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HIV Screening for Pregnant Women

Part 1: Introduction

Part 2: Characteristics for Success: HIV Screening for Pregnant Women

Part 3: Implementation of Quality Measure: HIV Screening for Pregnant Women

Part 4: Improvement Strategies: HIV Screening for Pregnant Women

Part 5: Holding the Gains and Spreading Improvement

Part 6: Supporting Information


Part 3: Implementation of Quality Measure: HIV Screening for Pregnant Women 

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:

  • Developing an aim statement
  • Creating an infrastructure for improvement
  • Obtaining commitment from leadership

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.

The case study continues....

The Case Study: The Approach

Critical Pathway for 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.

Note: Please consider the following regarding critical pathways:
  • There can be more than one way to depict the idealized critical pathway.
  • Authorities vary on critical issues that have an impact on important decisions in medicine, and there is latitude within guidelines for variation related to less critical matters.
  • It is important that an organization agrees on the guidelines with which to align. References are located in n Part 6: Supporting Information at the end of this module.

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.

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:

  • The scheduling procedure for new prenatal patients
  • Testing discussion that includes the importance of early detection of HIV for mother and baby in pregnancy
  • Training about the importance of HIV screening and HIV pre-test counseling skills for staff who perform prenatal intake visits
  • Using the opt-out approach to screening with prenatal patients; that is, HIV testing is presented as a part of the basic testing panel for all pregnant patients and is only excluded if she declines to be tested
  • Availability of convenient and affordable lab services
  • Availability of skilled interpretation services if the patient and staff member do not speak the same language
  • Accuracy in filing and documentation of test results

A couple of important notes:

  • An organization may adopt additional prenatal guidelines that include important care parameters beyond early access. The Institute for Clinical Systems Improvement, by the National Guidelines Clearinghouse, describes guidelines for comprehensive prenatal care in Routine Prenatal Care, [link to guideline] including recommendations for HIV testing at the first visit (six to eight weeks).(26)
  • A critical pathway can also be constructed to illustrate how care is currently provided within an organization (the existing pathway). Understanding the gap between an organization's existing critical pathway (how you provide care now), and the idealized critical pathway (how to provide reliable, evidence-based care aligned with current guidelines), form the basis for improvement efforts.
Factors That Impact the Critical Pathway

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:

  • Age-Women over the age of 25 may be more likely to appreciate the importance of HIV screening.
  • Cultural differences-Immigrant women may have practices or beliefs in their native countries that do not view HIV testing as a priority.
  • Health literacy-Women unaware of the importance of positive health behaviors are less likely to agree to screening. Ability to read and language proficiency may be barriers to understanding health information.
  • Education-Women may not be aware of the availability of the HIV early treatment and the direct correlation with positive outcomes for the baby.

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:

  • Staff skilled in scheduling prenatal appointments and rescheduling those appointments
  • Provide culturally competent care to address the patient's cultural norms
  • Provide planned care for pregnant women
  • Educate staff on counseling patients on the importance of early HIV testing in pregnancy and success of early interventions

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.

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:

Example 3.1: A Team's Brainstorming Session

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).Example 3.1: A Team's Brainstorming Session.

 

Factor CategoryFactors pertinent to our organization - Steps
PatientSome patients are resistant to being seen for nurse visit before seeing a provider
Care TeamSchedulers do not always schedule prenatal intake visit appropriately (schedule with a provider instead or result in excessive delay)
Health SystemScheduling 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.

Data Infrastructure: HIV Screening for Pregnant Women

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.

Data Infrastructure to Monitor the Performance Measure-An Overview

There are three major purposes for maintaining a data infrastructure for quality improvement work:

  • To know the starting baseline
  • To track and monitor performance as changes are implemented
  • To perform systematic analysis and interpretation of data in preparation for action

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.

Implementation: HIV Screening for Pregnant Women  

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.

  1. 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
    • All patients, regardless of age, who were seen for at least two prenatal care visits during the measurement year, are included in the denominator.
    • A prenatal care visit is defined as a visit with a prenatal care provider and does not include pregnancy testing, registration, or lab testing.
    • 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.
    c. Exclusion criteriaDocumentation 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:
    1. a note with documentation of screening for HIV infection including: enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), Western blot (WB), indirect immunofluorescence assay (IFA), rapid test, during the first or second prenatal visit.
    2. If a patient transfers into care during pregnancy, documentation of prenatal HIV screening done elsewhere for the same pregnancy must be dated within the first or 2nd visit.
    3. Do not include HIV testing from a previous pregnancy or from before the pregnancy.
    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:

    • All women who were seen for at least two prenatal care visits during the measurement year are included in the denominator.
    • A prenatal care visit is defined as a visit with a prenatal care provider and does not include pregnancy testing, registration, or lab testing.
    • Data source is documentation (electronic or paper chart) of HIV infection screening (enzyme immunoassay [EIA], enzyme-linked immunosorbent assay [ELISA], Western blot [WB], indirect immunofluorescence assay [IFA], and rapid test during the first or second prenatal visit).
    • If a woman transferred to an organization's care, she is counted in the numerator if she had documented HIV screening with the previous provider during the first or second visit, or if HIV testing was performed at her first or second prenatal visit with the new provider.
    • If a woman has two pregnancies in the same measurement year, she is counted twice.
    • Do not include HIV testing from a previous pregnancy or from before the pregnancy.
    • Women previously documented as HIV positive are excluded.

