According to the CDC, More than 37 million US adults have diabetes, and 1 in 5 of them don’t know they have it. 29% of diabetics are 65 years or older. In the last 20 years adults diagnosed with diabetes have doubled. With the population aging, these percentages will be expected to increase.
Since being diabetic or pre-diabetic is a risk factor for acute and long-term health complications, and even death, there has been a national awareness to educate the public of managing diabetes and preventing diabetes through lifestyle changes.
Medical insurance companies are keen mitigating diabetes risk since diabetic complications incur costly expenses for the insurance companies. Over 90% of insurance companies use HEDIS criteria when reimbursing providers. The theory is that meeting HEDIS criteria in Diabetes Care saves lives and reduces healthcare costs. Trickled down, this means insurance companies base provider reimbursement rates in how well they hit target HEDIS goals.
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Provider will focus on meeting the following goals in their diabetic/ pre-diabetic patients:
- HbA1c adequately controlled <8%
- A yearly retinal or dilated eye exam
- Received a kidney health evaluation,
- Blood pressure is adequately controlled
In the given scenario, the office nurse is to convert 1,000 patient health records into electronic health records (EHR). Electronic records are helpful because the inputted data can generate reports and charts for a patient or patients trends. For example, EHR can generate a run chart of a patient HbA1c data and blood pressure readings over a period of time. It can also assist in tracking when a patient is due for an eye exam and kidney exam. Sharing EHR with insurance companies to determine if the provider is meeting HEDIS benchmarks is not a violation of HIPAA.
Kyeremanteng, K., Robidoux, R., D’Egidio, G., Fernando, S. M., & Neilipovitz, D. (2019). An Analysis of Pay-for-Performance Schemes and Their Potential Impacts on Health Systems and Outcomes for Patients. Critical care research and practice, 2019, 8943972. https://doi.org/10.1155/2019/8943972
Perla, R. J., Provost, L. P., & Murray, S. K. (2011). The run chart: A simple analytical tool for learning from variation in healthcare processes. BMJ Quality & Safety, 20(1), 46. doi:https://doi.org/10.1136/bmjqs.2009.037895Links to an external site.
Healthcare Effectiveness Data and Information Set (HEDIS) is a performance measurement tool created in 1991 by a Health Maintenance Organization (HMO) known as the Alliance of Community Health plans (McIntyre & Heier, 2001). This first draft of standard set measurements was moved to the National Committee for Quality Assurance (NCQA) which further developed the project and released HEDIS in 1993 (McIntyre & Heier, 2001). HEDIS is a highly utilized performance improvement tool to improve six domains: effectiveness of care, access and availability of care, the experience of care, utilization and risk-adjusted utilization, health plan descriptive information, and measures reported using electronic clinical data systems (NCQA, n.d.).
A staff nurse is tasked with finding how many diabetic patients are within a provider’s care and discerning if they meet HEDIS core measures. The challenge of this task is even more significant, considering the data information is in manual charts. The nurse could find this information one of two ways. Either she must manually read every patient’s chart and account for the information she needs by hand, or she can digitize the data. It is preferable to digitize the information for quick access in the future. To expedite the process, the nurse can scan the paper files into a computer system (5 Patient Data-Collection Best Practices, 2022). Another option would be to purchase a HIPAA-compliant electronic health record (5 Patient Data-Collection Best Practices, 2022).
After collecting the data, the nurse can determine if the patients meet the HEDIS core measures for diabetes care. For example, the NCQA recommends that adults between the ages of 18 and 75 with diabetes should be assessed if they have hemoglobin A1C with poor control and control, a retinal exam, medical attention for nephropathy, and blood pressure control if the bp is less than 140/90 mm Hg. These standards will help cover diabetic patients with the diseases’ sequelae and prevent further damage to the body.
5 Patient Data-Collection Best Practices. (2022, May 10). The Jotform Blog. https://www.jotform.com/blog/patient-data-collection/Links to an external site.
McIntyre, D., Rogers, L., & Heier, E. J. (2001). Overview, History, and Objectives of Performance Measurement. Health care financing review, 22(3), 7–21.
NCQA. (n.d.). HEDIS and Performance Measurement. https://www.ncqa.org/hedis/
Applying Measurement Tools
All healthcare professionals are charged with the responsibility of providing high-quality care that is efficient and safe. Continuously improving healthcare management helps organizations accomplish their goals. This, therefore, means the quality of care has to be verified over time to analyze if all the objectives are being met (Huser et al.,2018). The Healthcare Effectiveness Data and Information Set (HEDIS) is currently being used by 90 percent of healthcare facilities in the US to measure performance. The tool has 71 metrics and eight subsections of care. The subsections specifically look into access to care, the efficacy of care, risks associated with care, and other healthcare information collected through electronic clinical data systems.
On matters related to diabetes, HEDIS considers patients with Type 1 and Type II diabetes, and are above 18 years. It also puts emphasis on this group of patients who have a HbAIc of more than 9.0 percent, whose eyes have been examined, a blood pressure that is below normal (140/90 mm Hg), and who have recorded neuropathic pain. As a nurse leader who happens to work in this facility and has few personnel and limited space, I would ensure everyone contributes by coming up with information that would be suitable for diabetic patients (Kutz et al.,2018). The office has been operating for one year and this means that at least there has been a follow-up treatment plan for diabetes patients. The first people I would involve are nurse informaticists in the billing office; I would ask them to locate all the ICD-10 codes for all diabetes patients.
Once the codes have been identified, I would call out for medical staff to locate the records of all those patients. Once this has been done, every staff would now need to collaborate by going through the records and identifying patients who complied with the HEDIS routine, those who did not comply, and which of the eight subcategories dis the patients comply with. The analysis will also help understand whether the majority of patients had their HbA1c routinely checked and whether it was in a good or poor range (Owen et al.,2021). It would also be important to compare the information received with that of insurance companies. Most insurance companies are keen on knowing whether a patient has diabetes as it results in expensive costs. Getting this information is within The Health Insurance Portability and Accountability Act (HIPAA) requirements.
Huser, V., Kahn, M. G., Brown, J. S., & Gouripeddi, R. (2018). Methods for examining data quality in healthcare integrated data repositories. In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018: Proceedings of the Pacific Symposium (pp. 628-633). https://doi.org/10.1142/9789813235533_0059
Kutz, T. L., Roszhart, J. M., Hale, M., Dolan, V., Suchomski, G., & Jaeger, C. (2018). Improving comprehensive care for patients with diabetes. BMJ open quality, 7(4), e000101. http://dx.doi.org/10.1136/bmjoq-2017-000101Links to an external site.
Owen, R., Wyckoff, M., & Rogers, R. (2021). Increasing Access to Diabetes Care in Underserved Populations Using Mobile Medicine: How Point-of-Care Testing on Mobile Clinics Strengthens Hemoglobin A1c Adherence in Vulnerable and Underserved Populations. Journal of Doctoral Nursing Practice, 14(3), 204-212. DOI: 10.1891/JDNP-D-20-00066