Before following the steps in Part 3, an organization should first make a commitment to improve HIV screening for pregnant women and complete the initial steps outlined in the previous section that include:
Performance on this measure indicates how effectively all the steps of the processes used to deliver care work together so that prenatal patients will receive HIV screening. Because there are a variety of factors that can have an impact on the success of screening, it helps to visualize how these steps are mapped. The next section defines Critical Pathway and illustrates the application of this concept to implement HIV Screening for Pregnant Women.
A critical pathway, also known as a clinical pathway, is a visual depiction of the process steps that result in a particular service or care. The sequence and relationship among the steps are displayed, which reveals a map of the care process. Additional information, including tools and resources regarding the mapping of care processes, can be found in the Redesigning a System of Care to Promote QI module. In an ideal world, the care process is reflective of evidence-based medical guidelines. Evidence-based medicine aims to apply the best available evidence gained from the scientific method for medical decision making. (25) A map of the care process steps that incorporates all of the known evidence and follows respected evidence-based medical guidelines can be considered the idealized critical pathway.
While the needs of individual patients should always be considered, clinical guidelines synthesize the best evidence into a pragmatic set of action steps that strive to provide the optimum health care delivery system. It is important to emphasize that clinical evidence and guidelines will evolve as knowledge progresses; therefore, the idealized critical pathway may evolve over time and not meet the needs of every individual.
In Figure 3.1, the schematic for Critical Pathway for HIV Screening for Pregnant Women incorporates available evidence and represents an idealized critical pathway for HIV screening for pregnant women. The boxes represent typical steps in care delivery. If these steps happen reliably and well, HIV screening for pregnant women will occur.
Figure 3.1: Critical Pathway for HIV Screening for Pregnant Women
Walkthrough of the Idealized Critical Pathway
The critical pathway for HIV Screening for Pregnant Women is not overly complex and integrates well with processes already in place in most prenatal care settings, such as, the intake visit and other routine testing. Strategies or program elements that are critical to successful performance on this measure include:
A couple of important notes:
In addition to understanding the steps for providing HIV screening for pregnant women, factors that interfere with optimal care should be understood. As there may be several of these factors, a QI team may find it helpful to focus its attention on factors that interfere with ideal outcomes. This becomes especially useful as plans are developed to mitigate these factors.
Factors that have an impact on HIV Screening for Pregnant Women can be organized into those that are patient-related, relative to the care team, and a result of the health system. Overlaps exist in these categorizations, but it is useful to consider factors that have an impact on care processes from each perspective to avoid overlooking important ones.
Patient factors are characteristics that patients possess, or have control over, that have an impact on care. Examples of patient factors are age, race, diet, and lifestyle choices. Examples of how patient factors may influence HIV screening for pregnant women include:
Care team factors are controlled by the care team. These types of factors may include care processes, workflows, how staff follows procedures, and how effectively the team works together. Care team factors that may influence HIV screening for pregnant women include the processes and procedures that:
Health system factors are controlled at the high level of an organization and often involve finance and operational issues. Health system factors that may influence HIV screening for pregnant women include:
These factors, when added to the critical pathway, create another dimension to the map as shown in Figure 3.2:
Figure 3.2: Critical Pathway for HIV Screening for Pregnant Women with Potential Factors that Have an Impact on Early HIV Testing
Next, a team may identify specific factors that pertain to the way care is provided for its patients. A team may look at Step 1: Patient presents for prenatal care services, and Step 2: Patient completes intake process which includes discussion and order for routine lab test of the critical pathway. What factors have an impact on how effectively, timely, and reliably Step 2 follows Step 1? It is tempting to consider the first thoughts that come to mind, but a QI team is best served by systematically thinking through the potential impact of each category. In Example 3.1: A Team's Brainstorming Session a QI team's output is illustrated:
|The team brainstorms on factors that have an impact on the arrow (or opportunity) between Steps 1 and 2 of the Critical Pathway for HIV Screening for Pregnant Women (from Figure 3.1).|
|Factor Category||Factors pertinent to our organization - Steps|
|Patient||Some patients are resistant to being seen for nurse visit before seeing a provider|
|Care Team||Schedulers do not always schedule prenatal intake visit appropriately (schedule with a provider instead or result in excessive delay)|
|Health System||Scheduling system does not always allow for adequate time for intake visit, bi-lingual staff or interpreter not always available, or no-shows not always rescheduled appropriately|
The team continues to look at different parts of the pathway to identify relevant impacts for each part. Once it is able to evaluate where there are potential opportunities for improvement, it can use this information to target its efforts. Additional examples of strategies to improve care for the measure, HIV Screening for Pregnant Women, are described in the Improvement Strategies section of this module.
