Dysphagia in geriatric hospital units: Prevalence and risks factors

2012 ◽  
Vol 3 ◽  
pp. S32
Author(s):  
M. Saume ◽  
V. Panier ◽  
V. Lardinois ◽  
J. Kengni ◽  
G. Fayt ◽  
...  
2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Kilian Rapp ◽  
Johannes Ravindren ◽  
Clemens Becker ◽  
Ulrich Lindemann ◽  
Andrea Jaensch ◽  
...  

2020 ◽  
pp. 1-8
Author(s):  
Julie Hias ◽  
Lorenz Van der Linden ◽  
Karolien Walgraeve ◽  
Jean-Claude Lemper ◽  
Laura Hellemans ◽  
...  

1993 ◽  
Vol 32 (01) ◽  
pp. 79-81 ◽  
Author(s):  
P. Millard ◽  
S. McClean

Abstract:The flow of patients through geriatric hospitals has been previously described in terms of acute and long-stay states where the bed occupancy at a census point is modelled by a mixed exponential model. Using data for sixteen years the model was fitted to successive annual census points, in order to provide a description of temporal trends. While the number of acute patients has remained fairly stable during the period, the model shows that there has been a decrease in the number of long-stay patients. Mean lengths of stay in our geriatric hospital before death or discharge have decreased during the study period for both acute and long-stay patients.Using these fits of the mixed exponential model to census data, a method is provided for predicting future turnover of patients. These predictions are reasonably good, except when the turnover patterns go through a period of flux in which assumption of stability no longer holds. Overall, a methodology is presented which relates census analysis to the behaviour of admission cohorts, thus producing a means of predicting future behaviour of patients and identifying where there is a change in patterns.


Author(s):  
Vo Que Son ◽  
Do Tan A

Sensing, distributed computation and wireless communication are the essential building components of a Cyber-Physical System (CPS). Having many advantages such as mobility, low power, multi-hop routing, low latency, self-administration, utonomous data acquisition, and fault tolerance, Wireless Sensor Networks (WSNs) have gone beyond the scope of monitoring the environment and can be a way to support CPS. This paper presents the design, deployment, and empirical study of an eHealth system, which can remotely monitor vital signs from patients such as body temperature, blood pressure, SPO2, and heart rate. The primary contribution of this paper is the measurements of the proposed eHealth device that assesses the feasibility of WSNs for patient monitoring in hospitals in two aspects of communication and clinical sensing. Moreover, both simulation and experiment are used to investigate the performance of the design in many aspects such as networking reliability, sensing reliability, or end-to-end delay. The results show that the network achieved high reliability - nearly 97% while the sensing reliability of the vital signs can be obtained at approximately 98%. This indicates the feasibility and promise of using WSNs for continuous patient monitoring and clinical worsening detection in general hospital units.


BMJ ◽  
1917 ◽  
Vol 2 (2959) ◽  
pp. 373-373
Author(s):  
R. Mitchell

Neurosurgery ◽  
2019 ◽  
Vol 86 (1) ◽  
pp. 132-138
Author(s):  
Christopher D Shank ◽  
Nicholas J Erickson ◽  
David W Miller ◽  
Brittany F Lindsey ◽  
Beverly C Walters

Abstract BACKGROUND Neurosciences intensive care units (NICUs) provide institutional centers for specialized care. Despite a demonstrable reduction in morbidity and mortality, NICUs may experience significant capacity strain with resulting supraoptimal utilization and diseconomies of scale. We present an implementation study in the recognition and management of capacity strain within a large NICU in the United States. Excessive resource demand in an NICU creates significant operational issues. OBJECTIVE To evaluate the efficacy of a Reserved Bed Pilot Program (RBPP), implemented to maximize economies of scale, to reduce transfer declines due to lack of capacity, and to increase transfer volume for the neurosciences service-line. METHODS Key performance indicators (KPIs) were created to evaluate RBPP efficacy with respect to primary (strategic) objectives. Operational KPIs were established to evaluate changes in operational throughput for the neurosciences and other service-lines. For each KPI, pilot-period data were compared to the previous fiscal year. RESULTS RBPP implementation resulted in a significant increase in accepted transfer volume to the neurosciences service-line (P = .02). Transfer declines due to capacity decreased significantly (P = .01). Unit utilization significantly improved across service-line units relative to theoretical optima (P < .03). Care regionalization was achieved through a significant reduction in “off-service” patient placement (P = .01). Negative externalities were minimized, with no significant negative impact in the operational KPIs of other evaluated service-lines (P = .11). CONCLUSION Capacity strain is a significant issue for hospital units. Reducing capacity strain can increase unit efficiency, improve resource utilization, and augment service-line throughput. RBPP implementation resulted in a significant improvement in service-line operations, regional access to care, and resource efficiency, with minimal externalities at the institutional level.


