Evaluation of Strategies from the Urgent Matters Learning Network II
Executive Summary
Contract Final Report
This document summarizes a project funded by the Agency for Healthcare Research and Quality (AHRQ) to evalute nine strategies designed to improve patient flow and reduce crowding in a hospital emergency department (ED).
Prepared by Health Research & Educational Trust: Megan McHugh, Kevin Van Dyke; Northwestern University: Julie Yonek; Urban Institute: Embry Howell, Fiona Adams.
Executive Summary
Purpose
From November 2008 to May 2010, six diverse hospitals participated in the Urgent Matters Learning Network II (UMLN II), a national program funded by the Robert Wood Johnson Foundation dedicated to finding, developing, and disseminating strategies to improve patient flow and reduce emergency department (ED) crowding. The hospitals adopted nine patient flow improvement strategies in an effort to reduce ED crowding. Under a contract with the Agency for Healthcare Research and Quality (AHRQ), the Health Research& Educational Trust (HRET) conducted an evaluation of the strategies. There were three goals for the evaluation:
- Determine the factors that supported or impeded the implementation of the strategies.
- Determine the time and expenses associated with implementation of the strategies.
- Examine changes in patient flow that occurred after the implementation of the strategies.
Methods
The research team used a mixed-methods approach. We conducted 129 interviews using a semi-structured interview protocol to obtain information about the planning and implementation process. Interviews were recorded, transcribed, and coded using a qualitative software program. Additionally, we collected retrospective data on ED patient visits before and after implementation of the patient flow-improvement strategies. We used multivariate regression models and binary logistic regression models to investigate changes in ED length of stay (LOS) and the percentage of patients who left without being seen (LWBS). Our hypothesis was that the improvement strategies would be associated with reductions in ED LOS and LWBS.
Facilitators and Barriers to Implementation
Factors that enhanced implementation included participation in the learning network, strategic selection of planning team members, executive support, and staff-led improvement teams. Common barriers included staff resistance, entrenched organizational culture, previous failures of past improvement projects, and staffing shortages. Some of the challenges encountered were mitigated through additional staff education, department leaders' constant reinforcement of the strategy, and strategy modifications to address staff resistance.
Time and Resources Expended
Time spent planning and implementing the strategies ranged from 40 to 1,017 hours per strategy. The most time-consuming strategies were those that involved extensive staff training, large implementation teams, or complex process changes. Many respondents said that more time should have been allocated for staff training. Only three strategies involved sizable expenditures, ranging from $32,850 to $490,000. Construction and the addition of new personnel represented the most costly expenditures.
Changes in Measures of Patient Flow
By the end of the project, six hospitals had implemented nine strategies (go to Table 1). Results showed statistically significant improvement in LOS or LWBS at four hospitals:
- An effort to expedite care for a subset of mid-acuity patients at Good Samaritan Hospital was associated with a 16 percent decline in ED LOS.
- Front-end improvements and additional resources to the fast track were associated with an 11 percent decline in ED LOS for patients at Hahnemann University Medical Center.
- Front-end improvements at St. Francis Hospital were associated with a 4.3 percent reduction in ED LOS.
- Improved communication between the ED and inpatient units at Westmoreland Hospital was associated with a 33 percent decline (from 0.6 percent to 0.4 percent) in LWBS patients.
Conclusions and Lessons Learned
The interventions at four of the six hospitals were associated with modest improvements in patient flow. It appears that a variety of factors influenced the impact of strategies in this Learning Network, including the ability to overcome implementation challenges, the timing of implementation, and the type of strategy selected. Although we believe that hospital and ED leaders should focus on all of these issues, more research is needed to determine the relative impact of each.
The relatively modest changes in ED LOS and LWBS identified through this evaluation are not surprising. All of the hospitals adopted strategies that were somewhat limited in scope, some of which targeted only a subset of ED patients. Larger declines in LOS or LWBS may require hospitals to adopt more significant hospital-wide patient flow strategies, such as smoothing the elective surgery schedule.
The most notable improvements in ED LOS occurred at the hospitals that had considerably longer ED LOS than the national average. In 2007, two-thirds of patients nationally had a length of stay less than 180 minutes (3 hours). The largest drop in LOS in this study occurred at Good Samaritan Hospital, where a subset of mid-acuity patients had a wait time of 426 minutes (just over 7 hours) before the improvement strategy was implemented. Similarly, Hahnemann University Medical Center also experienced a large, significant decline in LOS. The average LOS was 482 minutes (just over 8 hours) before the improvements were implemented (Table 1). Improvement interventions may be most effective at hospitals (or for patient populations) with relatively long LOS.
