What Factors Should You Take into Account when Designing ePROs - Electronic Patient Reported Outcomes Solutions?

[00:00:00] Dear listeners, welcome to Faces of Digital Health, a podcast about digital health and

[00:00:05] how healthcare systems around the world adopt technology with me, Tjasa Zajc.

[00:00:14] Patient-reported outcomes have become increasingly integral in healthcare for assessing the

[00:00:20] effectiveness of treatments from the patient's perspective.

[00:00:23] It sounds as a logical next step in improving clinical research and care provision,

[00:00:32] however gathering data can be more difficult than you may think.

[00:00:39] It's difficult to get to marginalized communities, there's language barriers in collecting data

[00:00:45] and there are cultural aspects that impact patient responses.

[00:00:50] So how can you design useful electronic solutions for patient-reported outcomes and make sure

[00:00:56] that the interpretation of the results is correct?

[00:01:04] I discuss this with Mustafa Ali Saed, researcher at the Manchester Academic Health Science Center

[00:01:11] at the University of Manchester and Ben James, co-founder and chief design officer at U Motif,

[00:01:19] an electronic patient-reported outcomes and engagement platform designed to help

[00:01:26] with clinical and real-world research.

[00:01:30] Both speakers are co-authors of recently published paper titled,

[00:01:35] Exploring the Cross Cultural Acceptability of Digital Tools for Pain Self-Reporting.

[00:01:42] Enjoy the show that might give you food for thought about how to approach patient populations

[00:01:47] and what to consider when trying to get their feedback about new therapies or digital solutions.

[00:01:55] And if you haven't yet, make sure to check out our newsletter

[00:01:59] you can find it at fodh.substake.com.

[00:02:07] Now let's dive in today's discussion.

[00:02:29] We will help to discuss patient-reported outcomes.

[00:02:33] How can we actually measure them?

[00:02:35] How is the whole perception changing around why patient-reported outcomes matter?

[00:02:41] What can companies even do with them?

[00:02:44] What's the impact?

[00:02:45] And also some of the challenges when it comes to gathering patient feedback

[00:02:50] when we're talking about different patient groups, patients with different ethnicity,

[00:02:55] different age groups. So when we start digging into this discussion,

[00:03:00] we can see that it's actually very complex.

[00:03:04] So maybe we can start with a very general discussion.

[00:03:08] Why do patient-reported outcomes matter?

[00:03:12] I can take that. Thanks so much.

[00:03:14] The patient voices hugely important in all phases of research.

[00:03:18] So when you're collecting data, it's methoding that voices there and that data is collected.

[00:03:22] At this point, it's being mandated by the FDA, part of the 21st century KIAZ Act

[00:03:27] and regulators, one electronic data capture patients,

[00:03:31] two ensure that submissions are really high quality

[00:03:34] and most new drug findings include PLROs or real-world data to ensure that sort of voices there.

[00:03:41] It's a fundamental when you're talking about humans that you have that patient input and that data.

[00:03:47] You've been in this space for over 12 years.

[00:03:50] What do you see has been changing in this time?

[00:03:53] So what's measured in patient-reported outcomes is the idea from the research perspective

[00:04:00] changing in terms of what should be measured.

[00:04:04] Yeah, I think it's been around for a long while.

[00:04:08] I think what has changed is the ability to capture that data.

[00:04:11] I think the importance is increasing the increasing in terms of the need for the data,

[00:04:16] also the way of collecting it has become much easier and much more effective.

[00:04:20] And things that are usually measured are things like symptoms, quality of life, treatment,

[00:04:23] satisfaction, things like that that are critical to the research and having that patient voice

[00:04:29] and making sure that it's captured in the data.

[00:04:32] I think the importance of it is increased over the last few years just because it's being mandated

[00:04:37] by the FDA and things like that but also I think it's been recognised as the right thing to do as well.

[00:04:42] There's a huge importance in that voice and ensuring that people are represented in the trials.

[00:04:48] The ability to capture as well has increased.

[00:04:50] So obviously the last 10 years, the technology shift has been absolutely massive and people

[00:04:55] have in their own personal devices that are capable of capturing this data as

[00:04:58] it's not only become more important from a regulatory point of view but also just

[00:05:02] it's been much more feasible and much easier.

[00:05:04] Yeah, one of the speakers in the previous episodes that is an advisor to Metek and Pharma said

[00:05:10] that if in the past the FDA didn't took you seriously if you were using wearables for your clinical trial

[00:05:17] today it's expected that you have them because of the measurements that you might want to take.

