Cardiac Arrest (Disorder), Patient Died in Hospital (Finding), Patient Transfer to Intensive Care Unit (Procedure), Respiratory Arrest (Disorder)
Conditions
Keywords
Information Science: Algorithms, Information Science: Informatics: Nursing Informatics, Information Science: Data Display: Computer graphics, Diagnosis: Diagnostic Techiques and Procedures: Monitoring, Physiologic, Health care facilities, Manpower and services: Health services: Emergency Medical Services: Triage
Brief summary
The overall goal of the project is to reduce the number of unexpected patient deteriorations by 50% at Emergency Departments (ED) by investigating if the novel Patient Deterioration Warning Systems (PDWS), can improve clinicians' ability to identify deterioration at an earlier stage. A third of all acute medical patients with normal vital signs at arrival, experience a deterioration in vital signs during the 24 first hours. This can potentially lead to dire consequences for these patients, as the risk of deterioration is present across all severity levels. The utilization of patient monitoring systems in the dispersed and shared working environments of EDs and acute wards may help to identify some of the reasons for failure to rescue patients. Thus, quantifying the extent to which a patient is being monitored, may be an aid to bridge the current gap between usage of automated and manual monitoring as clinical work will continue to depend on tacit knowledge and intuition. Several systems and protocols have been established to swiftly deal with identified deterioration. Most systems struggle with issues of clinical adherence and are difficult to assess on-the-fly, and in some cases nurses failed to notice abnormality in 43% of patients experiencing deterioration. Although the trajectories of patients' vital signs have been identified as more important than the initial scoring value, most of the widely used Track and Trigger systems lack a temporal aspect. Furthermore, a limited number of these Track and Trigger systems have been integrated into real time clinical decision support systems, which has not evolved much in the last decades. The PDWS deals with these challenges by aggregating and summarizing all vital values measured with the ED's patient monitors in the ongoing admission to intuitively present the state and trajectory. The investigators intend to determine if making the PDWS system available to nurses and physicians throughout the entire ED improves their ability to identify patients at risk of deterioration. To make this assessment, the PDWS will be evaluated in a cluster randomized trial (CRT) at two ED facilities in Denmark. The CRT is structured in three 5-week intervention, and three 5-week control periods, separated by a washout period of at least one week. The primary outcome is in-hospital deterioration - defined as transfer to the intensive care unit, heart/respiratory failure or death. The effect the PDWS will be assessed by comparing the proportions of events in each study arm using Pearsons's chi-squared test on these two samples. Furthermore, the technical and economical effects are evaluated using the Technology Acceptance Model, and the Model for Assessment of Telemedicine.
Interventions
Patients admitted to the emergency department in the intervention period will have their vital values presented on the PDWS in addition to existing patient monitoring systems
Sponsors
Study design
Eligibility
Inclusion criteria
* All adult patients admitted to the emergency department
Exclusion criteria
* Critically ill patients who die during their admission * Orthopedic patients with minor injuries
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| In-hospital deterioration | Admission length (1-7 days) | Defined as transfer to intensive care units, heart/respiratory failure, and in-hospital mortality. Events are reviewed by experts to exclude cases expected of deterioration at time of arrival. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Economic effect of PDWS | 35 weeks | Cost effective analysis of savings in DKK between differences in primary outcomes in intervention and control arms of the study when inspecting DRG associations. |
| Acceptance of novel patient monitoring system | 35 weeks | Technology Acceptance Model based evaluation of the clinicians' perception of the novel system's usefulness and ease of use. |
| Reduction in length of stay | Admission length (1-7 days) | Evaluation of differences in length of stay for patients admitted during the intervention and control arms respectively. |
| Monitoring load effect | Admission length (1-7 days) | Does the automatic presentation of patients average severity defined by registered vital signs affect how much patients are monitored during their admission. Degree of monitoring as defined as monitor load. |
Countries
Denmark