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Key Insights in SNF Data from the Transition to PDPM

> Blog > Key Insights in SNF Data from the Transition to PDPM

By Maria Arellano, MS, RN, RAC-CT

Product Manager, PointRight


This is the first blog in a series focused on looking back at PDPM data thus far so you can plan. Part 2 focuses on compliance and risk. Part 3 focuses on developing strategies to address unfavorable trends.

SNF Data Impacted by COVID

Most of us have been chomping at the bit to look at the first PDPM era data to evaluate this new payment model, but so far CMS FY2021 hasn’t been a typical year to analyze. Quarter 1 of the fiscal year was a transition quarter and with the high number of Interim Payment Assessments (IPAs) in October along with the learning curve of a new model, the data may not be reflective of the true impact of PDPM. Quarter 2 looked promising until COVID-19 took hold in March; and quarter 3 has COVID-19 written all over it, so as we look at the PDPM data captured in the first 3 quarters of 2020, we must keep these factors in mind.

What story does the PDPM data tell us so far?

As you can imagine, there are many different elements to consider when analyzing the PDPM data but looking at the per diem rate drivers will give you a leg up on imminent CMS analysis and provider scrutiny. Just like the patterns observed in the highest RUGs levels in the old model, CMS has vowed to monitor these payment drivers to ensure program integrity. Naturally, these will be an area of focus under PDPM.

PDPM Payment Drivers

Let’s start with a review of the MDS Items that impact the PDPM components.

  • Are we seeing any coding pattern changes since the start on 10/1/2019?
  • If so, are the changes true changes in the resident population?
  • Are the changes related to improvements in how these items are assessed, or are there signs that there may be some “gaming” of the system?

These questions and others are critical to answer to have full awareness of where you may stand if scrutiny comes to your facility in the form of audits or surveys.

One simple way to evaluate your data is to look at the percentage of assessments in the individual drivers for each component. The data represented in the charts below illustrates the average percentage of assessments for these rate drivers within PointRight nation*. If the same data was available prior to 10/1/19, data from pre-PDPM CYQ3 2019 through CYQ2 2020 is displayed to better evaluate the changes pre and post-PDPM implementation.

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*PR National average – PointRight processes over 25,000 MDS’ per day – watching for patterns and emerging trends to help identify potential problem areas early

Nursing component:

For the nursing component the percentage of assessments for these 3 rate drivers were analyzed:

  • Extensive Services,
  • Depression,
  • Restorative Nursing
PDPM Payment Drivers Nursing Component Data
CY = Calendar Year; FY = Fiscal Year

 

Depression coding rose dramatically, doubling from FYQ4 (2019) to FYQ1 (2020). Restorative nursing nearly doubled at a much slower pace over a 3-quarter period. The use of extensive services remained nearly unchanged until the pandemic when, not surprisingly, a significant increase occurred in FYQ3 (2020), which is likely due to the increased need for isolation related to COVID-19.

Speech Therapy Component:

For the SLP component, the percentage of assessments for these 3 rate drivers were analyzed:

  • Cognitive Impairment
  • Mechanical Altered Diet
  • Swallowing Disorders
PDPM Payment Driver Speech SLP

 

Rapid and dramatic increases were observed in the percentage of assessments with cognitive impairment and swallowing disorders, especially in the first quarter post implementation of PDPM. Cognitive impairment continues to show an upward trend and swallowing disorders continued to climb for another quarter before leveling off. Mechanically altered diet has demonstrated a more gradual but steady increase across all 3 quarters.

Non-Therapy Ancillaries (NTA) Component:

For the NTA component, the distribution of the percentage of assessments for each of the NTA case mix groups was analyzed. Over 50% of the assessments landed in the lowest two case mix groups which have case mix indices (CMIs) lower than 1.

One point separates each of these categories, so missing one diagnosis or special treatment can mean the difference between a base rate multiplier of 0.96 vs.1.34, or 1.34 vs. 1.85. Does this truly reflect the complexity of the residents you care for?

 

As you know, with averages some providers will be higher, and others will be below the average.

  • Where do you fit within these averages?
  • Do you know where you stand today?
  • Could you explain why your numbers land where they do?
  • If you’ve had some of these dramatic increases, could you defend them in the event of an audit?
  • If you’re significantly below average, are you missing opportunities?

If you are among those with increases, let me go back to my original questions. Did your resident population change from one quarter to the next? Not likely. So, what is the reason for the change in your facility? And better yet, what will you do about it?  I’ll give you 720 reasons why you should know.

PDPM: Looking Back and Planning Ahead Series

Check out Part 2 focused on compliance and risk, followed by Part 3 of this PDPM series focused on successful strategies to address unfavorable trends.


About the Author

Photo of post author Maria Arellano, MS, RN, RAC-CT

Maria Arellano, MS, RN, RAC-CT

Product Manager, PointRight

Maria has over 35 years’ experience as a registered nurse in the post-acute industry. Maria has served in a variety of roles directly in or supporting the long-term post-acute care sector including staff development, director of nursing and corporate consultant where she demonstrated regulatory excellence and exemplary quality outcomes. Previously she was a nursing home quality specialist with a quality improvement organization (QIO) and worked to enhance the quality of care for residents. Maria participated on CMS Technical Expert Panels as well as various committees and advisory boards throughout her career. She is focused on quality improvement, which fuels her passion for transforming data into knowledge and actionable insights.

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