While not definitive or generalizable, the experiences of these six hospitals provide useful insight for other hospitals that may be interested in adopting patient flow improvement strategies. We found that the staff time and expenses involved in the adoption of the ED strategies are highly variable. Hospital and department leaders should set realistic expectations for the staff time and resources needed to support planning and implementation and recognize the variety of staff members likely to be involved. Further, implementation challenges are common. Developing a strategy to address common barriers (e.g., staff training, consistent reinforcement of the strategy) may leave leaders better prepared to implement the improvements (go to Table 2).
Finally, none of the six hospital improvement teams implemented their strategies within the original timelines they had established. And, once their strategies were implemented, many of the teams had to make adjustments based on initial feedback. Hospital teams considering the implementation of patient flow improvement strategies should recognize that planning and implementation may take several months, and that refining and sustaining the strategies may require attention for at least a year in order to make sure they are effective as possible.
Results in Brief
Facilitators and Barriers to the Implementation of Improvement Strategies
Across the six hospitals, there were several common facilitators and barriers to implementation of the improvement strategies.
Facilitators
Participation in UMLN II
The URGEBT Matters Learning Network (UMLN) II collaborative provided the hospitals with structure, a firm timeline for implementation, and external accountability needed to ensure that the proposed improvement strategies received appropriate attention among many competing priorities.
Staff-Driven Strategies
At three hospitals, staff resistance was minimized by implementing a strategy that involved staff in the planning and implementation of the strategies.
Executive Support
Executive leadership and support were paramount to securing additional resources needed for the successful implementation of several strategies. Because of capital limitations and many competing priorities, it was almost impossible to secure significant resources without executive support.
Aligned Reporting Structure
An aligned reporting structure was critical to the success of several strategies. Having a common supervisor who was supportive of the strategy was helpful in emphasizing collaboration and compliance with the implementation.
Robust IT System
Two hospitals in the learning network had sophisticated health information technology (IT) resources and in-house expertise that facilitated customization of forms and templates (e.g., forms for specialty consult requests, checklists for patient assessments) to support their strategies.
Careful Selection of Staff for Pilot
Having capable, adaptable, and willing staff pilot the changes proved useful to the launch of strategies at two hospitals. The planners knew that the strategies would not be embraced by all staff members initially, and they selected individuals who would put significant effort toward testing the process change.
Inclusive Planning Team
For strategies that affected units or individuals outside of the ED, it was important to have an inclusive planning team, including representatives from all affected units.
Barriers
Staff Resistance
Staff resistance often arose due to increased workloads or a disruption of familiar staff workflow patterns after the implementation of the strategies.
Culture Change
Some of the proposed strategies ran counter to the culture of the department, and many of the patient flow-improvement teams found it difficult to change attitudes and habits.
Lack of Staffing Resources
Several of the strategies required the addition of personnel, and some of the hospitals struggled with recruitment and hiring freezes. Due to the economic recession and overall financial pressures, hiring additional staff was not an option for some hospitals.
Previous Failures
Previous failures to implement or maintain quality improvements led to cynicism among some staff members.
Lack of Data
Two hospitals struggled with obtaining the needed data to track whether their changes were effective in improving patient flow.
Approaches to Overcoming Barriers
The patient flow improvement teams employed a number of approaches to overcome the barriers described above (Table 2).
Time and Expenses Associated with Implementation of Improvement Strategies
Implementation Time
The total number of hours spent planning and implementing strategies ranged from 65 to 1,017 hours (Table 3). Strategies that involved extensive, standardized training of staff were the most time-consuming. As one would expect, larger staff teams had a higher total number of hours devoted to planning and implementation. Other factors include the complexity of the strategies and the extent to which the strategies represent a significant change to ED processes.
ED nurse managers, charge nurses, and staff nurses spent more time planning and implementing strategies than others, primarily because several of the strategies involved extensive nurse training.
Implementation Expenses
Of the eight strategies adopted under the learning network, five required little or no new expenditures ($200 or less) (Table 4). Many of these interventions involved a change in policy or a shift in staff responsibilities rather than the addition of new resources.
