Tune up your data collection skills!
As peritoneal dialysis (PD) nurses, you spend hours collecting notebooks full of clinical outcome data on your patients. But, unless data are organized and trended in a user-friendly manner, it is just numbers taking up space on a page.
Assess Your Methods
To assess your data collection methods, ask yourself these questions (and be honest with your answers):
* Is your data hidden in reports or tables that no one reads?
* Do you spend hours flipping back through previous monthly reports to determine if patient outcomes have changed for better or worse over time and to what extent outcomes have changed?
* Do you collate your data periodically (monthly or quarterly), and then drag it out to be discussed at the next QM meeting?
If you answered yes to any of the above questions, you are doing yourself a disservice. A data collection system that requires resurrection and review of notebooks full of previous reports, or is used solely for QM reporting offers limited benefits to you and your patients; furthermore, it is a drain on your limited resources. For data to be useful and relevant, it needs to be timely, trended, easy to retrieve and displayed simply and visually. Now answer these questions:
* Do you review aggregated patient data to determine if clinical outcome goals are being met?
* Do you know the percent of your patients that meet your PD program's clinical outcome goals for adequacy of dialysis, and anemia and renal osteodystrophy management (in addition to your program's average for that outcome)?
* Do you know how your program's clinical outcomes compare to other PD programs locally, statewide, network-wide, or nationwide?
* Do you know how your program's clinical outcomes compare to applicable clinical practice guideline recommendations?
* Do you know how your program's peritonitis rate compares to rates from previous years?
* If you change a process/procedure (i.e., exit site care) or implement a new process/procedure (i.e., IV iron administration) do you trend and compare data before and after the change or implementation?
If you answered no to any of the above questions, review the data you collect for your program and ask yourself why you collect specific data points? Does the data you collect tell you where you've been and where you are currently? Does it help you drive program changes and enable you to justify and validate changes in protocol and patient management? An organized, systematic data collection process that includes trending and analysis can do all that and more--it is the backbone of a successful PD program!
Change Your System!
So--how do yon make this happen? Start by using the following steps as a guide to learn how to make data work for you and your patients. The steps may look overwhelming at first glance, but most of the steps are simply another way of looking at what you do everyday as a PD nurse--using the nursing process, clinical decision making and patient advocacy. As you start developing your own questions for data collection, try jotting down a brief outline of what you are studying (who, what, why, when, how, etc.) to "keep you on track" and to look back on for future reference.
First step: Start with a simple question. Your question can refer to clinical outcomes such as dialysis adequacy, postoperative exit site infections, peritonitis rates, renal osteodystrophy outcomes, etc. or it can reflect pro gram variables or expenses, such as number of call hours, supply utilization, number of training days, etc. Anemia management will he used as an example throughout this column.
Question: Are my patients receiving optimal anemia management?
Second step: Define what you need to know to answer the question.
a. Define the parameters (lab value or charge, etc.) that need to be measured.
b. Decide which parameters are most valuable and prioritize them if numerous parameters exist.
c. If you have never trended a particular area of program or outcome data, it may be helpful to identify the broadest, most encompassing parameter.
d. Determine how often the parameter(s) is currently measured.
e. Define where the parameters are found (patient's lab slips, peritonitis flow sheet, clinic visit sheet, computer database, supply orders, program cost reports, etc.)
Parameters: Hemoglobin, Serum Ferritin, Transferrin Saturation (in that order)
Third step: Using identified parameters, redefine your question to make it more specific.
a. Use measurable goals.
b. To encourage proactive intervention on patient outcomes, it's helpful to define goals for both the desirable and the undesirable outcomes. (See an example of this below for Hgb).
c. Decide whether you need aggregated data or individual data.
d. For evaluation of over all program or population goals your question should reflect aggregate data.
e. When a single patient's data is being trended (i.e., a severely anemic patient's Hgb tracked over time with data related to interventions), the question will reflect individual data.
f. Clinical practice guidelines are useful resources for setting program goals.
Question with Desirable Goal: What % of my patients have a Hgb greater than or equal to ([greater than or equal to]) 11.0 gm/dl?
Question with Undesirable Outcome: What % of my patients have a Hgb less than or equal to ([less than or equal to]) 10.0 gm/dl?
Fourth step: Determine the time frame you want to study and incorporate time frame into question. a. Run chart data must be collected over time in an orderly time sequence (monthly, weekly, quarterly, etc.).
b. Retrospective data tells you where you've been.
c. Current data reflects your current status.
d. Future data collection points will help you evaluate the effectiveness of interventions on patient outcomes.
e. Time frame can be influenced by your selected parameter. For example, if you draw labs with monthly clinic visits, you could track Hbg monthly.
f. Time frame can also be influenced by how long it will take to effect change after an intervention. An outcome that takes a long time to achieve might be measured quarterly, every, 6 months or possibly annually.
g. Adequacy data should be maintained and aggregated using continuously updated adequacy values for every patient. If you have 50 patients, then 50 numbers should be aggregated for your data. Looking at aggregated adequacy data for only patients tested in a certain month is inaccurate and misleading. For example, if 5 patients got tested in October, it would be inaccurate to base your facility's adequacy outcome data on the lab results of these 5 patients. Adequacy values for all 50 patients should be aggregated, using each patient's most recent adequacy value.
