Person-Centered Measures of Multimorbidity

Join Dr. Sarah Berry Harvard Medical School and Michelle Xue, PhD-c, RN, nurse scientist and doctoral candidate at the Duke University School of Nursing, as they discuss the similarities and differences between person-centered outcomes and patient-reported outcomes and touch on how to apply person-centered approaches and measures in both clinical settings and research.

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Transcript

Sarah Berry, MD, MPH: Hi, I'm Sarah Berry. I'm an associate professor of medicine at Harvard Medical School and Marcus Institute at Hebrew Senior Life. And I'll be talking today with Michelle Xue, who's a, a nursing PhD candidate at Duke University School of Nursing. We'll be talking today about Person-Centered Measures as part of the AGS/ AGING LEARNING Curriculum. Michelle, thanks so much for joining us today. 

Tingzhong (Michelle) Xue, PhD-c, RN: Hi, Dr. Barry. Thank you so much for having me. It's an honor for me to be here. 

Sarah Berry, MD, MPH: I really enjoyed the slides that you put together, and one of the things that hit home for me is this distinction between person-centered outcomes and patient reported outcomes. Can you remind the audience of what the difference in those is? Some of the similarities and differences? 

Michelle Xue, PhD-c, RN: Yes. So before we talk about person-centered outcomes, and [00:01:00] patient reported outcomes, we can first talk about what person-centeredness is. So person-centeredness really concerns what matters to the person and what healthcare professionals can do to help the person to achieve their goals and develop care and treatment plans according to the person's preferences. So by that definition, person-centered outcomes in the context of multimorbidity, are health outcomes that are important for the person, whereas patient reported outcomes are outcomes that are reported directly by the patient. So patient reported outcomes are likely to be person-centered outcomes, but they are not exactly the same.

So person-centered outcomes can be broader. For example, mortality or life expectancy is not a patient reported outcome, but can be [00:02:00] considered as a person-centered outcome, because most people will think living longer is important, holding other factors constant. 

Also, proxy reported and clinician reported outcomes can also be important in some scenarios when considering person-centeredness. For example, when taking care of patients with cognitive impairment, who might be unable to report their own outcomes. In that case, we would need proxies and clinicians to report outcomes on their behalf. So that's kind of the distinction. 

Sarah Berry, MD, MPH: That's a great point. And, and you know, it's so important to hear from the patients and from the proxies, in particular in persons with cognitive impairment and dementia.

How do we apply this person-centered approach that you mentioned that's, that's so important in a research [00:03:00] setting. 

Michelle Xue, PhD-c, RN: So, in research setting, we want to select outcome measures for our research, and we want to think about when to select disease specific, measures and measures of the overall impacts of multimorbidity.

So considering the overall impact, such as function and quality of life is usually more desirable than focusing on managing specific diseases. If the individuals are dealing with two or more conditions at the same time, however, sometimes it's helpful to know something about the disease focused measures, those measures can help us think about development of multimorbidity measures.

For example, symptom burdens are often measured in patients with cancer, like the Memorial Symptom Assessment Scale, it assesses the frequency, [00:04:00] severity, and distress of the symptoms. So the measure has been validated in the context of some other chronic diseases and also used in multimorbidity symptom burden in some studies.

But we do need more validated measures specifically developed for assessing multimorbidity symptom burden.

Sarah Berry, MD, MPH: I see. And then this is all gonna get even more complicated in a longitudinal study, I guess, you know? Have you, have you thought a little bit, can you tell us a little bit about collecting longitudinal measures?

Michelle Xue, PhD-c, RN: Yes. I'm a big fan of longitudinal measures, because we know that that can help us to understand more about the patient, because no patient is average. So timing is a super important factor to consider in multimorbidity research, because diseases contributing to multimorbidity may progress and [00:05:00] symptoms may interact with each other.

So the experiences of affected individuals over time can be very different. Understanding that those trajectories of health outcomes can help us better capture individuals' changing needs and preferences and therefore facilitate developing individualized treatment and care paths. 

Sarah Berry, MD, MPH: And then I was thinking also, Michelle, what is the bright balance between qualitative data and these quantitative data that we're collecting from patients?

Michelle Xue, PhD-c, RN: Yes, that's a really good question. So we just talk about using longitudinal measures to capture the individual differences. And another way to capture individual differences is to leverage information gathered through qualitative method. So the reaching information of the qualitative data can give us more insights of the person, [00:06:00] like how their lives are affected by multimorbidity and what goals are important for them to achieve.

So this information can help us select outcomes that are important for them and generate hypotheses in the phase of intervention development, or help interpret the quantitative findings in the phase of evaluation. 

Sarah Berry, MD, MPH: Yeah. Selecting the, the right outcomes to really, get at this person centered approach, seems to be key. Do you recommend that investigators use a, a framework to help them do that, accuse these outcomes? How do you do this? 

Michelle Xue, PhD-c, RN: Yes. So choosing the right theoretical framework as a guide can be really helpful to select the right outcomes. So, looking into the framework and really think about, you know, what's the direct effect of the [00:07:00] intervention and what's the causal effect in there can help us to select the right measure.

And also at the same time, it will be very informative to learn from previous research studies. So in trials that focus on multimorbidity management. For example, in a large trial conducted in UK using a patient centered care model, the impact on individual health outcomes may not show significant differences in the short term. Or the measures may not be sensitive enough to capture the differences before and after the intervention, but the intervention may have direct effects on care quality or better address the patient's needs and the preferences so that the person-centered measures, such as satisfaction of care, may be more likely to be influenced. So choosing that as one of those outcomes [00:08:00] can help us to understand if the intervention works. 

Sarah Berry, MD, MPH: Yeah. Really good advice for young investigators. That is, you know, starting with a theoretical framework and looking to see what's out there and what's been done, before you, you choose your measures. It seems that one of the real take homes is that, you know, it's key to select the right measures for the right population . That there's no one size that that fits all. Any tips that you have or sort of closing thoughts about how to select the right measure for the right population? 

Michelle Xue, PhD-c, RN: Yes. So populations are,different when we talk about like different cultures and different backgrounds, and we want to consider the needs and preferences of diverse populations to fully enhance person centeredness in the context of multimorbidity care and research.

So the items of matters may mean different things to [00:09:00] individuals with diverse backgrounds. As one example mentioned in the Pat Covax paper, so people from different cultures or different socioeconomic backgrounds may read certain items differently. . So one example they gave is how people from different cultures with different socioeconomic backgrounds read the item on the impacts of disease on managing job differently.

So for people who are doing more manual labor, they may read higher on the importance of physical health, managing jobs. And several measures we mentioned in the module, such as Quality of Life measures have already been adapted to different countries with different languages. So that's one example of considering different populations' needs and preferences. So more [00:10:00] population characteristics should be included in the future in the context of measure development. 

Sarah Berry, MD, MPH: This is great. and those were just great examples. We certainly need more research in this area on patient-centered outcomes, but in particular, as you say, in diverse populations, underserved populations. And Michelle, I've certainly learned a lot today. I really appreciate your time. 

Michelle Xue, PhD-c, RN: Thank you so much for having me.