Two strategies involved significant expenses. The Fast Track Improvement at Hahnemann University Medical Center required new space and staffing. Physically separating the fast track from the ED required a construction project ($150,000), and four nurse practitioners (NPs) were hired to staff the fast track (estimated at $85,000 each). For Good Samaritan's strategy to expedite care for mid-acuity patients, one ED physician was hired ($267,293) to provide triage and initial treatment to mid-acuity patients in a renovated triage room ($8,000). A tech was also hired ($33,390) to escort these patients to a separate area where a nurse practitioner would continue treatment under the guidance of the ED physician. An OB chair was purchased ($12,000) for this area.
A final strategy, the Standardized Registration and Triage Process, at St. Francis Hospital, involved moderate expenses associated with improving front-end operations. The hospital adopted a zoning strategy for registration (i.e., assignment of one registrar to a set of geographically close rooms), that was aided by the addition of two computers on wheels ($8,000 each). Also, two nurses attended a train-the-trainer standardized triage course and then trained the ED nursing staff ($16,850).
As our study demonstrates, it is overly simplistic to say that patient flow improvement efforts are inexpensive to implement, as some have asserted. There are some relatively low-cost patient flow improvement strategies involving simple process changes that are successful. For the many hospitals that have not yet begun to address the challenges of ED crowding, such process changes can be a place to start. However, for those hospitals that have already implemented the simple process changes, further actions to address ED crowding may require the adoption of more sophisticated strategies that involve substantial investment in both staff time and money.
Changes in Measures of Patient Flow after the Implementation of Improvement Strategies
We compared the average ED LOS and average LWBS before and after the implementation of the strategies. We constructed multivariate linear regression models for each hospital to assess changes in ED LOS after controlling for patient (age, sex, acuity) and visit attributes (use of lab, use of x-ray, date and time of visit, hospital occupancy rate, number of other ED visits at the time, disposition). Binary logistic regression models were constructed with LWBS as the outcome measure. The regression-adjusted results are shown in Table 5.1
Four hospitals showed modest but statistically significant improvement in measures of patient flow:
- An effort to expedite care for a subset of mid-acuity patients at Good Samaritan Hospital was associated with a 69 minute (16 percent) decline in the ED LOS for those patients.
- Front-end improvements coupled with additional resources to the Fast Track were associated with a 52 minute (11 percent) decline in ED LOS for patients at Hahnemann University Medical Center.
- Front-end improvements at St. Francis Hospital were associated with a 9 minute (4.3 percent) reduction in ED LOS.
- Improved communication between the ED and inpatient units at Westmoreland Hospital was associated with a 33 percent decline (from 0.6 percent to 0.4 percent) in LWBS patients.
There was no significant improvement in ED LOS or LWBS at the remaining hospitals—after controlling for patient and visit characteristics—after implementation of the strategies.
Perceptions of Impact
We asked staff from each of the hospitals about whether they believe the strategies resulted in improvements in patient flow. Their responses matched our quantitative findings. The majority of respondents from Good Samaritan Hospital, Hahnemann University Medical Center, St. Francis Hospital, and Westmoreland hospital said that their strategies resulted in improvement in LOS or LWBS. Most of our interview respondents from Stony Brook University Medical Center and Thomas Jefferson University Medical Center did not believe that their efforts had resulted in improved patient flow. At the end of the assessment period, the team from Stony Brook was making adjustments to their physician consult request process, moving from a paper to electronic system. If our assessment period was 6 months later, we may have seen statistical improvements in the patient flow measures, and the perceptions of change may have been more optimistic. As part of their strategy to improve the Fast Track, the team from Thomas Jefferson proposed the addition of staff resources. At the end of our assessment period, these staff resources were not consistently in place, which led the team to believe that their efforts were not successful in reducing LOS or LWBS.
Hospital Snapshots
Good Samaritan Hospital—The team from Good Samaritan implemented an innovative strategy, but one with a limited evidence base. They overcame some initial challenges very quickly and successfully implemented their strategy. The new process for expediting care for a subset of mid-acuity patients benefited the ED patients targeted (712 visits in the post-implementation period), but the impact was not strong enough to affect patient flow for the entire ED (22,233 visits in the post-implementation period). It is possible that directing resources to this limited group of patients had the unintended consequence of diverting resources from other patients.
