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* Context.--Intermountain Healthcare hospitals use a single, standardized laboratory and automated testing process for estrogen receptor/progesterone receptor (ER/ PR) tests to minimize testing errors. Objectives.--To test the (1) variability in ER/PR negativity among hospitals and (2) association between specimen handling conditions and ER/PR negativity. Design.--Retrospective study of women who had breast cancer surgery at 7 Intermountain hospitals and ER/PR tests ordered between 1997 and 2003. Data were extracted from cancer registry. Frequency of ER/PR negativity was calculated for each surgery day and compared among hospitals and between 2 groups: regular (specimens obtained Sunday through Thursday, more likely to be tested within 24 hours of surgery) and prolonged (specimens obtained on Friday and Saturday, more likely to be tested more than 24 hours after surgery) specimen handling conditions. Results.--Five thousand seventy-seven women were tested for ER/PR. The frequency of ER and PR negativity was 20.9% and 27.9%, respectively. It increased with each day of the week for both ER (P = .03) and PR (P = .059) and tended to be higher for prolonged specimens for ER (23.6% versus 20.4%; P = .03) and for PR (30.1% versus 27.4%; P = .11) compared with regular specimens. After controlling for age and tumor size, both ER (P = .02) and PR (P = .02) negativity was significantly different among the hospitals and was associated with prolonged specimens for ER (P = .04) but not for PR (P = .09). Conclusions.--Estrogen receptor and PR negativity remained highly variable among hospitals despite use of a single laboratory and tended to be significantly associated with prolonged specimen handling. More studies are needed to confirm these findings. (Arch Pathol Lab Med. 2010;134:606-612), The estrogen receptor/progesterone receptor (ER/PR) test is an important prognostic indicator in the management of patients with breast cancer. It is also an important predictive test used to select women [...]

1. Participants will be able to identify differences in antidepressant prescriptions in family members of patients with and without dementia before and after death. 2. Participants will be able to identify additional caregiver and patient factors associated with antidepressant prescriptions including demographics, caregiver-patient relationship, nursing home status, and family size. Persons with dementia often have more protracted long-term care and end-of-life caregiving needs, compared to those with other chronic illnesses with caregiving typically managed by spouses and adult children. Dementia-related responsibilities are particularly stressful, with high rates of depression before death and more intense grief reactions following death. Little is known, however, about how the emotional stress of caregiving may differ before and after the death of persons with and without dementia. Given that antidepressant prescriptions are a strong indicator of clinically relevant levels of caregiver stress and bereavement outcomes, we explored the rates of antidepressant prescriptions among family members of decedents with and without dementia, during the one year prior and one year following a family member's death. To examine antidepressant prescriptions in kin of decedents with and without dementia before and after death. This retrospective cohort study used data from the novel Utah Caregiving Population Science (Utah C-PopS) dataset and focused on the widows/widowers (n=47,117) and sons/daughters (n=210,572) of decedents who died in Utah between 1998-2016 with dementia (n=29,401) or without dementia (n=98,017). Descriptive statistics were used to describe the study sample and multivariable logistic regressions were used to predict whether someone had an antidepressant prescription during the one-year prior and one-year post death by dementia status, controlling for decedent age, kin age, co-residence status, presence/type/number of other kin, prior antidepressant use, and other variables. Multivariable logistic regression models revealed spouses of dementia decedents had higher odds of an antidepressant prescription compared to spouses of non-dementia decedents during pre-death (End of life caregiving) [OR = 1.22 (1.01, 1.46)], including higher odds of prescriptions in female spouses [OR =1.64 (1.39, 1.96)]. Adult children of dementia decedents had higher odds of an antidepressant prescription compared to adult children of non-dementia decedents during pre-death (End of life (EOL) caregiving) [OR = 1.36 (95% 1.17, 1.58)], including higher odds of prescriptions in daughters [OR=1.74 (1.51, 2.00)]. Conversely, there was no difference in odds of antidepressant prescription 1 year after death in either spouses or adult children of decedents with and without dementia [OR = 1.12 (0.93, 1.33); OR = 1.12 (0.96, 1.31) respectively]. Differences in antidepressant prescriptions during pre-death periods suggests that caregiving and bereavement may be more intense for spouses and adult children of persons with dementia and for females compared to males. Dementia status did not have a significant effect on the odds of post-death grief antidepressant prescriptions. These results underscore the need for pre-death support for caregivers of persons with dementia, particularly female caregivers. Furthermore, these results demonstrate the utility of population-based datasets to investigate associations between dementia kin and medical outcomes including antidepressant prescriptions. Further analysis should examine if larger family sizes and multiple available caregivers impact antidepressant prescriptions an indicator that other family members may be more likely to share the caregiving responsibilities and provide grief support. Family caregivers/Psychosocial support [ABSTRACT FROM AUTHOR]

