786 resultados para Patient-centered care


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The prognosis from thyroid cancer subtypes in humans covers a spectrum from "cured at almost 90%" to "100% lethal." Invasive and poorly differentiated forms of thyroid cancer are among the most aggressive human cancers, and there are few effective therapeutic options. Genetically engineered mice, based on mutations observed in patients, can accurately recapitulate the human disease and its progression, providing invaluable tools for the preclinical evaluation of novel therapeutic approaches. This overview details models developed to date as well as their uses for identifying novel anticancer agents. © 2015 by John Wiley & Sons, Inc.

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Communication : If there is one topic that comes up over and over again as we discuss ways to make John Dempsey Hospital the safest hospital, it is “communication.” In fact, several of the 2006 and 2007 National Patient Safety Goals are centered around improving the effectiveness of communication among caregivers. There are many ways of doing this, and we have implemented several already. These include handoffs, medication reconciliation, “SBAR,” etc. On page two, we will talk in more detail about hand-offs and the use of “SBAR.”

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Geographic health planning analyses, such as service area calculations, are hampered by a lack of patient-specific geographic data. Using the limited patient address information in patient management systems, planners analyze patient origin based on home address. But activity space research done sparingly in public health and extensively in non-health related arenas uses multiple addresses per person when analyzing accessibility. Also, health care access research has shown that there are many non-geographic factors that influence choice of provider. Most planning methods, however, overlook non-geographic factors influencing choice of provider, and the limited data mean the analyses can only be related to home address. This research attempted to determine to what extent geography plays a part in patient choice of provider and to determine if activity space data can be used to calculate service areas for primary care providers. ^ During Spring 2008, a convenience sample of 384 patients of a locally-funded Community Health Center in Houston, Texas, completed a survey that asked about what factors are important when he or she selects a health care provider. A subset of this group (336) also completed an activity space log that captured location and time data on the places where the patient regularly goes. ^ Survey results indicate that for this patient population, geography plays a role in their choice of health care provider, but it is not the most important reason for choosing a provider. Other factors for choosing a health care provider such as the provider offering "free or low cost visits", meeting "all of the patient's health care needs", and seeing "the patient quickly" were all ranked higher than geographic reasons. ^ Analysis of the patient activity locations shows that activity spaces can be used to create service areas for a single primary care provider. Weighted activity-space-based service areas have the potential to include more patients in the service area since more than one location per patient is used. Further analysis of the logs shows that a reduced set of locations by time and type could be used for this methodology, facilitating ongoing data collection for activity-space-based planning efforts. ^

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Purpose. The overall purpose of the study was to evaluate the patient experience relevant to the Chronic Care Model as measured by the PACIC. Chronic illness care of patients with diabetes was compared to those with other chronic illnesses. In addition, chronic illness care of Hispanics was compared to those of other race/ethnicity. ^ Methods. The setting of this study was 20 primary care practices located in San Antonio, TX. The subjects in this study were consecutive adult patients age >18 yrs. Data was collected via a survey (PACIC) administered to 40-60 consecutive adult patients in each primary care clinic who presented for a scheduled appointment. ^ Results. Patient experience of the Chronic Care Model is different among those with diabetes than those with other chronic diseases: those with diabetes report a higher PACIC score. (P = 0.012) Although Hispanic patients report a higher PACIC score, patient experience of the Chronic Care Model among Hispanic patients is not significantly different than that of patients of other race/ethnicity regardless of chronic disease. (P = 0.053) After controlling for the patient characteristics of age, education, health status, and race/ethnicity, the diabetes status of the patient remains significantly associated with the outcome, the PACIC score. (P = 0.033) ^ Conclusions. Diabetes is associated with a greater experience of the Chronic Care model. Contributing factors to diabetes patients’ greater experience of the Chronic Care Model include the greater heath care use and higher self-care needs unique to individuals with diabetes. Special consideration must be given to the specific needs diabetic patients to ensure effective interventions, higher patient education, greater patient compliance, and lower health care costs. ^

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Sepsis is a significant cause for multiple organ failure and death in the burn patient, yet identification in this population is confounded by chronic hypermetabolism and impaired immune function. The purpose of this study was twofold: 1) determine the ability of the systemic inflammatory response syndrome (SIRS) and American Burn Association (ABA) criteria to predict sepsis in the burn patient; and 2) develop a model representing the best combination of clinical predictors associated with sepsis in the same population. A retrospective, case-controlled, within-patient comparison of burn patients admitted to a single intensive care unit (ICU) was conducted for the period January 2005 to September 2010. Blood culture results were paired with clinical condition: "positive-sick"; "negative-sick", and "screening-not sick". Data were collected for the 72 hours prior to each blood culture. The most significant predictors were evaluated using logistic regression, Generalized Estimating Equations (GEE) and ROC area under the curve (AUC) analyses to assess model predictive ability. Bootstrapping methods were employed to evaluate potential model over-fitting. Fifty-nine subjects were included, representing 177 culture periods. SIRS criteria were not found to be associated with culture type, with an average of 98% of subjects meeting criteria in the 3 days prior. ABA sepsis criteria were significantly different among culture type only on the day prior (p = 0.004). The variables identified for the model included: heart rate>130 beats/min, mean blood pressure<60 mmHg, base deficit<-6 mEq/L, temperature>36°C, use of vasoactive medications, and glucose>150 mg/d1. The model was significant in predicting "positive culture-sick" and sepsis state, with AUC of 0.775 (p < 0.001) and 0.714 (p < .001), respectively; comparatively, the ABA criteria AUC was 0.619 (p = 0.028) and 0.597 (p = .035), respectively. SIRS criteria are not appropriate for identifying sepsis in the burn population. The ABA criteria perform better, but only for the day prior to positive blood culture results. The time period useful to diagnose sepsis using clinical criteria may be limited to 24 hours. A combination of predictors is superior to individual variable trends, yet algorithms or computer support will be necessary for the clinician to find such models useful. ^

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Improving patient self-management can have a greater impact than improving any clinical treatment (WHO). We propose here a systematic and comprehensive user centered design approach for delivering a technological platform for diabetes disease management. The system was developed under the METABO research project framework, involving patients from 3 different clinical centers in Parma, Modena and Madrid.

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The respiratory effects of dexmedetomidine were retrospectively examined in 33 postsurgical patients involved in a randomised, placebo-controlled trial after extubation in the intensive care unit (ICU). Morphine requirements were reduced by over 50% in patients receiving dexmedetomidine. There were no differences in respiratory rates, oxygen saturations, arterial pH and arterial partial carbon dioxide tension (PaCO2) between the groups. Interestingly the arterial partial oxygen tension (PaO2) : fractional inspired oxygen (FIO2) ratios were statistically significantly higher in the dexmedetomidine group. Dexmedetomidine provides important postsurgical analgesia and appears to have no clinically important adverse effects on respiration in the surgical patient who requires intensive care.