Innovations in assessing patient outcomes

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Published: 3 May 2016
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Dr Heather Jim - Moffitt Cancer Center, Tampa, USA

Dr Heather Jim speaks with ecancertv at AACR 2016 about novel technologies in evaluating and integrating patient responses to therapies.

She describes how patient involvement has altered data acquisition and trial design, using technologies commonly available to patients at home with less cost and wider engagement than existing techniques.

With consideration of patients symptoms and response to therapies, Dr Jim hopes that novel technologies and even current wearable technologies could enable a more responsive, personalised care programme at low cost and significant benefit. 


AACR 2016

Innovations in assessing patient outcomes

Dr Heather Jim - Moffitt Cancer Center, Tampa, USA

This is really an exciting time to be doing patient reported outcomes research because there’s really starting to be an awareness of the importance of incorporating patient outcomes into clinical trials. So there has been a variety of new methodologies that are enabling the development of patient reported outcomes through the internet and also through smartphone technologies, wearable sensors, all kinds of things that are happening. And it’s really terrific because I think it gives patients a voice and so not only are there new measures that allow patients to comment on their own adverse events and toxicities to complement clinician rated toxicities but then we can learn a lot more about why these side effects occur and what we can do to prevent them or treat them.

What results can you report on?

It’s interesting because when you talk to patients themselves, a lot of times they will talk about how their physician told them that they might experience this or that side effect but they didn’t understand how that would negatively affect their quality of life. So new initiatives by the NIH are being developed to have patient reported single item measures of symptoms that can be used in clinical trials, multi-item measures that are short and reduce patient burden in terms of answering the questions. Then also objective measures as well so, for example, cognitive effects of cancer and its treatment like “chemo-brain”. Before we would have to bring patients into a lab and have them do long neuro-psychological tests under very controlled conditions but now they can do some of those tests at home on the internet and also they can do them through an iPad app. Now this allows researchers to collect lots more data than ever before with less cost and less burden for the patients and it really enables us to do some big data analyses which I think are sorely needed at this point in time.

How does that affect the design of trials?

It’s interesting because there are trials where perhaps in the past if you only looked at survival or progression free survival and there were no differences in the two drugs but you didn’t collect patient reported outcomes, then it might look like a wash. Whereas if you collect patient reported outcomes and there’s a clear benefit to one, that’s really important knowledge to have. So you’ll continue to see in the future more trials that are designed that way and the FDA is encouraging that as well.

What technologies could potentially speed up or aid diagnosis?

When you have so much data that’s coming up how do you handle all that data and make sense of it? There are two aspects of that, one aspect is in terms of the clinical time and resources that are required to do much more symptom management, remote monitoring and that kind of thing that has the potential to really beneficially impact the patient but potentially requires a lot of clinical resources, so that’s something that needs to be addressed. But then also these wearable sensors can give you data minute by minute on a patient and how do you, as a researcher, take all that data and distil it down into something meaningful. The opportunities are there but it can be very challenging as well.

What are your thoughts on bringing practices from other fields?

A lot of patient phenotypes in the past have been diagnosis – yes/no and I would actually like to see it get even more complicated because I’d like to see patient phenotypes in terms of their symptomatology. Why is it that some patients just seem to sail through treatment with no real problems whereas other patients seem to have a lot of problems and a lot of toxicities on treatments. Can we predict that ahead of time based on some of the biological or genetic characteristics of the patients? Can we even, at some point down the road, use that to develop perhaps almost like a customised informed consent to treatment where you could say, ‘OK, you have a 60% chance of this side effect and a 40% chance of that side effect,’ not only so that patients really understand what the possible consequences of treatment are but then can you also provide very early supportive care to prevent or treat some of those side effects early?

How are patients responding to new treatments?

I found them to be very interested and engaged in patient-oriented outcomes research. They appreciate the opportunity to share their experiences with the hope that they will be helping other patients down the road in the future and have been very willing to engage in the kinds of studies that we’ve been doing where we’ll have them wear wearable sensors, for example, to see how well they’re sleeping or how active they are during the day or report on their side effects using their smartphone or over the internet or that kind of thing. So I think that patients really seem to be partial to this type of research.

What is your take-home message?

The more people are aware of some of these new measurement technologies and patient reported outcomes research, the more they can incorporate those not only into research and clinical care, the better will be all in terms of helping patients make it through their diagnosis and treatment and then long-term survivorship as well.