[00:05:24] But from the patient perspective I always wonder what actually happens when you measure

[00:05:30] that data to which extent is that insights that you get just something that companies do

[00:05:36] because regulators demand that you know you have to have a contact with the patient you have

[00:05:41] to have the contact with the customer to get a specific certification.

[00:05:46] But do they actually change anything based on that feedback?

[00:05:52] I think it depends on which phase of clinical trials you're talking about.

[00:05:56] In the real world there's often a lot of patient data captured that is used from a

[00:06:00] and a kind of more general way and all the way through to marketing phases to understand how

[00:06:04] our products are being used.

[00:06:05] In the earlier phases of clinical trials that sort of primary endpoints are collected from patients

[00:06:10] and you can become critical to how stuff is being used.

[00:06:13] I think it can be used for methods of understanding the additional information around drugs

[00:06:18] and the effectiveness but also it's it can be actually kind of primary endpoints.

[00:06:25] One of the big challenges in clinical trials that has been highlighted a lot in the last few years

[00:06:32] is the diversity of the sample.

[00:06:36] In the past women in their reproductive time were traditionally excluded from clinical trials

[00:06:44] was being shown during the COVID pandemic because of the oxygen monitors was that basically

[00:06:51] a different skin tone can impact the measurements.

[00:06:54] So we opened up this whole field of the impact of diversity and ethnicity on the way

[00:07:02] we measure outcomes and also create solutions.

[00:07:05] And you actually did a study where you tried to highlight how pain perception

[00:07:13] differs among ethnic groups and what does that actually mean also on patient reported outcomes.

[00:07:20] Can you talk a little bit more about that?

[00:07:22] So what did the study look at?

[00:07:24] What did you try to find out and what were the findings that you came to?

[00:07:30] Yeah thanks very pleased to be here.

[00:07:33] So I think so the the more than thing to recognize here is that there's a dual burden

[00:07:38] so not only prevenous among some groups is higher than other groups but also the impact of pain

[00:07:43] is also higher and it is very important to understand that why is that?

[00:07:50] And for that we started to work on just developing that distinction between ethnicity and culture

[00:07:56] because ethnicity is that variable which we normally see in research but actually so

[00:08:00] and to present that problem we use ethnicity as a variable.

[00:08:04] But when we will try to find out solution for that we need to look at the culture

[00:08:08] because culture is encompasses all those factors which are related to life stalls people

[00:08:13] perceptions believes religion so all those factors which would really impact how people would

[00:08:19] perceive about their pain and how they will report it and who they will report it.

[00:08:24] And second reason was actually my own ethnicity so my own cultural background so that also kind of

[00:08:30] you know telling me that what I am reading in the literature it may not be exactly the same for

[00:08:36] people who are from different ethnic backgrounds.

[00:08:39] People would assign different meaning to different things just because of their cultural

[00:08:42] background and belief and this really motivated us to start exploring that how pain is perceived

[00:08:48] in different ethnic groups we know that different ethnic groups are affected with chronic pain

[00:08:52] differently but how they perceive about it and how they deal with it because their management

[00:08:59] practices would be different so how they deal with it and this is the kind of information perhaps

[00:09:03] which clinicians would want to know that what people are doing to manage their pain

[00:09:08] and how they can account in that factor when they are having pain consultation with those patients

[00:09:13] so that they will have a holistic view and they could have better treatment options for patients.

[00:09:19] In our work we engage with three different ethnic groups,

[00:09:23] why British, Black Africans and South Asians and definitely particularly focus was on culture

[00:09:28] so how they perceive pain so we found differences in their perception and how they talk about pain

[00:09:33] who whom they talk about pain and it really refers to people's pain reporting behavior.

[00:09:40] If I am having pain then whom will I go to speak maybe a doctor if not doctor then maybe

[00:09:48] few people will prefer to talk about their pain with their loved ones with the family members

[00:09:51] or friends before even going to a doctor so their behaviors are different which are linked with

[00:09:56] their cultural background this is what we need to understand and this is what we try to explore

[00:10:00] in this work. And to which extent have you further discussed the findings of the study? So we know

[00:10:08] that differences exist there's been a lot of kind of harm and misconception in the past about how

[00:10:15] for example Black people experience pain and what I have more questions here than anything so

[00:10:24] is it just that the perception is different are the pain levels actually different and how do you

[00:10:30] even start addressing that? I think there's also in digital health there's this question if we

[00:10:37] could find some sort of a more objective measurement of pain because at the moment it's just on a scale

[00:10:44] of one to ten how would you rate your pain? What kind of innovation do you see in this field?

