Welcome to the Nursing Outlook Blog – “3 Questions” – Timely Interviews with Thought Leaders in Nursing and Health Care Policy

With the complexity of our health care system in the United States growing, it has become even more important for nursing to parse out the domains most important to patients and families and contribute to the knowledge related to illness and comorbidity through nursing research. Symptom domains such as insomnia, pain, anger, anxiety, depression, nausea and fatigue are among the numerous troublesome problems that patients experience with illness. A symptom or cluster of symptoms may be the result of disease or other etiology, and we need to draw our research attention beyond the disease to formulate evidence based interventions that work.

The nursing science upon which to build our understanding of symptom management is aided by the development of the National Institutes of Health (NIH) development of the Patient-Reported Outcomes Measurement Information System (PROMIS), with the National Institute of Nursing Research (NINR) playing a lead role. This national attention to realize a goal of standardizing research approaches to measurement offers us new ways to advance the state of symptom science. Empirical findings related to symptom assessment and management are essential to inform health policy in efforts to restructure health care systems that meet patients’ needs.

The September October 2014 issue of Nursing Outlook focuses on symptom science with articles that highlight current developments and contributions of the NINR to advance the state of the science in symptom measurement. Dr. Elizabeth Corwin and colleagues discuss a vision for the future with the PROMIS system as a key accelerator, particularly with the potential of “big data” and “common data elements” (CDEs).

Elizabeth J. Corwin, Associate Dean for Research and Professor at the Nell Hodgson Woodruff School of Nursing, Emory University in Atlanta Georgia. She is also PI and Co-PI on NIH grants related to clinical symptoms, and author of the textbook Handbook to Pathophysiology.

For links to the PROMIS website, click here: PROMIS.

We invite commentary that is thoughtful and provocative! Join the online dialogue!


We invite commentary that is thoughtful and provocative! Join the online dialogue!

Veronica D. Feeg, PhD, RN, FAAN

Elizabeth J. Corwin, PhD, RN, FAAN
Nell Hodgson Woodruff School of Nursing
Emory University, Atlanta, GA

Question 1. What is different about nursing symptom science now compared to previous years that led your group to call for new ways to envision the future?

To listen, click here.

The culture has really changed in terms of what is expected and what are the possibilities for nursing symptom science. For example, in the past it has often been individual researchers working alone or with just one or two others to ask questions that involve small groups of patients or families. And now we have the capability to think about the big picture including complex interactions between symptoms and across populations. We can share data. We can utilize big data. It’s really an opportunity to jump forward with our research. And the emphasis to start thinking about the bigger picture, and pulling together diverse groups and patients and families has come from the National Institutes of Health itself, which has encouraged data sharing for the common good to really improve outcomes for families and patients.

So, it really is coming from that emphasis: to share data, use resources widely and for the best good for all. Also, with the National Institute for Nursing Research, the emphasis on centers and across all NIH institutes, the emphasis on interdisciplinary work and teams has allowed for new perspectives on diseases, interventions and self-management to really come into light in a way that hadn’t been before.

Another thing I think that has changed that is really driving the future of nursing symptom science is that so many nurses have moved into very very important advance practice roles. And when you’re in advance practice, it is obvious that diseases and symptoms are very complex. And there’s a great deal of overlap between the biological underpinnings of any given symptom, for example fatigue or depression, or pain. There are biological underpinnings [that] overlap across diseases and across populations. And so being able to look at the complex interactions, both from seeing it in real life as a clinician and then thinking about the mechanisms, has made symptom science jump forward. And we have to connect across diseases and across populations to ask questions that are complex but still patient focused. Maybe the best way to describe this is that ultimately it’s personalized healthcare – that includes patient’s perceptions of his or her symptoms as well as the individual context of that symptom, his or her genetics and epigenetics – all are available to us now – these huge opportunities to pull together these complex pieces that will allow patient care and prevention and reduction of symptoms to really be individualized in a way that we never had opportunity to do before. And never even had a perspective of how to think about this potential!

Question 2. What are CDEs and how will consensus on CDEs be attained and their use actualized?

To listen, click here.

Common data elements (CDEs) is a broad umbrella, and it includes measures of symptoms for example – so screening forms, questionnaires – different types of ways to get at individuals’ symptoms. So it does include measures. But, common data elements are more than that. It is any pieces of data – all pieces of data – that are gathered in a study that can be shared. For example: demographic data is a common data element. You can have demographics on gender, age, socioeconomic status, race, ethnicity and a number of other pieces of common data elements that are demographic in nature.