Question & Time Frame: What % of my patients had a Hgb [greater than or equal to] 11.0 gm/dl during the last quarter?
Fifth step: Collect and enter your data into a simple table format (see Table 1).
a. If aggregate data is being used, use time-saving computer databases to aggregate data whenever possible.
b. Design your table so it makes sense to you!
c. It doesn't have to be fancy--you can draw it with pencil and paper or use the "Table" function in your word processing program. Remember--this is just the "scratch sheet" for developing your trending graph.
Sixth step: Now comes the fun part--displaying your data. An easy way to display data is the run chart (also called a trend chart). The inpatient TPR chart used to document a patient's vital signs is an example of a run chart. Run charts give you the capability to quickly and easily:
* Display clinical outcomes, program processes or financial data.
* Trend data over time (how outcomes behave over time)
* Identify aggregate PD clinical outcomes that meet, exceed or fall short of program goals without the need for statistical calculations. (Run charts most commonly focus on population outcomes rather than individual outcomes. Focusing on populations gives you the most benefit in the least amount of time).
* Compare your clinical outcomes to practice guidelines and state, Network or national averages.
* Compare clinical outcomes of 2 subsets of patients (i.e., peritonitis rates in CAPD vs CCPD)
* Identify variations in clinical outcomes over time.
*Chart baseline data prior to a protocol/process change and assist you in evaluating the results of those changes by trending outcomes over time.
Strictly speaking, a run chart is made up of plotted points and connecting lines, but sometimes bars are easier to interpret than the lines. It's your run chart--it's up to you !
There are several ways to make your run chart. If you are familiar with Excel or PowerPoint programs, you can use these. (For samples of run charts in PowerPoint, go to www.esrdnetwork.org, select PD Resource Guide and then select Run Charts). If you're not comfortable with these programs, you can start by making your run charts on graph paper and when you're ready, find someone to teach you these programs. A run chart (see Figure 1) includes these components:
[FIGURE 1 OMITTED]
a. A horizontal access line that represents the time flame being studied
b. A vertical access line represents what we care about and what we want to measure (the parameter you've chosen denoted by either a % scale or a numerical scale).
c. Comparative data bars that run horizontally across the face of the run chart. Examples of comparative values that can be used are corporate data comparisons, national Clinical Performance Measurement PD data and Network data, clinical practice recommendations.
d. Notations of interventions and dates of the interventions
e. A data table located below the run chart for clarification (optional)
f. Your data.
Last Step: Practice using your data and run charts in your day to day practice. Data talks--and when it talks, this is what it says, I can:
* Measure the success of your current protocols and processes.
* Identify protocols or processes in your program that can be improved.
* Assist you in presenting information during QM meetings.
* Help you generate interest in outcome data.
* Assist you in comparing physician practice in a multi physician program.
Data collection is not just another task that needs doing--it is a valuable part of your PD practice. Participation in data collection not only helps you justify and validate changes in protocol and patient management, it also expands your role as a PD nurse, allowing you to demonstrate and utilize team leading skills, critical thinking abilities and patient advocacy responsibilities.
Table 1 Simple Table Format October 2003 Hgb Ranges # patients % patients [greater than or equal to] 10.0 gm/dl 5 10% 10.1- 10.0 gm/dl 25 50% [less than or equal to 11.0 gm/dl 20 40% November 2003 Hgb Ranges # patients % patients [greater than or equal to] 10.0 gm/dl 5 10% 10.1- 10.0 gm/dl 20 40% [less than or equal to 11.0 gm/dl 25 50% December 2003 Hgb Ranges # patients % patients [greater than or equal to] 10.0 gm/dl 3 6% 10.1- 10.0 gm/dl 17 34% [less than or equal to 11.0 gm/dl 30 60%
Bobbie Knotek, RN, BSN, CNN, CPHQ., is Assistant Quality Management Coordinator at ESRD Network of Texas, Inc., Dallas, TX. She is a member of ANNA's Dallas Chapter and the ANNA PD SIG.
Maria Luongo, RN, BA, MSN, is CAPD Nurse Manager at Mass General Hospital, Boston, MA. She is a member of ANNA's Mass Bay Chapter and the Chairperson of the ANNA PD SIG.
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|Title Annotation:||PD News and Views|
|Author:||Knotek, Bobbie; Luongo, Maria|
|Publication:||Nephrology Nursing Journal|
|Date:||Dec 1, 2003|
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