Quality Metrics for Chronic Disease Management
According to the CDC, chronic diseases are the leading cause of death in the United States, with almost 50% of the population suffering from at least one chronic illness. As a result, almost 80% of health care spending is devoted to its management (CDC, 2010). To this end, the National Committee for Quality Assurance (NCQA) developed performance measures. These performance measures allow organizations to compare yearly quality improvement outcomes in the management of chronic diseases. As a nurse engaged in advanced practice, you may find yourself at the forefront of prevention and care management efforts.
- Review the National Committee for Quality Assurance report, presented in the Learning Resources, and examine current trends and measures associated with at least two chronic diseases. This information will form the basis for this Discussion.
- Review examples of measures that address the management of chronic diseases for an inpatient setting that might not be relevant in an outpatient setting. Be sure to explore the companion metrics that influence a patient’s ability to manage chronic disease.
- Consider how these metrics facilitate change and improve the management of chronic disease.
- Examine the efficiency of current automated trigger systems for managing patient safety. Ask yourself: How do these automated trigger systems help improve quality of health care, patient education, and management of chronic illnesses?
By tomorrow 12/27/2017, write a minimum of 550 words essay in APA format with 2 references from the list below. Include the level one headers as numbered below:
post a cohesive response that addresses the following:
1) Compare one quality metric for managing chronic disease that applies to your practice setting to a metric that applies in a different practice setting (i.e. hospital nurse compared to home health nurse).
2) Evaluate how these quality metrics facilitate change and improve the management of chronic disease.
3) Take a stance on the efficiency of current automated trigger systems to help manage patient safety. Do you believe these to be proactive or reactive responses when educating patients on disease management?
Joshi, M.S., Ransom, E.R., Nash, D.B., & Ransom, S.B., (Eds.). (2014). The Healthcare Quality Book, 3rd ed. Chicago, IL: Health Administration Press.
Chapter 9: “Measuring and Improving Patient Experiences of Care”
Frith, K. H., Anderson, F., & Sewell, J. P. (2010). Assessing and selecting data for a nursing services dashboard. Journal of Nursing Administration, 40(1), 10–16. doi:10.1097/NNA.0b013e3181c47d45
This article highlights the benefits of nurses using dashboards to help with staffing issues. It considers the sharing of data that dashboard can facilitate from the perspectives of nurses, units, hospitals, and patients.
Grossmeier, J., Terry, P. E., Cipriotti, A., & Burtaine, J. E. (2010). Best practices in evaluating worksite health promotion programs. American Journal of Health Promotion, 24(3), TAHP 1–9.
In this article, the authors discuss how to measure success when analyzing worksite health promotion (WHP). They then cover how to organize these measurements, assess WHP programs, and determine factors related to best-practice evaluation frameworks.
Stanley, R., Lillis, K. A., Zuspan, S. J., Lichenstein, R., Ruddy, R. M., Gerardi, M. J., & Dean, J. M. (2010). Development and implementation of a performance measure tool in an academic pediatric research network. Contemporary Clinical Trials, 31(5), 429–437.
The details of this article focus on a multi-center research network that initiated an evaluation method using balanced scorecards. The first three years of the measurement tool’s implementation are covered, and the achievements and challenges are discussed.
Laureate Education, Inc. (Executive Producer). (2011). Organizational and systems leadership for quality improvement: Benchmarking outcomes. Baltimore: Author.
Note: The approximate length of this media piece is 10 minutes.
In this program, Barbara Epke and Carrie Brady discuss methods that health care organizations use to gather data for measuring outcomes, and explain how data are used to measure key indicators of quality and safety.
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As chronic diseases account for 86 percent of the U.S. healthcare costs, caregivers are concerned with accurate evaluation of their chronic condition management activities. Accordingly, there is much discussions and research on how to improve health outcomes for chronic patients. However, there is not much written on what precedes actual improvement — that is, medical data analytics where caregivers define, measure, and analyze these outcomes in order to base their progress with clinical processes on real-world data.
Only when this chain of defining, measuring, analyzing, and improving stays uninterrupted can providers reach a number of crucial milestones in the value-based care environment such as:
- improving care delivery by recognizing dependencies between medications, procedures, and lifestyle factors
- finding gaps in clinical processes and fixing them
- enhancing patient engagement to positively influence chronic patients’ health outcomes
- reporting to CMS and complying with the value-based care model
- analyzing their facilities, doctors, and nurses to both adopt best practices and fix the gaps in performance
But before the chain kicks off, caregivers may face certain challenges, including these three that chronic diseases bring into health outcomes analytics.
- A patient still stays ill: There is no way caregivers can use any outcome related to a complete recovery. Even with cancer, there can only be remission but this period can end anytime.
- Differences in initial health statuses: It is harder to evaluate health outcomes when patients’ initial general states differ a lot. One patient’s regular health condition can be life-threatening for another (for example, individual normal values of blood glucose vary among diabetes patients).
- Slow treatment progress: When speaking of clinical process improvements via outcome analysis specifically for long-term conditions, it is useless to analyze short-time outcome data (apart from complications and exacerbations), such as monthly measurements. This lag between an instance of care and the following health status improvement makes outcome data analytics even more challenging.
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