was not statistically significant at P < .05. These findings shed light on physician cognitive load as a clinically-relevant factor in the context of pain care quality and equity. PERSPECTIVE: These findings highlight the clinical relevance of physician cognitive load (eg, mental workload) when providing pain care for diverse patients. This line of work can support the development of interventions to manage physician cognitive load and its impact on pain care, which may ultimately help reduce pain disparities.
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Dor Crônica , Classe Social , Humanos , Masculino , Feminino , Dor Crônica/terapia , Dor Crônica/etnologia , Adulto , Pessoa de Meia-Idade , Internato e Residência , Manejo da Dor , Cognição/fisiologia , Médicos , Tomada de Decisão Clínica , Tomada de Decisões/fisiologiaRESUMO
Objective: The study aimed to investigate otolaryngologists' knowledge, trust, acceptance, and concerns with clinical applications of artificial intelligence (AI). Methods: This study used mixed methods with survey and semistructured interviews. Survey was e-mailed to American Rhinologic Society members, of which a volunteer sample of 86 members responded. Nineteen otolaryngologists were purposefully recruited and interviewed until thematic saturation was achieved. Results: Seventy-six respondents (10% response rate) completed the majority of the survey: 49% worked in academic settings and 43% completed residency 10 or fewer years ago. Of 19 interviewees, 58% worked in academic settings, and 47% completed residency 10 or fewer years ago. Familiarity: Only 8% of survey respondents reported having AI training in residency, although 72% had familiarity with general AI concepts; 0 interviewees had personal experience with AI in clinical settings. Expected uses: Of the surveyed otolaryngologists, 82% would use an AI-based clinical decision aid and 74% were comfortable with AI proposing treatment recommendations. However, only 44% of participants would trust AI to identify malignancy and 53% to interpret radiographic images. Interviewees trusted AI for simple tasks, such as labeling septal deviation, more than complex ones, such as identifying tumors. Factors influencing AI adoption: 89% of survey participants would use AI if it improved patient satisfaction, 78% would be willing to use AI if experts and studies validated the technologies, and 73% would only use AI if it increased efficiency. Sixty-one percent of survey respondents expected AI incorporation into clinical practice within 5 years. Interviewees emphasized that AI adoption depends on its similarity to their clinical judgment and to expert opinion. Concerns included nuanced or complex cases, poor design or accuracy, and the personal nature of physician-patient relationships. Conclusion: Few physicians have experience with AI technologies but expect rapid adoption in the clinic, highlighting the urgent need for clinical education and research. Otolaryngologists are most receptive to AI "augmenting" physician expertise and administrative capacity, with respect for physician autonomy and maintaining relationships with patients. Level of Evidence: Level VI, descriptive or qualitative study.