Patient and Physician Race and the Allocation of Time and Patient Engagement Efforts to Mental Health Discussions in Primary Care: An Observational Study of Audiorecorded Periodic Health Examinations

The Relationship of Race/Ethnicity Concordance to Physician-Patient Communication: A Mixed-Methods Systematic Review

The concept of race or ethnic concordance between health care provider and patient has emerged as a dimension of the patient-physician relationship that could influence health outcomes for patients from minoritized groups, particularly through differences in the way physicians communicate with patients based on race or ethnicity. However, two decades of study on concordance and physician-patient communication have produced contradictory results. Given the heightened societal awareness of racism and the persistence of health disparities, there is a need for a comprehensive review of the current state of knowledge. This review sets out to determine how communication patterns differ in race/ethnicity concordant versus discordant patient-physician medical encounters. Thirty-three studies employing a range of methodologies were identified. In most analyses, after accounting for covariates, no relationship was found between race/ethnicity concordance and communication variables. Race/ethnicity concordance with their physician does not appear to influence the quality of communication for most patients from minoritized groups. A number of methodological weaknesses were identified in existing research, among them: few studies investigated potential explanatory variables, the heterogeneity of ethnic and cultural experience was over-simplified, there was little consistency in operationalization of communication variables, and the physician-patient dynamic was inadequately conceptualized.

Do I care for you more when you really need help? An experimental test of the effect of clinical urgency on compassion in health care

OBJECTIVES

To experimentally investigate whether more urgent patient presentations elicit greater compassion from health care professionals than less urgent, facilitating future research and thinking to address systemic barriers to compassion in health care.

DESIGN

This is a pre-registered online study with an experimental, within-subjects repeated-measure study design. Two clinical vignettes that systematically varied the urgency of patient presentation were utilized. Both vignettes depicted a patient with difficult behaviours typically associated with lower compassion.

METHODS

Health care professionals (doctors, nurses and allied health practitioners) recruited from all 20 District Health Boards across Aotearoa/New Zealand completed two vignettes in a counterbalanced order. Paired-sample t-tests were used to test the effect of the presentation urgency on indices of compassion.

RESULTS

A total of 939 participants completed the vignettes (20% doctors, 47%, nurses and 33% allied health professionals). As expected, participants reported greater care and motivation to help the more urgent patient. However, the more urgent patient was also perceived as less difficult, and exploratory analyses showed that perceived patient difficulty was associated with lower caring and motivation to help, particularly in the less urgent patient.

CONCLUSIONS

This is the first work to experimentally test the relationship between the urgency of patient presentation and compassion in health care. Although the association between urgency and difficulty is complex, our findings are consonant with evolutionary views in which urgent distress elicits greater compassion. A system-wide orientation towards efficiency and urgency may exacerbate this 'bias' which must be addressed to ensure more equitable compassion in health care.

"Mm-hm," "Uh-uh": are non-lexical conversational sounds deal breakers for the ambient clinical documentation technology?

OBJECTIVES

Ambient clinical documentation technology uses automatic speech recognition (ASR) and natural language processing (NLP) to turn patient-clinician conversations into clinical documentation. It is a promising approach to reducing clinician burden and improving documentation quality. However, the performance of current-generation ASR remains inadequately validated. In this study, we investigated the impact of non-lexical conversational sounds (NLCS) on ASR performance. NLCS, such as Mm-hm and Uh-uh, are commonly used to convey important information in clinical conversations, for example, Mm-hm as a "yes" response from the patient to the clinician question "are you allergic to antibiotics?"

MATERIALS AND METHODS

In this study, we evaluated 2 contemporary ASR engines, Google Speech-to-Text Clinical Conversation ("Google ASR"), and Amazon Transcribe Medical ("Amazon ASR"), both of which have their language models specifically tailored to clinical conversations. The empirical data used were from 36 primary care encounters. We conducted a series of quantitative and qualitative analyses to examine the word error rate (WER) and the potential impact of misrecognized NLCS on the quality of clinical documentation.

RESULTS

Out of a total of 135 647 spoken words contained in the evaluation data, 3284 (2.4%) were NLCS. Among these NLCS, 76 (0.06% of total words, 2.3% of all NLCS) were used to convey clinically relevant information. The overall WER, of all spoken words, was 11.8% for Google ASR and 12.8% for Amazon ASR. However, both ASR engines demonstrated poor performance in recognizing NLCS: the WERs across frequently used NLCS were 40.8% (Google) and 57.2% (Amazon), respectively; and among the NLCS that conveyed clinically relevant information, 94.7% and 98.7%, respectively.

