54 resultados para HEALTH-CARE


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In the current climate of escalating health care costs, defining value and accurately measuring it are two critical issues affecting not only the future of cancer care in particular but also the future of health care in general. Specifically, measuring and improving value in cancer-related health care are critical for continued advancements in research, management, and overall delivery of care. However, in oncology, most of this research has focused on value as it relates to insurance industry and payment reform, with little attention paid to value as the output of clinical interventions that encompass integrated clinical teams focusing on the entire cycle of care and measuring objective outcomes that are most relevant to patients. ^ In this study, patient-centered value was defined as health outcomes achieved per dollar spent, and calculated using objective functional outcomes and total care costs. The analytic sample comprised patients diagnosed with three common head and neck cancers—cancer of the larynx, oral cavity, and oropharynx—who were treated in an integrated tertiary care center over an approximately 10-year period. The results of this study provide initial empirical data that can be used to assess and ultimately to help improve the quality and value of head and neck cancer care, and more importantly they can be used by patients and clinicians to make better-informed decisions about care, particularly what therapeutic services and outcomes matter the most to patients.^

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The purpose of this study was to understand the scope of breast cancer disparities within the Texas Medical Center. The goal was to increase the awareness of breast cancer disparities at the health care organization level, and to foster the development of organizational interventions to reduce breast cancer disparities. The study seeks to answer the following questions: 1. Are hospitals in the Texas Medical Center implementing interventions to reduce breast cancer disparities? 2. What are their interventions for reducing the effects of non clinical factors on breast cancer treatment disparities? 3. What are their measures for monitoring, continuously improving, and evaluating the success of their interventions? ^ This research project was designed as a mixed methods case study. Quantitative breast cancer data for the years 2000-2009 was obtained from the Texas Cancer Registry (TCR). Qualitative data collection and analysis was done by conducting a total of 20 semi-structured interviews of administrators, physicians and nurses at five hospitals (A, B, C, D and E) in the Texas Medical Center (TMC). For quantitative analysis, the study was limited to early stage breast cancer patients: local and regional. The dependent variable was receipt of standard treatment: Surgery (Yes/No), BCS vs Mastectomy, Chemotherapy (Yes/No) and Radiation after BCS (Yes/No). The main independent variable was race: non-Hispanic White (NHW) , non-Hispanic Black (NHB), and Hispanic. Other covariates included age at diagnosis, diagnosis date, percent poverty, grade, stage, and regional nodes. Multivariate logistic regression was used to test the adjusted association between receipt of standard care and race. Qualitative data was analyzed with the Atlas.ti7 software (ATLAS.ti GmbH, Berlin). ^ Though there were significant differences by race for all dependent variables when the data was analyzed as a single group of all hospitals; at the level of the individual hospitals the results were not consistent by race/ethnicity across all dependent variables for hospitals A, B, and E. There were no racial differences in adjusted analysis for receipt of chemotherapy for the individual hospitals of interest in this study. For hospitals C and D, no racial disparities in treatment was observed in adjusted multivariable analysis. All organizations in this study were aware of the body of research which shows that there are disparities in breast cancer outcomes for patient population groups. However, qualitative data analysis found that there were differences in interest among hospitals in addressing breast cancer disparities in their patient population groups. Some organizations were actively implementing directed measures to reduce the breast cancer disparity gap in outcomes for patients, and others were not. Despite the differences in levels of interest, quantitative data analysis showed that organizations in the Texas Medical Center were making progress in reducing the burden of breast cancer disparities in the patient populations being served.^

