Data Driven Approaches to Define Acute Care Referral Regions and Resources

2016 ◽  
Author(s):  
David J. Wallace
Keyword(s):  
Acute Care ◽  
Data Driven

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

scholarly journals 210. Vancomycin Infusion Frequency and Intensity: Analysis of Real-World Data Generated from Automated Infusion Devices

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S108-S108
Author(s):  
Cynthia Yamaga ◽  
David L Bostick ◽  
Ying P Tabak ◽  
Ann Liu-Ferrara ◽  
Didier Morel ◽  
...  
Keyword(s):  
Acute Care ◽  
Real World ◽  
Acute Care Hospitals ◽  
Data Driven ◽  
Stopping Times ◽  
Becton Dickinson ◽  
Infusion Device ◽  
Session Duration ◽  
Number Of Patients ◽  
Infusion Devices

Abstract Background Automated infusion devices captures actual infused medication administration data in real-time. Vancomycin use is now recommended to be driven by AUC (area under the curve) dosing. We evaluated automated infusion device data to depict vancomycin administration practices in acute care hospitals. Figure 1. Distribution of vancomycin infusion dosing Figure 2. Distribution of time intervals between each vancomycin infusion session (mostly around 8 or 12 hours) Methods We analyzed archived vancomycin infusion data from 2,417 patients captured by automated infusion systems from 3 acute care hospitals. The infusion device informatics software recorded a variety of events during infusion – starting and stopping times, alarms and alerts, vancomycin dose, and other forms of timestamped usage information. We evaluated infusion session duration and dosing, using data-driven clustering algorithms. Results A total of 13,339 vancomycin infusion sessions from 2,417 unique adult patients were analyzed. Approximately 26.1% of patients had just one infusion of vancomycin. For the rest of the patients, the median number of infusion sessions per patient was 4; the interquartile range was 3 and 8. The most common dose was 1.0 gram (53.7%) or 1.5 gram (24.6%) (see Figure 1). The distribution of infusion session duration (hours) was 4.2% (≤1.0 hh); 40.1% (1.01–1.5 hh); 29.1% (1.51–2.0 hh); and 26.6% (>2.0 hh). The dosing frequency was 39.5% (q8 hh), 42.9% (q12 hh), 11.1% (q24 hh), and 6.5% (>q24 hh) (Figure 2), demonstrating clinical interpretability. Conclusion A considerable number of patients received just one vancomycin infusion during their hospital stay, suggesting a potential overuse of empiric vancomycin. The majority of infusion doses were between 1 to 1.5 grams and most infusion sessions were administered every 8 or 12 hours. The actual infusion duration for each dose often exceeds the prescribed 1- or 2-hour infusion orders, which may be due to known instances of infusion interruptions due to patient movement, procedures or IV access compromise. The data generated by infusion devices can augment insights on actual antimicrobial administration practices and duration. As vancomycin AUC dosing becomes more prevalent, real world infusion data may aid timely data-driven antimicrobial stewardship and patient safety interventions for vancomycin and other AUC dosed drugs. Disclosures Cynthia Yamaga, PharmD, BD (Employee) David L. Bostick, PhD, Becton, Dickinson and Co. (Employee) Ying P. Tabak, PhD, Becton, Dickinson and Co. (Employee) Ann Liu-Ferrara, PhD, Becton, Dickinson and Co. (Employee) Didier Morel, PhD, Becton, Dickinson and Co. (Employee) Kalvin Yu, MD, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding)

QUEST ® : A Data-Driven Collaboration to Improve Quality, Efficiency, Safety, and Transparency in Acute Care

2016 ◽  
Vol 42 (6) ◽  
pp. 247-253
Author(s):  
Mary M. Crimmins ◽  
Timothy J. Lowe ◽  
Monica Barrington ◽  
Courtney Kaylor ◽  
Terri Phipps ◽  
...  
Keyword(s):  
Acute Care ◽  
Data Driven ◽  
Improve Quality

Theory and Practice in Acute Care of Psychological Trauma

2002 ◽  
Vol 17 (S2) ◽  
pp. S8
Author(s):  
Rob Gordon
Keyword(s):  
Acute Care ◽  
Psychological Trauma ◽  
Theory And Practice

Improving Patient Safety and Patient–Provider Communication

2019 ◽  
Vol 4 (5) ◽  
pp. 1017-1027 ◽  
Author(s):  
Richard R. Hurtig ◽  
Rebecca M. Alper ◽  
Karen N. T. Bryant ◽  
Krista R. Davidson ◽  
Chelsea Bilskemper
Keyword(s):  
Acute Care ◽  
Augmentative And Alternative Communication ◽  
Health Care Team ◽  
Provider Communication ◽  
Alternative Communication ◽  
Patient Provider Communication ◽  
Acute Care Settings ◽  
Nurses Perceptions ◽  
Communication Needs ◽  
The Impact

Purpose Many hospitalized patients experience barriers to effective patient–provider communication that can negatively impact their care. These barriers include difficulty physically accessing the nurse call system, communicating about pain and other needs, or both. For many patients, these barriers are a result of their admitting condition and not of an underlying chronic disability. Speech-language pathologists have begun to address patients' short-term communication needs with an array of augmentative and alternative communication (AAC) strategies. Method This study used a between-groups experimental design to evaluate the impact of providing patients with AAC systems so that they could summon help and communicate with their nurses. The study examined patients' and nurses' perceptions of the patients' ability to summon help and effectively communicate with caregivers. Results Patients who could summon their nurses and effectively communicate—with or without AAC—had significantly more favorable perceptions than those who could not. Conclusions This study suggests that AAC can be successfully used in acute care settings to help patients overcome access and communication barriers. Working with other members of the health care team is essential to building a “culture of communication” in acute care settings. Supplemental Material https://doi.org/10.23641/asha.9990962

Early Feeding Intervention: Transitioning From Acute Care to Early Intervention

ASHA Leader ◽  
2010 ◽  
Vol 15 (7) ◽  
pp. 12-14 ◽  
Author(s):  
Kelly VanDahm
Keyword(s):  
Early Intervention ◽  
Acute Care ◽  
Early Feeding

New Post-Acute Care Quality Measures Take Effect Oct. 1

ASHA Leader ◽  
2016 ◽  
Vol 21 (6) ◽  
pp. 34-35
Author(s):  
Sarah Warren ◽  
Tim Nanof
Keyword(s):  
Acute Care ◽  
Quality Measures ◽  
Care Quality ◽  
Post Acute Care

Benefits of a Multidisciplinary Tracheostomy Team: Acute Care Experience

2018 ◽  
Vol 3 (13) ◽  
pp. 89-100
Author(s):  
Carmin Bartow ◽  
Nina Collins ◽  
Eugene Kopp ◽  
Oscar Guillamondegui
Keyword(s):  
Acute Care ◽  
Care Experience

Speech-Language Pathology in a Freestanding Acute Care Children's Hospital

1991 ◽  
Vol 19 ◽  
pp. 5-10
Author(s):  
Sharon Lockwood
Keyword(s):  
Acute Care ◽  
Speech Language Pathology ◽  
Children's Hospital ◽  
Children’S Hospital ◽  
Language Pathology
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