Context: The transition to population health management has changed the healthcare landscape to identify high risk, high cost patients. Various measures of patient risk have attempted to identify likely candidates for care management programs. Pre-screening patients for outreach has often required several years of data. Intermountain Healthcare relied on cost-ranking algorithms which had limited predictive ability. A new risk-adjusted algorithm shows improvements in predicting patients' future cost status to facilitate identifying patient eligibility for care management., Case Description: A retrospective cohort study design was used to evaluate high-cost patient status for two of the next three years. Modeling was developed using logistic regression and tested against other decision tree methods. Key variables included those readily available in electronic health records supplemented by additional clinical data and estimates of socio-economic status., Findings: The risk-adjusted modeling correctly identified 79.0% of patients ranking among the top 15% of costs in one of the next three years. In addition, it correctly estimated 48.1% of the patients in the top 15% cost group in two of the next three years. This method identified patients with higher medical costs and more comorbid conditions than previous cost-ranking methods., Major Themes: This approach improves the predictive accuracy of identifying high cost patients in the future and increases the sensitivity of identifying at-risk patients. It also shortened data requirements to identify eligibility criteria for case management interventions., Conclusion: Risk-adjustment modeling may improve management programs' interface with patients thus decreasing costs. This method may be generalized to other healthcare settings.

Introduction: Intermountain Healthcare is a fully integrated delivery system based in Salt Lake City, Utah. As a learning healthcare system with a mission of performance excellence, it became apparent that population health management and our efforts to move towards shared accountability would require additional patient-centric metrics in order to provide the right care to the right patients at the right time. Several European countries have adopted social deprivation indices in measuring the impact that social determinants can have on health. Such indices provide a geographic, area-based measure of how socioeconomically deprived residents of that area are on average. Intermountain's approach was to identify a proxy measure that did not require front-line data collection and could be standardized for our patient population, leading us to the area deprivation index or ADI. This paper describes the specifications and calculation of an ADI for the state of Utah. Results are presented along with introduction of three use cases demonstrating the potential for application of an ADI in quality improvement in a learning healthcare system., Case Description: The Utah ADI shows promise in providing a proxy for patient-reported measures reflecting key socio-economic indicators useful for tailoring patient interventions to improve health care delivery and patient outcomes. Strengths of this approach include a consistent standardized measurement of social determinants, use of more granular block group level measures and a limited data capture burden for front-line teams. While the methodology is generalizable to other communities, results of this index are limited to block groups within the state of Utah and will differ from national calculations or calculations for other states. The use of composite measures to evaluate individual characteristics must also be approached with care. Other limitations with the use of U.S. Census data include use of estimates and missing data., Conclusion: Initial applications in three meaningfully different areas of an integrated health system provide initial evidence of its broad applicability in addressing the impact of social determinants on health. The variation in socio-economic status by quintile also has potential for clinical significance, though more research is needed to link variation in ADI with variation in health outcomes overall and by disease type.