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The management of HIV infection with antiretroviral drugs has succeeded in increasing survival rates, but the subject of pregnancy in HIV-positive women continues to garner debate. Discrimination and stigma have been identified as barriers to health care, suggesting that women with HIV may be disinclined to seek prenatal care if health-care workers exhibit negative attitudes toward the women's pregnancies. To optimize prenatal and medical care for women with HIV infection, it is important to understand the general social conditions and cultural context in which these women have children. Goffman's treatise on stigma, Foucault's discussion of the knowledge/power matrix, and Bandura's Social Cognitive Theory offer theoretical perspectives by which we can evaluate the gender, race, and class issues that are inherent in pregnancy decision-making for women with HIV infection. It is also necessary to evaluate prevailing attitudes on childbearing toward HIV-positive women and to review the historical background of prejudice in which HIV-positive women make decisions regarding childbearing. ^ This qualitative study used a survey instrument and one-on-one interviews with HIV-infected women to elicit their perceptions of how they were treated by care providers when they became pregnant. It also included interviews with health-care workers to determine what their feelings are about pregnancy within the context of HIV infection. Results of the ethnographic inquiry reveal that most of the women had negative experiences at some point during a pregnancy, but that the situation improved when they sought care from a provider who was familiar with HIV infection. The health-care providers interviewed were firm in their belief that HIV-positive women deserved optimal care and treated the women with respect, but these are individuals who are also experts in providing care to HIV-positive patients. The question remains as to what kind of care HIV-positive women are receiving generally and what types of attitudes they are being subjected to if they see less experienced providers. Further research is also needed to determine whether HIV-positive women from a broader ethnic representation and higher socioeconomic status experience similar negative attitudes. ^

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Background. Lack of coverage, lack of access, and failure to utilize health care services have all been linked to dismal health outcomes in the US. Such consequences have been a longstanding challenge that US minorities are faced with, in the context of a health care system believed to be lacking efficiency and equity. National population surveys in the US suggest that the number of uninsured approaches 50 millions, while some concerns and suspicions are raised by opponents to the growing number of foreign born US residents, many of whom are Hispanic. Research shows that race is a significant predictor of lack of coverage, access, and utilization, while age, gender, education, and income are also linked to these outcomes. We investigated the potential effect of immigration status or duration in the US on the association between coverage, access, use, and race. Methods. Using National Health Interview Survey (NHIS) data of 2006, we selected 22, 667 individuals of Non-Hispanic Black, Hispanic, and Non-Hispanic White descent, at least 18 years of age, US-born and foreign-born who reported their duration of residence in the US. Through complex sample survey logistic regression analysis, we computed odds ratios, beta coefficients, and 95% confidence intervals using models which excluded then included immigration status. Results. Although a significant predictor of the outcomes, immigration status did not change the relationship between each of the dependent variables (coverage, access, utilization), and the factor race, while adjusting for age, gender, education, and income. Our results show that Hispanics were least likely to have coverage (OR=.58; 95% CI[.49, .68]), access (OR=.62; 95% CI[.50, .76]), and to utilize services (OR=.60; 95% CI[.46, .79]) followed by Non-Hispanic Blacks, and Non-Hispanic Whites. These results were not changed by stratification, or the inclusion of interaction terms to eliminate the potential effect of relationships between independent variables. Recent immigrants (<5 years in US) were 0.12 times less likely to be insured, but also 0.26 times less likely to utilize services (p<0.001), and in addition they represented only 7.3% of the uninsured and 1.9% of the US population in 2006. Furthermore, 12% of the Non-Hispanic White population in the US was not covered, and 65% of the uninsured individuals were US-Born Citizens. Other predictors of lack of coverage, access and use were age below 45, male gender, education at high school or below, and income of less than $20,000. Conclusion. This investigation shows that the high percentage of uninsured was not directly caused by Hispanics, and immigration status alone could not explain racial differences in coverage, access, and utilization. An immigration reform may not be the solution to the healthcare crisis, and more specifically, will not stop the increase in the number of uninsured in the US, nor reduce the cost of health care. As a better alternative, universal health insu rance coverage should be considered, when aiming to eliminate racial disparities, and to solve the health care crisis. ^ Keywords. health insurance, coverage, access, utilization, race, immigration, disparities.^