[00:10:49] How can we improve the measurement of pain to make it more objective? We need to understand that

[00:10:56] how the pain perceptions are developed. It's not a static thing it develops over the period of time

[00:11:01] and it is developed based on people's experience so how they interact with health system

[00:11:05] and then they develop that perception so for example if I go to a doctor and speak about my pain

[00:11:10] and I'm telling them for example my pain is five and they don't take any action then what will

[00:11:15] they do next time? They will say okay my pain is eight so now they have exaggerated this is what

[00:11:21] provider has maybe considered an exaggeration and now there is a provider bias which is at play

[00:11:27] and now they are not given the right treatment or they're not given the treatment which they are

[00:11:31] expecting so I think so there's a huge opportunity in technologies like these like this when you

[00:11:37] start collecting this information and you are collecting that information over the period of time

[00:11:41] then instead of having a snapshot which a clinician used to have just a snapshot of a one day

[00:11:47] of pain situation instead of having that and relying on in making decision now they will have

[00:11:53] a better picture of pain how it is changing over the period of time and then you can start

[00:11:59] and not even clinician would start thinking more objectively but patients also who are reporting

[00:12:04] their pain they would also see okay I was maybe reporting too much pain but actually that was

[00:12:10] and then they will start correcting their pain reporting also so I think so in the context of

[00:12:15] technologies and especially making the design easy enough so that people can use it regularly

[00:12:21] it will can it can create massive difference in how health system would respond on these

[00:12:27] outcome years. Ben how do these findings translate into the design of the solutions in one of

[00:12:36] the the blog posts on your website you mentioned among other things that it's really important for

[00:12:42] example if there's a human body that's used in an app where patients would mark their pain

[00:12:49] that's not just a body of a white male or female so there's a lot yeah to unpack there so how does

[00:13:01] for on the one hand research impact that and also how do the existing models impact the reporting

[00:13:09] that patients do patients did you for example detect that patients didn't want to report on their

[00:13:16] outcomes because they didn't feel that the design of the app is representing who they are.

[00:13:25] Yeah I think it's a complex issue and I think the findings were really interesting we

[00:13:30] you know the context of this study was done in a we've been working on an application for

[00:13:34] capturing pain on a body map so very much when you go to a clinician and you'd be after drawing

[00:13:39] a piece of paper and you sketch where your pain was now they're they're really crude and if you've

[00:13:44] seen one the drawing is not very realistic of her turn it's very male and this has been used

[00:13:49] for years so we were exploring with Manchester as a wider piece of research and collaboration around

[00:13:54] how we would improve that data capture and make it a little bit more objective because obviously

[00:13:59] you've got you have a pencil on a piece of paper is interpreted we can obviously capture a

[00:14:03] base similar thing but we've also looked at ways of dividing the body into grids and looking at

[00:14:07] and if I would have had different levels of pain and it's a very complex area one of the

[00:14:11] focuses of the workshops was looking at what the user interface would look like and what the

[00:14:16] body map looks like we found that we people expressed the real desire to have the body to look more

[00:14:22] like them so it's to do gender body stuff but actually when we tested it it was really mixed

[00:14:26] feedback based on that so I think it it just shows you really need to test these ideas and

[00:14:31] one of the other challenges you have around capturing data is that you need a sort of consistency

[00:14:35] in the way the data is captured if you're talking about body area and talking about percentage

[00:14:39] of pain or intensity of pain if you need to do that over 4,000 people you need some consistent way

[00:14:44] of measuring between those people and I think there's a real difference between capturing

[00:14:50] data for self-management on the standing of your own pain versus essentially where a clinical

[00:14:54] trial where you've got to have consistency between the data and unique have objectivity and

[00:14:58] ensure that there's consistent way of recording data. You mentioned that you got mixed feedback

[00:15:03] or results when you tried to adapt the outlook of a human body can you talk a bit more about that

[00:15:12] so what was the challenge? I can talk about it from a design point of view

[00:15:16] I think people have a different way of perceiving the body that they're communicating pain onto

[00:15:22] it's very often it's very too dimensional we look at 3D versions and we just get quite complicated

