Applying Measurement Tools

Applying Measurement Tools

The Healthcare Effectiveness Data Information Set (HEDIS) was established by the National Committee for Quality Assurance (NCQA), and it was meant to enhance health care performance by providing a measurement technique for health issues in the country. The system is used to measure diabetes, and the comprehensive diabetes care provided involved HbA1c poor control (>9.0%), HbA1c control (<8.0%), LDL-C control (<100 mg/dL), HbA1c control (<7.0%), examination of the eye retinal, nephropathy medical attention, LDL-C screening, and Hemoglobin A1c (HbA1c) testing (National Committee for Quality Assurance, n.d). The Health Effectiveness Data and Information Set (HEDIS) is a standardized standard used to determine the appropriate and/or overuse of chronic disease-oriented services, as well as preventive services, behavioral health services, and preventable hospitalizations and it is also a performance measurement tool (Perla, Provost, and Murray, 2011). Patient satisfaction created by the National Quality Assurance Committee of Medicare & Medicaid Services (NCQA). HEDIS is designed to improve health care by measuring the quality of care provided by service providers and practitioners, allowing consumers to better choose health plans by distinguishing effects. HEDIS has also certified health insurance plans. Suppliers, nurses, and hospitals can use the HEDIS standardized measurement tool to track the success of quality improvement efforts, track development, and provide a set of metrics that can be compared to other plans (Curran et al., 2020).

In this scenario, two doctors in this small clinic are interested in getting quality incentives to help diabetics. The author needs to review more than 1000 charts identifying patients diagnosed with diabetes, corresponding to the eight components of HEDIS. Exploring these diagrams manually is inefficient, this task requires more staff, and training in data collection is required to ensure consistent and accurate data collection (Curran et al., 2020). Electronic medical records have been used for the past year and have proved to be effective. This is the most practical way to use it. The author can create a spreadsheet that collects data containing eight diabetes components. Data collectors need to train information that needs to be collected to ensure the reliability of the report. Obtaining data from the EHR is the most accurate, reliable, and suitable method (Curran et al., 2020). After receiving the information, she will have to decide whether to use sampling according to the recommendations of the Joint Committee. In this case, if the population of diabetics is less than 30 cases, the sample will contain 30 cases, but if the population is more than 500, a sample of 70 cases is statistically sufficient (Curran et al., 2020)..

Finally, information is provided to the physician to review and evaluate the efficacy and areas in which each of the eight components of HEDIS comprehensive diabetes treatment can be improved. If there is room for improvement, the timeline can also be presented as an analysis tool (Anhøj & Wentzel-Larsen, 2018). Including the results table in the results display gives a visual assessment, and the physician gets ideas for areas that need improvement, followed by an intervention plan (Curran et al., 2020).

References

National Committee for Quality Assurance. (n.d.). HEDIS Measures. Retrieved January from http://www.ncqa.org/hedis-quality-measurement/hedis-measures

National Committee for Quality Assurance. (n.d.). Disease Management Performance Measures. Retrieved from http://www.ncqa.org/tabid/1109/Default.aspx

Curran, R. L., Lippman, J. M., Raber, H., Gondor, K., Li, M., & Fortenberry, K. T. (2020). Diabetic Nephropathy Screening in a Residency Clinic: Improvements Also Reveal Limits of Healthcare Effectiveness Data and Information Set (HEDIS) Metric. Clinical Diabetes.

Anhøj, J., & Wentzel-Larsen, T. (2018). Sense and sensibility: on the diagnostic value of control chart rules for the detection of shifts in time series data. BMC medical research methodology, 18(1), 100.