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Health Center Data Validation Tool

Weight Assessment and Counseling for Nutrition and Physical Activity for Children and Adolescents Validation for UDS Reporting

Weight Assessment and Counseling for Nutrition and Physical Activity for Children and Adolescents Data Validation

  • Download the Excel Tool at the bottom of this page. Open it and click Enable at the top, it is a macro-enabled Excel file.
  • This data validation tool is specifically for the following 2021 UDS Clinical Quality Measure: Weight Assessment and Counseling for Nutrition and Physical Activity for Children and Adolescents (Table 6B, Line 12), CMS155v9. Review the measure beginning on Page 94 of the 2021 UDS Manual.        

Before you jump into data validation, it may be helpful to review your recent Child Weight Assessment and Counseling UDS data and reporting. Access your health center's HITEQ UDS Clinical Dashboards to see recent trends. Watch this quick video if you are new to the health center clinical quality measure dashboards, and email HITEQinfo@jsi.com with your grant number if you need your login information.

Getting Started with this Data Validation Tool for Weight Assessment and Counseling for Nutrition and Physical Activity for Children and Adolescents

Watch this 3-minute video for an overview. Click the four arrows in the lower right of the video to make the video full screen,

 

Follow the steps below to use the data validation tool at the bottom of this page to validate your Child and Adolescent Weight Assessment and Counseling UDS data:

  • STEP 1: First, you need a list of patients in your patient population for this particular measure for the given year. Generate this list from your EHR or data system. Be sure to include or add a column that denotes whether the patient met the measure (Yes) or did not meet the measure (No). This column will be what the data validation tool uses to identify discrepancies. Include the full universe of patients, the tool will select a random sample for you later.
    • Note: You can determine which patients you want to include-- for example, you may just want to include all the patients included in the measure who did not meet the measure, as this may be most useful for identifying opportunities for improvement. If you choose to go this route, be sure you are including all patients from the denominator who do not meet the measure, all marked with a No. 
  • STEP 2:  Paste that patient list into the third tab, EHR Chart Universe, aligning with the three columns defined there. The first column is medical record or unique ID, the second is last name (used only for your own verification during chart review), and the last is Yes/ No for whether the patient was compliant with the measure or not.
    • Note: Be sure to align with these three columns, so the tool works as expected.
  • STEP 3: Go to the first tab, Issue Assessment. In the table provided there, enter your compliance rate from the most recent reporting year in the first row of the table. In the second row of the table, enter the number of charts that you want to review in this validation process.
    • Note: Remember, reviewing less charts will take less time and resources to complete, but also is less likely to be representative. More charts takes more time and resources, but it is more likely to be representative. We recommend somewhere between 20 and 50 charts to get a sense.
    • The total patients included in the EHR Universe Chart List needs to be larger than the sample of charts you want to review. You will see the statement "There are less records in your Universe List than the selected sample size" in red until this is correct.
    • Once information is entered, you will see an estimated confidence interval, which tells you the range in which the actual compliance rate falls. A smaller confidence interval for this purpose means more certainty that your sample of charts reviewed more closely represents your actual compliance rate. Note that a general confidence interval calculation is used, and the size of your universe impacts this but is not included in the calculation.
  • STEP 4: After you have entered the number of charts to review in the table, click the Draw a Random Sample button. This will populate the second tab, Chart Abstract Input, with a random sample of that number of patients from your EHR list on the third tab. For example, if you entered 25 patients in the table on the first page, then when you click Draw a Random Sample, the second tab will be populated with 25 random patients from the third tab.
  • STEP 5: Then, use the second tab to conduct your chart review. Each column E-R (with blue headings) needs to be filled in for the assessment.
    • Note: Be sure to follow the format specified. For example, if it calls for a date, enter a date or 0 if there is no date. If the cell has a dropdown, be sure to use the dropdown for responses. Some columns call for qualitative information, such as where the information was found, who documented it, and where the service was provided. Standardize this information when entering (for example, use the same structure or abbreviations throughout). This information is helpful later, when determining where potential issues may exist.
    • Column T indicates whether all the columns needed for chart abstract are complete. If any records are shown as Incomplete in Column T, go back and fill in the needed columns.
  • STEP 6: Once the chart review is complete, go back to the first tab, Issue Assessment, and click Reveal Chart Abstract & Results Detail button to show the results on the second tab. You will be asked to enter a code, the code is HITEQvalid. Once the code is entered a whole set of additional columns on the second tab will be revealed as well as producing a table indicating potential issues identified on the first tab.
  • STEP 7: Review this resulting information (the new table on the first tab and the new columns shown on the second tab) to understand whether there are potential report inconsistencies or documentation issues that are depressing your performance rate. 
    • Step 7.1: Start with reviewing the Results Analysis table that shows up on the first tab (the Issue Assessment tab).
      • This table provides an overview of how many charts were abstracted and whether any were not fully abstracted. Then the next section breaks down the compliance rate from the charts abstracted by providing the number that met the measure and therefore were compliant and then providing a range in which actual compliance rate most likely falls based on 95% confidence interval.
      • Then, the "Sampled rate statistically different from EHR?" row notes whether the EHR compliance rate, entered at the top, falls into this range. If this row says yes, it will also be highlighted in red. This indicates that chart review is yielding compliance results that are notably different from the EHR-reported compliance.
      • The last two rows of the table specify whether the difference between the chart abstracted and EHR compliance is in the Universe (denominator) or in meeting compliance requirements for the measure (Numerator).
    • Step 7.2: If discrepancies are identified, then go to the second tab and review the new Results columns. The Columns are in three sections: First, EHR vs. Chart Abstract Result Comparison; second: Universe Inclusion Parameters; Third: Compliance Parameters.
      • In the EHR vs. Chart Abstract Result Comparison section, the first column shows the EHR result from the 3rd tab, indicating whether the patient met the measure, per the EHR. The Second result column in this section indicates whether the patient was eligible for the universe, according to the chart review. The Third result column indicates whether, based on chart abstract, the patient met the measure requirements. Then the fourth and fifth columns indicate whether the EHR and the chart abstract results agree or disagree for Universe and Compliance, for each record. These help you narrow down on where exactly the data issues exist.
      • The next two sections break down this information in even more detail-- indicating where exactly each abstracted chart meets or does not meet the various aspects of the measure. First, the requirements for the universe, and then the requirements for compliance with the measure. Again, these indicate where exactly data issues were seen.
  • STEP 8: Use this resulting information to act on the specific areas that need to be addressed. For example, if there are areas where the chart review suggests that documentation exists in the chart but it’s not being picked up as compliant, then investigate the next steps to update the documentation or mapping as needed or bring the example to your vendor. There may be providers or sites where documentation does not meet the requirements, use those as training opportunities. And of course, for those patients who have indeed not received the required service or test, circle back with them if time allows!
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