Applying Measurement Tools One example of a measurement tool is the Healthcare Effectiveness Data and Information Set (HEDIS) comprehensive care measures


The Healthcare Effectiveness Data and Information Set (HEDIS) is one of the most widely used tools in healthcare performance, according to the NCQA association (2021).  This week’s scenario closely resembles my time working in the clinic about ten years ago.  I was a staff nurse working with a provider, and we were transitioning to the electronic medical record.  All quality work was done via paper, which was not always ideal but was the only way we had at that time.

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Although time-consuming, it would not be difficult for the staff nurse to find how many diabetics there were in the facility.  Most likely, the facility has some form used for each person with diabetes, and it could manually be found by looking at which people with diabetes have been in over the past two years.  Since the EMR has been live for one year, the EMR would be a great place to start.  Most of the HEDIS information should be found via the work that has been done in the EMR since it went live.  Once the nurse has obtained the information from the EMR, historic paper charts would need to be looked at to include the data from these patients that have not been in over the past year.  These would be the patients that would need to be looked at closely to get them in to see if they meet the HEDIS measures. 

Since physicians are starting to inquire about quality incentives regarding their diabetic patients, I would advise them to look at their “low hanging fruit.” In other words, encourage the providers to work on the patients that are just out of range for meeting the measurements.  Those patients will be much easier to get to the HEDIS goal than those who have not been in or are not taking their medications.  Organization is vital; since there are only 1,000 patients in the practice, finding and sifting through to see the diabetic patients shouldn’t take a lot of work if the EMR has been active for a year. 

I also think it would be beneficial for the providers to list all of their diabetic patients and where they are out of range.  It is an inconvenience that all of the information is not in the EMR, but since it is now active, it will be easier for the providers to track their patient’s measures in the future. 


 As an employee of the primary care practice, I would need to discover who else within our small network of employees would

be interested in being part of a team to gather information, interpret the results, and be facilitators of change. The collaboration could

consist of one team or various teams based on using individual employee strengths. After creating the team or teams, delegating the

appropriate roles would be the next step towards implementing the quality improvement process.

     Based on the information from Spath (2018), the first step is identifying the topic, followed by developing a system for collecting

the data. The next step would involve developing a system of measuring the percentage of patients who have diabetes and are

meeting all of the HEDIS components. In this case, the topic is diabetic patients of the medical practice. Next, one would need to

determine if the information gathering is from the electronic health record (EHR), the paper charts, or both. The information would

be more substantial and reflective if both EHR charts and paper charts were analyzed. After agreeing on the best representation of

charts for the data collection, the process then involves sorting through the charts.

      A starting point is to use the current electronic health record. For example, by sorting by billing codes, one could determine

which patients have diabetes. Through utilizing the EHR and identifying the diabetic patients, the sorting process of the manual

charts is less daunting by generating a list of patient names. Also, determining a timeframe is essential to know how far back in time

the data collection process would benefit the results.

     After collecting the diabetic patient files, the next step involves collecting and measuring the data. Based on Spath (2018), an

expectation should be created for every measure. For example, the HEDIS includes the following measures: testing for Hemoglobin

A1c, controlling Hemoglobin A1c within specific measures, having a retinal eye exam, managing and caring for nephropathy, and

regulating blood pressure (National Committee for Quality Assurance, 2021). Again, a checklist-type spreadsheet would help record

which components of the HEDIS are met.

    In developing a system of measurement, one might write an equation. A percentage is determined using a numerator and

denominator in this scenario and multiplying that number by 100. For example, the numerator would be ” how many diabetic

patients meet all components of HEDIS” over the denominator of “total number of diabetic patients” and then multiplied by 100

(Spath, 2018). Creating an equation to represent each measure will help discover the percentage for each HEDIS component. Keep

in mind that a potential gap in this system is miscoding and mistakes in billing. A more straightforward measurement system uses a

run chart, as Perla et al. (2011) mentioned.

     The data collected can be compared internally with set expectations in changes to the HEDIS components or compared to other

facilities. After completing the measurements, further studies can determine what causes the meeting of the various components and

improve upon them (Spath, 2018).

     After gathering evidence and assessing data, sharing that information with the staff is crucial in implementing change. The results

can be expressed by implementing line graphs, histograms, or flowcharts and help decipher what expectations are being met, thus

deciding how to improve upon quality incentives for the diabetic patients to meet more of the HEDIS components (Yoder-Wise,


     Collectively as a practice and a team committed to being facilitators of change, decisions can be made to change quality

management and incentives, thus increasing the commitment to quality whole patient-centered patient care.


As a staff nurse working in a small private practice, determining the total number of diabetics in a 1000 patient practice can be challenging.  Yet, with time, and a combination approach of tackling the situation, it should bring us very close to an accurate estimate.  Acquiring EHR last year gives me the opportunity of having a large portion of our 1000 patient population already in the EHR system.  Through code systems in EHR, diabetics should be fairly and quickly recognizable.  The remaining charts still being manual will have to undergo the old fashioned style of chart reviews.  Utilization of a check sheet may sorting the diabetic charts for future analysis might prove productive (Spath, 2018).  Tried true and tested, until we have every diabetic patient accounted for.  

The HEDIS comprehensive diabetes care measure promotes a complete patient picture of the clinical medical needs of diabetic patients.  These patients require constant monitoring, as to reduce the risk of severe diabetic complications.  HEDIS comprehensive diabetes care measures include HbA1C control, retinal eye exams, nephropathy assessments, and blood pressure control measures.  HEDIS seems simple in the fact that HEDIS rewards providers in preventive care.  The noncompliant patient can create the opposite effect , penalizing providers in financial charges.  Therefore, not all will meet HEDIS comprehensive diabetic care.  These are the ones who miss out on positive outcomes.   Successful preventive care with compliant patients leads to fewer appointment visits and quality patient outcomes.  Hedis measures have shown positive results reducing morbidity and mortality from diabetes (Eddy et al., 2008). 

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