scholarly journals A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Narges Razavian ◽  
Vincent J. Major ◽  
Mukund Sudarshan ◽  
Jesse Burk-Rafel ◽  
Peter Stella ◽  
...  
Keyword(s):  
Decision Making ◽  
Positive Predictive Value ◽  
Real Time ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision ◽  
Front Line ◽  
Average Precision ◽  
Time Prediction ◽  
Parsimonious Model

Abstract The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

scholarly journals Task Shifting: The Use of Laypersons for Acquisition of Vital Signs Data for Clinical Decision Making in the Emergency Room Following Traumatic Injury

2017 ◽  
Vol 41 (12) ◽  
pp. 3066-3073 ◽  
Author(s):  
Bryce E. Haac ◽  
Jared R. Gallaher ◽  
Charles Mabedi ◽  
Andrew J. Geyer ◽  
Anthony G. Charles
Keyword(s):  
Decision Making ◽  
Emergency Room ◽  
Clinical Decision Making ◽  
Traumatic Injury ◽  
Vital Signs ◽  
Clinical Decision ◽  
Task Shifting

scholarly journals Utility and Acceptability of Influenza Surveillance amongst Emergency Providers

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Andrea F. Dugas ◽  
Howard Burkom ◽  
Anna L. DuVal ◽  
Richard Rothman
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
Additional Information ◽  
Provider Surveys

We provided emergency department providers with a real-time laboratory-based influenza surveillance tool, and evaluated the utility and acceptability of the surveillance information using provider surveys. The majority of emergency department providers found the surveillance data useful and indicated the additional information impacted their clinical decision making regarding influenza testing and treatment.

Gaps Between Alarm Capabilities and Decision-making Needs: An Observational Study of Detecting Patient Decompensation

2018 ◽  
Vol 7 (1) ◽  
pp. 112-116 ◽  
Author(s):  
Chelsea R. Horwood ◽  
Michael F. Rayo ◽  
Morgan Fitzgerald ◽  
E. Asher Balkin ◽  
Susan D. Moffatt-Bruce
Keyword(s):  
Decision Making ◽  
Early Warning ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision ◽  
Unintended Consequences ◽  
Warning Signs ◽  
Medical Setting ◽  
Mental Picture ◽  
Wide Range

Decompensation is a change in the overall ability to maintain physiological function in the presence of a stressor or disease. In the medical setting, clinicians utilize a wide range of technological tools to aid in their clinical decision making and to identify early warning signals for decompensation. However, many of these technologies have underperformed and are not aligned with the actual role of practitioners, resulting in unintended consequences and adverse events. The primary aim of this study is to explore how different nurses interpret early warning signs in order to anticipate decompensation. The secondary aim is to assess which technologies nurses rely on when anticipating decompensation, and if those technologies are adequately aiding them in their clinical decision making. Two researchers performed semi-structured ethnographic interviews that were recorded and transcribed during the summer of 2017. In total, 43 nurses were interviewed from different medical and surgical floors within the same hospital. Participants were asked questions focused on how they use and respond to alarms and how they anticipate patient decompensation. Constant Comparative Analysis was used to reveal patterns of responses between participants. Based on the qualitative analysis 6 major themes emerged:  1. Anticipating patient decompensation requires creating a complete mental “picture of the patient” by the nurses  2. Nurse-to-nurse communication and expertise is essential to understanding the patient’s history  3. Warning signs for decompensation were largely determined by a patient’s baseline  4. Change over time, or trends, is informative for anticipating decompensation. Numbers (regarding vital signs and labs) alone are not  5. Consistent care of patients improved nurse’s confidence in decision making  6. Anticipating decompensation requires “staying ahead of the machines Our research suggests that there is a gap between the information practitioners need to accurately anticipate patient decompensation, and the information current alarm technologies provide. Alarms are the primary tool provided to nurses to aid them in detecting hazardous events, however, current alarms are not well-suited in supporting signals that anticipate patient decompensation before it happens.

