In this insightful webinar, Amit Singh (Senior Manager at Deloitte) and Jamil Siddiqui (Managing Director at Deloitte) discuss the critical role of holistic patient data management and consent in advancing patient health. With the life sciences industry's growing focus on patient-centricity, effective data management strategies have become essential for improving patient outcomes and experiences.
Key topics covered include:
The shifting industry landscape towards patient-centric care models
- Key business drivers and use cases for implementing patient master data management (MDM) solutions
- Navigating the complexities of patient consent management and data governance
- Best practices for maintaining patient data privacy and compliance
- How patient MDM and consent management enable capabilities like patient support programs and analytics Integration considerations between patient MDM, CRM, CDP and other marketing technology
Whether you're a life sciences professional, healthcare provider, or anyone interested in the future of patient data management, this webinar offers valuable insights into streamlining patient data unification while prioritizing privacy and consent. Don't miss this opportunity to learn from Deloitte's industry experts!
Check out the transcript:
Chris: Welcome everyone to another Reltio Community Show. Today's special topic and one that I'm really excited about is one called Advancing Patient Health, The Role of Holistic Patient Data Management and Consent. Amit and Jamil, they actually presented at a conference, data driven conference back in October, some of this information, and I thought it was amazing.
Chris: So I wanted to make sure that we shared this with the entire community, if you haven't seen this. Really excited. Today we have Amit Singh, Senior Manager at Deloitte. How's it going, Amit?
Amit: Doing great. Thank you. Can't be really much more excited than this.
Chris: And then we also have Jamil Siddiqui . He's the Managing Director over at Deloitte.
Chris: Jamil, how are you? I
Chris: think
Jamil: he's pretty good. Doing well, Chris. Happy Friday, I said.
Chris: Perfect. Thank you. So the [00:01:00] rules of the show, keep yourself on mute. All questions should be asked in the chat, or feel free to take yourself off mute if you have a particular question, around the presentation. The community show is going to be recorded and posted to the community.
Chris: My hope is to get this out by Monday. And everyone of you that are here will get a recording. So quickly, upcoming events that we have is today's show on advancing patient health. And then on the 30th, we have achieving real time data quality with a custom Reltio UI. We have another partner that's helping with that as well.
Chris: Really excited about that. And then the next three shows is some really cool innovations that Reltio has come out with. One is discovering Reltio's AI ML powered fern for data unification. To learn more make sure you sign up for that and then we'll go even a deeper dive And have our ml expert talk about unlocking entity resolution with ai with fern And how that's revolution [00:02:00] data matching really exciting there.
Chris: And then we have a show with our senior vp of product finke around customer 360 data product powering ai Data driven unification for enhanced CX. Lots of really cool stuff to show and share. I have some other shows that are coming up that I'm just in the works of. Putting together late May into June.
Chris: So we'll have a full agenda on that. Lastly, and I'll put the link in the chat we do have a Reltio life science industry event coming on May 9th. It's going to be a half day Princeton Marriott at Forstell and that's going to be in Princeton, New Jersey. A lot of great speakers we have a few life sciences speakers there and then we also have our CEO and founder Manish Sood, and then we also have product roadmap session on that day.
Chris: So really excited about that, and I'll stop sharing here [00:03:00] and Amit or Jamil, I'll let you guys share.
Jamil: Sure, Amit is going to share his screen. Beautiful,
Chris: and you're on mute, Amit.
Amit: Thanks, Chris. No worries. Yeah. Give me one second. Just let me do this and let me know if my screen is visible and you guys can see it. We can see it. We can see it. Yeah. Alrighty.
Amit: And I believe it's a full screen for you all. It's
Chris: not. We see the, yeah, just I would put it, there you go. Perfect. No no. We see next slide. So it's not the full screen yet. Okay. Gimme just one second.
Jamil: Technology always right? Yeah. Always of doing these live .
Amit: Yeah. There, there are lot of stuff. Wait a second.[00:04:00]
Jamil: Yeah, Chris Amit and I were just. Doing the simulation of how the slides coming up and it was fine earlier, so I'm not sure. Give it a shot. We'll get
Chris: it. Either way, if you can't. How about now? Perfect. Beautiful. Good?
Jamil: Yeah. Looks good. Awesome. Jamil? Yeah. Thank you. Thanks, Chris. Good morning.
Jamil: Good afternoon, everyone. Depending upon where you are assuming it's a global audience. My name is Jamil Siddiqui, and as Chris was saying and then I had the privilege and opportunity to co present on this very important topic at relative of 2023 data driven conference, and there were a lot of interest on, on this topic.
Jamil: When Chris reached out we said, absolutely. I would love to talk again. My name is Jamil Siddiqui. [00:05:00] I'm a Managing Director in our Deloitte Life Sciences Strategy and Analytics Offering Portfolio. 25 plus years of global life science industry and consulting experience, I help and advise our life science clients around digital and commercial transformation.
Jamil: Leveraging the power of data first, and then obviously, AI, Gen AI and larger analytics capability excited to be talking to you today. And my one request would be, let's make it an interactive session. We want to learn from each other's experience. I'm going to hand over to Amit to introduce himself.
