Stress, Burnout, Compassion Fatigue, and Mental Health in Hospice Workers in Minnesota

Background: Working in hospice care is a highly challenging yet rewarding profession. However, the challenges of working with dying patients and their families can overwhelm even the most highly dedicated professional, leading to burnout, compassion fatigue, anxiety, and depression. Objective: The aim of this study was to better understand how stress affects the mental health of hospice workers in terms of burnout and compassion fatigue and how they cope with these issues. Methods: Data for this study are from Compassion Fatigue and You, a cross-sectional survey of hospice staff from across Minnesota. We surveyed 547 hospice workers throughout Minnesota to better understand the overall mental health of staff, including levels of stress, burnout, and compassion fatigue, and how they cope with these issues. The study was conducted in 2008 and 2009 through a private, not-for-profit research institute affiliated with a large Midwestern health plan. Results: Hospice staff reported high levels of stress, with a small but significant proportion reporting moderate-to-severe symptoms of depression, anxiety, compassion fatigue, and burnout. Staff reported managing their stress through physical activity and social support, and they suggested that more opportunities to connect with coworkers and to exercise could help decrease staff burnout. Conclusions: Poor mental health places staff at risk for burnout and likely contributes to staff leaving hospice care; this is a critical issue as the profession attempts to attract new staff to meet the expanding demands for hospice care

The Effects of Patient-Centered Depression Care on Patient Satisfaction and Depression Remission

Background: While health systems are striving for patient-centered care, they have little evidence to guide them on how to engage patients in their care, or how this may affect patient experiences and outcomes. Objective: To explore which specific patient-centered aspects of care were best associated with depression improvement and care satisfaction. Methods: Design - observational. Setting - 83 primary care clinics across Minnesota. Subjects - Primary care patients with new prescriptions for antidepressants for depression were recruited from 2007 to 2009. Outcome measures - Patients completed phone surveys regarding demographics and self-rated health status and depression severity at baseline and 6 months. Patient centeredness was assessed via a modified version of the Patient Assessment of Chronic Illness Care. Differences in rates of remission and satisfaction between positive and negative responses for each care process were evaluated using chi-square tests. Results: At 6 months, 37% of 792 patients ages 18–88 achieved depression remission, and 79% rated their care as good-to-excellent. Soliciting patient preferences for care and questions or concerns, providing treatment plans, utilizing depression scales and asking about suicide risk were patient centered measures that were positively associated with depression remission in the unadjusted model; these associations were mildly weakened after adjustment for depression severity and health status. Nearly all measures of patient centeredness were positively associated with care ratings. Conclusion: The patient centeredness of care influences how patients experience and rate their care. This study identified specific actions providers can take to improve patient satisfaction and depression outcomes

Clinician Burnout and Satisfaction with Resources in Caring for Complex Patients

Objective: To describe primary care clinicians\u27 self-reported satisfaction, burnout and barriers for treating complex patients. Methods: We conducted a survey of 1554 primary care clinicians in 172 primary care clinics in 18 health care systems across 8 states prior to the implementation of a collaborative model of care for patients with depression and diabetes and/or cardiovascular disease. Results: Of the clinicians who responded to the survey (n=709; 46%), we found that a substantial minority (31%) were experiencing burnout that was associated with lower career satisfaction (P\u3c.0001) and lower satisfaction with resources to treat complex patients (P\u3c.0001). Less than 50% of clinicians rated their ability to treat complex patients as very good to excellent with 21% rating their ability as fair to poor. The majority of clinicians (72%) thought that a collaborative model of care would be very helpful for treating complex patients. Conclusions: Burnout remains a problem for primary care clinicians and is associated with low job satisfaction and low satisfaction with resources to treat complex patients. A collaborative care model for patients with mental and physical health problems may provide the resources needed to improve the quality of care for these patients

Adolescent Healthcare Contacts in the Year Before Suicide: a case control study

Introduction: Suicide rates among adolescents have risen steadily since 2007, creating a dire need to expand prevention protocols. Healthcare systems have been identified as a key avenue for identification and intervention. To date, no comprehensive analysis has been done to understand adolescent-specific characteristics and healthcare utilization prior to suicide death. Methods: A case-control study was conducted using records from eight healthcare systems nationwide. Data from 450 subjects aged 10-24 who died by suicide between the years 2000-2013 was matched with 4500 controls based on health system and time period of membership. We examined past-year health diagnoses and patterns of visit types and frequency. Results: Adolescents who died by suicide were more likely to have at least one mental health disorder (52% vs 16%), as well as each individual disorder. Physical health disorders were also more likely among this group. Close to half (49%) and nearly all (89%) of youth who died by suicide had a health care visit in the month and year prior to their death, respectively. Outpatient visits were most common, with suicide decedents averaging 8 in the year before death. Conclusion: With nearly half (48%) of adolescents who died by suicide lacking a mental health diagnosis in the year prior to their death, it is no longer sufficient to rely on mental health services to capture at-risk youth. High rates of healthcare utilization among those who died by suicide indicate a strong need for improving identification of youth while they are seeking services, thereby preventing future deaths