2020 ◽  
Vol 41 (S1) ◽  
pp. s168-s169
Author(s):  
Rebecca Choudhury ◽  
Ronald Beaulieu ◽  
Thomas Talbot ◽  
George Nelson

Background: As more US hospitals report antibiotic utilization to the CDC, standardized antimicrobial administration ratios (SAARs) derived from patient care unit-based antibiotic utilization data will increasingly be used to guide local antibiotic stewardship interventions. Location-based antibiotic utilization surveillance data are often utilized given the relative ease of ascertainment. However, aggregating antibiotic use data on a unit basis may have variable effects depending on the number of clinical teams providing care. In this study, we examined antibiotic utilization from units at a tertiary-care hospital to illustrate the potential challenges of using unit-based antibiotic utilization to change individual prescribing. Methods: We used inpatient pharmacy antibiotic use administration records at an adult tertiary-care academic medical center over a 6-month period from January 2019 through June 2019 to describe the geographic footprints and AU of medical, surgical, and critical care teams. All teams accounting for at least 1 patient day present on each unit during the study period were included in the analysis, as were all teams prescribing at least 1 antibiotic day of therapy (DOT). Results: The study population consisted of 24 units: 6 ICUs (25%) and 18 non-ICUs (75%). Over the study period, the average numbers of teams caring for patients in ICU and non-ICU wards were 10.2 (range, 3.2–16.9) and 13.7 (range, 10.4–18.9), respectively. Units were divided into 3 categories by the number of teams, accounting for ≥70% of total patient days present (Fig. 1): “homogenous” (≤3), “pauciteam” (4–7 teams), and “heterogeneous” (>7 teams). In total, 12 (50%) units were “pauciteam”; 7 (29%) were “homogeneous”; and 5 (21%) were “heterogeneous.” Units could also be classified as “homogenous,” “pauciteam,” or “heterogeneous” based on team-level antibiotic utilization or DOT for specific antibiotics. Different patterns emerged based on antibiotic restriction status. Classifying units based on vancomycin DOT (unrestricted) exhibited fewer “heterogeneous” units, whereas using meropenem DOT (restricted) revealed no “heterogeneous” units. Furthermore, the average number of units where individual clinical teams prescribed an antibiotic varied widely (range, 1.4–12.3 units per team). Conclusions: Unit-based antibiotic utilization data may encounter limitations in affecting prescriber behavior, particularly on units where a large number of clinical teams contribute to antibiotic utilization. Additionally, some services prescribing antibiotics across many hospital units may be minimally influenced by unit-level data. Team-based antibiotic utilization may allow for a more targeted metric to drive individual team prescribing.Funding: NoneDisclosures: None


Author(s):  
Andreas Follmann ◽  
Franziska Schollemann ◽  
Andrea Arnolds ◽  
Pauline Weismann ◽  
Thea Laurentius ◽  
...  

The bans on visiting nursing homes during the COVID-19 pandemic, while intended to protect residents, also have the risk of increasing the loneliness and social isolation that already existed among the older generations before the pandemic. To combat loneliness and social isolation in nursing homes, this trial presents a study during which social networks of nursing home residents and elderly hospital patients were maintained through virtual encounters and robots, respectively. The observational trial included volunteers who were either residents of nursing homes or patients in a geriatric hospital. Each volunteer was asked to fill in a questionnaire containing three questions to measure loneliness. The questionnaire also documented whether video telephony via the robot, an alternative contact option (for example, a phone call), or no contact with relatives had taken place. The aim was to work out the general acceptance and the benefits of virtual encounters using robots for different roles (users, relatives, nursing staff, facilities). Seventy volunteers with three possible interventions (non-contact, virtual encounters by means of a robot, and any other contact) took part in this trial. The frequency of use of the robot increased steadily over the course of the study, and it was regularly used in all facilities during the weeks of visitor bans (n = 134 times). In the hospital, loneliness decreased significantly among patients for whom the robot was used to provide contact (F(1,25) = 7.783, p = 0.01). In the nursing homes, no demonstrable effect could be achieved in this way, although the subject feedback from the users was consistently positive.


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