Hospital-Based Physicians\u27 Intubation Decisions and Associated Mental Models when Managing a Critically and Terminally Ill Older Patient.

BACKGROUND: Variation in the intensity of acute care treatment at the end of life is influenced more strongly by hospital and provider characteristics than patient preferences. OBJECTIVE: We sought to describe physicians\u27 mental models (i.e., thought processes) when encountering a simulated critically and terminally ill older patient, and to compare those models based on whether their treatment plan was patient preference-concordant or preference-discordant. METHODS: Seventy-three hospital-based physicians from 3 academic medical centers engaged in a simulated patient encounter and completed a mental model interview while watching the video recording of their encounter. We used an expert model to code the interviews. We then used Kruskal-Wallis tests to compare the weighted mental model themes of physicians who provided preference-concordant treatment with those who provided preference-discordant treatment. RESULTS: Sixty-six (90%) physicians provided preference-concordant treatment and 7 (10%) provided preference-discordant treatment (i.e., they intubated the patient). Physicians who intubated the patient were more likely to emphasize the reversible and emergent nature of the patient situation (z = -2.111, P = 0.035), their own comfort (z = -2.764, P = 0.006), and rarely focused on explicit patient preferences (z = 2.380, P = 0.017). LIMITATIONS: Post-decisional interviewing with audio/video prompting may induce hindsight bias. The expert model has not yet been validated and may not be exhaustive. The small sample size limits generalizability and power. CONCLUSIONS: Hospital-based physicians providing preference-discordant used a different mental model for decision making for a critically and terminally ill simulated case. These differences may offer targets for future interventions to promote preference-concordant care for seriously ill patients

"Doctor, what would you do?": physicians' responses to patient inquiries about periviable delivery

OBJECTIVE: To qualitatively assess obstetricians' and neonatologists' responses to standardized patients (SPs) asking "What would you do?" during periviable counseling encounters. METHODS: An exploratory single-center simulation study. SPs, portraying a pregnant woman presenting with ruptured membranes at 23 weeks, were instructed to ask, "What would you do?" if presented options regarding delivery management or resuscitation. Responses were independently reviewed and classified. RESULTS: We identified five response patterns: 'Disclose' (9/28), 'Don't Know' (11/28), 'Deflect' (23/28), 'Decline' (2/28), and 'Ignore' (2/28). Most physicians utilized more than one response pattern (22/28). Physicians 'deflected' the question by: restating or offering additional medical information; answering with a question; evoking a hypothetical patient; or redirecting the SP to other sources of support. When compared with neonatologists, obstetricians (40% vs. 15%) made personal or professional disclosures more often. Though both specialties readily acknowledged the importance of values in making a decision, only one physician attempted to elicit the patient's values. CONCLUSION: "What would you do?" represented a missed opportunity for values elicitation. Interventions are needed to facilitate values elicitation and shared decision-making in periviable care. PRACTICE IMPLICATIONS: If physicians fail to address patients' values and goals, they lack the information needed to develop patient-centered plans of care

Using Simulation to Assess the Influence of Race and Insurer on Shared Decision-making in Periviable Counseling

Introduction: Sociodemographic differences have been observed in the treatment of extremely premature (periviable) neonates, but the source of this variation is not well understood. We assessed the feasibility of using simulation to test the effect of maternal race and insurance status on shared decision making (SDM) in periviable counseling. Methods: We conducted a 2 × 2 factorial simulation experiment in which obstetricians and neonatologists counseled 2 consecutive standardized patients diagnosed with ruptured membranes at 23 weeks, counterbalancing race (black/white) and insurance status using random permutation. We assessed verisimilitude of the simulation in semistructured debriefing interviews. We coded physician communication related to resuscitation, mode of delivery, and steroid decisions using a 9-point SDM coding framework and then compared communication scores by standardized patient race and insurer using analysis of variance. Results: Sixteen obstetricians and 15 neonatologists participated; 71% were women, 84% were married, and 75% were parents; 91% of the physicians rated the simulation as highly realistic. Overall, SDM scores were relatively high, with means ranging from 6.4 to 7.9 (of 9). There was a statistically significant interaction between race and insurer for SDM related to steroid use and mode of delivery (P < 0.01 and P = 0.01, respectively). Between-group comparison revealed nonsignificant differences (P = <0.10) between the SDM scores for privately insured black patients versus privately insured white patients, Medicaid-insured white patients versus Medicaid-insured black patients, and privately insured black patients versus Medicaid-insured black patients. Conclusions: This study confirms that simulation is a feasible method for studying sociodemographic effects on periviable counseling. Shared decision making may occur differentially based on patients’ sociodemographic characteristics and deserves further study

Survival after Acute Hemodialysis in Pennsylvania, 2005- 2007: A Retrospective Cohort Study

Abstract Background: Little is known about acute hemodialysis in the US. Here we describe predictors of receipt of acute hemodialysis in one state and estimate the marginal impact of acute hemodialysis on survival after accounting for confounding due to illness severity

Value Awareness: A New Goal for End-of-life Decision Making

The principal policy tool for respecting the preferences of patients facing serious illnesses that can prompt decisions regarding end-of-life care is the advance directive (AD) for health care. AD policies, decision aids for facilitating ADs, and clinical processes for interpreting ADs all treat patients as rational actors who will make appropriate choices, if provided relevant information. We review barriers to following this model, leading us to propose replacing the goal of rational choice with that of value awareness , enabling patients (and, where appropriate, their surrogates) to be as rational as they can and want to be when making these fateful choices. We propose approaches, and supporting research, suited to individuals’ cognitive, affective, and social circumstances, resources, and desires

Clinically informed machine learning elucidates the shape of hospice racial disparities within hospitals

Abstract Racial disparities in hospice care are well documented for patients with cancer, but the existence, direction, and extent of disparity findings are contradictory across the literature. Current methods to identify racial disparities aggregate data to produce single-value quality measures that exclude important patient quality elements and, consequently, lack information to identify actionable equity improvement insights. Our goal was to develop an explainable machine learning approach that elucidates healthcare disparities and provides more actionable quality improvement information. We infused clinical information with engineering systems modeling and data science to develop a time-by-utilization profile per patient group at each hospital using US Medicare hospice utilization data for a cohort of patients with advanced (poor-prognosis) cancer that died April-December 2016. We calculated the difference between group profiles for people of color and white people to identify racial disparity signatures. Using machine learning, we clustered racial disparity signatures across hospitals and compared these clusters to classic quality measures and hospital characteristics. With 45,125 patients across 362 hospitals, we identified 7 clusters; 4 clusters (n = 190 hospitals) showed more hospice utilization by people of color than white people, 2 clusters (n = 106) showed more hospice utilization by white people than people of color, and 1 cluster (n = 66) showed no difference. Within-hospital racial disparity behaviors cannot be predicted from quality measures, showing how the true shape of disparities can be distorted through the lens of quality measures. This approach elucidates the shape of hospice racial disparities algorithmically from the same data used to calculate quality measures