Amit: Thank you, Jamil. And happy Friday, everyone. I'm really excited to be here. And as Jamil said, when Chris reached out to us about this particular topic, we, without any further delay, was like, yeah, we're going to talk about this. And not just because it gives us the opportunity to connect with you. But more than anything, [00:06:00] we are really passionate about how the industry is shaping right now.
Amit: And it would be a complete dismanimer if we talk and say that we are still in the HCP, HCO enabled ecosystem. Yeah. HCP, HCO still play the critical role, but everything is now turning and shifting towards the patient. And that's what Jamil will tell you a little bit more in detail. Talking about my experience, my name is Amit and I have been with Deloitte for the last 10 years.
Amit: Over a decade, I have had experience in doing the global data modernization project, which includes the data strategy, MDM, data quality, and a lot of other kind of MDM enabled technology or data enabled technologies and capability building. As part of this whole experience, one thing that I have seen that every time you do any kind of a transformation, now companies are looking for the scale, right?
Amit: And if you have experience under your belt, which is similar to the global not one geography, but multiple geography, [00:07:00] this helps tremendously to the organization. And that's where I bring the value to my clients, to my partners and to my team as well. And once again, really excited. With the note that I am going through the tough weather, so you see me coughing a bit here and there.
Amit: So apologies for that, but I will make sure that it doesn't interrupt your discussion, but that Jim into you.
Jamil: Okay, thanks. I appreciate it. So basically, we are going to cover 40 topics. One, I'm going to talk about the changing industry landscape as Amit was talking about the focus. It's not shifting is already shifted right from more customer to patient, right?
Jamil: Life science industry is also maturing like other retail and consumer industry in terms of how do you engage with not just the customer, but the end consumer or in patient. We'll talk about what are some of the enabler right, from MDM from consent identity management to [00:08:00] drive this digital transformation focusing on patient and consumers.
Jamil: So that's what we want to cover and then we'll go more deep on the consent and preference management topic. If you go to the next slide. So historically, if you really look at life science companies were in primarily focusing on customer, whether that's at CP healthcare professional or healthcare organization or other institutions.
Jamil: Who are the decision maker, but the power has shifted from customer to patient, right? One is because the industry moved from more of a mass market business to more of a specialty and now more rare specialty, including CAR T and next gen therapy. And with that, there has been a proliferation of digital technology and availability of data.
Jamil: We have a lot more data. That, the industry is generating and collecting to [00:09:00] drive insights, whether it's through first party data or through second party data or through third party traditional syndicated data. And also the, with the proliferation of digital technology, the awareness among the patient or consumer has increased.
Jamil: We all have smartphones. We all like, have well being apps, right? We all, a lot of us subscribe to digital services, focusing on our well being. I serve one of the biomedtech client and, another biopharma client focusing on diabetic business. And we know diabetic is a chronic disease, right?
Jamil: And, taking care of your glucose is a discipline in itself, right? It's a collective and holistic lifestyle. Throw the smartphone, throw well being apps, throw digital device. We are a lot more cognizant about our [00:10:00] lifestyle collective and holistic lifestyle and being to maintain some of these chronic diseases, right?
Jamil: And and so with this there's so much of learning for the industry by collecting all of this data the way the retail industry, right? We all get this emails, we all get this text messages, right? From like a consumer industry, whether it's a sports. Goods or, retail goods and so on.
Jamil: What I'm seeing is the life science companies, whether it's a metric companies or biopharma companies, they are partnering with a lot of digital ecosystem players, whether it's a Fitbit or WellDoc, Cecilia. There's so much of data that you get through a specialty pharma channel through the hub channel, and then the companies are trying to understand holistic patient journey and all of the interactions in that patient journey, right?
Jamil: And and basically one of my client [00:11:00] what their focus is identifying the patient who may have a metabolic symptom way before they are even pre diabetic. And, they're trying to improve the life of those potential patient population who could eventually become a diabetic and focus on the prevention, then managing the disease itself.
Jamil: Amit, anything you want to highlight here?
Amit: You called out very profoundly, sir. What I would say that yes, everything is changing and shifting towards that. One of the biggest thing that at least I can observe in the industry is that getting the access to the digital power that the patients, right?
Amit: So for example, even me whenever I look at for any drug that has been prescribed by a doctor, or even when I wanted to go WebMD, I check it. So this gives me enough power as a patient that I can make my own decision which never used to happen way back In the decades, right? So this gives enough power and enough, substance [00:12:00] for the patients and not just a patient But the caregiver as well, we shouldn't forget about there's a patient and there's a caregiver So caregiver is also playing the similar role Which we can put it in the similar kind of a box when it's come for the data.
Amit: But yeah that's where the shift is happening back to you.
Jamil: Thanks. So I think i'm done with this slide Let's move to the next slide. I think I already set the context right in terms of the changing landscape And again, you are trying to look at the holistic Patient journey and providing that elevated patient experience in this journey all the way from, the discovery, the clinical trials to prelaunch to post launch and ensuring that, you're able to continue to provide that elevated patient experience and it's very complex very complex, right?
Jamil: And, we have four different calls. And I'll just touch upon these calls, right? So these are like very diverse set of [00:13:00] companies, global, multinational companies that, we have partnered in delivering this digital transformation focusing on patient, right? So the first one is, it's like really enabling and a patient MDM.
