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E4 – Advancing Malnutrition Care Through De Novo Electronic Clinical Quality Measures (eCQMs)

Summary

Avalere's Angel Valladares is joined by Dr. Ken Nepple in episode 4 of our maltnutrition podcast series discussing the implementation of eMeasures. Dr. Nepple is an associate professor in the Department of Urology at the University of Iowa Health System with great professional interest in nutrition care. He has led the MQii project at his facility as a measure testing site and participated in the hospital learning collaborative piloting the standardized toolkit.
“Medical providers are not always trained to identify malnutrition and may not use a common language with other care providers. By improving both malnutrition identification and communication, we have the potential to make an impact.” Dr. Ken Nepple, Urologist

Panelists

Moderator
Angel Valladares , Senior Manager, Center for Healthcare Transformation

Angel Valladares provides research and analytic support for clients within the field of evidence-based medicine and healthcare quality measurement and improvement.

Guest Speaker
Ken Nepple, MD , Clinical Associate Professor, Department of Urology, University of Iowa Health System
Dr.

Listen to Other Podcasts in This Series

E5 – A National Call Action: Using Tools to Defeat Malnutrition Care Challenges in the United States

E3 – The Critical Role of Patients in Optimal Malnutrition Care

E2 – Improving Patient Outcomes by Reducing Clinical Practice Variability in Malnutrition Care

E1 – Malnutrition Among Hospitalized Adults: An Opportunity to Improve Care and Reduce Readmissions

Transcript

Angel: Welcome to today’s discussion on improving malnutrition care. As some of you may know, Malnutrition among hospitalized patients often leads to longer hospital stays, increased medical complications, higher morbidity and mortality, and increased readmissions, particularly for older adults. Unfortunately, malnutrition is often not diagnosed or effectively treated and thus represents a gap in quality healthcare. However, new tools developed by the Malnutrition Quality Improvement Initiative (MQii)—a collaboration of the Academy of Nutrition and Dietetics, Avalere Health, and other organizations dedicated to improving nutrition care—are now available for healthcare professionals to help close this critical gap and potentially improve health outcomes.

I am Angel Valladares, a manager at Avalere Health. I am the Avalere project lead for the development and testing of a set of electronic clinical quality measures focused on malnutrition. Joining me today to talk about the MQii, specifically about the development and implementation of the eMeasures, is Dr. Ken Nepple. Ken is an associate professor in the Department of Urology at the University of Iowa Health System with great professional interest in nutrition care and has lead the MQii project at his facility, as a measure testing site as well as a participant of the learning collaborative of hospitals piloting the standardized toolkit. His team collected data to test the newly developed eMeasures, which required significant internal buy-in from a cross-disciplinary team to understand both internal clinical care processes and how they reflect in the EHR. He is here today to share some of his lessons and address how technology will be critical to improving malnutrition care.

Thank you, Ken, for joining Avalere for our podcast today. With that, we are excited to address this critically important topic, so let’s get started.

Ken: The process is supportive of appropriately identifying the importance of malnutrition, and the impact that malnutrition has on patients. Medical providers are not always trained to identify malnutrition and we may not be using a common language with other care providers. By improving both identification and communication regarding malnutrition, we have the potential to impact malnutrition which may be a modifiable factor in patient care.

Additionally, I’d like to emphasize not only how identification of malnutrition has an impact on patient care, but also with hospital reimbursement and risk adjustment. Patients with malnutrition clearly require more resources. From an institutional perspective, it is important to appropriately identify patient with malnutrition from a reimbursement and risk adjustment perspective. But more importantly, early identification allows improved allocation of resources to intervene and improve patient care.

Angel: In your efforts to test and eventually implement these measures, there probably was some collaboration that needed to be completed, who were the folks involved in the process and what were some of the activities that needed to be completed to appropriately test and implement the measures in your EHR?

Ken: Collaboration was absolutely necessary for this project. It was very important to involve all the stakeholders who are involved with malnutrition care; this includes nurses, dietitians, medical providers, informatics personnel and multidisciplinary communication across these groups. Particularly for the EHR work, you want to have a workflow that is as streamlined as possible.

You want to involve stakeholders to understand how we can best develop a workflow that both provides good patient care and captures discrete data. This allows us to monitor how the process changes are influencing patient care, and allows access to a large volume of patients.

For example, since thousands of patients can possibly be admitted in a month, collecting data on screening rates and the timing captured in a discreet fashion greatly reduces the burden on monitoring and tracking of quality improvement by a manual process.

Angel: Granted, it has not been very long since you first tested these eMeasures in your EHR. Can you provide any initial findings such as areas of process improvement that come to mind after reviewing your performance on the eMeasures?

Ken: Sure. We have a few lessons learned across the measure data which we have collected. To date, we have looked at four processes: nutritional screening, assessment, diagnosis, and care plan.

For screening, we identified that having a validated screening tool embedded into the nursing EHR workflow led to a high rate of utilization. This was the measure that was most clearly automated and ingrained in the workflow at our institution. For assessment, we had recently improved the capture of discrete data in dietitian notes with an emphasis on the ASPEN/Academy Consensus Statement. This resulted in a successful capture of data, but led us to further improve that workflow to capture more specific data about the characteristics of each patient. Now we are looking at the six characteristics of malnutrition in more detail, and better characterizing if patients have evidence of any of the six characteristics, allowing for more granularity.

For both provider diagnosis documents and care plans, there was room for improvement. Some patient with a malnutrition diagnosis had that entered as text and not as discrete data. Additionally, our documentation of malnutrition care plans are largely text based. As a result of the project, we are trying to improve education about how best to document the status of the patient and how to best communicate between care teams on implementing the care plan once the patient has been assessed and is diagnosed with malnutrition.

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