[00:15:26] but I think people immediately have a different way of thinking about what they're actually drawing

[00:15:32] onto does it there's the corner of the arm go around the side or is it front and so it's so there

[00:15:37] was that but also just down to the actual details is it a more generic non-genres or non-sex body

[00:15:42] it all versus something that's there's more realistic and then obviously they've looked at the

[00:15:47] ability to customize that based on you which people didn't seem to respond to I think you really only

[00:15:51] get that information once you present people with a prototype or something to look out and see visually

[00:15:57] because it's a very visual thing as well so there's another interpretation of that and that's also

[00:16:03] given through when you see it in cultural context so people would like to report their pain on a

[00:16:08] gender-specific mannequin but when they want to transfer that information to clinician say they

[00:16:13] want gender-neutral mannequin so that their identity is not obvious from the pain mannequin report

[00:16:20] that's what I think so one of the participants was concerned about having a mannequin and reporting

[00:16:26] it as such if it is gender-specific you did a lot of research around this topic even before you

[00:16:32] actually did your own study so Mustafa can you maybe talk a bit more about what you generally see

[00:16:39] in the fields of gender and ethnicity and the impact on medical care and how we need to take

[00:16:46] this into account in future research are there any research projects or efforts that you see that

[00:16:55] seem very promising what's your general perception of this whole complexity that we are now

[00:17:00] starting to address yeah so I think the recently we start working on a research program that focuses

[00:17:06] around understanding digital health inequities so what I mean from this so when I when we are

[00:17:13] introducing digital technologies and care pathways so they are benefiting all rather than creating

[00:17:19] inequities for for some groups and as part of that work we started working with South Asians

[00:17:25] because when you look at the literature you will see women are affected more

[00:17:30] and to understand that we need to look at the look at the culture so they are the normally don't work

[00:17:36] and and when you look at the social economic status that's also poor their education level is poor

[00:17:43] because when we try to say South Asians are affected more actually we are not saying every

[00:17:47] South Asian living in the UK is affected more so we need to start thinking about and that's only

[00:17:52] possible if you start bringing an intersectionality lens into understanding these inequities so who are

[00:17:58] those subdued within South Asians who are affected more and we need to engage with them more these

[00:18:04] are very basic steps we haven't done anything massive but even those small steps will already help

[00:18:09] us to reach those who actually need help who are impacted by these digital technologies who are not

[00:18:15] achieving better health outcomes like others are achieving so we are just in in that program you're

[00:18:21] working on identifying those subgroups within the sub population and normally these are seldom heard

[00:18:28] very hard to reach and we need to figure out the ways through which we can connect with them because

[00:18:32] trust is a big issue not only for health research but also for digital technologies and trust

[00:18:38] issue will not be addressed by researchers like us we need to collaborate with these communities so

[00:18:43] that we have models in place through which we can introduce these technologies that means we are

[00:18:49] not directly going into community but we there are gatekeepers who are helping us to reach these

[00:18:54] these communities and then we can start understanding these issues and then start developing these

[00:18:59] technologies because even so far in our research we have engaged with South Asians and I don't think

[00:19:04] so we have so far being successful in engaging with those South Asians who are suffering from chronic

[00:19:10] pain more than others so I think that's what we are learning at the stage and the only

[00:19:17] way we can create impact engaging with right groups engaging with the right people and developing

[00:19:23] technologies who are addressing their needs rather than my need because I'm also South Asians so

[00:19:28] my needs are different from their needs so we need to understand their needs basically what you

[00:19:33] mentioned is that as always the problem isn't the technology but the biggest challenge is

[00:19:40] how do you actually get to those targeted populations are you seeing any specific trends there or

[00:19:46] like how did you for example try to get a representative sample for the study that you did?