Jamil: It's a US based pharma was And what it the impact we were able to deliver was the ability to quickly identify and recruit that eligible patient population, right? Through the clinic for the clinical trial process. The other 1, is around again, focused on more on the drug safety side for their MS product, right?
Jamil: Where we were able to track patient data and understand the efficacy and safety related issue and, throughout the patient MDM capability. The third one is more focused on the commercial world where we were able to provide that better outreach to patient and help them in their access.
Jamil: Into in [00:14:00] terms of getting onto the drug and once they were on the drug, making sure they continue to stay on the drug. And, there's the following all the compliance in terms of the discipline, right? So that was like, again, more focused on adherence and the patient outcome.
Jamil: And the last one was, really elevating the patient experience from multi channel. Omni channel, engagement looking at end to end engagement and experience and then driving better ROI through those omni channel, multi channel engagement and digital marketing program.
Jamil: And we'll go more in deep, as Amit is going to talk about how we enable these capabilities. But let's move to the next slide.
Jamil: Two messages I want to deliver this, right? You will see the, there's an integrated patient hub. You are interacting, with the patient through various channels, which I talked about. Like through portal, through call center, through various campaigns. And then, you are [00:15:00] enabling patient with a lot of great capabilities or services from patient education to preferences, to helping them with their Q& A if they have certain disease to reimbursement and so on.
Jamil: And then to do all of that, what you need, what above the surface is all high sky, high rise buildings and all, but you need that. The foundation below the surface, which is where you need that robust. And comprehensive 360 degree patient MDM patient hub, which is allows you to provide that consent management, identity management and the services you're delivering is in compliance fashion.
Jamil: from HIPAA perspective from PII, PHI perspective. So with that I'm going to hand over to Amit and obviously looking forward to the Q& A where we can further drill down and have more interactive conversation. So back to you, Amit. [00:16:00]
Amit: Thanks, Jamil. So before jumping on to the next slide, I want you guys to remember this particular view, which says that integrated patient hub, because further along our discussion, I will be referring to this.
Amit: Particular integrated patient hub and it becomes very important because now let's think about it. You saw the integrated patient hub and then there are many other function in the life sciences organization that execute the business and bring the values. So there are R& D, there's a commercial and market access, there's a regulatory, legal, IT, bunch of different other functions, right?
Amit: And when any organization wants to build a patient capability, it's not just one function gets impacted. All other functions also gets impacted. For an example one of the, one of the help that we are doing to another client building the patient capability, it all started from the legal and compliance point of view.
Amit: Because they are in the, in, in the verge of launching a [00:17:00] specialty ultra specialty drug in the pipeline for the next five or 10 years. And their legal team came and said Hey guys what about if we have to really master the patient information but without really bringing in-house? So that's where the conversation is started.
Amit: And now you can imagine when we got into the discussion, we found out. The legal team also wanted to make sure that it is managed and owned completely with a complete compliance and regulatory purposes. And the discussion further went to the place where it was an idea that how we can leverage that information.
Amit: And again, the two part that you will always face the challenge whenever you either as a technical person implement the patient MDM solution, or as a business leader you are going to set the tone for the overall strategy. One is. Is it going to be the patient identifiable data or it is going to be the anonymized or de identified data, right?
Amit: These are the two things you will be asked 200 times or maybe 5, 000 times throughout the journey, right? And [00:18:00] this is the place where it gets tricky because depending on the different kind of a function that you're going to address. You can't be living just with one kind of a data set, you may have to find out the right balance how much deep you want to go onto the identifiable data, because you have to also serve, remember we talked about the patient and the caregiver, through the patient support you have to help them, but at the same time, if you wanted to generate any kind of an insight, you can't do that on the actual patient data, so you have to think about how you're going to bring in the de identified data, anonymized data So that's a very intricate area.
Amit: That's a very tricky area to balance it out. And that's where I think I was referring back to the integrated view, right? So you see that as Jamil was talking There's a high rises and then there is also the baseline which is the foundation in the foundation You shot saw two areas right the MDM and also the consent and these two areas actually help you answer these kind of a question whether you need the identified data, you need the de [00:19:00] identified data, you need it anonymized, or how you're going to balance both, right?
Amit: So keep that in consideration. And as we fast forward, started helping this client, what we realized that, and actually we did the scan of the entire life sciences industry, and what we found out, that yet where the patient journey and the patient capabilities are not novel there's not really anyone who has really pioneered or innovated the space completely not because that it's not doable It's because of the fact that it's very complex There's a lot of regulation, attached to these kind of use cases and at the same time Technologically, you have all the building blocks, like you have the MDM solution in the market.
Amit: You also have a consent solution, but where the real value comes out is that how you're going to build all this solution that talk to each other within the compliance sphere. And then still they are serving the patient and the caregiver, right? So it's a complete complex journey. And especially if you put that [00:20:00] lens to the, excuse me.
Amit: To the rare and ultra rare disease it becomes even more complex because just think about it right if someone is going through the rare disease care spectrum right the journey start not from the pharma or biopharma or medtech companies it starts all the way from the hospitals right and sometimes even before that it comes from the educational groups right so The moment you start seeing the symptoms and things like that, you don't really know.
Amit: And sometimes, like I said, as a patient, you go to the Google, you go to the WebMD, you go to the Mayo Clinic website, and you try to see all that, right? So all the way from there, it starts. And we don't really look out on those kind of a, the tricking point or interaction points that patients and caregivers are generating.