Predicting suicide attempts and suicide deaths among adolescents following outpatient visits

BACKGROUND: Few studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved. METHODS: We used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]). LIMITATIONS: The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement. CONCLUSIONS: Prediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation

Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction

Suicide risk prediction models can identify individuals for targeted intervention. Discussions of transparency, explainability, and transportability in machine learning presume complex prediction models with many variables outperform simpler models. We compared random forest, artificial neural network, and ensemble models with 1500 temporally defined predictors to logistic regression models. Data from 25,800,888 mental health visits made by 3,081,420 individuals in 7 health systems were used to train and evaluate suicidal behavior prediction models. Model performance was compared across several measures. All models performed well (area under the receiver operating curve [AUC]: 0.794-0.858). Ensemble models performed best, but improvements over a regression model with 100 predictors were minimal (AUC improvements: 0.006-0.020). Results are consistent across performance metrics and subgroups defined by race, ethnicity, and sex. Our results suggest simpler parametric models, which are easier to implement as part of routine clinical practice, perform comparably to more complex machine learning methods

The DIAMOND Initiative: Implementing Collaborative Care for Depression in 75 Primary Care Clinics

Background: The many randomized trials of the collaborative care model for improving depression in primary care have not described the implementation and maintenance of this model. This paper reports how and the degree to which collaborative care process changes were implemented and maintained for the 75 primary care clinics participating in the DIAMOND Initiative (Depression Improvement Across Minnesota–Offering a New Direction). Methods: Each clinic was trained to implement seven components of the model and participated in ongoing evaluation and facilitation activities. For this study, assessment of clinical process implementation was accomplished via completion of surveys by the physician leader and clinic manager of each clinic site at three points in time. The physician leader of each clinic completed a survey measure of the presence of various practice systems prior to and one and two years after implementation. Clinic managers also completed a survey of organizational readiness and the strategies used for implementation. Results: Survey response rates were 96% to 100%. The systems survey confirmed a very high degree of implementation (with large variation) of DIAMOND depression practice systems (mean of 24.4 ± 14.6%) present at baseline, 57.0 ± 21.0% at one year (P = \u3c0.0001), and 55.9 ± 21.3% at two years. There was a similarly large increase (and variation) in the use of various quality improvement strategies for depression (mean of 29.6 ± 28.1% at baseline, 75.1 ± 22.3% at one year (P = \u3c0.0001), and 74.6 ± 23.0% at two years. Conclusions: This study demonstrates that under the right circumstances, primary care clinics that are prepared to implement evidence-based care can do so if financial barriers are reduced, effective training and facilitation are provided, and the new design introduces the specific mental models, new care processes, and workers and expertise that are needed. Implementation was associated with a marked increase in the number of improvement strategies used, but actual care and outcomes data are needed to associate these changes with patient outcomes and patient-reported care

Substance use disorders and risk of suicide in a general US population: a case control study

BACKGROUND: Prior research suggests that substance use disorders (SUDs) are associated with risk of suicide mortality, but most previous work has been conducted among Veterans Health Administration patients. Few studies have examined the relationship between SUDs and suicide mortality in general populations. Our study estimates the association of SUDs with suicide mortality in a general US population of men and women who receive care across eight integrated health systems. METHODS: We conducted a case-control study using electronic health records and claims data from eight integrated health systems of the Mental Health Research Network. Participants were 2674 men and women who died by suicide between 2000-2013 and 267,400 matched controls. The main outcome was suicide mortality, assessed using data from the health systems and confirmed by state death data systems. Demographic and diagnostic data on substance use disorders and other health conditions were obtained from each health system. First, we compared descriptive statistics for cases and controls, including age, gender, income, and education. Next, we compared the rate of each substance use disorder category for cases and controls. Finally, we used conditional logistic regression models to estimate unadjusted and adjusted odds of suicide associated with each substance use disorder category. RESULTS: All categories of substance use disorders were associated with increased risk of suicide mortality. Adjusted odds ratios ranged from 2.0 (CI 1.7, 2.3) for patients with tobacco use disorder only to 11.2 (CI 8.0, 15.6) for patients with multiple alcohol, drug, and tobacco use disorders. Substance use disorders were associated with increased relative risk of suicide for both women and men across all categories, but the relative risk was more pronounced in women. CONCLUSIONS: Substance use disorders are associated with significant risk of suicide mortality, especially for women, even after controlling for other important risk factors. Experiencing multiple substance use disorders is particularly risky. These findings suggest increased suicide risk screening and prevention efforts for individuals with substance use disorders are needed