[00:19:53] Not only us many researchers and I have seen many people who are making right at first in the

[00:19:59] right direction and trying to engage with those communities I think so the first impact would be

[00:20:04] improved participation in research so that's the first effect we need to achieve first

[00:20:09] improving health outcome will come later on because first we need to understand why for example

[00:20:14] these people are suffering more than others once we understood that we started developing these

[00:20:18] technologies then you will start seeing the impact their in health outcomes so I think so expecting

[00:20:24] impact on health outcome now is maybe not the right thing to expect maybe we need to have

[00:20:31] these communities in our research more frequently and particularly those because our language

[00:20:39] is a big barrier and we start just thinking about how only language barrier can translate into

[00:20:44] multiple disadvantages so you don't you will not have better economic opportunities you will not

[00:20:50] have better health literacy you will not have better digital literacy so all these disadvantages will

[00:20:55] you know combine together and then they will have poorer health outcomes so we need to engage with

[00:21:01] those people and at the moment we focus more on bilingual rather than uni-lingual who can

[00:21:06] only speak non-English language because at the moment there are not infrastructure available within

[00:21:15] research institute where they could run these activities in their local languages so this is I

[00:21:20] think so the biggest challenge which many researchers are recognizing focus on linguistic and cultural

[00:21:26] adaptation of these digital tools is huge at this moment which is which is very great opportunity

[00:21:31] and if we keep capitalizing it then I'm sure we will get there we will want to go

[00:21:36] yeah I was just thinking the other day how for example now that we are increasingly communicating

[00:21:43] with clinicians or family doctors through online means for example here on the patient portal you

[00:21:50] have a limited number of characters that you can write in one message to the doctor which obviously

[00:21:57] makes sense because the GP won't have time to read novels but at the same time this means that

[00:22:03] patients have to be very articulate in clarifying what their challenges even if you're a native speaker

[00:22:10] you already have that problem and now add to that the complexity of a different language

[00:22:15] than communication then how does all this translate into designing the solutions there's there seems

[00:22:22] to be that so many things that you need to think of there is almost impossible to imagine

[00:22:27] a solution that would work for everyone I think that's a really good point actually obviously there

[00:22:31] is a huge advantage in having translated digital technology can be translated and it can be

[00:22:37] adapted to communicate different languages and capture the same data but you've got the questions

[00:22:42] are written in different languages so you can get that quality across and that happens in

[00:22:47] clinical studies now you'd have instruments that have been translated into different language

[00:22:51] and they're obviously ensured that the data is then consistent and that's part of the process

[00:22:56] but yeah I think the thing that we found over being doing working with patients for a long time

[00:23:00] and building interfaces and technology I think the one thing that we always find is having the ability

[00:23:05] to configure your technology to the context I think there's a lot of health and certainly when you

[00:23:10] get into the clinical space there's a lot of technology that is very focused on the data

[00:23:14] and it doesn't often work in the context and give people the prompts that are relating to them

[00:23:19] so I think the thing that we found is that you can design experience that perfectly fits one

[00:23:24] sort of situation but then you then apply something else and you've got the wrong language

[00:23:28] you've got the wrong prompts you've got so what we found is configurability is key so you need a

[00:23:33] system underneath that's highly robust and collects data in a very sort of organised way but

[00:23:38] actually on top of that you need to put a layer of communication that gives people much more

[00:23:42] of a contextual experience that speaks to them if you're a patient on a real world evidence

[00:23:46] trial where you're not speaking to a site or you're someone that's in a very short focused

[00:23:51] window where you're collecting diaries you need to use a different language and a different

[00:23:54] experience and I think so much of the technology that's been put into certain clinical trials

[00:23:59] and research has been coming from the point of data systems for clinicians that are adapted

[00:24:04] for patients and that's a completely wrong way of doing it where we've come from as we've worked

[00:24:08] with patients to start with and then brought that experience into this space and it gives you

[00:24:11] a totally different experience and a different set of rules to work with so I think but the thing is

[00:24:16] there's no one right answer what you need to do is make sure that the language you're using

[00:24:20] is contextual right it's right for that patient group it's right for the cadence of the study or

[00:24:25] the research is it a study that's over five years you're going to talk to people in a different

[00:24:29] way than if it's over 30 days there's a different intensity of that so I think the one word I was

[00:24:33] come back to is the design points for this configurability you need a robust platform that does

[00:24:37] the things that you know of best practice but then you have to build this layer on top it gives

[00:24:40] it that that user experience that they're expecting and it is customized to that that environment

[00:24:46] and context. So what's your experience in terms of what kind of standards so data standards should

[00:24:53] be used in research what kind of data standards do you use in research and also what's the impact

[00:25:00] that for example generative AI now has on the interpretation of the results because I'm assuming

[00:25:06] that there's still a lot of free text that's used in some cases for patient reported outcomes it's

[00:25:12] not all just one to five or yes or no questions. Yeah I think it depends on again it depends on

[00:25:19] the type of research you're doing when you go through more the real world and the kind of more

[00:25:24] marketing and the bit you end up with more free text and it's much more open when you're in a

[00:25:27] clinical trial setting but you wouldn't really you wouldn't often collect free text you and try and

[00:25:32] things would be validated instruments so the questions are consistent the way you collect the data

[00:25:37] and you present the data has to be is regulated so you have to have data standards there.