Amit: And then fast forward, minimizing that complexity and build that. So in a nutshell, the journey is complex. But we have seen that their organization and majority of the organization are either in reactive or practicing phase. So what does that mean? Reactive phase. [00:21:00] So in the reactive phase, companies are aware that they need the patient master strategy, right?
Amit: So the way they are trying to do it, either they collaborate with a third party or outside vendors or providers, right? To get that access in the de identified. So that's one way. There are companies that are practicing, right? Meaning they understand that the patient MDM or the patient strategy is going to be very important from building the overall data capability as well as the business capability.
Amit: So they are launching those initiatives and as part of that, they are trying to figure out what should be the path should be the path to bring the identified data in house. Or should they still continue with the de identified because just imagine right bringing the identified data Brings the business value and edge against the competitor, but at the same time increase your risk part as well so you gotta find that and then optimizing it in overdo.
Amit: I it's self explanatory We can talk endlessly about that, but given the time and constraint i'll just pause here just to check that Is there any question?[00:22:00]
Chris: I have one. What are some of the key business drivers? And maybe Go into a little bit deeper use cases on implementing a patient mastering solution, because, I've heard one company say, look, we want to do this and. It's just trying to get buy in, trying to get, legal and security to buy in and things like that.
Chris: So it's been a, I assume it's going to be a big, lift in that and getting that buy in.
Amit: Yeah. So I'll take this and Jamil, feel free to chime in. So the use case that we are trying to solve for another organization, they are in the pipeline of launching. Rare and ultra rare disease products and also the drugs in the couple of years in the 10 years time frame or 20 years time frame, right?
Amit: And their biggest thing is they wanted to be the patient first for the patient organizations meaning they wanted to not just Think about like how they provide their care to the patient but overall organization point of view. They just want to think about How they [00:23:00] help all the way from the acumen to the overall prescription to the overall care to the patient and in that strategy, there's multiple reasons, right?
Amit: Of course, it gives you a very strong edge against the other competitors. That's a novel and that's a usual business but more than that it's actually brings the Organization close to the patient and the close to the caregiver and that actually increase the trust
Jamil: You
Amit: know that when it's increased the trust within the organization and the patient It automatically started reflecting onto the payer side as well It also started reflecting onto the overall ecosystem in the landscape, right?
Amit: So that's one of the use case they are trying to solve as part of that they pick up like You Quite a number of use cases, but one of the very important or actually two of the very important one in my opinion is one the patient support program. So once you know that how their caregivers are coming to the platform, providing them all kind of resources, providing them all kind of support 24 by 7.
Amit: [00:24:00] if there is any challenges with the consumption of the drug or things like that. So that's one of the use cases. The 2nd use cases is around. Finding out the analytics and insights again, not onto the identified patient information, but on the de identified aggregate, right? That is not the real patient information.
Amit: So those are the top use cases that we can at least I can see in the market. Jamil, feel free.
Jamil: No, I think you covered it right. And, you touched upon the whole patient support program end of the day. If you can bring the manufacturer and patient close to each other, and if you can help address the unmet needs of the patient and improve the quality of life and, bring, I'm getting a little emotional, to be honest with you, I got into the life science industry by accident, but what keeps me motivating every day when I get up is directly or indirectly, I'm bringing smiles to some people.[00:25:00]
Jamil: Someone somewhere, right? And that would not be possible or, or wouldn't be that easy if you don't enable this kind of capabilities, right? So to me, that aspect is very motivating personally, right? Outside that. Yeah, end of the day, it's a business. But, bringing the manufacturer and the patient together and helping address unmet need is what's most important to me.
Chris: Love that. Thank you guys. I have other questions, but I'll wait a little bit.
Amit: All right. So with that, let's get a little bit technical and deeper here, right? So far you heard about so much on the patient overall strategy. You heard that how the industry landscape is shaping. You also heard that what are the different nuances and challenges.
Amit: in the overall journey, right? Now, shift the gear a little bit around the patient MDM and the consent, right? Talking about the patient MDM, there are three different ways that organizations are tapping into this. Number one, in source MDM, right? So what they do with it, actually, they [00:26:00] bring the whole capability within in house for the de identifying the patient information, or even for that matter, depending on the use cases identified, mastering them, and then feeding into the consuming applications, right?
Amit: That's one way to do it, and I think Reltio is there. The option two is basically the outsource MDM services. So what is actually happening in this one is, and by the way, you will see the option three as well, which is again, outsource. There's a slight little bit differences. That in option two, basically they are doing onto the de identified data by the other vendors.
Amit: So other vendors is taking the risk of owning the de identified patient information, doing the mastering and then giving it back to the organization, Pharma companies, right? In, in that situation, Pharma company is still going to be at the risk, right? Reputational risk, because they are actually going to consume the data from the other side, right?
Amit: In option three, basically managing the risk, what they're doing is they are completely giving that Full capability outsource, meaning that there are organizations like for example, IQVIA I, [00:27:00] and then the, there are other organizations Epsilon that is doing the patient data hub work. So they are actually managing the entire patient data profile.