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Research examining programs designed to retain patients in health care focus on repeated interactions between outreach workers and patients (Bradford et al. 2007; Cheever 2007). The purpose of this study was to determine if patients who are peer-mentored at their intake exam remain in care longer and attend more physicians' visits than those who were not mentored. Using patients' medical records and a previously created mentor database, the study determined how many patients attended their intake visit but subsequently failed to establish regular care. The cohort study examined risk factors for establishing care, determined if patients lacking a peer mentor failed to establish care more than peer mentor assisted patients, and subsequently if peer mentored patients had better health outcomes. The sample consists of 1639 patients who were entered into the Thomas Street Patient Mentor Database between May 2005 and June 2007. The assignment to the mentored group was haphazardly conducted based on mentor availability. The data from the Mentor Database was then analyzed using descriptive statistical software (SPSS version 15; SPSS Inc., Chicago, Illinois, USA). Results indicated that patients who had a mentor at intake were more likely to return for primary care HIV visits at 90 and 180 days. Mentored patients also were more likely to be prescribed ART within 180 days from intake. Other risk factors that impacted remaining in care included gender, previous care status, time from diagnosis to intake visit, and intravenous drug use. Clinical health outcomes did not differ significantly between groups. This supports that mentoring did improve outcomes. Continuing to use peer-mentoring programs for HIV care may help in increasing retention of patients in care and improving patients' health in a cost effective manner. Future research on the effects of peer mentoring on mentors, and effects of concordance of mentor and patient demographics may help to further improve peer-mentoring programs. ^

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Health Information Exchange (HIE) will play a key part in our nation’s effort to improve healthcare. The evidence of HIEs transformational role in healthcare delivery systems is quite limited. The lack of such evidence led us to explore what exists in the healthcare industry that may provide evidence of effectiveness and efficiency of HIEs. The objective of the study was to find out how many fully functional HIEs are using any measurements or metrics to gauge impact of HIE on quality improvement (QI) and on return on investment (ROI).^ A web-based survey was used to determine the number of operational HIEs using metrics for QI and ROI. Our study highlights the fact that only 50 percent of the HIEs who responded use or plan to use metrics. However, 95 percent of the respondents believed HIEs improve quality of care while only 56 percent believed HIE showed positive ROI. Although operational HIEs present numerous opportunities to demonstrate the business model for improving health care quality, evidence to document the impact of HIEs is lacking. ^

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This study aims to address two research questions. First, ‘Can we identify factors that are determinants both of improved health outcomes and of reduced costs for hospitalized patients with one of six common diagnoses?’ Second, ‘Can we identify other factors that are determinants of improved health outcomes for such hospitalized patients but which are not associated with costs?’ The Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database from 2003 to 2006 was employed in this study. The total study sample consisted of hospitals which had at least 30 patients each year for the given diagnosis: 954 hospitals for acute myocardial infarction (AMI), 1552 hospitals for congestive heart failure (CHF), 1120 hospitals for stroke (STR), 1283 hospitals for gastrointestinal hemorrhage (GIH), 979 hospitals for hip fracture (HIP), and 1716 hospitals for pneumonia (PNE). This study used simultaneous equations models to investigate the determinants of improvement in health outcomes and of cost reduction in hospital inpatient care for these six common diagnoses. In addition, the study used instrumental variables and two-stage least squares random effect model for unbalanced panel data estimation. The study concluded that a few factors were determinants of high quality and low cost. Specifically, high specialty was the determinant of high quality and low costs for CHF patients; small hospital size was the determinant of high quality and low costs for AMI patients. Furthermore, CHF patients who were treated in Midwest, South, and West region hospitals had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. Gastrointestinal hemorrhage and pneumonia patients who were treated in South region hospitals also had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. This study found that six non-cost factors were related to health outcomes for a few diagnoses: hospital volume, percentage emergency room admissions for a given diagnosis, hospital competition, specialty, bed size, and hospital region.^