DISCUSSION AND CONCLUSION

Current ASR solutions are not capable of properly recognizing NLCS, particularly those that convey clinically relevant information. Although the volume of NLCS in our evaluation data was very small (2.4% of the total corpus; and for NLCS that conveyed clinically relevant information: 0.06%), incorrect recognition of them could result in inaccuracies in clinical documentation and introduce new patient safety risks.

Influencing factors of high PTSD among medical staff during COVID-19: evidences from both meta-analysis and subgroup analysis

Background

PTSD (Post-traumatic stress disorder, PTSD) had a great impact on health care workers during the COVID-19 (Corona Virus Disease 2019, COVID-19). Better knowledge of the prevalence of PTSD and its risk factors is a major public health problem. This study was conducted to assess the prevalence and important risk factors of PTSD among medical staff during the COVID-19.

Methods

The databases were searched for studies published during the COVID-19, and a PRISMA (preferred reporting items for systematic review and meta-analysis) compliant systematic review (PROSPERO-CRD 42021278970) was carried out to identify articles from multiple databases reporting the prevalence of PTSD outcomes among medical staff. Proportion random effect analysis, I² statistic, quality assessment, subgroup analysis, and sensitivity analysis were carried out.

Results

A total of 28 cross-sectional studies and the PTSD results of doctors and nurses were summarized from 14 and 27 studies: the prevalences were 31% (95% CI [confidence interval, CI]: 21%–40%) and 38% (95% CI: 30%–45%) in doctors and nurses, respectively. The results also showed seven risks (p < 0.05): long working hours, isolation wards, COVID-19 symptoms, nurses, women, fear of infection, and pre-existing mental illness. Two factors were of borderline significance: higher professional titles and married.

Conclusion

Health care workers have a higher prevalence of PTSD during COVID-19. Health departments should provide targeted preventive measures for medical staff away from PTSD.

Predictors of Physician Compassion, Empathy, and Related Constructs: a Systematic Review

BACKGROUND

Compassion in healthcare provides measurable benefits to patients, physicians, and healthcare systems. However, data regarding the factors that predict care (and a lack of care) are scattered. This study systematically reviews biomedical literature within the Transactional Model of Physician Compassion and synthesizes evidence regarding the predictors of physician empathy, compassion, and related constructs (ECRC).

METHODS

A systematic literature search was conducted in CENTRAL, MEDLINE, PsycINFO, EMBASE, CINAHL, AMED, OvidJournals, ProQuest, Web of Science, and Scopus using search terms relating to ECRC and its predictors. Eligible studies included physicians as participants. Methodological quality was assessed based on the Cochrane Handbook, using ROBINS-I risk of bias tool for quantitative and CASP for qualitative studies. Confidence in findings was evaluated according to GRADE-CERQual approach.

RESULTS

One hundred fifty-two included studies (74,866 physicians) highlighted the diversity of influences on compassion in healthcare (54 unique predictors). Physician-related predictors (88%) were gender, experience, values, emotions and coping strategies, quality of life, and burnout. Environmental predictors (38%) were organizational structure, resources, culture, and clinical environment and processes. Patient-related predictors (24%) were communication ease, and physicians' perceptions of patients' motives; compassion was also less forthcoming with lower SES and minority patients. Evidence related to clinical predictors (15%) was scarce; high acuity presentations predicted greater ECRC.

DISCUSSION

The growth of evidence in the recent years reflects ECRC's ongoing importance. However, evidence remains scattered, concentrates on physicians' factors that may not be amenable to interventions, lacks designs permitting causal commentary, and is limited by self-reported outcomes. Inconsistent findings in the direction of the predictors' effects indicate the need to study the relationships among predictors to better understand the mechanisms of ECRCs. The current review can guide future research and interventions.

