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Patient Centricity: A Pharma Industry Trend Defining New Ways of Patient Data Management

By Chris Detzel posted 23 days ago

  
Patient Centricity: A Pharma Industry Trend Defining New Ways of Patient Data Management

Find the PPT Here: Trend Defining New Ways of Patient Data Management

This webinar, titled "Patient Centricity: A Pharma Industry Trend Defining New Ways of Patient Data Management," was hosted by Chris Detzel and featured two guest speakers: Azim Ahmed and Harneet Kapil, both principals at Axtria and Ingenious Insights. The discussion centered on the growing importance of patient centricity in the pharmaceutical industry, emphasizing how this trend is reshaping patient data management practices. The speakers outlined the patient journey from initial consultation to therapy utilization, introducing the concept of Patient 360 - a comprehensive view of the patient that integrates data from various sources including wearable devices, clinical interactions, lab results, claims, and social determinants of health.

The webinar addressed several challenges in patient data management, such as data fragmentation, the need for timely diagnosis in rare diseases, ensuring data privacy compliance, and the industry shift towards internalizing patient management. The speakers highlighted the crucial role of Master Data Management (MDM) in creating a unified patient 360 view, integrating both identified and de-identified data while maintaining compliance with regulations like HIPAA. They presented different data integration scenarios and outlined a solution architecture that incorporates various data sources, an identity resolution platform, and a patient 360 data hub to enable analytics and insights.

The importance of patient consent management and its integration into MDM systems was emphasized. The speakers also presented a maturity model for patient data management capabilities, ranging from foundational to strategic levels. They provided examples of how Patient 360 can improve patient outcomes, such as identifying at-risk patients and matching them with effective interventions. The webinar concluded with a discussion of the Axtria Data Max platform, designed to work with various data scenarios and featuring specific components for life sciences. Overall, the webinar underscored how advanced data management techniques can support better patient outcomes while maintaining privacy and compliance in the evolving landscape of patient-centric healthcare.

Transcript: 

Patient Centricity: A Pharma Industry Trend Defining New Ways of Patient Data Management

[00:00:00]

Chris Detzel: All right. Why don't we go ahead and get started? So welcome to another community show. My name is Chris Detzel. And today's topic is around patient centricity. It's a pharma industry trend that's defining new ways of patient data management. That's really exciting because I know we've done a couple of these before, but today we have two special guests that's never been on a community show.

Chris Detzel: And Azim Ahmed, he's a principal over at AAxtria. Azeem, how are you? I'm doing good. Thanks. Good. Glad to have you on. And we have Harneet Kapil. She's a principal business information management at AAxtria - Ingenious Insights. How are you, Harneet? I'm doing good. Thank you. Thank you, Chris.

Chris Detzel: Great. Welcome. So before we get started, again, it's the same rules as usual. A lot of you already know, but keep yourself on mute. All questions could be [00:01:00] asked in chat or feel free to take yourself off mute and ask I will make sure to get most of those questions answered we are going to record this and we'll post it to the community as usual.

Chris Detzel: I'll post the link to all the recordings here After my little spill so upcoming events These are not all we have two or three more that I'm looking at potentially August pushing more out, but right now we have today's show around patient centricity and on the ninth we have one around unlocking the potential of relatio business, critical edition, enhanced security and resilience.

Chris Detzel: Pretty excited about that one. That's a new product that Reltio is, uh, excited to introduce. And then we have an improved data discovery with Reltio integration for Colibra. Excited about that as well. First time to really do anything with Colibra. It's pretty exciting on the 18th. And then Reltio data pipelines for Databricks on the 25th.

Chris Detzel: So that's on the 25th. That moved from the 11th to the 25th. [00:02:00] And then lastly, we do have a big conference coming up. Called data driven that Realteo is sponsoring in a big way. And. If you sign up today or anytime, and if you put community as your code right now, you'll get 200 off. So I'll put that in the comments as well.

Chris Detzel: So please make sure you sign up to that. There's the code and everything else. I'll put that in the chat. I'm going to stop sharing my screen. And we're neat. I'm going to give it over to you. Okay, thank

Harneet Kapil: you. We just

Harneet Kapil: let me know when you see my screen.

Chris Detzel: Perfect.