Amit: And based on that, they are doing also the DUP deduplication, they're also doing the de-identification, including the caregiver, and then give it back to, so these are the three spectrum. that we are seeing. Up until now, the majority of the companies has been either an option C and option B, but as the organizations are growing further, they are choosing for the option A.
Amit: And while they choose for the option A, the biggest focus is going to be how do we make sure that every organization or every patient or every caregiver data that we are collecting, they still own that data, right? And that is where the concept for the consent is coming in here. So when you capture the patient or caregiver data.
Amit: How do we capture their consent first before even capture their actual data, right? So the concept for the consent is it's nothing new, right? But the [00:28:00] way it is currently being implemented and it's a way that currently being seen in the life science industry Is becoming very complex. So think about this right as a caregiver I'm consenting, let's say, on behalf of my patient who can't give the consent, right?
Amit: They're not in that situation to give the consent. So question number one comes out. Can that caregiver give the consent on the behalf of somebody else, right? Another situation. I'm the patient, and yes, I wanted to give the consent, but for nothing else. except for the patient support, meaning when I need to call you, when there's a need for you to call me, you do it, but nothing around doing any kind of analytics and insights.
Amit: That's different kind of a concept, like owning the data is one thing, but deriving the insights and analytics out of that is another thing. The third situation I gave you the concern. And then at some point I said I am going to opt out completely from any kind of a data sharing or anything, right?
Amit: Whereas you as [00:29:00] a patient. Have been using this organization, let's say multiple drugs or multiple products, right? When you opt out and you say that I don't need anything, what does that really mean? Does that mean that this entire organization has stopped interacting with you? Or should they only not interact for that particular drug or that particular product or that particular services or support?
Amit: So that's where the implementation of the consents start becoming very tricky. And as you see that when we were talking about that, there are multiple functions around this, right? Like R& D is there, commercial is there, then the IT is there, right? Just imagine, do you think that giving a consent for the R& D work is going to be equally implicable to the market access or the commercial side?
Amit: So these are the question comes during the time when you set up the consent strategy and oftentimes what happened is that before you set up a patient MDM capability, you have to answer first the case, the consent part of it and the legal part of it, right? And this is the tricky part comes out. [00:30:00] So once you start answering or once you start helping get the answer to what those concerns are going to look like, how the hierarchy, how the structure of the concern is going to look like.
Amit: And mind me, um. These are the concern discussion, and not just the business specific. These are business, but at the same time, there's a lot of legal nuances in this, right? So that's where the other aspect of the complexity comes in. However, the good part is that when you put the consent strategy, You have to also think about how the data governance is going to work in, right?
Amit: Because the demographic data is going to be there. Then there's R& D related data is going to be there. There's APLD and a bunch of different things, right? So these are the things that come in. In a nutshell, what I would say. When it comes for the two capabilities or the technology capability we are talking about, right?
Amit: Like one, the patient MDM and the other one is the patient consent. They don't go in silos. They go hand in hand. And what does that mean is whether you start your discussion, whether you start solving the, or even implementing or putting the [00:31:00] patient strategy from the data point of view towards the patient data management, or you start from just the legal and compliance, eventually they come together.
Amit: They come in hand. So whenever you have the discussion, think about once you identified the consent, how are you going to tag that to one unique individual? Because at the end of the day, you have to find that one patient record, whether it is identified data or de identified data, however you track that, you have to link those two together.
Amit: So that's the part, it comes out that when you build a patient MDM and consent, you have to think about the integration too, that how they both going to work together. Oftentimes I have seen that Okay, I should first start thinking about the patient data management and then narrowing down to the patient MDM And once they build it Then they start thinking about like how should we figure out the consent because the consent is managing somewhere else And for the consent is also it's in the multiple.
Amit: So that's becomes a tricky. They're Quite a guiding principle where we as a Deloitte help other [00:32:00] organizations. And that's where our subject matter expertise and legal and privacy standards, including the delivery excellence comes in the picture. And with that Jameel, any final word? I think we are close to our slide here.
Jamil: There's a question around do you have some views on what data governance processes should be established for concerned data validity expiry and evocation, right? Yes, yeah, why don't you, why don't you address it on chime in?
Amit: I love this question. I'm not sure who asked this, but a lot of kudos to them.
Amit: So yes there's, Like I said, there's a clear challenges in this, right? There's really no one solution that fits all or one model in the consent I would say consent governance that fit across, right? But couple of things that has really worked out in this situation is that A, most of the times you would find that organization doesn't have the consent governance to begin with.
Amit: But what they have it already is the master data governance, right? And it's also become very tricky that consent [00:33:00] should be considered as a master data or as a transactional data or as a reference data. Because if you look at that just from the volatility point of view, the consent doesn't change so often.
Amit: So yeah, it may sound it can fit into the master data, right? But at the same time, it changes more faster than the master data. So yeah, it is not. That kind of a challenge comes out and what I have seen that or at least in our recent work with other clients that if they already have the master data governance or for that matter data governance You find the ways to embed the consent governance within that rather than creating the two separate governance structure When it's come for the data and the other thing that has worked well is that see the consent itself Can have a multiple role and multiple views across the different function, right?
Amit: So for example that I was talking the consent that I have given for the rnd function to look at my data, find out the new insights and genomics and things like that would not be the consent that I will give it to, let's say, marketing [00:34:00] and sales and support and things like that. Yes, I'm the same person.
