Predictors of Death in the Hospital for Patients with Chronic Serious Illness.

BACKGROUND: Most people prefer to die at home, yet most do not. Understanding factors associated with terminal hospitalization may inform interventions to improve care. OBJECTIVE: Among patients with chronic illness receiving care in a multihospital healthcare system, we identified the following: (1) predictors of death in any hospital; (2) predictors of death in a hospital outside the system; and (3) trends from 2010 to 2015. DESIGN: Retrospective cohort using death certificates and electronic health records. Settings/Subjects: Decedents with one of nine chronic illnesses. RESULTS: Among 20,486 decedents, those most likely to die in a hospital were younger (odds ratio [OR] 0.977, confidence interval [CI] 0.974-0.980), with more comorbidities (OR 1.188, CI 1.079-1.308), or more outpatient providers (OR 1.031, CI 1.015-1.047); those with cancer or dementia, or more outpatient visits were less likely to die in hospital. Among hospital deaths, patients more likely to die in an outside hospital had lower education (OR 0.952, CI 0.923-0.981), cancer (OR 1.388, CI 1.198-1.608), diabetes (OR 1.507, CI 1.262-1.799), fewer comorbidities (OR 0.745, CI 0.644-0.862), or fewer hospitalizations within the system during the prior year (OR 0.900, CI 0.864-0.938). Deaths in hospital did not change from 2010 to 2015, but the proportion of hospital deaths outside the system increased (p < 0.022). CONCLUSIONS: Patients dying in the hospital who are more likely to die in an outside hospital, and therefore at greater risk for inaccessibility of advance care planning, were more likely to be less well-educated and have cancer or diabetes, fewer comorbidities, and fewer hospitalizations. These findings may help target interventions to improve end-of-life care.

Temporal Trends Between 2010 and 2015 in Intensity of Care at End-of-Life for Patients With Chronic Illness: Influence of Age Under vs. Over 65 Years.

CONTEXT: Recent analyses of Medicare data show decreases over time in intensity of end-of-life care. Few studies exist regarding trends in intensity of end-of-life care for those under 65 years of age. OBJECTIVES: To examine recent temporal trends in place of death, and both hospital and intensive care unit (ICU) utilization, for age-stratified decedents with chronic, life-limiting diagnoses (<65 vs. ≥65 years) who received care in a large healthcare system. METHODS: Retrospective cohort using death certificates and electronic health records for 22,068 patients with chronic illnesses who died between 2010 and 2015. We examined utilization overall and stratified by age using multiple regression. RESULTS: The proportion of deaths at home did not change, but hospital admissions in the last 30 days of life decreased significantly from 2010 to 2015 (hospital b = -0.026; CI = -0.041, -0.012). ICU admissions in the last 30 days also declined over time for the full sample and for patients aged 65 years or older (overall b = -0.023; CI = -0.039, -0.007), but was not significant for younger decedents. Length of stay (LOS) did not decrease for those using the hospital or ICU. CONCLUSION: From 2010 to 2015, we observed a decrease in hospital admissions for all age groups and in ICU admissions for those over 65 years. As there were no changes in the proportion of patients with chronic illness who died at home nor in hospital or ICU LOS in the last 30 days, hospital and ICU admissions in the last 30 days may be a more responsive quality metric than site of death or LOS for palliative care interventions.

Effect of Psychiatric Illness on Acute Care Utilization at End of Life From Serious Medical Illness.

CONTEXT: Little is known about psychiatric illness and utilization of end-of-life care. OBJECTIVES: We hypothesized that preexisting psychiatric illness would increase hospital utilization at end of life among patients with chronic medical illness due to increased severity of illness and care fragmentation. METHODS: We reviewed electronic health records to identify decedents with one or more of eight chronic medical conditions based on International Classification of Diseases-9 codes. We used International Classification of Diseases-9 codes and prescription information to identify preexisting psychiatric illness. Regression models compared hospital utilization among patients with and without psychiatric illness. Path analyses examined the effect of severity of illness and care fragmentation. RESULTS: Eleven percent of 16,214 patients with medical illness had preexisting psychiatric illness, which was associated with increased risk of death in nursing homes (P = 0.002) and decreased risk of death in hospitals (P < 0.001). In the last 30 days of life, psychiatric illness was associated with reduced inpatient and intensive care unit utilization but increased emergency department utilization. Path analyses confirmed an association between psychiatric illness and increased hospital utilization mediated by severity of illness and care fragmentation, but a stronger direct effect of psychiatric illness decreasing hospitalizations. CONCLUSION: Our findings differ from the increased hospital utilization for patients with psychiatric illness in circumstances other than end-of-life care. Path analyses confirmed hypothesized associations between psychiatric illness and increased utilization mediated by severity of illness and care fragmentation but identified more powerful direct effects decreasing hospital use. Further investigation should examine whether this effect represents a disparity in access to preferred care.

Error disclosure: a new domain for safety culture assessment.

OBJECTIVE: To (1) develop and test survey items that measure error disclosure culture, (2) examine relationships among error disclosure culture, teamwork culture and safety culture and (3) establish predictive validity for survey items measuring error disclosure culture. METHOD: All clinical faculty from six health institutions (four medical schools, one cancer centre and one health science centre) in The University of Texas System were invited to anonymously complete an electronic survey containing questions about safety culture and error disclosure. RESULTS: The authors found two factors to measure error disclosure culture: one factor is focused on the general culture of error disclosure and the second factor is focused on trust. Both error disclosure culture factors were unique from safety culture and teamwork culture (correlations were less than r=0.85). Also, error disclosure general culture and error disclosure trust culture predicted intent to disclose a hypothetical error to a patient (r=0.25, p<0.001 and r=0.16, p<0.001, respectively) while teamwork and safety culture did not predict such an intent (r=0.09, p=NS and r=0.12, p=NS). Those who received prior error disclosure training reported significantly higher levels of error disclosure general culture (t=3.7, p<0.05) and error disclosure trust culture (t=2.9, p<0.05). CONCLUSIONS: The authors created and validated a new measure of error disclosure culture that predicts intent to disclose an error better than other measures of healthcare culture. This measure fills an existing gap in organisational assessments by assessing transparent communication after medical error, an important aspect of culture.