Building community at work: An exploratory study in healthcare system management.

Recent scholarship has demonstrated that experiences in the community (i.e., a sense of community [SOC] and a SOC responsibility) can enhance employee psychological and behavioral outcomes. Recent evidence also shows that the experiences of the community are better able to predict employee outcomes compared to long-regarded management and public management constructs. However, very little empirical evidence exists on antecedent conditions that help build community experiences at work. To evaluate this gap, we conducted interviews with executive leaders, and focus groups with administrative leaders, across four major facilities in a large nonprofit healthcare system that is headquartered in Pennsylvania, United States. The study confirmed the propositions of the Community Experience Model, and explored organizational conditions that appear to build community experiences. The findings help frame factors that scholars can empirically test in future studies, and assist executives, human resource professionals, and managers throughout an organization, in building community at work.

Characterizing primary care for patients with major depressive disorder using electronic health records of a US-based healthcare provider.

BACKGROUND: Major depressive disorder (MDD) is predominantly managed in primary care. However, primary care providers (PCPs) may not consistently follow evidence-based treatment algorithms, leading to variable patient management that can impact outcomes. METHODS: We retrospectively analyzed adult patients with MDD seen at Geisinger, an integrated health system. Utilizing electronic health record (EHR) data, we classified patients as having MDD based on International Classification of Disease (ICD)-9/10 codes or a Patient Health Questionnaire (PHQ)-9 score ≥5. Outcomes assessed included time to first visit with a PCP or behavioral health specialist following diagnosis, antidepressant medication switching, persistence, healthcare resource utilization (HRU), and treatment costs. RESULTS: Among the 38,321 patients with MDD managed in primary care in this study, significant delays between diagnosis with antidepressant prescribing and follow-up PCP visits were observed. There was also considerable variation in care following diagnosis. Overall, 34.9% of patients with an ICD-9/10 diagnosis of MDD and 41.3% with a PHQ-9 score ≥15 switched antidepressants. An ICD-9/10 diagnosis, but not moderately severe to severe depression, was associated with higher costs and HRU. More than 75% of patients with MDD discontinued antidepressant medication within 6 months. LIMITATIONS: The study population was comparable with other real-world studies of MDD, but study limitations include its retrospective nature and reliance on the accuracy of EHRs. CONCLUSIONS: Management of patients with MDD in a primary care setting is variable. Addressing these gaps will have important implications for ensuring optimal patient management, which may reduce HRU and treatment medication costs, and improve treatment persistence.

Caring for trafficked and unidentified patients in the EHR shadows: Shining a light by sharing the data.

OBJECTIVE: Healthcare providers have key roles in the prevention of, detection of, and interventions for human trafficking. Yet caring for trafficked persons is particularly challenging: patients whose identities are unknown, unreliable, or false could receive subpar care from providers delivering care in a vacuum of relevant information. The application of precision medicine principles and integration of biometric data (including genetic information) could facilitate patient identification, enable longitudinal medical records, and improve continuity and quality of care for this vulnerable patient population. Scant empirical data exist regarding healthcare system preparedness and care for the needs of this vulnerable population nor data on perspectives on the use and risks of biometrics or genetic information for trafficked patients. METHODS: To address this gap, we conducted mixed-methods research involving semi-structured interviews with key informants, which informed a subsequent broad survey of physicians and registered nurses. RESULTS: Our findings support the perception that trafficked persons obtain care yet remain unnoticed or undocumented in the electronic health record. Our survey findings further reveal that healthcare providers remain largely unaware of human trafficking issues and are inadequately prepared to provide patient-centered care for trafficked and unidentified patients. CONCLUSION: Meaningful efforts to design and implement precision medicine initiatives in an inclusive way that optimizes impacts are unlikely to succeed without concurrent efforts to increase general awareness of and preparedness to care for trafficked persons. Additional research is needed to examine properly the potential utility for biometrics to improve the delivery of care for trafficked patients.

Burden of Multiple Chronic Conditions among Patients with Urological Cancer.

PURPOSE: We describe age, multiple chronic condition profiles and health system contact in patients with urological cancer. MATERIALS AND METHODS: Using Geisinger Health System electronic health records we identified adult primary care patients and a subset with at least 1 urology encounter between 2001 and 2015. The Agency for Health Care Research and Quality Chronic Condition Indicator and Clinical Classifications Software tools were applied to ICD-9 codes to identify chronic conditions. Multiple chronic conditions were defined as 2 or more chronic conditions. Patients with urological cancer were identified using ICD-9 codes for prostate, bladder, kidney, testis and penile cancer. Inpatient and outpatient visits in the year prior to the most recent encounter were counted to document health system contact. RESULTS: We identified 357,100 primary care and 33,079 urology patients, of whom 4,023 had urological cancer. Patients with urological cancer were older than primary care patients (71 vs 46 years) and they had more median chronic conditions (7 vs 4). Kidney and bladder cancer were the most common chronic conditions (median 8 patients each). Coronary artery disease and chronic kidney disease were common in urological cancer cases compared to mental health conditions in primary care cases. Patients with urological cancer who had multiple chronic conditions had the most health system contact, including 32% with at least 1 hospitalization and 68% with more than 5 outpatient visits during 1 year. CONCLUSIONS: Urology patients are older and more medically complex, especially those with urological cancer than primary care patients. These data may inform care redesign to reduce the treatment burden and improve care coordination in urological cancer cases.