Amit: I'm giving the same kind of a concern, but it's not applicable across. So what happened in that situation is the moment you get the consent every small function and organization start behaving like this consent owns to me. But if you look at that at the end of the day, it's coming from the patient. So in the context of the governance, you also want to make sure that all the other functions are on board and they're not acting in the silos.
Amit: Because at the end of the day, what it matters and how the organization governance is going to reflect upon the patient is something that you have to control within the governance. So those are the two areas that I have seen once again. One, don't create a separate consent governance structure.
Amit: If there is an organization, embed that into it. And two, all the consents, wherever it's being used, shouldn't have their own set of governance too. And they should be on board to the entire governance strategy. Third piece [00:35:00] of either advice you take it or you consider that how things are working is, there's a lot of debate about what could be a good data governance structure here.
Amit: Would it be federated structure or would that be like completely distributed structure? What is that? Frankly speaking, it doesn't really impact that much in my opinion, because if you have a particular governance structure that has been working for years, And you don't really have a path forward where you are shifting your governance strategy completely.
Amit: I think it should work. But nonetheless, it's always a good approach to think about whether your consent governance would require a similar kind of approach or it's required a different. But in all possible scenarios everything is going to be linked together. So you've got to find the real balance in two.
Jamil: And if I may just chime in so obviously cross functional is very important. Participation from legal privacy, it's very important. And, the, what we have seen is from governance perspective there's no one [00:36:00] size fits all, it depends on your company, your culture, and a lot of time companies take like a big bang approach.
Jamil: It doesn't work, so keep it more practical and, simplify the process. You don't want too many levels of governance as well. So it's and again it depends on the need and the culture and the environment, right? And, there are obviously decentralized and federated models, but there's no right or wrong as Amit was talking about it.
Amit: Awesome. I see there are a few questions coming up. Gear up. Awesome.
Other: Yeah, I have a question. I have just posted it too. When it comes to HCP or HCO, we master like providers or the organization and we do have those attributes in place for mastering, right? When it comes to patients, we are saying like it's all de identified data.
Other: So what actual attributes are we using for mastering it? Any examples or any questions? Customers who have implemented, [00:37:00] what are those that they are using for deduping the data?
Amit: Absolutely. And thanks for asking this question. See the approach works out in such a way, right? This is the approach for a patient support program.
Amit: So you first capture you, you have the websites, for the support program where the patient or their caregiver can go and sign up for the support, right? That information comes. This is not the de identified data. This is the identified data. What is being captured is captured the first name last name in certain cases, you are also given the consent to provide your addresses in certain cases you are just given the consent to say that whether you are your gender basically And also whether you are minor or you're an adult and then Your contact numbers, right?
Amit: So either email or phone number and things like that, right? So these are some Good attributes to think about and these are not de identified. These are identified data. So once this comes I saw a question also around the quality part which I'll take it later, [00:38:00] but once it's, once it comes into the MDM, these are the information you use to do the mastering, right?
Amit: Exact name, same name same address, different address same phone number, different phone number, email, similar matching and things like that, the normal way of doing that, right? To identify that. Remember, it's a patient support program, so it doesn't have to be all the time super accurate, but I have seen the organization not implementing any auto match and auto merge.
Amit: So what does that mean is that every every matches that a patient MDM implement or run that it has to be reviewed by a human. That is the kind of structure they have built. Once you master this, then the part of the de identification comes depending on what is your use case. So de identify, de identification parts comes from there and then you start generating any other kind of insights and anything.
Amit: And again, you don't generate the insight at the individual level, but at the aggregated level. So there is also a part comes out the tokenization, right? So there's APLD data. There's [00:39:00] a bunch of other different kinds of data set that you can get it. And then you have the patient de identified data. You have to find the balance around how you're going to tokenize that.
Amit: Okay. And find those let's say either phone number after the de identification That you can use to link that and then still do the Insights onto the aggregated level instead of the individual level.
Jamil: Yeah, and greece you know the windows like live ramp and their technologies like Data Vant and I've done this for one of the leading life science company where they are tokenizing it, right?
Jamil: And as Amit was saying, you need to provide insights at the aggregated level, you have to be HIPAA compliant, right? You can have a cohort of five and above, each, so you have to adhere to those. Compliance guidelines and, obviously that's what I'm seeing is the latest trend in the industry as well.
Curt: Hey, guys, can I ask a question? Hey, thank you. I think I'm missing something here. So when you talk about the identified and aggregate [00:40:00] and the example you were getting before, so I've got this guy. with identified information. It's Bob Brown. I've got a master record for him. And as you say, I've got his name, I've got his phone number, I've got his email.
Curt: So I know who this person is, and I'm mastering golden information about him, right? And now are you suggesting that from a perspective of de identified information, what I do additionally is I somehow capture and gather de identified Information from an in aggregate form, so I somehow identify or a vendor gives to me a set of characteristics, right?
Curt: Therapeutic characteristics, profile characteristics that are in some strong way, very similar to the characteristics of this person. And I can use that information to make [00:41:00] predictions or I'm not really getting how this de identified information like what it is and how I leverage it as regards the master record I actually do have about this guy.