Training Front-Line Employees to Conduct Visual Analysis Using a Clinical Decision-Making Model

2019 ◽  
Vol 28 (3) ◽  
pp. 301-322 ◽  
Author(s):  
Kailie J. Kipfmiller ◽  
Matthew T. Brodhead ◽  
Katie Wolfe ◽  
Kate LaLonde ◽  
Emma S. Sipila ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Visual Analysis ◽  
Clinical Decision ◽  
Front Line ◽  
Decision Making Model

scholarly journals Clinical decision making regarding the use of vital signs in physical therapy

2016 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Virginia G Thistle ◽  
Allison L Basskin ◽  
Eric Shamus ◽  
Renee Jeffreys-Heil
Keyword(s):  
Decision Making ◽  
Physical Therapy ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision

Dashboard to Support the Decision-Making within a Chronic Disease

2012 ◽  
pp. 160-171 ◽  
Author(s):  
Leonor Teixeira ◽  
Vasco Saavedra ◽  
João Pedro Simões
Keyword(s):  
Decision Making ◽  
Chronic Disease ◽  
Real Time ◽  
Clinical Decision Making ◽  
Healthcare Providers ◽  
Clinical Decision ◽  
Heterogeneous Data ◽  
Information And Communications Technologies ◽  
The Right ◽  
Real Time Information

This chapter describes a monitoring system based on alerts and Key Performance Indicators (KPIs), applied in clinical context, within a chronic disease (haemophilia). This kind of disease follows the patient through his/her life, and its treatment requires an almost permanent exchange of data/information with healthcare professional (HCPs), with the information and communications technologies (ICTs) a key contribution in this process. However, most applications based on those ICTs do not allow the analysis of heterogeneous data in real-time, requiring the availability of clinicians to check the data and analyze the information to support the clinical decision process. Since time is a scarce resource in the context of healthcare providers, and information a crucial resource in the decision support process, real-time monitoring systems can help finding the right balance between those two resources, presenting the key information in an appropriate format, through alerts and KPIs. The system described in this chapter, named hemo@care_dashboard, aims to support clinical decision-making of healthcare professionals of a specific chronic disease, providing real-time information in a push-logic through alerts and KPIs, displayed on a dashboard.

Real-time clinical application of next-generation sequencing (NGS): Results from a multicenter program.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 11016-11016 ◽  
Author(s):  
Aaron Richard Hansen ◽  
Andrew M. K. Brown ◽  
Philippe L. Bedard ◽  
Sebastien J. Hotte ◽  
Eric Winquist ◽  
...  
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Clinical Decision Making ◽  
Illumina Miseq ◽  
Clinical Decision ◽  
Metastatic Lesion ◽  
Second Phase ◽  
Targeted Ngs ◽  
Approved Drugs ◽  
The Impact

11016 Background: NGS techniques enable the identification of actionable mutations in clinical tumor samples. The objective of this study is to assess feasibility and explore the impact of real-time targeted NGS on therapeutic decision-making. Methods: Patients (pts) with advanced solid tumors underwent a biopsy of a metastatic lesion. The first phase was performed with Sequenom MassARRAY somatic genotyping and Pacific Biosciences RS-targeted NGS. The second phase broadened genomic coverage in both Sequenom and Illumina MiSeq. All pts had a molecular profiling (mp) report issued after identified actionable mutations were verified by Sanger sequencing in a CLIA-lab and reviewed by an expert panel. “Actionability” was defined as having prognostic, predictive or diagnostic implications on patient management. Details of clinical outcomes and subsequent matched therapy, if applicable, were captured. Referring physicians were surveyed on the impact of mutation results on their treatment recommendations. Results: These are summarized in the Table. Conclusions: Broader mp platforms resulted in more identified actionable mutations which required a longer time for verification prior to reporting, but may yield a greater impact on clinical decision-making. However, the matching of pts to drugs based on their molecular profiles depends highly on drug access. For mp to be clinically relevant, it must be coupled with access to approved drugs or to investigational agents on clinical trials. Clinical trial information: NCT01345513. [Table: see text]

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