Dataset Information

What is full capacity protocol, and how is it implemented successfully?

ABSTRACT:

Background

Full capacity protocol (FCP) is an internationally recognized intervention designed to address emergency department (ED) crowding. Despite FCP international recognition and positive effects on hospital performance measures, many hospitals, even the most crowded ones, have not implemented FCP. We conducted this study to identify the core components of FCP, explore the key barriers and facilitators associated with the FCP implementation, and provide practical recommendations on how to overcome those barriers.

Methods

To identify the core components of FCP, we used a non-experimental approach. We conducted semi-structured interviews with key informants (e.g., division chiefs, medical directors) involved in the implementation of FCP. We used the Consolidated Framework for Implementation Research (CFIR) to guide data collection and analysis. We used a template analysis approach to determine the relevance of the CFIR constructs to implementing the FCP. We analyzed the responses to the interview questions about FCP definition and FCP key principles, compared different hospitals' FCP official documents, and consulted with the original FCP developer. We then used an adaptation framework to categorize the core components of FCP into three main groups. Finally, we summarized practical recommendations for each barrier based on information provided by the interviewees.

Results

A total of 32 interviews were conducted. We observed that FCP has evolved from the idea of transferring boarded patients from ED hallways to inpatient hallways to a practical hospital-wide intervention with several components and multiple levels. The key determinant of successful FCP implementation was collaboration with inpatient nursing staff, as they were often reluctant to have patients boarded in inpatient hallways. Other determinants of successful FCP implementation were reaching consensus about the criteria for activation of each FCP level and actions in each FCP level, modifying the electronic health records system, restructuring the inpatient units to have adequate staffing and resources, complying with external regulations and policies such as fire marshal guidelines, and gaining hospital leaders' support.

Conclusions

The key determinant in implementing FCP is creating a supportive and cooperative hospital culture and encouraging key stakeholders, including inpatient nursing staff, to acknowledge that crowding is a hospital-wide problem that requires a hospital-wide response.

SUBMITTER: Alishahi Tabriz A

PROVIDER: S-EPMC6637572 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

What is full capacity protocol, and how is it implemented successfully?

Alishahi Tabriz Amir A Birken Sarah A SA Shea Christopher M CM Fried Bruce J BJ Viccellio Peter P

Implementation science : IS 20190718 1

<h4>Background</h4>Full capacity protocol (FCP) is an internationally recognized intervention designed to address emergency department (ED) crowding. Despite FCP international recognition and positive effects on hospital performance measures, many hospitals, even the most crowded ones, have not implemented FCP. We conducted this study to identify the core components of FCP, explore the key barriers and facilitators associated with the FCP implementation, and provide practical recommendations on ...[more]

PMID: 31319857

Similar Datasets

Project description:IntroductionEarly identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse events. However, little research focuses on how these systems are implemented and how system design impacts clinicians' decisions or patient outcomes. This protocol outlines the steps to review the designs of these tools.Methods and analysisWe will use scoping review methods to explore how tools that leverage machine learning algorithms in predicting adverse events are designed to integrate into clinical practice. We will explore the types of user interfaces deployed, what information is displayed, and how clinical workflows are supported. Electronic sources include Medline, Embase, CINAHL Complete, Cochrane Library (including CENTRAL), and IEEE Xplore from 1 January 2009 to present. We will only review primary research articles that report findings from the implementation of patient deterioration surveillance tools for hospital clinicians. The articles must also include a description of the tool's user interface. Since our primary focus is on how the user interacts with automated tools driven by machine learning algorithms, electronic tools that do not extract data from clinical data documentation or recording systems such as an EHR or patient monitor, or otherwise require manual entry, will be excluded. Similarly, tools that do not synthesise information from more than one data variable will also be excluded. This review will be limited to English-language articles. Two reviewers will review the articles and extract the data. Findings from both researchers will be compared with minimise bias. The results will be quantified, synthesised and presented using appropriate formats.Ethics and disseminationEthics review is not required for this scoping review. Findings will be disseminated through peer-reviewed publications.

Project description:BACKGROUND:Full-scale implementation of lung cancer screening in the United States will increase detection of early stages. This study was aimed at assessing the capacity required for treating those cancers. METHODS:A well-established microsimulation model was extended with treatment data from the National Cancer Database. We assessed how treatment demand would change when implementing lung cancer screening in 2018. Three policies were assessed: 1) annual screening of current smokers and former smokers who quit fewer than 15 years ago, aged 55 to 80 years, with a smoking history of at least 30 pack-years (US Preventive Services Task Force [USPSTF] recommendations); 2) annual screening of current smokers and former smokers who quit fewer than 15 years ago, aged 55 to 77 years, with a smoking history of at least 30 pack-years (Centers for Medicare and Medicaid Services [CMS] recommendations); and 3) annual screening of current smokers and former smokers who quit fewer than 10 years ago, aged 55 to 75 years, with a smoking history of at least 40 pack-years (the most cost-effective policy in Ontario [Ontario]). The base-case screening adherence was a constant 50%. Sensitivity analyses assessed other adherence levels, including a linear buildup to 50% between 2018 and 2027. RESULTS:The USPSTF policy would require 37.0% more lung cancer surgeries in 2015-2040 than no screening, 2.2% less radiotherapy, and 5.4% less chemotherapy; 5.7% more patients would require any therapy. The increase in surgical demand would be 96.1% in 2018, 46.0% in 2023, 38.3% in 2028, and 24.9% in 2040. Adherence strongly influenced results. By 2018, surgical demand would range from 52,619 (20% adherence) to 96,121 (80%). With a gradual buildup of adherence, the increase in surgical demand would be 9.6% in 2018, 38.3% in 2023, 42.0% in 2028, and 24.4% in 2040. Results for the CMS and Ontario policies were similar, although the changes in comparison with no screening were smaller. CONCLUSIONS:Full-scale implementation of lung cancer screening causes a major increase in surgical demand, with a peak within the first 5 years. A gradual buildup of adherence can spread this peak over time. Careful surgical capacity planning is essential for successfully implementing screening.

Dataset Information

What is full capacity protocol, and how is it implemented successfully?

Background

Methods

Results

Conclusions

Publications

What is full capacity protocol, and how is it implemented successfully?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets