D159: Evidenced-Based Measures for Evaluating Healthcare Improvement

D159: Evidenced-Based Measures for Evaluating Healthcare Improvement

Team Member Collaboration

Effective collaboration is a strategy used during project management to improve aspects like communication and timely completion of tasks. The first part of ensuring team member collaboration will involve setting weekly timelines to discuss completed elements of the project and new plans. The meeting to discuss the data elements will begin with brainstorming sessions. Brainstorming is an important tool for idea generation that is conducted to stimulate creative thinking and create innovative solutions to problems. During the sessions, I will encourage the team to list sensible ideas regarding the data elements first before bringing on board wild ideas. I will encourage all ideas from the team and allow members to discuss which elements will be critical in determining the success of the project. The final aspects of collaboration and identification of the data elements will involve voting to identify three crucial elements that will be used to determine the success of the project.

Data Elements

During the brainstorming meeting, the team will identify three key data elements that will be used to evaluate the success of the project. The first element that I believe will stand out is the patient fall rates in the surgical unit. This element is calculated by figuring out the number of beds occupied each day and summed to find the total number of beds occupied for the month. The number of falls is then divided by the number of patient bed days for the month and multiplied by 1000 to get the fall rate per 1000 patient bed days. This data element will be used by the team to compare the baseline fall rate in the surgical unit with the observed patient falls after implementing the new strategy. I believe this measure will be the key determinant of the effectiveness of intentional rounding to reduce patient falls in the surgical unit.

The second data element that will be identified during the brainstorming meeting is patient satisfaction scores. Patient satisfaction is an important and commonly used indicator for measuring the quality of healthcare provided to patients. The quality of care is dependent on important aspects like maintaining safety through fall prevention alongside other safety measures. I believe patient satisfaction scores will improve if the patient falls will be minimized through the implementation of the new strategy. The informatics nurse will be an important team player in monitoring, updating, and reviewing patient satisfaction published each quarter. The patient satisfaction scores will be retrieved from the CMS website and analyzed to ascertain the success of the project.

The third data element that will be monitored is compliance with hourly rounding using a designed checklist. The implementation of hourly rounding can be challenging given the busy nature of nursing and the number of activities carried out throughout the day. To ensure compliance, a checklist will be used to ascertain the hours that rounding is performed in a day including night checks. This data element will be used to compare the average number of hours patients were seen and the difference in fall rates observed in the unit. I believe increasing the number of hours rounding is done can translate to better results. This data element will be recorded in an excel spreadsheet and analyzed to ascertain the success of the project.

Data Source

The same approach used to identify the data elements will be applied to identify the data source for measuring the success of the project. A meeting will be held to give stakeholders an opportunity to air their views on possible data sources. A brainstorming session will be the most appropriate approach during meetings to give members a chance to give their ideas. Additionally, I will ensure to include experts from the IT department to help stakeholders understand the various data sources available and their effectiveness in project management. The team will be informed about the need to compare data throughout the HIP timeline to facilitate the selection of efficient data source tools. Upon identification of the data sources, voting, if necessary will be used to select one data source that can effectively serve the purpose of the project.

A data source represents the digital or physical location where data is stored in the form of a table, object, or other formats. Based on the information required for the successful completion of the project, an excel workbook will be the most appropriate source of data. This data source helps users to identify trends and organize and sort data into meaningful categories. This data source will be used to record the number of rounding hours per day and the observed falls throughout the implementation phase. The data source will also provide insight on the number of staff available during each critical event to help identify factors like staffing that may impact project outcomes. Excel workbook will be used to calculate fall rates and analyze data using graphs to give a clear picture of the project outcomes. Additionally, the team members are used with excel spreadsheets making it the most convenient data source for everyone.

KPI and Benchmarks

Key performance indicators (KPIs) are quantifiable measures used to gauge organizational performances against set targets. The KPIs provide a focus for strategic and operational improvement and create a platform for decision-making (Sipes, 2020). The first key performance indicator that will be used to measure the success of the project will be the number of patient falls in the surgical unit. The project’s goal is to reduce the number of patient falls in the surgical unit by 20% after the implementation of the hourly rounding approach. Data will be measured by the number of falls in the unit within the implementation period. The excel spreadsheet will be updated weekly to indicate the number of falls observed, rounding hours completed by nurses, and other pertinent information like the number of staff during shifts.