Hahnemann University Medical Center—The team at Hahnemann implemented two strategies shown to be effective in reducing LOS at other organizations. The result was a statistically significant reduction in LOS. While they encountered a lot of initial resistance from staff, department leaders consistently reinforced the importance of the strategy, provided training for staff, and made modest adjustments after implementation so that the strategies were successfully implemented. However, the team was not able to implement all of its changes to the fast track by the post-implementation period.
St. Francis Hospital—St. Francis Hospital also implemented front-end improvements, which have a strong evidence base for reducing LOS. Like the other implementation teams, staff resistance was initially a problem, especially because the registration staff had not been included in the planning stage. Strong leadership coupled with training efforts led to diminished resistance over time, the strategy was successfully implemented, and it resulted in a statistically significant reduction in LOS.
Stony Brook University Medical Center—The Stony Brook team selected an innovative strategy without a strong evidence base, but one that addresses a major challenge to patient flow for many hospitals. The effort to speed physician consultations was not fully implemented prior to the post-implementation period, and we found no significant improvement in the patient flow measures. After the post-implementation period, the team continued to make adjustments to respond to critiques from staff, including moving from a paper request process to one that is electronic to better match the work flow. Regression results indicate no decline in the patient flow measures, though our results may have been different if we had delayed the assessment period by 6 months to allow more time for the team to make adjustments.
Thomas Jefferson University Medical Center—The implementation team at Thomas Jefferson developed a number of recommendations for improving patient flow in the Fast Track, and central to these reforms was a dedicated Fast Track team consisting of a nurse practitioner, nurse, and technician. Although many of the recommendations were implemented, there were still times during the post-implementation period when Fast Track staff and resources were diverted to support the care of patients elsewhere in the ED. The inconsistent implementation of the strategy likely contributed to the lack of change in the patient flow measures.
Westmoreland Hospital—The team at Westmoreland Hospital successfully implemented a strategy that has been effective in reducing LOS at other hospitals. The strategy was focused on expediting the admission process by improving communication between the ED and inpatient units. They implemented the strategy within the timeframe of the learning network, but like the other hospitals, made adjustments in response to staff resistance. It was surprising that the regression results indicated improvement in LWBS but not LOS, which was the measure targeted by this intervention.
Areas for Future Research
Our findings highlight the importance of a mixed-methods approach for evaluating patient flow improvement efforts. Information about implementation provided important context for explaining our results. They highlight that a variety of factors may influence the success of strategies, including the challenges encountered, the timeframe of implementation, and the type of strategy selected. Although we believe that hospital and ED leaders should focus on all of these issues, more research is needed to determine the relative impact of each.
Future research also should be directed to identifying which patient flow measures are more sensitive to improvements in patient flow. It was surprising that improvements in LOS were not also coupled with improvements in LWBS and vice versa. This suggests that hospital and ED leaders should monitor multiple measures in order to assess changes in patient flow.
Finally, it would be useful to investigate the efforts of hospitals that undertook system-wide patient flow improvement efforts and compare those outcomes to the outcomes of hospitals that focused on localized ED improvements. Results from such a study could provide some insight regarding the extent to which system-wide efforts may result in larger improvements in patient flow measures than ED-specific strategies.
1Raw changes in Measures of Patient Flow are provided in Appendix 1 of the final report.
This document is in the public domain and may be used and reprinted without permission.
None of the investigators have any affiliations or financial involvement that conflict with the material presented in this report.
The opinions presented in this report are those of the authors, who are responsible for its content, and do not necessarily reflect the position of the U.S. Department of Health and Human Services or the Agency for Healthcare Research and Quality.
Note: The complete final report from which this executive summary is drawn is available from Dina Moss, Agency for Healthcare Research and Quality, Center for Delivery, Organization, and Markets, 540 Gaither Road, Rockville, MD 20850; Dina.Moss@ahrq.hhs.gov.
AHRQ Publication No. 11(12)-0094-EF
Current as of September 2011
Internet Citation:
McHugh M, Van Dyke K, Yonek J, et al. Improving Patient Flow and Reducing ED Crowding: Evaluation of Strategies from the Urgent Matters Learning Network II. Executive Summary. (Prepared by Health Research & Educational Trust under contract 290-200-600022). AHRQ Publication No. 11(12)-0094-EF, September 2011. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/qual/ptflow/ptflowsum.htm
540 Gaither Road Rockville, MD 20850 