Once the team visualizes the pathway and identifies opportunities for improved care, the next step is to collect and track data to test and document them. First, a QI team needs to determine how to collect data to support its improvement work. This step is essential for understanding the performance of its current care processes, before improvements are applied, and then monitoring its performance over time.
T his section begins to address the important role of data throughout the improvement process. It is important to recognize that different types of data are collected during the improvement project. First, data to calculate and monitor the HIV Screening for Pregnant Women performance measure results is needed. Monitoring a performance measure involves calculating the measure over time and is used to track progress toward a numerical aim. This section provides an overview of what is needed. A detailed and stepwise approach follows to explain the types of infrastructure elements needed to gather data to support improvement. Second, changes an organization is making to improve care processes and their effects must be tracked. Tracking the impact of changes reassures the team that the changes caused their intended effects.
There are three major purposes for maintaining a data infrastructure for quality improvement work:
After determining what data is needed for a specific measure, the next step to creating a data infrastructure for monitoring the performance measure is to determine the baseline. A baseline is the calculation of a measure before a quality improvement project is initiated. It is later used as the basis for comparison as changes are made throughout the improvement process. For the HIV Screening for Pregnant Women measure, an organization can determine the percentage of prenatal patients that receive HIV testing as a result of established systems of care. Systems of care reflect the current organizational infrastructure and the patient's interactions with existing care processes and the care team.
Baseline data is compared to subsequent data calculated similarly to monitor the impact of quality improvement efforts. The details of how to calculate the data must be determined to ensure that the calculation is accurate and reproducible. The difference between how an organization provides care now (baseline) and how it wants to provide care (aim) is the gap that must be closed by the improvement work.
The next step of data infrastructure development involves a process in place to calculate the measure over time as improvements are tested. A QI team's work is to make changes, and it is prudent to monitor that those changes result in achieving the stated aim. This involves deciding how often to calculate the measure and adhering to the calculation methodology.
Finally, an organization's data infrastructure must include systematic processes that allow analysis, interpretation, and action on the data collected. Knowledge of performance is insufficient for improvement. It is important for an organization to understand why performance is measured and to predict which changes will improve HIV Screening for Pregnant Women based on an organization's specific situation. Collecting data related to specific changes and overall progress related to achieving an organization's specified aim are important to improvement work. The next section describes in more detail how to develop a data infrastructure to support improvement.
This section explores each step to create the data infrastructure used to improve performance on the measure, HIV Screening for Pregnant Women. This measure is intended for an organization that provides or assumes primary responsibility for some or all of a patient's prenatal care services, regardless if it performs the delivery. Since the HIV Screening for Pregnant Women measurement must include all prenatal patients, consideration of the mechanism for data collection is critical, especially if an organization is not using an electronic database as its source of information for this measure. If data is accessed through chart audits, consistent documentation of HIV screening and reasons for not screening are especially important.
Step 1 - Determine and Evaluate the Baseline
As previously discussed, a baseline for improvement is a calculation that provides a snapshot of the performance of the systems of care for a measure before improvements are applied. The baseline is determined by calculating the measure and collecting the information for the numerator and denominator.
The following tables depict a calculation guideline for the measure, HIV Screening for Pregnant Women. The guideline outlines the calculation for determining baselines and monitoring improvements for HIV Screening for Pregnant Women: (27)
|Identify the Denominator|
|The denominator for this measure is all pregnant women who were seen for two or more prenatal visits during the measurement year.|
|a. Use a one-year date range: the measurement year. A date range to audit.|
|b. Inclusion criteria|
|c. Exclusion criteria||Documentation of medical reason(s) for not screening for HIV during the first or second prenatal visit (e.g., patient has known HIV).|
|Identify the Numerator|
|The numerator for this measure is all women from the denominator with documentation (electronic or paper chart) that they were screened for HIV infection during the first or second prenatal visit.|
|a. Patients included in numerator have: |
|c. There are no exclusions for the numerator. There is no exclusion for patient refusal.|
|Calculate the Measure|
|Divide the numerator by the denominator and multiply by 100 to get the percentage of women who received HIV screening.|
Special considerations related to sampling and collecting data for this measure include the following:
In selecting charts for tracking data on this measure, consider ICD-9 codes to include all pregnant women (i.e., women who experienced a spontaneous abortion or stillbirth after their initial visit, women who transferred out of care during pregnancy, etc.). It is important to remember that for the purpose of reporting, this measure must include all pregnant women cared for by an organization as defined in the Managing Data for Performance Improvement module.