The second KPI that will be applicable in this project is the patient satisfaction scores. There is a close relationship between reducing the number of falls and patient satisfaction (LeLaurin & Shorr, 2019). As a short-term measure, the excel spreadsheet will be used to record patient satisfaction scores in the unit to ascertain if an increasing trend will be observed. For long-term monitoring, the patient satisfaction scores will be retrieved from the CMS website to determine improvement upon implementation of the project. Benchmarking for the two KPIs will be done using both organizational and national standard values. For the aspect of patient falls, national benchmarks indicate a rate of 3.44 falls per 1000 patient days (Agency for Healthcare Quality and Research (AHRQ), 2017). The patient fall rates will be calculated at the end of the project using excel values and measured against the previous unit values and the national average. Regarding the aspect of patient satisfaction, the values collected from the CMS website will be compared with the previous hospital scores to ascertain improvement.

Data Collection Method and Parameters

The best way to collect quantitative data for patient falls will be through the electronic medical record (EMR) system. According to the hospital policies, all incidences of falls are documented in the EMR system alongside incident reports that are submitted to the unit manager. The parameters collected will include the number of falls, the patient’s age, diagnosis, and the frequency of rounding. An excel spreadsheet will be used to document the data collected from the EMR. For instance, the number of falls will be documented weekly and a line graph used to ascertain an increase or decrease in falls monthly. The data and analysis will be presented weekly during meetings and any changes made. The project manager will be tasked with updating the data to ensure consistency.

The second set of quantitative data that will be collected will be patient satisfaction scores. This information will be accessed via the CMS website and downloaded for comparison. The informatics nurse will be a key member during the data collection process and the data will be documented in an excel spreadsheet for interpretation. Unlike other occasions where patient satisfaction scores are compared with other surrounding institutions, scores from this project will be compared within the facility. To effectively measure progress, the informatics nurse will retrieve the quarter when the HIP was initiated followed by another quarter during the implementation phase and the last quarter after the HIP. The excel spreadsheet will be used to assess progress by plotting the scores graphically.

Data Analysis

Collected data will be analyzed using different methods to generate a clear picture of the effect of purposeful hourly rounding on falls prevention. The first part of the analysis will focus on the effect of the HIP on fall rates. The Agency for Healthcare Quality and Research (AHRQ) provides guidelines on how to calculate fall rates using the average daily census of a unit or hospital. According to the guidelines, the healthcare provider should count all falls that occur during a given month from the incident reporting system including those resulting in injury (AHRQ, 2017). The second step involves figuring out the number of beds occupied each day of the month in the unit and adding the total to get the number of occupied beds for the month. To calculate the fall rates, the following formula is used.

The number of patient falls in a given month     × 1000

The number of patient bed days

Data for analysis will be retrieved from the EMR and RL solutions risk software that is used in the institution for storing incident reports. The team will collect the information and use the fall rate calculation formula to calculate the fall rates in the unit. For example, if 3 falls occur during the first month of implementation and 879 beds are occupied during that time, the fall rate will be as follows:

3 (falls)             × 1000

879 (Occupied bed days)         = 3.4 falls per 1000 occupied bed days

The second part of the data analysis will focus on compliance with the hourly rounding protocol in the unit. The nurses will be required to complete a rounding checklist that will be submitted daily to the unit manager for review. Data on daily rounding will be collected and analyzed weekly to ascertain the compliance rate in percentage form. For example, the average rounding hours in a day will be 20 hours considering aspects like time for lunch, giving reports, and visiting. The calculation for compliance will follow the approach below.