Amit: I think I'll clarify that. Once you got the complete actual information, the identified information, and then after that you did the de identification. But imagine, let's say you implemented this program or this approach for the patient support capability. There could be other functions in the same organization, they are capturing also the similar kind of details.
Amit: For example, if you have the clinical and R and D function, they are doing the trials. They are even before the trials, they are capturing all the details, right? So they are capturing the similar kind of details in terms of the name, phone, everything. And then also along with the demographic, they're also capturing other.
Amit: Information, which can be clinical information, which can be other set of the medical information. So where is coming is that now you have the same organization, there's one patient support [00:42:00] capability, then there is a clinical R& D capabilities when they all come into the in house, then you do the de identification using the same approach.
Amit: It's not that you are using the de-identification for one set of the data with the, let's say hashed in values or alphanumeric values or things like that, or using another technology or the platform like data bank and for the other one is doing different. So if you do that using the same approach, it will result into the similar or common functions.
Amit: For example, like if you have the phone number A, B, C or 1, 2, 3, 4, use the function, it de-identified, applied the same. Now you got the link between the two. So that's how you are combining the dataset. And then after that, you're doing the aggregated level of insights. So again, you're not doing at the individual level, you're doing at the aggregated level.
Jamil: Yeah. And, Kurt it's different use cases, right? Identified data, you are in the the patient support the, you can't use that data for promotional activities and all, right? The de identified work where, you're using it for more Understanding the [00:43:00] patient population, understanding the behavior, and all of that which you're using in the commercial world.
Jamil: So two different set of use cases, that we are addressing here, and that's how we differentiate between identified data versus how do you use the de identified data more at cohort level, where you are, have to be compliant.
Amit: You brought up a very good point, Jamil. Let me add one more thing probably that I missed it, right?
Amit: So again, depending on the use cases, One side in the patient support program, you don't have a chance to incorrectly reach out to someone, right? But based on the insights and generations, if you wanted to see that where you wanted to either launch the drug, new drug experimentation, or for that matter, for your commercial and marketing purpose, you wanted to send more comprehensive material there.
Amit: You can still be like, don't have to be like as precise as you have to be on the patient support side, right? So that's another aspect that you wanted to consider to this.
Chris: So guys, let me organize some of these questions real quick. And I'll ask this one we have. Can we have a view on [00:44:00] mastering and maintaining the identify patient data in DM?
Chris: You guys covered that, but
Amit: mastering and okay, I see that. De identified data. Actually, I have not seen anyone actually doing the mastering within the MDM on the identified data and also maintaining the de identified data there, right? The way that it has worked is you master the the identified data because that's what you need.
Amit: And then once you create the mastered view, then in the lake or in the other storage area, you have the de identified data. Okay. So that there's no clear linkage, because remember, this is also again the DRN, you don't want to create a linkage between what you have versus what you have in the actual, because if anybody gets the key, it's again demystify the whole purpose or actually break the whole purpose of DRN.
Amit: doing this whole work. So you have that. The intent for doing the de identification is not about there is Amit Singh and here's his de identified information. The intent of doing the de identification [00:45:00] is that, hey, here's Amit Singh, but here's the de identified information, which may belong to Amit Singh, which may belong to Jamil.
Amit: But the idea is that after all doing the de identification, this whole set of information is intact and representing somebody in the world. So that's why you don't maintain that within MDM rather you maintain outside of it. I hope it gives a point of view
Chris: Yeah, I think it was I really like this question interesting.
Chris: So patient crm versus patient mdm How do you carve out like specific use cases for mdm as against the already existing data governance within? Patient CRM. So example would be like health cloud. So mostly patient supports and analytics organizations leverage CRM for most of their cases like patient journey and sense, et cetera, and data needs.
Chris: So how would you or how do you drive an MDM or what drives an MDM need for patients?
Amit: Very good question. I would say see patient [00:46:00] MDM and the consent is not for everyone. And that's why when you remember when we were talking about the journey, I mentioned that there are almost 50 to 60 percent organization is within that reactive and then it's still the practicing mode.
Amit: Right? There's really nobody. So it totally depends on the use case. If your use case is not around improving or being the patient first if your use case is not about that you have the rare and ultra rare disease coming in the pipeline, let's say, for five years and ten years period, if your use case is not about deriving the insights and also the analytics, and building the organization foolproof from the legal and compliance point of view, because you are actually getting the patient data, then there's really no problem at all.
Amit: I would say, I would not call it out value, but I would say there's really no strong read and the strong reason that why you have to build a standalone patient MDM solution. And in the case that the question, if the CRM is there and if CRM is capturing the complete It information. Great. If it's [00:47:00] capturing the complete consent information to the level of complexity that we have been talking about.
Amit: Great. I doubt though, because I have seen multiple of the CRM system. They are mainly designed for the engagement for the interaction. They're not designed for master. They're not designed for capturing that level of complex consent. And especially if the organization has like multiple products, right?
Amit: So in the multiple products, yes, you can have the consent and the product level, but even within that, the kind of a governance on the consent alone is very difficult. It's purely designed for the interaction, purely designed for the engagement. Yeah, it can provide you the insights analytics, but those are not designed for the use cases that we have been talking.
Amit: So once again, going back to if there is really no strong need we have not seen organization building this separate capability, but the use cases that we talked about the very first basic fundamental things comes out is how to build that capability by enabling the patient MDM consent and combining that two together.
