Dataset Information

Development of a minimization instrument for allocation of a hospital-level performance improvement intervention to reduce waiting times in Ontario emergency departments.

ABSTRACT:

Background

Rigorous evaluation of an intervention requires that its allocation be unbiased with respect to confounders; this is especially difficult in complex, system-wide healthcare interventions. We developed a short survey instrument to identify factors for a minimization algorithm for the allocation of a hospital-level intervention to reduce emergency department (ED) waiting times in Ontario, Canada.

Methods

Potential confounders influencing the intervention's success were identified by literature review, and grouped by healthcare setting specific change stages. An international multi-disciplinary (clinical, administrative, decision maker, management) panel evaluated these factors in a two-stage modified-delphi and nominal group process based on four domains: change readiness, evidence base, face validity, and clarity of definition.

Results

An original set of 33 factors were identified from the literature. The panel reduced the list to 12 in the first round survey. In the second survey, experts scored each factor according to the four domains; summary scores and consensus discussion resulted in the final selection and measurement of four hospital-level factors to be used in the minimization algorithm: improved patient flow as a hospital's leadership priority; physicians' receptiveness to organizational change; efficiency of bed management; and physician incentives supporting the change goal.

Conclusion

We developed a simple tool designed to gather data from senior hospital administrators on factors likely to affect the success of a hospital patient flow improvement intervention. A minimization algorithm will ensure balanced allocation of the intervention with respect to these factors in study hospitals.

SUBMITTER: Leaver CA

PROVIDER: S-EPMC2706789 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Publications

Development of a minimization instrument for allocation of a hospital-level performance improvement intervention to reduce waiting times in Ontario emergency departments.

Leaver Chad Andrew CA Guttmann Astrid A Zwarenstein Merrick M Rowe Brian H BH Anderson Geoff G Stukel Therese T Golden Brian B Bell Robert R Morra Dante D Abrams Howard H Schull Michael J MJ

Implementation science : IS 20090608

<h4>Background</h4>Rigorous evaluation of an intervention requires that its allocation be unbiased with respect to confounders; this is especially difficult in complex, system-wide healthcare interventions. We developed a short survey instrument to identify factors for a minimization algorithm for the allocation of a hospital-level intervention to reduce emergency department (ED) waiting times in Ontario, Canada.<h4>Methods</h4>Potential confounders influencing the intervention's success were id ...[more]

PMID: 19505308

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Project description:BackgroundGlobally, reduction of patient waiting time has been identified as one of the major characteristics of a functional health system. In South Africa, 83% of the general population visiting primary healthcare (PHC) facilities must contend with long waiting times, overcrowding, staff shortages, poor quality of care, an ineffective appointment booking system, and a lack of medication. These experiences may, in turn, affect how patients view service quality.MethodsThis scoping review was guided by Arksey and O'Malley methodological framework. The primary literature search of peer-reviewed and review articles was achieved through PubMed/MEDLINE, Google Scholar, Science Direct, and World Health Organization (WHO) library databases, using waiting times, outpatient departments, factors, interventions, and primary healthcare facilities as keywords. Two independent reviewers screened abstracts and full articles, using the set inclusion and exclusion criteria. We used NVIVO® version 10 software to facilitate thematic analysis of the results from included studies.ResultsFrom the initial 250 records screened, nine studies were eligible for inclusion in this scoping review. Seven papers identified the factors contributing to waiting time, and five papers mentioned effective interventions implemented to reduce waiting times within PHC facilities. Our analysis produced three (patient factors, staff factors, and administrative systems) and two (manual-based waiting time reduction systems and electronic-based waiting time reduction systems) main themes pertaining to factors contributing to long waiting times and interventions to reduce waiting times, respectively.ConclusionOur results revealed that the patients, staff, and administrative systems all contribute to long waiting times within the PHC facilities. Patient waiting times recorded a wider and more evenly spread patient arrival pattern after the identified interventions in our study were implemented. There is a need to constantly strategize on measures such as implementing the use of an electronic appointment scheduling system and database, improving staff training on efficient patient flow management, and regularly assessing and optimizing administrative processes. By continuously monitoring and adapting these strategies, PHC facility managers can create a more efficient and patient-centered healthcare experience.

Project description:ObjectivesLittle academic investigation has been done to describe emergency department (ED) practice structure and quality improvement activities. Our objective was to describe staffing, payment mechanisms, and quality improvement activities among EDs in a nationwide quality improvement network and also stratify results to descriptively compare (1) single- versus multi-site EDs and (2) small-group versus large-group EDs.MethodsObservational study examining EDs that completed activities for the 2018 wave of the Emergency Quality Network (E-QUAL), a voluntary network of EDs nationwide that self-report quality improvement activities. EDs were defined as single-site or multi-site based on self-reported billing practices; additionally, EDs were defined as large-group if they and a majority of other sites with the same group name also identified as multi-site. All other sites were deemed small-group.ResultsData from 377 EDs were included. For staffing, the median number of clinicians was 17 overall (16 single-site; 19 multi-site). For payment, 376 of 377 EDs (99.7%) participated in the Merit-Based Incentive Payment System. Thirty-five EDs (9.2%) participated in a federal alternative payment model, and 19 (5.0%) participated in a commercial alternative payment model. For quality improvement, single- and multi-site EDs reported similar progress on quality improvement strategies; however, small-group EDs reported more advanced quality improvement strategies compared to large-group EDs for 8/10 quality improvement strategies included in a survey (eg, "achieved a formal plan to eliminate waste").ConclusionAmong EDs in E-QUAL, staffing, payment, and quality improvement activities are similar between single- and multi-site EDs. Group-level analysis suggests that practice structure may influence adoption of quality improvement strategies. Future work is needed to further evaluate practice structure and its influence on quality improvement activities and quality.

Dataset Information

Development of a minimization instrument for allocation of a hospital-level performance improvement intervention to reduce waiting times in Ontario emergency departments.

Background

Methods

Results

Conclusion

Publications

Development of a minimization instrument for allocation of a hospital-level performance improvement intervention to reduce waiting times in Ontario emergency departments.

Similar Datasets

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