The Fierce Urgency of Now

Barrett, Nadine; Hasan, Marisette; Bethea, Kenisha; Johnson, Kimberly S.

doi:10.18043/ncm.81.4.254

Cited by 4 publications

(5 citation statements)

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“…Moreover, the clinical use-case for this model is to trigger a palliative care consultation, which the treating clinician, patient or their family may decline and, unlike hospice care, can be provided concurrently with curative or restorative interventions. In contrast, given the well-documented disparities in provision of palliative care among marginalised patients and their families,35 48 implementation of the model evaluated here could reproduce biased clinical decision making by other means (eg, by reinforcing clinicians’ explicit or implicit beliefs).…”

Section: Discussionmentioning

confidence: 96%

“…Resource scarcity, in addition to evidence suggesting that existing inequities in palliative care are driven in part by hospital-level variation in the availability of resources, highlights the critical need for structural interventions beyond clinical decision support tools to advance palliative care inequity. This includes policies to improve coverage and payment for these services and to expand, diversify and improve equity education for the palliative care workforce 35 79 80…”

Section: Discussionmentioning

confidence: 99%

“…A recurrent example of this is the use of prognostic models to predict risk of death or other undesirable outcomes in an effort to improve targeted delivery of supportive or palliative care interventions for serious illness,24–31 long a pressing national priority 31–34. Yet, no studies to date have rigorously evaluated the myriad published EHR-based prognostic models for potential differential predictive performance among patient subgroups, particularly for structurally marginalised patients with reduced access to high-quality serious illness care at baseline 35. Such evaluations are needed to help ensure these models do not exacerbate inequities in access to high-quality serious illness care when incorporated into daily practice.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis

Teeple

Chivers²,

Linn

et al. 2023

BMJ Qual Saf

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ObjectiveEvaluate predictive performance of an electronic health record (EHR)-based, inpatient 6-month mortality risk model developed to trigger palliative care consultation among patient groups stratified by age, race, ethnicity, insurance and socioeconomic status (SES), which may vary due to social forces (eg, racism) that shape health, healthcare and health data.DesignRetrospective evaluation of prediction model.SettingThree urban hospitals within a single health system.ParticipantsAll patients ≥18 years admitted between 1 January and 31 December 2017, excluding observation, obstetric, rehabilitation and hospice (n=58 464 encounters, 41 327 patients).Main outcome measuresGeneral performance metrics (c-statistic, integrated calibration index (ICI), Brier Score) and additional measures relevant to health equity (accuracy, false positive rate (FPR), false negative rate (FNR)).ResultsFor black versus non-Hispanic white patients, the model’s accuracy was higher (0.051, 95% CI 0.044 to 0.059), FPR lower (−0.060, 95% CI −0.067 to −0.052) and FNR higher (0.049, 95% CI 0.023 to 0.078). A similar pattern was observed among patients who were Hispanic, younger, with Medicaid/missing insurance, or living in low SES zip codes. No consistent differences emerged in c-statistic, ICI or Brier Score. Younger age had the second-largest effect size in the mortality prediction model, and there were large standardised group differences in age (eg, 0.32 for non-Hispanic white versus black patients), suggesting age may contribute to systematic differences in the predicted probabilities between groups.ConclusionsAn EHR-based mortality risk model was less likely to identify some marginalised patients as potentially benefiting from palliative care, with younger age pinpointed as a possible mechanism. Evaluating predictive performance is a critical preliminary step in addressing algorithmic inequities in healthcare, which must also include evaluating clinical impact, and governance and regulatory structures for oversight, monitoring and accountability.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis

Teeple

Chivers²,

Linn

et al. 2023

BMJ Qual Saf

View full text Add to dashboard Cite

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“…The COVID-19 pandemic raised the US health care community's acknowledgement of both the historic and current inequities in health care access, treatment, and outcomes. The pandemic therefore highlighted the need for increased engagement of diverse communities to increase the validity of research studies [1][2][3]. This is true for all of health care, and serious illness research is no exception [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…The pandemic therefore highlighted the need for increased engagement of diverse communities to increase the validity of research studies [1][2][3]. This is true for all of health care, and serious illness research is no exception [3][4][5][6][7]. Serious illness communication (SIC) describes conversations that occur between patients with serious illness and clinicians to understand the patient's goals, values, preferences, and priorities so that health care can be aligned with those priorities [8].…”

Section: Introductionmentioning

confidence: 99%

Online Forums as a Tool for Broader Inclusion of Voices on Health Care Communication Experiences and Serious Illness Care: Mixed Methods Study

Davila,

Chan,

Gosline

et al. 2023

J Med Internet Res

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Background Existing health care research, including serious illness research, often underrepresents individuals from historically marginalized communities. Capturing the nuanced perspectives of individuals around their health care communication experiences is difficult. New research strategies are needed that increase engagement of individuals from diverse backgrounds. Objective The aim of this study was to develop a mixed methods approach with qualitative online forums to better understand health communication experiences of individuals, including people from groups historically marginalized such as Black and Latino individuals; older adults; and people with low income, disability, or serious illness. Methods We used a multiphase mixed methods, community-informed research approach to design study instruments and engage participants. We engaged a diverse group of collaborators with lived experience of navigating the health care system who provided feedback on instruments, added concepts for testing, and offered guidance on creating a safe experience for participants (phase 1). We conducted a national quantitative survey between April and May 2021 across intrapersonal, interpersonal, and systems-level domains, with particular focus on interpersonal communication between patients and clinicians (phase 2). We conducted two asynchronous, qualitative online forums, a technique used in market research, between June and August 2021, which allowed us to contextualize the learnings and test concepts and messages (phase 3). Using online forums allowed us to probe more deeply into results and hypotheses from the survey to better understand the “whys” and “whats” that surfaced and to test public messages to encourage action around health. Results We engaged 46 community partners, including patients and clinicians from a Federally Qualified Health Center, to inform study instrument design. In the quantitative survey, 1854 adults responded, including 50.5% women, 25.2% individuals over 65 years old, and 51.9% individuals with low income. Nearly two-thirds identified as non-Hispanic white (65.7%), 10.4% identified as non-Hispanic Black, and 15.5% identified as Hispanic/Latino. An additional 580 individuals participated in online forums, including 60.7% women, 17.4% individuals over 65 years old, and 49.0% individuals with low income. Among the participants, 70.3% identified as non-Hispanic white, 16.0% as non-Hispanic Black, and 9.5% as Hispanic/Latino. We received rich, diverse input from our online forum participants, and they highlighted satisfaction and increased knowledge with engagement in the forums. Conclusions We achieved modest overrepresentation of people who were over 65 years old, identified as non-Hispanic Black, and had low income in our online forums. The size of the online forums (N=580) reflected the voices of 93 Black and 55 Hispanic/Latino participants. Individuals who identify as Hispanic/Latino remained underrepresented, likely because the online forums were offered only in English. Overall, our findings demonstrate the feasibility of using the online forum qualitative approach in a mixed methods study to contextualize, clarify, and expound on quantitative findings when designing public health and clinical communications interventions.

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