So those are two types: demographics and measures. But then it goes beyond that and it can be clinical indicators. So you can have across populations: hemoglobin A1C, or brain imaging data, genetic data, epigenetic data, telomere length. All of these pieces of information that are gathered within one study can be shared if they are coded the same and available to other researchers. These pieces of information can be shared across studies, across populations, across disease conditions. For example, you can have individuals looking at telomere length, the marker of chronic stress exposure. And you can have those measures in a population of caregivers of Alzheimer’s patients or family members who are caregivers. You can also have telomere length data available for mothers who care for children with cystic fibrosis, or, for patients with heart failure. And these different populations of study participants or patients could be 90 years old, or they could be 12 years old. Yet some of their symptoms can be the same or they may be different – and you could look at the impact of common data elements, the measures, questionnaires you use, the age, the gender, the socioeconomic data, the clinical markers, the brain imaging information, the epigenetics, the genetics. You can share these common data elements between studies, between patients with different conditions, between many different levels to evaluate what’s similar and what’s different, for example, across gender in patients with different diseases.

Common data elements are those bits of information that we use to keep about just one population, one small study – that now, if they’re coded the same and individuals have access to them, can be shared across populations, diseases and studies.

Now in regard to your question on how can consensus on CDEs be actualized? Well that’s difficult in some ways because many of us use questionnaires, for example, that we have used for a long time and they might be (I’ve heard the term used – legacy surveys or questionnaires) “legacy measures.” And so it is sometimes difficult to give up a legacy measure that you have used for a long time and start using a different measure for that same symptom for example. There are reasons that people don’t want to give those up. You can refer back to your previous research if you’re using the common tool and common measures as opposed to not having any standard that you could go back. But the benefits are that then others will also be able to build on your research, extend it, and ask new questions – more complex questions.

So how does that consensus happen? How is it built or agreed upon? Well I think NIH has done that and many institutes or have done that already. It seems that the process is generally that individuals are brought together to perhaps discuss the idea at the earliest stages and then put it out to the community that will be involved, to get input, get feedback, get ideas going back and forth. Do this respectfully and as an inclusive group not exclusive, hear all points of view and work towards identifying the key common elements that studies can share, or, should be encouraged to share. And then perhaps there still could be other measures or tools that an individual researcher wants to include in his or her own research. But adding even just a few that across studies, for example, perhaps across centers that are all aiming towards the same research goals – like self-management centers or cognitive centers – having some common data elements across all centers that have been agreed to by consensus through mutual discussion and respect, and analysis of the available literature. Using these common measures, then, can be added to supplement other measures that people still want to use (some previously studied measures in their own populations). So it will take consensus. Consensus will take development, it takes time, it takes inclusion, it takes in listening to people and then it takes identifying the key pieces of information that most people will agree can be shared across studies.

Question 3. What challenges arise when nurses try to translate their findings into policy? How will the new future in symptom science described in this article address those challenges?

To listen, click here.

The challenges nurses face when attempting to translate their findings are not just unique to nursing. But one piece might be that we often have small sample sizes, especially if we’re interested in unique questions or populations, the sample sizes might be small. And so in the past when everyone was just doing research individually, it was difficult to change policy with sample sizes of 50 or 60 or 100 or 160. But now the opportunity to share data across groups by the use of common data elements and hopefully someday a common data repository that individuals could have access to. Being able to share that sort of data across populations gives power to our own smaller studies [so] that we can start asking bigger questions because we can add to our sample size. The power is improved. So by this way, we will have a chance to change policy.

In addition, as discussed in our manuscript, we will be able to ask more complex questions. For example, previously in our research, even though as nurses we knew that the expression of symptoms depend on the context for the patient – whether the patient is experiencing them at home versus in the hospital, or has his or her partner with them makes a difference, or, whether they are out with their grandchildren that day, or staying at home alone. Context matters. But until the time of big data, until we had the tools to analyze symptom outcomes and patient reports as part of a big data set, it was very difficult to influence policy because we couldn’t include context and how important context is into the presentation to try to affect policy. But now that we can address something like context by big data usage and common data elements, we have a chance, really, to impact patients in ways that are very very meaningful.

Also, previously we considered perhaps one aspect of an intervention, for example, patient satisfaction. But now, having access to other data sets that we can merge with, for example, that one outcome (patient satisfaction), we can now merge that with big data sets on hospital readmission, or, costs to the consumer or to the state. You can merge those data sets now. We will be doing that, and, especially if we code them and have access – common data registries. We will be able to ask those very very complex questions and answer them in ways that can lead to policy change.

So using big data – and that was one of the underpinnings of this discussion – big data will allow us to consider other contributors to patient outcomes, to symptoms, to satisfaction and self-management. We can consider these for larger impact, and so we will have a greater success in actually changing policy. When you can bring in more stakeholders by merging data sets that will become available, the impact of our research grows.