Harneet Kapil: You see it. Okay.

Chris Detzel: We do. Yeah. Great.

Harneet Kapil: So guys, I hope you all are having a great morning. I'm super excited to be here with you all and our partners to talk about the subject around [00:03:00] patient data management today with my with my peer Azeem. And just so you all know I come really from a lot of data management backgrounds, life science, about 25 plus years in data management.

Harneet Kapil: Both industry been in the industry for about more than half of my 25 years and then in the consulting as well so it's really what we're going to be sharing with you is patient management, that's really is the latest trend that are licensed industry. They've been doing it, but the way it's happening now is changed their new ways.

Harneet Kapil: So we'll be talking about that. And as seemed, could you do a brief intro of yourself?

Azeem Ahmed: Absolutely. Thanks. Sorry. Like her, you said that I'm really excited to be part of the community show here, speaking at the real to a community event. My name is Azeem. I'm actually a principal at Axtria. Actually, I've been leading the effort on the product development on the specialty in rare disease, data management, aggregation and analytics.

Azeem Ahmed: Actually, I'm also part of the patient center of excellence at Axtria. I have more than 25 years of [00:04:00] information management experience. A majority of them actually within the life sciences industry. And actually, I'm glad to be here and to be part of participating here.

Harneet Kapil: Thank you. Let's begin with really understanding the industry, right?

Harneet Kapil: Why the, what is the existence of an industry? Why does life science nursery exist? It's front and center is all about patience. It's all about the lives of the people making it better. If you read some of these credos that they have, what is discovered new ways to improve and extend people's life, right?

Harneet Kapil: We discovered and deliver innovative medicines and solutions that address complex health issues, enhance people's lives, right? We talked about Roche is developing medicines and diagnostics to help patients. Everywhere, it's all about patient and making patients lives better, right? And healthier.

Harneet Kapil: With this credo, our client's mission is to ensure patient safety, ensure protection of patient privacy, optimal, Experience delivery to our patients, to your patients, right? [00:05:00] And this has become all the more important as the patient centricity continues to evolve, right? You all must have heard about precision medicine, photography, therapies, personalized treatment plans, outcome based reimbursements, right?

Harneet Kapil: Digital health and telemedicine picked up a lot during COVID and all of the digital channels and the era of digital that we sit in optimizing patient engagement. Is. It's not an option. It has to be there to compete. It's not even thrive. I would say to survive in the industry.

Harneet Kapil: That's really wide. And so is that of new insights, right? So how with the amount of data we have, how that guides drug development, regulatory decision making and post market surveillance, right? So this whole patient centricity that's coming up and there's a whole lot of other use cases. I think we tried to get more broad categories, right?

Harneet Kapil: This is. Warranting for industry to know their patients holistically. It has really become mission critical. It's really [00:06:00] supporting directly supporting the why the existence of the industry today. The next I will hand over to to a zine to take us through illustration of the patient journey.

Harneet Kapil: What does this really mean? And then also what entails this whole patient 360 and knowing a patient when a stupid. I think.

Azeem Ahmed: Thank you, honey. Um, like he was talking about, basically, it's all about the patient. So this is actually in a kind of generic July's patient journey that the typically the patient journey starts with the the doctor's consultation that the patient come to the doctor's office.

Azeem Ahmed: The diagnosis is being made in the disease of this disease state of the determine the therapy plan for the patient. And once they do that, they actually have to overcome a series of hurdles, basically, especially with the specialty and rare diseases, they need to make sure that they have the the prioritization with the peers involved.

Azeem Ahmed: Also, make it affordable to reduce the out of pocket costs, basically, to basically essentially do all these. Access [00:07:00] enablement affordability, labor of depo to make sure that it's affordable for the patients to get onto the to therapy. This is in addition to the psychological behavioral aspect development that actually is there, that the pa that the doctors have to work out with the patients.

Azeem Ahmed: So essentially the specialty rate actually makes it, several more steps to the overall therapy initiation. So once the therapy stars, essentially the adherence and persistence and patient support and counseling kind of kicks in life sciences companies are spending billions of dollars in this area, basically patient support programs and that the, they want to actually be able to make sure that the patients are adhering.