COVID-19 Mental Health Disparities

Communities of color in the United States have been disproportionately impacted by the COVID-19 pandemic. Studies exploring the mental health implications of these disparities have only just begun to emerge. The purpose of this study is to better understand mental health concerns and test whether social determinants of health and COVID-19-related experiences influence these concerns. In April 2020, we launched a community-based survey for adults across the United States. A total of 341 respondents completed the survey, which included questions about demographics, depression, social isolation, work environment, and preexisting mental health conditions. We generated matched controls by adding county data from the Robert Wood Johnson Foundation to our survey. Chi square, Pearson product-moment correlation, point biserial correlation, and logistic regression were estimated. Our analysis revealed that respondents who identified as Latinx, Latin@, or Hispanic were 10 times more likely to meet the threshold score for depression. Similarly, individuals with prior mental health conditions and those who expressed feelings of social isolation due to COVID-19 were 3 times more likely to meet the threshold score for depression. These results confirm our hypothesis that communities of color will likely experience disproportionate mental health impacts of COVID-19-specifically, the mental health sequela that emerge from exposure, cumulative burden, and social isolation. We discuss the implications for expanding access and quality of health and mental health services to address current inequities.

Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions

Objective

Amid electronic health records, laboratory tests, and other technology, office-based patient and provider communication is still the heart of primary medical care. Patients typically present multiple complaints, requiring physicians to decide how to balance competing demands. How this time is allocated has implications for patient satisfaction, payments, and quality of care. We investigate the effectiveness of machine learning methods for automated annotation of medical topics in patient-provider dialog transcripts.

Materials and Methods

We used dialog transcripts from 279 primary care visits to predict talk-turn topic labels. Different machine learning models were trained to operate on single or multiple local talk-turns (logistic classifiers, support vector machines, gated recurrent units) as well as sequential models that integrate information across talk-turn sequences (conditional random fields, hidden Markov models, and hierarchical gated recurrent units).

Results

Evaluation was performed using cross-validation to measure 1) classification accuracy for talk-turns and 2) precision, recall, and F1 scores at the visit level. Experimental results showed that sequential models had higher classification accuracy at the talk-turn level and higher precision at the visit level. Independent models had higher recall scores at the visit level compared with sequential models.

Conclusions

Incorporating sequential information across talk-turns improves the accuracy of topic prediction in patient-provider dialog by smoothing out noisy information from talk-turns. Although the results are promising, more advanced prediction techniques and larger labeled datasets will likely be required to achieve prediction performance appropriate for real-world clinical applications.

Experiences of shared decision-making in community rehabilitation: a focused ethnography

BACKGROUND

Shared decision-making (SDM) can advance patient satisfaction, understanding, goal fulfilment, and patient-reported outcomes. We lack clarity on whether this physician-focused literature applies to community rehabilitation, and on the integration of SDM policies in healthcare settings. We aimed to understand patient and provider perceptions of shared decision-making (SDM) in community rehabilitation, particularly the barriers and facilitators to SDM.

METHODS

We used a focused ethnography involving 14 community rehabilitation sites across Alberta, including rural, regional-urban and metropolitan-urban sites. We conducted semi-structured interviews that asked participants about their positive and negative communication experiences (n = 23 patients; n = 26 providers).

RESULTS

We found SDM experiences fluctuated between extremes: Getting Patient Buy-In and Aligning Expectations. The former is provider-driven, prescriptive and less flexible; the latter is collaborative, inquisitive and empowering. In Aligning Expectations, patients and providers express humility and openness, communicate in the language of ask and listen, and view education as empowering. Patients and providers described barriers and facilitators to SDM in community rehabilitation. Facilitators included geography influencing context and connections; consistent, patient-specific messaging; patient lifestyle, capacity and perceived outlook; provider confidence, experience and perceived independence; provider training; and perceptions of more time (and control over time) for appointments. SDM barriers included lack of privacy; waitlists and financial barriers to access; provider approach; how choices are framed; and, patient's perceived assertiveness, lack of capacity, and level of deference.

CONCLUSIONS

We have found both excellent experiences and areas for improvement for applying SDM in community rehabilitation. We proffer recommendations to advance high-quality SDM in community rehabilitation based on promoting facilitators and overcoming barriers. This research will support the spread, scale and evaluation of a new Model of Care in rehabilitation by the provincial health system, which aimed to promote patient-centred care.

Mental health delivery in primary care: The perspectives of primary care providers

PURPOSE

To explore perspectives of primary care providers (PCPs), including physicians and nurse practitioners (NPs), about delivery of mental health care in primary care settings.

METHODS

We used a qualitative descriptive designed convenience sample of physicians (N = 12) and NPs (N = 14) through face-to-face interviews in New York State.

RESULTS

Three themes emerged: 1) prioritization of patient needs; 2) applicability of mental health care in primary care settings; and 3) physician and NP approaches to mental health care.

CONCLUSIONS

PCPs recognized importance of addressing patients' mental health care needs and barriers in primary care practices.