[00:25:41] Would there's just been a paper that I've been part of as part of the E-calibur consortium which is

[00:25:46] is about best practices for migrating, as well patients with cutting out times two in an electronic

[00:25:50] format because there's a whole way of migrating these measures that has to be consistent with the

[00:25:54] way that you present on the screen so there's always a tension I think between ensuring that your

[00:25:59] data capture is valid and it is validated in its meeting the requirements of the data but also

[00:26:04] then you want to wrap that in a human experience that people can I think that's a difficult balance and

[00:26:08] I think you just have to that's why you need to configure better to ensure that you sometimes you're

[00:26:12] going to be catching free text in a completely different type of study but other times you'll

[00:26:16] be very locked down but you also want to wrap that as much human-centered behavior as possible to give

[00:26:21] the best chance of engagement because that is a huge issue in in trials and researches the engagement

[00:26:27] you get from participants. You basically mentioned the differences between real world evidence and

[00:26:34] basically clinical trials that have very specific endpoints and data that they want to capture so can

[00:26:39] you dive into that a little bit deeper so how does data gathering differ in clinical real world

[00:26:47] and post marketing research to which extent do also the data sets in terms of the data standards

[00:26:54] differ I think is probably the question for both of you. Yeah this sort of is obviously generalizations

[00:27:01] are clinical based you're driven often by a site a research site and you generally will get quite

[00:27:06] high compliance kind of later because you've got the site monitoring data collection there's

[00:27:11] much more engagement it's still cited ease of use the software is recently cited as sort of

[00:27:17] there's one of the biggest concerns for sponsors who are our sponsor in this study and this is

[00:27:21] a recent report so there's a lot of work to be done there and you need to remove those barriers

[00:27:26] to to compliance and engagement with the study in our in real world evidence you tend to have a

[00:27:31] much wider study where they may not be a site or an intermediary and there's a much more direct to

[00:27:35] the patient so you generally get a lower engagement rate but again using the technology in the

[00:27:40] right way can really increase that engagement but the data sets are clinical much more

[00:27:45] script written from a practical things are much more locked down and it's more

[00:27:49] less exploratory whereas something rwb much more open and you'd be collecting lots of different

[00:27:54] data and it's more open and you do get lower engagement in that environment though so it's

[00:27:58] important to use those techniques. I'm a staff I don't know if you've gotten on that one.

[00:28:02] Yes I'm in a better answer than Ben but from my experience I can tell you how not really

[00:28:08] setting certain standards because Ben was saying that it's very hard to compare things across

[00:28:12] population if you have customizable variables within your digital tool but I think the one thing is

[00:28:17] very important because we're talking about here long-term conditions where symptoms of these

[00:28:23] conditions vary across time and they are quite long term span you know over the period of a couple

[00:28:29] of decades you can imagine so in order to provide better cure to these important thing is that

[00:28:36] the symptom variations are captured through these tools and it's like I was saying before so for

[00:28:41] a pain intensity if that's a variable that we want to capture my five would differ from other

[00:28:46] persons five because our perception is different but one thing important where we are capturing

[00:28:51] here is that you can I can compare my own results rather than looking at somebody of this results

[00:28:58] and then see where the pain treatment given to me is having an impact on my pain intensity or not

[00:29:04] but when you put that in the larger context I think so we might need advanced analytical

[00:29:10] techniques to see and tell how we can compare all these variables across population where they are

[00:29:16] there are differences because of people's different perception about pain but this is what we need

[00:29:21] to achieve yet but I think so one opportunity is right there which like Ben was also saying initially

[00:29:28] that these digital data collection tools so this is the recent change within this space but I think

[00:29:35] so one biggest strength of these tools is the computational power the analytical power so instead

[00:29:41] because before you use what used to happen you will have all these paper-based questionnaire you

[00:29:46] will combine the results you will analyze it and that information will be used quite later in the time

[00:29:51] but through these digital tools if you're using right analytical techniques you will get the

[00:29:56] information there and then so that you can decide what next steps you should take so I think so this

