Module Components

• Overview
• Assignments

Topics

• Evidence Review
• Frameworks
• Planning the Implementation
• Data Measure

NDNP 810 - DNP Project Identification

Mod 3: Planning

Data Measure

In your last module you assessed the context for your project and developed structure, process, and outcome goals. It is important to understand what measures you will monitor to meet these goals; it is often difficult to identify these measures in our complex health care system. We have many systems that help collect data and you will need to understand what specific data elements are needed and how to query them. In this module your focus needs to be on establishing what measures are needed.

You also assessed the current and desired process maps which helped identify areas you need to focus on for data collection. Thinking of these areas will help you establish the structure, process, and outcome measures. Your evidence review table should have identified the interventions and what measures were used to assess the improvement in like studies. Using this information as a guideline; think of the different areas for data you will need.

Remember, you must use a reliable tool to measure your intervention that has been documented in the literature, for instance, using the Braden scale to measure pressure ulcers. Whenever possible, use validated measures that are endorsed by the National Quality Forum or another national organization such as the Joint Commission or Leap Frog. You will however create a data entry form to record data using the validated measures and/or guidelines that have been identified for your area of interest. You may have more than one type of data to assess the improvement for your project. For instance, you may have a data report from the EHR, data from a chart audit and data from staff surveys. Having these different measures enhances the ability to document improvement.

There are four major types of data collections approaches for Quality Improvement projects are:

• Electronic data: The use of electronic data or e-measures are possible, but you may not have the option to get new reports created for your specific needs in a timely manner. Using e-measures pulled by someone in Informational Technology avoids the intensive labor of manual chart audits and the hand collection of data. If the request is for de-identified data, the use of e-measures also reduces risks associated with the collection of patient identifiers.
• Chart audits:  Chart audits can be done on items that are currently being charted. Creating a data collection form for your specific needs can be done with REDCap so it is important to identify what you will be measuring. Again, stressing that you do not create your own instrument, rather, use already validated instruments.
• Random observations: Sometimes the clinical setting has not yet added documentation of the intervention to the medical records. In this situation, random observations of the practice change may be useful such as the number of eye masks and ear plugs that are at patient’s bedside. These items can be collected and compared with other collected data such as supply par counts.
• Patient or staff interviews: Random interviews of the patients or staff regarding the practice change may also be useful (e.g., the number of patients who say they have used eye masks or ear plugs). Interviews or surveys can be completed in REDCap with your data collection form. You should have other data elements to collect than just a survey or interview data.  
  

Structure, Process and/or Outcome Measures

In planning data collection, students should collect structure, process and possibly outcome measures. Structure measures indicate whether the system itself has been changed (e.g., a change in the electronic medical record to record an intervention or an increase in the number of providers who have demonstrated competency in an assessment or procedure). Process measures demonstrate the practice change itself was being done (e.g., the percentage of times the process change has been documented as being implemented in the electronic health record). Outcome measures reflect the impact of that process change on patient outcomes (the fall rate on a unit).

Outcome measures are often seen as the “gold standard” in measuring improvement, but there are reasons why the student may not be able to collect outcomes in a DNP project. First, DNP students should be implementing interventions that have already been shown to be efficacious -- This is why you do an iterative literature review. The student does not necessarily have to provide additional evidence to support the intervention based on outcomes. Secondly, because of the short implementation phase for DNP projects (i.e., a maximum of 15 weeks), it may not be possible to demonstrate how the change in a process(es) altered an outcome(s). A reason to capture outcome measures is to assure that the change made does not adversely impact a key process, or result in unintended consequences.

Example:

If the DNP project goal was to ensure that 100% of patients in a specific clinic received their flu vaccine, then the focus of the data collection should be on measuring the number of patients (outlining inclusion, exclusion criteria and numerators and denominators), who received the vaccine over a specific time period. It is also helpful to capture implementation data on when specific implementation tactics were introduced and the response in vaccination rates related to these implementation tactics. This information may help determine why patients did or did not receive the vaccine. Were more patients vaccinated on different days of the week or at different times of the day? Did specific individuals on the staff affect the rate? Did the availability of the vaccine affect the rate? Again, looking at your literature, what measures were able to show an improvement in other studies. The implementation data will help the students as QI Project leaders tweak the implementation strategies and tactics outlined in their plans.

Students should focus data collection efforts on outlining barriers and facilitators to implementation effectiveness and progress. This can best be achieved by describing their baseline data and how much improvement they saw over time based on what types of strategies and tactics they implemented.

Quantitative or Qualitative Data

Never collect data you do not:

• need to collect
• know how to analyze - for every method of data collection, determine exactly how the quantitative data will be analyzed statistically

DNP students are advised to avoid collecting qualitative data through extensive interviews or focus groups, unless they have taken a course on qualitative analysis and have the skills and expertise to collect and analyze this type of data. If questionnaires are used that include short answer questions or a call for comments, students should have a plan in place on how to analyze this data.