Jamil: Yeah, and if I may just chime in, right? So when you're looking about the [00:48:00] holistic capability. You have to think of your CRM, you have to think of your MDM, you have to think of your consent management, you have to think of your identity management, and then you have to think of your CDP, right?
Jamil: Because as Amit said, patient CRM is a very transaction. I know there are platforms now who are enabling, let's say, data CDP kind of capability. But consent is very important. The way you use patient CRM is for to address certain use cases on the commercial side, but the MDM is a lot more broader, right?
Jamil: The set of use cases are a lot more broader across the value chain.
Chris: Yeah, speaking of CDPs, can you give us a view on how a CDP solution can coexist with a patient MDM solution as part of a Martech landscape? And would we even trust a CDP for unification or leverage the MDM batch?
Jamil: Go ahead. No, you go ahead.
Amit: [00:49:00] Honestly, I will say in the overall patient landscape, right? And again, I will go back to the journey part, right? There's like reactive, there's practicing, and then there's a notion and there's a bunch of other leaders. Haven't seen anyone who is at the patient MDM. And at the same time integrating with the CDP platform, right?
Amit: And the reason why I would say that, both kind of a solution are designed for the different purposes, right? And the majority of the use cases on the patient side that we have seen is the patient support program and insights and analytics. But those kind of insights and analytics are happening right now onto the de identified datasets, right?
Amit: And in my opinion and again, I have not seen that anywhere in our clients at least that i'm serving that doing this integration. Jameel has a vast experience. He can chime in. But When you do the CDP, there's also comes a part that you have to, link and cross link with the actual set of information.
Amit: So yes, for the identified part, if you wanted to [00:50:00] link that you can do that, but then again, why would you link that everything in the CDP when they're you, when your use cases purely dominating from the market point of view, one technology point of view, right? Whereas your purpose to build the patient MDM to begin with is a different use case.
Amit: It's the use case around the patient support program. It's the use case around driving the insights for the drug delivery or drug experimentation, right? So that's where maybe I have not seen that. And if I find something, I would love to be in touch and share that a little bit more details.
Jamil: Yeah, and no, you summarized it right?
Jamil: And there are nuances, right? Talking about patient CRM and patient CDP and MDM versus customer CRM and. Customer CDP and MDM. So patient given the sensitivity around being compliant from HIPAA perspective from PII and PHI perspective. What sort of the guardrails between the support world versus the commercial world.
Jamil: You have to factor in. So it's not an easy question [00:51:00] to address. There are a lot of nuances that that needs to be factored in as you're thinking about. End to end patient engagement experiences and enabling that sort of a patient transformation program.
Chris: Great. We have four minutes and I'm going to ask at least one more question. If that's okay. Great. So Chandra asked. I like this question. So if I'm understanding this right, one should have a dedicated MDM instance for patient domain with an identified data alone for security and privacy purposes and don't include it.
Chris: Okay. Thank you. Other domains like hcp and hco is in that case. How do we establish linkage between them?
Amit: Great question. First of all Yes, you got it correctly. And the second one establishing the hcp hco linkage. It's very tough frankly speaking because when you do the data collection, yes, yeah when you do the collection Yeah, because you can imagine, right?
Amit: The patient and their caregiver is not going to provide you that who's [00:52:00] your HCP, HCO that you're visiting, right? That's purely on the other side. HCP HCO is not going to talk about who's your patient. So frankly speaking, there's really no very clear link that we have been able to establish.
Other2: Not just HCP.
Other2: I just used them as examples, but any domain. Even, let's say, drug domain, right? For insights, you may need drug domain, right? But yeah, go
Amit: ahead. Sorry. Yeah no, you're absolutely right. And that has been the biggest challenge. Not because of the data sets are not available. It's because of the legal and compliance.
Amit: Because the moment you start linking, then you're basically deciphering the whole privacy. You're killing the whole privacy part of it, right? And that has been the biggest reason that You, like you rightly said, and you got it right, you have the patient domain, you have the consent and the preference solution tied to this, you're managing that consent and the [00:53:00] actual patient domain MDM, to enable the patient support program.
Amit: When it's come for your insights and analytics, depending on what kind of insights you want to generate, what kind of analytics you want to build. There you can do the de identification and the tokenization part as I was talking about that and then you generate the insights and things like that. But you're right, I clearly see that a big challenge and I have not seen, even though one organization is thinking about it not there yet.
Other2: Got it. So link case has to be done outside of MDM, this particular instance, that case. Okay, thank you.
Chris: Alright unfortunately, we have to go but I met and Jamel. Thank you so much for coming on and providing a great presentation. And thank you for coming in and answering a bunch of these questions.
Chris: I love it. So thank you guys. for
Jamil: the
Chris: opportunity. [00:54:00] Yeah, of course anytime and we might do this again if you're up for it and so when you leave please give us a Rating and how things went and what other things you want to to listen to your feedback is a gift as usual So I will see you guys in the next Couple of weeks where I'll be traveling next week, so we will not have a show, but we will continue on with the next three or four weeks after that, with the show every single week.
Chris: So thank you, everyone. I look forward to seeing you and thank you for coming on. And thanks so much for our speakers. Really do appreciate the time and effort that you guys put into this. Thank you, everyone.