Detailed specifications, including instructions to identify the denominator and numerator for the measure, HIV Screening for Pregnant Women, can be accessed on the HRSA Clinical Quality Performance Measures Web site, or the Physician Consortium for Performance Improvement. Prenatal Care, Physician Performance Measurement Set, September 2007.
Evaluate the baseline. Initially, a team compares its baseline to the performance it hopes to achieve. It is important to remember this gap in performance is defined as the difference between how the care processes work now (baseline) and how an organization wants them to work (aim). An organization may often modify its aim or timeline after analyzing its baseline measurement and considering the patient population and organizational constraints.
As an organization moves forward, the baseline is used to monitor and compare improvements in care over time. While it is important for an organization to stay focused on its aim, it is equally significant to periodically celebrate the interim successes.
Step 2 - Create a reliable way to monitor performance over time as improvements are tested. An organization should:
An organization should standardize its processes and workflows to ensure the team collects and calculates performance data the same way over time. An organization should:
Note: Frequent team meetings are not necessarily required for success. Many successful teams meet once a week while others may meet bi-weekly when focusing their improvement efforts on any given measure. These meetings can be short but intently focused on tests, findings and next steps. Successful meetings are based on the output of the team members' active engagement and being prepared to report on recent improvement findings. More information, including resources and tools supporting developing and implementing effective team meetings can be found in the Improvement Teams module.
|Number in universe||Number excluded|
|Number in compliance|
|#||Medical Record # and/or Name||Two PN Visits in year?||Date of HIV Test||Excluded||In Compliance: testing in 1st or 2nd PN visit||Notes|
Act: Make decisions based on data. Once a QI team has a better understanding of what the data means, efforts should be targeted to further advance the performance toward the aim. Often the decisions are made at the team level about what to tackle first. Then small tests of change can be accomplished to determine what improvements could be implemented to enhance performance. The practice of using small tests of change actually allows multiple changes to be tested simultaneously.
Note: An advanced discussion on how to use the data collected to advance an organization's improvement, including resources and tools to support improvement, can be found in the Managing Data for Performance Improvement module.
Example 3.2: QI Team at Big Valley Health Care Organization
The Quality Improvement (QI) Team at Big Valley worked diligently to improve HIV screening for prenatal patients within their first or second prenatal visit increasing its rate from 65 to 80 percent over the last several months. The team focused on staff education and scheduling issues and had streamlined those processes. But during the last three months, the performance remained the same, which was below its aim of 90 percent.
Analysis: The team noted improvement initially. Registry input, care processes and patient volumes seemed to be stable but performance was flat for the last four months.
The team leader asked for a list of those patients who did not have HIV screening during the past month–outliers for the measure. Further study of these specific cases found that of the 42 new prenatal patients seen during the month, 33 had received the HIV test (80 percent), 8 had not had any prenatal lab work and 1 had declined the HIV test. Of the 8 who did not have testing, 5 were uninsured and waiting for Medicaid eligibility determination, and 3 did not return for their next scheduled appointment.
Interpretation: The team leader interpreted the data to mean that initial changes provided some improvement, but more work was needed. The team leader employed a common strategy to find additional opportunities; i.e., looked at the population not in compliance (the outliers) for a common cause to be addressed. In this case, a common thread was that patients who were uninsured were not receiving testing. The delay between applying for Medicaid and receiving notification of eligibility could be a problem. In addition, a small number of patients were being lost to follow-ups.
This information allowed the team to consider processes that might help to improve the screening rate. The team brainstormed about ideas and suggestions based on its own patient population. It decided to look into a way of streamlining Medicaid eligibility since this had proven to be a problem in other areas of the clinic. The team also decided to refer prenatal no-shows to the prenatal case manager. The improvement team will continue to monitor its performance to determine if these changes are effective for achieving its aim statement goals.
Act: The information gathered from the analysis and interpretation of the data allowed the team to focus its next efforts. This enabled the team to focus on PDSAs (Plan-Do-Study-Act) to test changes specific to these areas and monitor its progress.
A QI team leader needs to monitor the pace of the progress over time. If there is insufficient progress to meet the specified aim, reasons should be analyzed and addressed. One organization may choose to accelerate its improvement efforts; another may decide to extend its initial allotment of time to achieve its aim and consider other constraints within the organization.
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