Azeem Ahmed: They actually are persistent on the therapy so that the therapy is able to help them better their lives, essentially. So this whole thing is basically, it's. It becomes very important for them to actually measure the effectiveness of these patient support programs. And they see a lot of data gets collected and analyzed to be able to do that.

Azeem Ahmed: And there's billions of dollars in addition to the co pay support that is done by the pharma [00:08:00] company. It's very important for pharma company to actually get that level of data about the patients, how they're actually behaving and how their how they actually health is progressing and how's the therapies affecting them, actually making their lives better.

Azeem Ahmed: You're welcome. So this is the the therapy utilization aspect comes in the security long term. Actually, that actually done more at the almost strictly level that across the patients is the therapy really helping the patients is a therapy really making it later lives better. So patient important outcomes becomes really foreign center and having the analyzing the electronic health records of the patients identified and anonymized, obviously.

Azeem Ahmed: To be able to actually make sure that the patients are getting their support and their lives are getting better, essentially. This is actually the way that they can actually take this data to improve the the therapy pathways. And this is how the overall patient kind of works. To make all of this.

Azeem Ahmed: Function patient 360 is the key capability that they need to [00:09:00] understand the the patient how the patient outcome is doing better. This kind of a dev skills into the patient 360 degree view that is shown here. Basically, patients at the center and all around this lots of areas and vendors actually are actually in the ecosystem that actually are helping the patients get better.

Azeem Ahmed: And also, these systems are generating lots of data. So 1 of the things actually, it's not news been more than a 15 to 12 years. Essentially the variable devices, like internet of things, essentially. Those actually are producing data on a regular basis about the patient's health conditions and the key vitals essentially.

Azeem Ahmed: And that actually. Become sometimes even lifesavers. I know that one of my relatives was wearing one of those while sitting in the hospital, and his life was saved because of this wearable device. That was pretty interesting. I think that actually seeing that these wearable devices actually are really lifesavers and sometimes actually help us help the clinicians gauge how well the patient is doing, [00:10:00] not just point in time and the evaluation of Evaluation how things are going at that point, when they show up in the office.

Azeem Ahmed: In addition to that, basically patient interactions that is coming from the clinical interactions that the doctors are doing that also could also be the patient support programs in nursing educations providers. Basically, they actually interact with the patients. All of the data actually gets. Pulled in to figure out how well the patient is doing and to add to this essentially is the lab.

Azeem Ahmed: Results actually come in the claims that the patient has been doing. In addition to the current therapy, what other comorbidities the patient has. So that actually becomes the part of the analysis. And last but not least, the social and determinants of health becomes very important to understand how patients are responding to different therapies and what is the drivers and motivated essentially.

Azeem Ahmed: When we look at the the pharmaceutical companies in general, basically, they're looking at these cases. These are like the variety of 1st party, 2nd party and 3rd party data sources. All of this needs to be stitched together to create [00:11:00] like a better view about the patient and also the caregiver sometimes.

Azeem Ahmed: So the, to make all these data sets interoperable, we need to actually have both identify and de identify matching capabilities, and Hrnidha is going to go. A little bit more into a little bit in the few slides down in addition to matching we need to actually have the kid data management capabilities, for example, aggregation integration to make the data kind of analysis ready and all of this, essentially, when we talk about all this patient interaction.

Azeem Ahmed: We can use this integrated assets to look at the next. Best action, how to engage patients better to identify and predict what patients are at risk of not getting on to therapy and what patients are at risk of dropping out of therapy. Once we have that level of data that we just talked about, it makes it easier for the patient support system to identify those adverse patients and see which Interactions interventions basically help the patients get better outcomes.

Azeem Ahmed: And based on that data, they can actually suggest we can [00:12:00] use the algorithms to suggest the next best action for this patient. Essentially, at the patient level.

Harneet Kapil: Thank you seem so. We just went through through the variety of sources that have to come together. And this is where I think we want to talk about today.

Harneet Kapil: What are those challenges that the industry is facing in the patient data management? The 1st and foremost, for us who are working either you're in the industry or in the consulting, you would have this insight working with our, with the businesses that fragmented patient data costs, patient help, support services, affordability, support, it works, even management, care management.