[00:30:01] is the biggest strength of these tools standardization I don't know when we will be able to get there

[00:30:07] yeah but I think so this is already promising um given that Ben you're in this space for 12 years

[00:30:17] what's the device that you would give to other software providers that are also in the

[00:30:24] patient reported outcome space what are some of the key learnings that you got to

[00:30:30] in the course of all the work that you did so far yeah I think we started working with

[00:30:36] Parkinson's patients so we've got some technology around capturing symptoms and

[00:30:40] we ended up working patients very closely from start if your system is designed from a day's

[00:30:44] point of view and then migrate into a sort of patient or human experience I think that's where

[00:30:49] it's quite difficult I think you have to start with a patient and or user we always try and start

[00:30:55] from a human sense point of view and I think that's a really good approach because if you start your

[00:30:58] technology from the actual user and it's much more user-driven sort of human-centered design experience

[00:31:03] then you're going to get a much better result I think so my advice would be to start with the users

[00:31:07] that you're trying to work with if you can get to them I think that's also something that can

[00:31:13] be very challenging especially I guess for far my industry for example they have a lot of

[00:31:19] limitations in terms of how much they can reach out to patients which makes it very challenging

[00:31:23] to actually get useful information that they might want to need so Mustafa what would you add

[00:31:30] to this question I think we know what the problems are the challenges there's just no easy

[00:31:36] solutions in how to get all the feedback that you get and how to address everyone equally so

[00:31:43] where do you hope that the field is going to go or what are potentially some of the improvements

[00:31:50] that we are already aware of we just need to figure out how to enact them yeah like you said these

[00:31:59] these issues are not new for reason we have been seeing this disparity for centuries but I

[00:32:06] think so covid has brought this very nice opportunity that we have now started thinking about

[00:32:12] there are certain groups who might be affected more and we need to know about those

[00:32:16] and so I think removing all these biases so even researchers like us we need to also remove

[00:32:23] biases and think okay there are these groups who are disadvantaged remove this bias I would say

[00:32:29] just keep your eye out with an open approach and see where are these additional

[00:32:34] advantages because there are certain disadvantages groups but there might be others who are

[00:32:39] disinvantage but we are still not able to categorize them so I would say if we keep if you're not

[00:32:46] closed in identifying these groups and we keep our approach open and engage with those communities

[00:32:52] and develop these tools not keeping in mind what works for us or how we can help them ask them

[00:32:59] how they want us to help them I think so this will create a difference when we will start listening

[00:33:04] rather than telling them okay this is what I know from my experience saying you need to try this

[00:33:09] this is a kind of approach we need to take going forward so that because one thing is very

[00:33:15] commonly heard when I speak to these people that we are not heard so not being heard is a very

[00:33:20] common phrase which I guess you will all agree so we need to start ensuring that they are being heard

[00:33:26] and then we are developing those technologies which are there for helping them rather than

[00:33:30] developing these technologies to gain our whatever that is but it is for their benefit not for us

[00:33:37] hmm yeah I think that's a great point also from the in the era of the importance of data I think

[00:33:48] product managers or developers to often think about what would be useful for us what kind of

[00:33:56] data would be useful for us to have about this patient about this customer not taking into the

[00:34:02] account that just because something is good for the product that the user is going to be willing

[00:34:08] to take the time or share that specific data because there has to be something in it for the patient

[00:34:15] as well are there any things that you would adhere than any mistakes that you commonly see

[00:34:22] in the field that innovators are making I think what you said then is really key we always try

[00:34:28] to look at we have a diagram with a subend diagram where you have the me and the we in the middle

[00:34:33] where it's what's the common thing that you can introduce these different experiences that serves both

[00:34:38] the data collection purposes and the user's purposes and again I think it depends on what sort of

[00:34:43] phase of research or you know what if you're in digital health but generally we found the best

[00:34:47] results when there's something in it for the person and there's also that that sort of overlap is

[00:34:52] really nice with the objectives of the research and I think that's why you've got to have these

[00:34:56] experiences customized or allow people to make them relevant to the individual because that's

[00:35:02] the key to real engagement people are really aware of digital technology now and what how it

[00:35:07] works for them so I think if you introduce experience that is not too way then you're going to

[00:35:12] really struggle so I think you just it's going back to that point of looking at what's the

[00:35:17] what's in it for both parties of the transaction to ensure that there's a really good fit and I think

[00:35:22] that's a key to being successful for sure.

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