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Preventable Hospitalizations (PHs) are hospitalizations that can be avoided with appropriate and timely care in the ambulatory setting and hence are closely associated with primary care access in a community. Increased primary care availability and health insurance coverage may increase primary care access, and consequently may be significantly associated with risks and costs of PHs. Objective. To estimate the risk and cost of preventable hospitalizations (PHs); to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs, first alone and then simultaneously; and finally, to estimate the impact of expansions in primary care availability and health insurance coverage on the burden of PHs among non-elderly adult residents of Harris County. Methods. The study population was residents of Harris County, age 18 to 64, who had at least one hospital discharge in a Texas hospital in 2008. The primary independent variables were availability of primary care physicians, availability of primary care safety net clinics and health insurance coverage. The primary dependent variables were PHs and associated hospitalization costs. The Texas Health Care Information Collection (THCIC) Inpatient Discharge data was used to obtain information on the number and costs of PHs in the study population. Risk of PHs in the study population, as well as average and total costs of PHs were calculated. Multivariable logistic regression models and two-step Heckman regression models with log-transformed costs were used to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs respectively, while controlling for individual predisposing, enabling and need characteristics. Predicted PH risk and cost were used to calculate the predicted burden of PHs in the study population and the impact of expansions in primary care availability and health insurance coverage on the predicted burden. Results. In 2008, hospitalized non-elderly adults in Harris County had 11,313 PHs and a corresponding PH risk of 8.02%. Congestive heart failure was the most common PH. PHs imposed a total economic burden of $84 billion at an average of $7,449 per PH. Higher primary care safety net availability was significantly associated with the lower risk of PHs in the final risk model, but only in the uninsured. A unit increase in safety net availability led to a 23% decline in PH odds in the uninsured, compared to only a 4% decline in the insured. Higher primary care physician availability was associated with increased PH costs in the final cost model (β=0.0020; p<0.05). Lack of health insurance coverage increased the risk of PH, with the uninsured having 30% higher odds of PHs (OR=1.299; p<0.05), but reduced the cost of a PH by 7% (β=-0.0668; p<0.05). Expansions in primary care availability and health insurance coverage were associated with a reduction of about $1.6 million in PH burden at the highest level of expansion. Conclusions. Availability of primary care resources and health insurance coverage in hospitalized non-elderly adults in Harris County are significantly associated with the risk and costs of PHs. Expansions in these primary care access factors can be expected to produce significant reductions in the burden of PHs in Harris County.^

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Making healthcare comprehensive and more efficient remains a complex challenge. Health Information Technology (HIT) is recognized as an important component of this transformation but few studies describe HIT adoption and it's effect on the bedside experience by physicians, staff and patients. This study applied descriptive statistics and correlation analysis to data from the Patient-Centered Medical Home National Demonstration Project (NDP) of the American Academy of Family Physicians. Thirty-six clinics were followed for 26 months by clinician/staff questionnaires and patient surveys. This study characterizes those clinics as well as staff and patient perspectives on HIT usefulness, the doctor-patient relationship, electronic medical record (EMR) implementation, and computer connections in the practice throughout the study. The Global Practice Experience factor, a composite score related to key components of primary care, was then correlated to clinician and patient perspectives. This study found wide adoption of HIT among NDP practices. Patient perspectives on HIT helpfulness on the doctor-patient showed a suggestive trend that approached statistical significance (p = 0.172). Clinicians and staff noted successful integration of EMR into clinic workflow and their perception of helpfulness to the doctor-patient relationship show a suggestive increase also approaching statistical significance (p=0.06). GPE was correlated with clinician/staff assessment of a helpful doctor-patient relationship midway through the study (R 0.460, p = 0.021) with the remaining time points nearing statistical significance. GPE was also correlated to both patient perspectives of EMR helpfulness in the doctor-patient relationship (R 0.601, p = 0.001) and computer connections (R 0.618, p = 0.0001) at the start of the study. ^