Gustav Strikes the Louisiana Bayou: Leonard J. Chabert Medical Center Staff and OR-2 DMAT Team Up to Provide Acute Care for Houma

2008 ◽  
Vol 2 (4) ◽  
pp. 205-205
Author(s):  
Lewis Rubinson ◽  
Helen Miller ◽  
Jon Jui
Keyword(s):  
Acute Care ◽  
Medical Center ◽  
Center Staff

scholarly journals Cohort profile of Acutelines: a large data/biobank of acute and emergency medicine

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e047349
Author(s):  
Ewoud ter Avest ◽  
Barbara C van Munster ◽  
Raymond J van Wijk ◽  
Sanne Tent ◽  
Sanne Ter Horst ◽  
...  
Keyword(s):  
Acute Care ◽  
De Novo ◽  
Medical Center ◽  
Large Data ◽  
Future Research ◽  
Imaging Data ◽  
Novel Studies ◽  
Long Term Follow Up ◽  
Registration Number

PurposeResearch in acute care faces many challenges, including enrolment challenges, legal limitations in data sharing, limited funding and lack of singular ownership of the domain of acute care. To overcome these challenges, the Center of Acute Care of the University Medical Center Groningen in the Netherlands, has established a de novo data, image and biobank named ‘Acutelines’.ParticipantsClinical data, imaging data and biomaterials (ie, blood, urine, faeces, hair) are collected from patients presenting to the emergency department (ED) with a broad range of acute disease presentations. A deferred consent procedure (by proxy) is in place to allow collecting data and biomaterials prior to obtaining written consent. The digital infrastructure used ensures automated capturing of all bed-side monitoring data (ie, vital parameters, electrophysiological waveforms) and securely importing data from other sources, such as the electronic health records of the hospital, ambulance and general practitioner, municipal registration and pharmacy. Data are collected from all included participants during the first 72 hours of their hospitalisation, while follow-up data are collected at 3 months, 1 year, 2 years and 5 years after their ED visit.Findings to dateEnrolment of the first participant occurred on 1 September 2020. During the first month, 653 participants were screened for eligibility, of which 180 were approached as potential participants. In total, 151 (84%) provided consent for participation of which 89 participants fulfilled criteria for collection of biomaterials.Future plansThe main aim of Acutelines is to facilitate research in acute medicine by providing the framework for novel studies and issuing data, images and biomaterials for future research. The protocol will be extended by connecting with central registries to obtain long-term follow-up data, for which we already request permission from the participant.Trial registration numberNCT04615065.

scholarly journals Implementation of a QTc-interval monitoring protocol by pharmacists to decrease cardiac risk in at-risk patients in an acute care inpatient psychiatric facility

2019 ◽  
Vol 9 (2) ◽  
pp. 82-87 ◽  
Author(s):  
Nicole M. Daniel ◽  
Kim Walsh ◽  
Henry Leach ◽  
Lauren Stummer
Keyword(s):  
At Risk ◽  
Acute Care ◽  
Medical Center ◽  
Intervention Group ◽  
Psychiatric Hospitals ◽  
Control Group ◽  
Qtc Interval ◽  
Psychiatric Facility ◽  
End Point ◽  
Monitoring Protocol

Abstract Introduction Many medications commonly prescribed in psychiatric hospitals can cause QTc-interval prolongation, increasing a patient's risk for torsades de pointes and sudden cardiac death. There is little guidance in the literature to determine when an electrocardiogram (ECG) and QTc-interval monitoring should be performed. The primary end point was improvement of the appropriateness of ECGs and QTc-interval monitoring of at-risk psychiatric inpatients at Barnabas Health Behavioral Health Center (BHBH) and Monmouth Medical Center (MMC) following implementation of a standardized monitoring protocol. The secondary end point was the number of pharmacist-specific interventions at site BHBH only. Methods Patients who met the inclusion criteria were assessed using a standardized QTc-prolongation assessment algorithm for ECG appropriateness. A retrospective analysis of a control group (no protocol) from January 1, 2016, to July 17, 2017, was compared with a prospective analysis of the intervention group (with protocol) from December 11, 2017, to March 11, 2018. Results At BHBH, appropriate ECG utilization increased 25.5% after implementation of a standardized protocol (P = .0172) and appropriate omission of ECG utilization improved by 26% (P < .00001). At MMC, appropriate ECGs decreased by 5%, and appropriate ECG omissions increased by 28%, neither of which were statistically significant (P = 1.0 and P = .3142, respectively). There was an increase in overall pharmacist monitoring. Discussion The study demonstrated that pharmacist involvement in ECG and QTc-interval monitoring utilizing a uniform protocol may improve the appropriateness of ECG and QTc-interval monitoring in patients in an acute care inpatient psychiatric hospital.