Number of daily rounding hours       × 100%

Actual number of required hours (20 hrs)


For example, if the nurses manage to complete 18 hours of rounding in a day, the compliance rate will be as follows:

18 hours (Documented) × 100%

20 hours (Recommended)          = 90% compliance rate

The third part of the data analysis will focus on the patient satisfaction scores collected from the CMS website. To address these values, data will be directly entered into the excel spreadsheet for the scores observed during the first quarter of implementation. The same approach will be used for the quarter following implementation and post-implementation scores. Graphical presentation of the scores will be the most appropriate means to analyze the scores and ascertain improvement in patient satisfaction. Overall, frequency tables and descriptive statistics will be used to summarize the characteristics of patient falls in the unit. The calculated monthly fall rates in the unit will be plotted in a graph using Microsoft excel for easy interpretation of the HIP effect. Lastly, the incident rate ratio and Chi-Square using 95% confidence intervals will be used to compare patient fall rates for the given period.

The interpretation of the initial results on patient fall rates, patient satisfaction, and compliance to hourly rounding by nurses will be done by the team. The computer system will generate a comprehensive report that will be presented to the team members. The project manager will be tasked with explaining the initial results and allowing questions from the team members. The results will be assessed for accuracy by looking at the data in the spreadsheets. During the interpretation of the initial results, little improvement will be expected based on the time allowed for implementation and adoption of the protocol. In the event that no improvement will be observed, the team will reassess the steps of implementation including compliance with hourly rounding, and make necessary adjustments.

A contextual issue that may affect the results of the HIP will include data entry errors. The majority of the data that will be used during the project will be entered into the excel spreadsheet for analysis and interpretation. Entry errors that may include the number of falls, number of occupied bed days, and rounding hours completed by nurses will lead to poor quality data entry. Entry errors will also affect calculations and lead to the observation of fake results upon completion of the project.

Results Dissemination Plan

The professional way that I opt to present the results will be through a meeting. The professional meeting is an integral component of the research and academic universe used to communicate results. The meeting will be attended by the seven stakeholders and upper management alongside other healthcare members. The meeting will be scheduled for one and half hours to allow a comprehensive presentation of the results and answer questions raised. The main focus of the meeting will be to highlight the impact of hourly rounding on falls prevention in the surgical unit and how the strategy can be successfully implemented to improve patient outcomes.

The professional method to deliver the results during the meeting will be through a PowerPoint presentation. I opt to use this method because PowerPoints are visual ways to communicate information and allow for the inclusion of graphs, charts, and other methods of displaying data. Each slide will represent a snapshot of each week’s team meetings throughout the project including the Gantt chart. The excel spreadsheets with raw and analyzed data will also be presented to aid the members to understand how the observed results were obtained.

Project Closure Plan

Effective project closure is one of the ways that defines and recognizes a team’s or organization’s culture. The aim of this phase is to tie up loose ends and move on with a clear sense of accomplishment. The formal method that I will use to acknowledge the organization and project team members for their time and support will be through a luncheon. I will invite the key stakeholders and the upper management and show my appreciation for the time and resources availed to complete the project. After finishing the small speech during the luncheon, I will offer small plaques to the team members to acknowledge their participation in the project and to act as a reminder of what was accomplished. Secondly, a larger plaque will be presented to the upper management to act as an appreciation for their managerial efforts leading to the success of the HIP. Upon completion of the luncheon, a small photo session will follow to demonstrate togetherness and the will to work to improve quality outcomes.

Project closure unfolds in many ways that can include retrospectives, project de-briefings, and wrap-up sessions. The wrap-up session for the project will take place a few days after the closure of the project. During this session, I will summarize agendas in the previous discussions and outline important action items. I will proceed to remind the team members about their roles during the post-implementation phase and how to support the new change in the unit. I will ensure to highlight the lessons learned, areas of weakness, and how to address these weaknesses during the post-implementation phase. Additionally, I will communicate the need to have monthly meetings to re-assess the situation, address any issues, and discuss changes.


Agency for Healthcare Quality and Research. (2017). Module 5: How to measure fall rates  and fall prevention practices- Training guide. https://www.ahrq.gov/patient-safety/settings/hospital/fall-prevention/workshop/module-5/guide.html

LeLaurin, J. H., & Shorr, R. I. (2019). Preventing falls in hospitalized patients: State of the science. Clinics in Geriatric Medicine35(2), 273–283. https://doi.org/10.1016/j.cger.2019.01.007

Sipes, C. (2020). Project Management for the Advanced Practice Nurse, (Second Edition.). Springer Publishing Company.