Challenges to care coordination posed by the use of multiple health IT applications.

Coordinating care for hospitalized patients requires the use of multiple sources of information. Using a macroergonomic framework (i.e. the work system model), we conducted interviews and observations of care managers involved in care coordination across transitions of care. When information is distributed across multiple health IT applications, care managers experience a range of challenges, including organizational barriers, technology design problems, skills and knowledge issues, and task performance demands (i.e. issues related to individual information processing and management and sharing of information). These challenges can be used as a checklist to evaluate the proposed IT infrastructure that will allow the integration of multiple health IT applications and, therefore, support coordination across transitions of care.

Body mass index and the built and social environments in children and adolescents using electronic health records.

BACKGROUND: No prior studies in children have evaluated how age may modify relationships of the built and social environments with BMI, nor evaluated the range of scales and contexts over which places may influence health. PURPOSE: To systematically evaluate associations of 33 environmental measures in three domains (land use, physical activity, and social environments) with BMI in children and adolescents in five geographies. METHODS: A cross-sectional, multilevel analysis was completed in 2009-2010 of electronic health record data (2001-2008) from 47,769 children aged 5-18 years residing in a 31-county region of Pennsylvania. Associations of environmental measures with BMI were evaluated using 0.5-mile network buffers; census tracts; minor civil divisions (i.e., townships, boroughs, cities); a mixed definition of place (townships, boroughs, and census tracts in cities); and counties, overall and by age strata. RESULTS: Among all children, lower levels of community socioeconomic deprivation and greater diversity of physical activity establishments were associated with lower BMI. Associations of environmental measures differed by age, depending on scale and context. For example, higher population density was associated with lower BMI in older children; this effect was strongest in the larger geographies. Similarly, a lower level of county sprawl was associated with lower BMI in older children. CONCLUSIONS: Associations differed by age and definition of place, suggesting that the benefits of environmental intervention may not be uniform across the childhood age range. The study demonstrated the utility of using electronic patient information for large-scale, population-based epidemiologic research, a research area of growing interest and investment in the U.S.

Mental health experiences and needs among primary care providers treating OEF/OIF veterans: preliminary findings from the Geisinger Veterans Initiative.

This study describes the results of the Reaching Rural Veterans Initiative (RRVI) funded by the Commonwealth of Pennsylvania and the Federal Government. The purpose of this project was to address the needs of veterans and their family members in rural communities who were seen by non-VA primary care providers. As part of this project, an assessment of healthcare providers' knowledge and awareness of mental health-related issues and experiences with veterans' healthcare services was conducted. Following this assessment, an education program was developed and implemented at primary care sites within the Geisinger Health System and also made available to other area providers. The survey indicated that Geisinger's primary care providers are currently involved with providing mental health care to area service members and their families. It was estimated that these providers saw about 1,200 Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) patients and 3,600 of their family members in clinics over a 6 month period. A significant number of these persons had mental health problems. About two-thirds (65.4%) of providers reported having a mental health professional onsite and nearly 23% reported that over one-third of their patients have mental health problems. Significant mental health gaps discovered indicated that providers lacked knowledge of PTSD and other combat-related stress disorders, as well as knowledge of VA resources. In addition only 20% of the providers rated their mental health treatment skills as high and only about 8% reported that they had adequate knowledge of current mental health treatment strategies. Based on this needs assessment and the results of the provider intervention, further service improvements are planned.

Preventive health examinations: a comparison along the rural-urban continuum.

In this analysis, Medical Expenditure Panel Survey data from 2000 were used to examine differences in reports of preventive health service utilization in 4 types of counties: large metropolitan counties, small metropolitan counties, counties adjacent to metropolitan places, and counties not adjacent to metropolitan areas or with fewer than 10,000 residents. Women from counties with 10,000 or fewer residents and not adjacent to a metropolitan county, classified as rural residents, were less likely to report a number of preventive health examinations during the previous 2 years. Rural women were less likely to obtain blood cholesterol tests, dental exams, and mammograms during the previous 2 years when compared to women from large metropolitan counties. Rural women were more likely to obtain blood pressure checks during the previous year when compared to the metropolitan women. Findings for exams that occurred during the preceding 1- and 2-year periods are reported for blood pressure checks, blood cholesterol checks, physical exams, colon cancer screening, dental exams, breast exams, mammograms, and Pap smears.