Implementation of the Acute Care Nurse Practitioner Role

1994 ◽  
Vol 5 (3) ◽  
pp. 404-407
Author(s):  
Lynn A. Kelso ◽  
Lori M. Massaro
Keyword(s):  
Acute Care ◽  
Nurse Practitioners ◽  
Nurse Practitioner ◽  
Medical Center ◽  
Care Nurse ◽  
University Of Pittsburgh ◽  
Acute Care Nurse Practitioner ◽  
The University ◽  
Pittsburgh Medical ◽  
Practitioner Role

In this article, the experiences of two new acute care nurse practitioners working at the University of Pittsburgh Medical Center arc described. Included are the experiences they encountered in initiating the role and some of the responsibilities they assumed.

scholarly journals Using ICD-10-based Social Determinants of Health Categories to Assess Patients Risk for Acute Care Utilization

2021 ◽  
Author(s):  
Peter H. Nguyen ◽  
James Wang ◽  
Pamela Garcia-Filion ◽  
Deborah Dominick ◽  
Hamed Abbaszadegan ◽  
...  
Keyword(s):  
Social Determinants Of Health ◽  
Social Services ◽  
Acute Care ◽  
Social Determinants ◽  
Hospital Admissions ◽  
Medical Center ◽  
Determinants Of Health ◽  
Care Utilization ◽  
Icd 10 ◽  
Acute Care Utilization

ABSTRACTObjectiveSocial determinants of health (SDoH) play a pivotal role in health care utilization and adverse health outcomes. However, the optimal method to identify SDoH remains debatable. We ascertained SDoH based on International Classification of Disease 10 (ICD-10) codes in patient electronic health records (EHR) to assess the correlation with acute care utilization, and determine if social services interventions reduced care utilization.MethodsWe analyzed retrospective data for active patients at a Department of Veterans Affairs Medical Center (VAMC) from 2015-2017. Eleven categories of SDoH were developed based on existing literature of the social determinants; the relevant ICD-10 codes were divided among these categories. Emergency Room (ER) visits, hospital admissions, and social work visits were determined for each patient in the cohort.ResultsIn a cohort of 44,401 patients, the presence of ICD-10 codes within the EHR in the 11 SDoH categories was positively correlated with increased acute care utilization. Veterans with at least one SDoH risk factor were 71% (95%CI: 68% - 75%) more likely to use the ED and 71% (95%CI: 65%-77%) more likely to be admitted to the hospital. Utilization decreased with social service interventions.ConclusionThis project demonstrates a potentially meaningful method to capture patient social risk profiles through existing EHR data in the form of ICD-10 codes, which can be used to identify the highest risk patients for intervention with the understanding that not all SDoH codes are uniformly used and some SDoHs may not be captured.

scholarly journals A Model of Bed Demand to Facilitate the Implementation of Data-driven Recommendations for COVID-19 Capacity Management

2020 ◽  
Author(s):  
Teng Zhang ◽  
Kelly McFarlane ◽  
Jacqueline Vallon ◽  
Linying Yang ◽  
Jin Xie ◽  
...  
Keyword(s):  
Intensive Care ◽  
Acute Care ◽  
Capacity Planning ◽  
Medical Center ◽  
Capacity Management ◽  
Data Driven ◽  
Severe Illness ◽  
Hospital Capacity ◽  
Medicine Service ◽  
Number Of Patients

Abstract Background:We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is positive forcoronavirus disease 2019 (COVID-19) and the rate at which COVID-19 is spreading in the population. Our goal was to facilitate the implementation of data-driven recommendations for capacity management with a transparent mathematical simulation designed to answer the specific, local questions hospital leadership considered critical.Methods:The model facilitates hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool with modifiable parameters.Results:The use of the model is illustrated by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21. The model was used to inform COVID-19 planning and decision-making, including Intensive Care Unit (ICU) staffing and ventilator procurement.Conclusion:In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.Contributions to the literature:· Generation and implementation of data-driven recommendations for hospital capacity management early in the COVID-19 pandemic· The conceptualization, development, and deployment of an interactive simulation model in two weeks· Data-driven capacity management in the presence of significant uncertainty about the expected volume of patients, their clinical needs, and the availability of the workforceTrial Registration: Not applicable

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