Harneet Kapil: There is it's fragmented. The 1st and foremost thing for that challenge resolution is to create that unified. Patient 360 longitudinal view with patient concept, right? And we'll talk about how MDM enables that and what that creates. It's improved patient engagement and satisfaction, increased adherence to treatment plans and better communication with patients.

Harneet Kapil: So the outcome is very use case focused, right? That's what it's [00:13:00] really important. Looking to, I usually use, my left to sometimes we go left to right when we are building our solutions, but I like to see the right, here is the outcome that you're looking for. And then what is it that's needed to solve for that outcome?

Harneet Kapil: So the middle is really what seemed to solve to get to that outcome. As he touched on the specialty area, rare disease patient management requires, some timely diagnosis and also the nuance in that space is most of the data is to identify data. Right how do we. Integrate this data to find data yet to solve for some of the specialty values cases across our Indian commercial value chain.

Harneet Kapil: So we'll take you through how and not miss a detail. How are we talking to you about the solution that we have that brings this integration of identified and identified data together? Data, privacy, compliance, risk to patient information. It's a given in this industry. As it as is, yeah.

Harneet Kapil: The regulatory compliance is so high when it comes to patient, then it's even higher, right? With the additional compliances like the [00:14:00] HIPAAs. Now we need a platform that's compliant by design to ensure their privacy and security. So making sure that the access is enabled to the right users and it's compliant with HIPAA and all of the regulatory compliance requirements for patient data management.

Harneet Kapil: This ensures the outcome is it ensures protection of patient privacy, compliance to regulatory reporting, higher patient trust, right? So you get all of that for that. Now, the last is really I would say it's so there's a, I would say it's a paradigm shift that's happening in, traditional patient data management was more outsourced as we were working with our clients today.

Harneet Kapil: Also, we see a lot of them actually. Moving towards would be what we are have is a resolution, which is more internalizing patient management. So really bringing this patient data in house, right to enable the analytics that's needed to support the outcomes that we are going after. So we have covered some of those use cases and outcomes.

Harneet Kapil: So this is really a trend change that's happening. And it's really what we usually. Use a phrase here that turning that black [00:15:00] box kind of thing into more of a glass box, which is a transparent video into patient 360. when we say glass box, of course, it is in the peripheral of the compliance. For instance, that the authorized users have access to the.

Harneet Kapil: Identified information that the client has that right? It's not that everybody can have access to the information. In MDM, let's see, as an example, just like how we have for customers. So it doesn't work like that. In case of patient, it has to ensure that the access is limited. It's legit and it's compliant.

Harneet Kapil: But bringing that data in house even if we. mask or de identify the de identified information. They, it really supports a lot of use cases in the course of commercial R& D. Everything we spoke about the digital use cases, the precision medicine use cases, it enables all of that because you're able to know your customer, you're able to segment them.

Harneet Kapil: Patients you're able to know your patients and able to segment them and you can then have the fulfillment of the outcomes that you're [00:16:00] looking to achieve accordingly, right? Be it in drug discovery or be it in commercial. Patient MDM is a core capability. A little bit, I'll go back again, right?

Harneet Kapil: So we're talking about the unified patient 360 long range review with patient concept integration of identify de identify data, right? Those two, MDM is an enabler. That's where it comes to play very much front and center to ensure That all of this is achieved in a secure way. It's accurate to enable that long term review.

Harneet Kapil: What on the right hand side is a is kind of use cases to improve client child recruitment for Pharmacovigilance, aggregating patient data, photography therapies, patient adherence that Azeem spoke about, personalized patient engagement with all of the digital platforms coming in and the engagement of the patients through these platforms, post marketing surveillance as well, right?

Harneet Kapil: So what MDM enables is that managing that patient data securely and compliant with regulations and patient [00:17:00] concept. Patient MDM has its own nuance. , all of us dealing in this space know that we said repeatedly identify, de identify data, right? And that's one key nuance. And the other is I think making sure it's HIPAA compliant, right?

Harneet Kapil: So those two at the minimum, and there are more, there are a variety of I would say requirements of HIPAA compliance. We are not going to go deeper into those, but all of those have to be met and satisfied as a store for this industry, right? So what on the left hand side is a broad category of data scenarios that we call it.

