4 Data Don’ts for QAPI Success

For some critical access hospitals (CAHs), the start date for the Centers for Medicare and Medicaid Services’ Quality Assurance Process Improvement (QAPI) regulatory requirements came and went without much fanfare. Quality assurance already is addressed in current CAH Conditions of Participation, and QAPI, to a large extent, is simply an expansion of existing CAH requirements. But, as they say, the devil is in the details. And in the case of QAPI, the details many organizations are struggling with have to do with data.

In an upcoming webinar, Carolyn St.Charles, Chief Clinical Officer for HealthTechS3, will lay out the data challenges facing CAHs as they start up their QAPI program and how to address data for QAPI compliance. While every hospital will have its own unique organizational priorities related to QAPI, St.Charles says there are data don’ts that are near universal. Avoid these 4 data mistakes for QAPI success:

Don’t confuse the difference between Quality Control, Quality Assurance and Performance Improvement: Although it may not seem important to differentiate the three aspects of QAPI, understanding the differences can ensure that your CAH has a robust QAPI program.

Quality Control (QC) measures are product oriented and focus on defect identification such as refrigerator temperatures, code cart checks, and generator checks as examples.

Quality Assurance (QA) is process oriented and focuses on doing the right things the right way.

Performance Improvement (PI) focuses on enhancing current processes and identifying new approaches.

CMS noted in their change to QAPI from QA that they expect hospitals to spend more time on PI, which is prospective and less time on QA, which is retrospective. And yet, St.Charles sees many CAHs rely heavily on QC or, measuring the same thing for multiple years, without an appreciable change or improvement in the data.

“These activities are not going to help drive improvements in card,” she says. “Departments should be spending the majority of their time on identifying ways to improve care and implementing best practices.”

Don’t forget the importance of a data plan: To comply with QAPI requirements, CMS requires hospitals to use high-volume, high-risk and problem-prone criteria to identify organizational priorities. St.Charles emphasizes that before you can even start thinking about collecting data, you need to understand why you are collecting data and what purpose it will serve. “The data you collect needs to be in alignment with what you are trying to achieve,” she says.

A data plan that clearly describes each measure ensures that the data you collect is reliable and reproducible. Typically, a data plan includes information such as: aim (purpose of collecting data), name of measure, who will collect data, how often will data be collected, what is the sample size, what is the baseline data, what is the external benchmark, what is the target, who will analyze data, who will report data, and how often will data be reported.

Don’t disregard sample size: With 25 or fewer acute-care beds, CAHs certainly aren’t drowning in data. And therein lies the next biggest mistake hospitals make when it comes to QAPI success—they frequently disregard sample sizes and how to glean meaningful insights when data volume is low.

“Many hospitals don’t understand how important sample size is to prevent your data from being skewed,” she says. “Sample size can make a significant difference in the accuracy of the data.”

Bottom line: The right sample size is based on the confidence level, which is a measure of certainty regarding how accurately a sample reflects the population being studied. 

However, St.Charles also notes that in some instances, a sample size of one is sufficient. “If you have one harm event, such as a patient fall with injury, or a significant medication error (even if it didn’t reach the patient), you don’t need to wait for more occurrences to decide if it’s statistically significant, or there is a problem that needs to be addressed,” she says.

Don’t rely on your data alone: Remember that sample size of one? It can be given a confidence boost with chart audits, expert opinions and by being compared against established best practices. “That’s sometimes going to tell you more than a large random sample size or 100-chart audit can,” she says. Don’t miss this webinar, happening May 21 at noon CST. In addition to the don’ts above St.Charles also will cover how to conduct effective chart audits and how to analyze and report your data for QAPI. Sign up today.