    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, Exit Disclaimer. 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.

  2. 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:

    1. Standardize its processes and workflows to ensure the team collects and calculates performance data the same way over time. An organization should document exactly how the data is captured so staff turnover does not interfere with the methodology.
    2. Determine the frequency that performance will be calculated. Frequent data collection is often associated with higher levels of improvement. Monthly measurement is recommended if feasible, as it is associated with a higher level of team engagement and success. If it is infeasible, quarterly measurements may be obtained. Less frequent performance measurements are adequate for reporting purposes, but do not adequately support improvement efforts. An advanced discussion can be found in the Managing Data for Performance Improvement module.
    3. Chart and display results. A simple chart audit form as shown in Figure 3.3 is appropriate for manual audits and can be repeated frequently as desired. Results of multiple audits can be presented in a graphic format to demonstrate trends. Refer to the Managing Data for Performance Improvement module for more information and examples of data displays that have been used to communicate information about improvement efforts to a variety of stakeholders.

    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.

    Measure: HIV Screening for Pregnant Women 

    Number in universe  Number excluded
    Number reviewed  
    Number in compliance  
    #Medical Record # and/or NameTwo PN Visits in year?Date of HIV TestExcludedIn Compliance: testing in 1st or 2nd PN visitNotes
    1      
    2      
    3      
    4      
    5      
    6      
    7      
    8      
    9      
    10      
    Figure 3.3: Chart Audit Form for HIV Screening for Pregnant Women Measure
  3.  

  4. Step 3 - Create systematic processes that allow an organization to analyze, interpret, and act on the data collected.Having the data is not enough. Improvement work involves thinking about the data and deciding what to do based on that analysis. A QI team needs to put processes in place – team meetings, scheduled reports, and periodic meetings with senior leaders, to use the data tracked. This section describes how a QI team may accomplish the work of creating actionable plans based on the data collected. In Example 3.2: QI Team at Big Valley Health Care Organization, the scenario illustrates how a team may use these concepts to act on its data:
    1. Analyze: What are the data trends? Tracking performance over time for the measure, HIV Screening for Pregnant Women, is critical to successful improvement, but calculation of performance is not enough. It is important for a team to meet to analyze the data on a regular basis. QI teams that are experienced in looking at data recognize these common patterns:
      • Performance is improving
      • Performance is decreasing
      • Performance is flat
      • Performance has no recognizable pattern
      • The results bring attention to other issues involved in the process that may not directly reflect on performance
      Additional examples of common data patterns are provided with further explanation in the Managing Data for Performance Improvement module. It is typical for a team to see little movement in its data over the first several months. If a team has chosen to monitor an associated process measure, such as, the percent of no-show prenatal patients who are rescheduled, performance improvement may be evident more quickly. Regardless, it is important that a QI team review performance progress regularly. A QI team that meets regularly and calculates performance monthly should spend part of one meeting each month reviewing its progress to date.
    2. Interpret: What do these data trends mean? A QI team needs to then interpret what these data trends mean within the context of its own organization. If performance is increasing, but has not yet reached the numerical aim, perhaps the changes in place are having the desired effect and the aim will be reached over time. If performance is decreasing, what has changed? Are there new care process changes, a failure of registry data input, or a large increase in those patients included in the registry? If performance is flat, did the organization maximize the benefits from changes implemented or was there some regression to the former way of doing things? Improvement trends that have reached a plateau may indicate that an organization needs to think differently about future changes. A few suggestions that an organization may consider when experiencing a plateau in improving HIV Screening for Pregnant Women are listed below:
      1. Consider looking at outliers that may create barriers to patients accessing timely prenatal care, for example, lack of insurance, transportation, or language and cultural differences.
      2. Consider changes in a different part of the framework to get improvement back on track. If using a critical pathway approach, an organization may look at the steps prior to where the problem seems to be. If a Care Model approach is used and the team worked hard on delivery system design issues, opportunities to better leverage the clinical information systems or engage the community may be considered.
      Interpretation of data over time is critical in determining where a team will target its efforts. Additional tools that can assist a team in understanding underlying causes for data trends are beyond the scope of this toolkit but are discussed in detail in a monograph that was published by the NQC, A Modern Paradigm for Improving Healthcare Quality. Exit Disclaimer.
    3. 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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