Women's health care utilization and expenditures.

This study examines women's use and expenditures for medical care in the US. In 2000, 91% of women aged 18 years and older used any form of health care services. Overall, 82% of adult women reported an ambulatory care visit, and 11% had an inpatient hospital stay. Mean expense per person with expenses was 3219 dollars for that year. We examined use and expenditures by sociodemographic characteristics. The most notable findings indicate that women with private insurance and those on Medicaid are more likely to use health services than uninsured women. White women, compared to black and Hispanic women, are more likely to have an ambulatory care visit, buy prescription drugs, and use preventive health care services. In addition, white and Hispanic women pay a higher proportion of medical care expenses out-of-pocket than do black women. Finally, nearly 30% of older women in fair or poor health spent 10% or more of their income on medical care. Preventable disparities in access to and receipt of care are unacceptable. To improve the quality of health care for all women, it is important for policymakers to understand the factors that influence their utilization and expenditures for medical care. Data collection, analysis, and reporting by race, ethnicity, and primary language across federally supported health programs are essential to help identify, understand the causes of, monitor, and eventually eliminate disparities.

Racial and ethnic differences in the mental health problems and use of mental health care.

OBJECTIVES: We compared rates of mental health problems and use of mental health care across multiple racial and ethnic groups using secondary data from a large, nationally representative survey. METHODS: We pooled cross-sectional data from the 2001-2003 National Surveys on Drug Use and Health. Our sample included 134,875 adults classified as white, African American, American Indian/Alaskan Native, Asian, Mexican, Central and South American, Puerto Rican, other Hispanic-Latino, or those with multiple race and ethnicities. For each group, we estimate the past year probability of: (1) having 1 or more mental health symptoms in the past year, (2) having serious mental illness in the past year, (3) using mental health care, (4) using mental health care conditional on having mental health problems, (5) reporting unmet need for mental health care, and (6) reporting unmet need for mental health care conditional on having mental health problems. RESULTS: We found significantly higher rates of mental health problems and higher self-reported unmet need relative to whites among American Indian/Alaskan Natives and lower rates of mental health problems and use of mental health care among African American, Asian, Mexican, Central and South American, and other Hispanic-Latino groups. These differences generally were robust to the inclusion of clinical and socio demographic covariates. CONCLUSIONS: Overall, our study shows wide variation in mental health morbidity and use of mental health care across racial and ethnic groups in the United States. These results can help to focus efforts aimed at understanding the underlying causes of the differences we observe.

Rural-urban differences in employment-related health insurance.

CONTEXT: Rural residents are disproportionately represented among the uninsured in the United States. PURPOSE: We compared nonelderly adult residents in 3 types of nonmetropolitan areas with metropolitan workers to evaluate which characteristics contribute to lack of employment-related insurance. RESEARCH DESIGN AND ANALYSIS: Data were obtained from the Medical Expenditure Panel Survey, pooled across 3 panels (1996--1998) to enhance the rural sample size. Econometric decomposition was used to quantify the contribution of employment structure to differences in the probability of being offered employment-related health insurance. FINDINGS: The most rural workers are 10.4 percentage points less likely to be offered insurance compared with urban workers; the difference is smaller for residents of other rural areas. In rural counties not adjacent to urban areas, lower wages and smaller employers each account for about one-third of the total difference. CONCLUSIONS: Health insurance disparities associated with rural residence are related to the structure of employment. Major factors include smaller employers, lower wages, greater prevalence of self-employment, and sociodemographic characteristics.

Rural-urban differences in usual source of care and ambulatory service use: analyses of national data using Urban Influence Codes.

BACKGROUND: Rural-urban disparities in access to and utilization of medical care have been a long-standing focus of concern. OBJECTIVE: Using the nine-category Urban Influence Codes, this study examines the relationship between place of residence and having access and utilization of ambulatory health services. RESEARCH DESIGN: Data come from the Medical Expenditure Panel Survey, conducted in 1996. Linear and logistic regression analyses assess the relationship between county type and having a usual source of care and ambulatory visits, controlling for demographic and health status measures. RESULTS: Residents of counties that were totally rural were more likely to report having a usual source of care (adjusted OR: 1.98; CI: 1.01, 3.89) than residents of large metropolitan counties. Residents of places without a city of 10,000 or more, but adjacent to a metropolitan area, were also more likely to report having a usual source of care (adjusted OR: 1.92; CI: 1.16, 3.22). In a regression analysis, residents of the most rural places reported fewer visits during the year (B = -2.42, CI: -3.68, -1.32). CONCLUSIONS: Results suggest that using rural and urban definitions that go beyond the traditional dichotomy of metropolitan and non-metropolitan may assist policymakers and researchers in identifying types of places where there is a disparity in access and subsequent utilization of health care. Rural residents, defined as totally rural in the urban influence coding scheme, may report having a health care provider but report fewer visits to health care providers during a year.