Harneet Kapil: Data integration scenarios, right? It's and there are clients that could have either 1 of this scenario or a combination of that, or it could be starting 1 and maturing to another. Our medical device clients that we are working with, for instance, mostly have, they're starting that hit with the identified patient data.

Harneet Kapil: So. They are devices, they know the customer, and then you all using devices day in, day out know how which use [00:18:00] cases, how the proactive and the views are given to to the patients that are using the devices. So that's 1 of the primary use cases. Driving this patient MDM adoption in medical devices, then comes the identified patient data.

Harneet Kapil: This is very common with our specialty from our clients. And there is a variation of the data sets that they have. They could be coming in with 1 common tokenizer, which means it's identified. And I would just use a simple term of token is really you can, use it as a decrypted key to identify, right?

Harneet Kapil: So let's say Harneet is coming in four sources, Harneet Kapil is coming in four sources, and in all four sources, instead of Harneet Kapil and the address and all that, you're really seeing token one, two, three, token one, two, three, token one, two, three, right? So it's de identified, but you're getting the same token.

Harneet Kapil: The Then there's another variation of the G identified data where it could be coming with different tokenizers. So specialty pharma companies have contracts with the tokenizers so that they use, so some of them may be [00:19:00] using different tokenizers, right? So there are some of the tokenizers you may know, IKEA tokenizes tokenizes, there's a variety of tokenizers out there.

Harneet Kapil: So when the data is coming with different tokenizers, then her need couple is not going to come with one token, it's just going to come with different tokens, right? So it's going to be token 1, 2, 3, but it could be token 646, maybe, right? But because the vendor is different, right? It's still the same.

Harneet Kapil: So how do we solve for that? How do we bring that together? And then on the right hand side it's a very interesting use case where the clients may have identified data. So let's say they have their own hub. Patients are existing there. We have that information, but then we also have de identified data that's coming in.

Harneet Kapil: So how do we bring that together? So what we provide the solution that we have are, I would say the it's an accelerator as we call it in, in, in the concerning world right now. It it's built on relative platform and it provides a critical set of capabilities to drive matching accuracy.

Harneet Kapil: And what we do is because when there's a tokenized [00:20:00] match, we also have a concept of contextual matching to further true up and refine the match. So one of the nuance with tokenizers is that they work off, it's really based on the quality of the data, right? So the tokenizers are not really doing that much of data standardization.

Harneet Kapil: So when we bring it in MDM, we have, we standardize the data, right? So if the data is not standard it's coming with different values. And it is possible. The data quality is not good, right? When the tokenizers are working, let's say this example, specialty pharma and let's say her need. Okay.

Harneet Kapil: couple had, the gender came as female in one source, another source gave blank, for instance. It may end up creating a token that's different than across the two farms, especially farmer sources. One had blank, one had female, and it ended up creating two different tokens, even though it's still same ID.

Harneet Kapil: So that is the scenario where we apply that contextual matching to actually minimize those. Match inaccuracies or [00:21:00] basically a level above to organizers to really bring in that match accuracy. Then the MDM platform is used for match adjudication because they could still be, as you all know, they could be there are potential matches and there could be an action needed for any kind of false negatives or positives.

Harneet Kapil: But I would say at least in the potential matches area or you will hear me emphasize again that match adjudication is again given to the only authorized users. Particularly such as patient support services, right? It's compliant with HIPAA, right? So there, there will be data stewards or those users that get identified, which are authorized to have access to this data that will action on that, right?

Harneet Kapil: So to enable all of what I just mentioned, trust framework is key. So it's really, comes with that trust framework and it creates that longitudinal important record. The data access is managed. The ratio we know is compliant by design, right? We have that combined platform. And then the other big piece of patient is that versus customer as an SEP or [00:22:00] SEOs is the patient consent.

Harneet Kapil: So patient consent is really integral part of patient MDM as it starts to try to be observing and really taking to our clients in case of SEP and SEO, that, that constant consolidation It's not that it's always happening in MDM for SCPs, right? Or contacts with NCOs. But for patient, it is really integral component of consent on 80 20 rule.

Harneet Kapil: I would say it's 80%. It is integral part of patient MDM. Chris, at this point, if you need me to, if you need to bring some questions, we could, or we can continue to progress.

Chris Detzel: I don't see any questions as of yet. So until then, feel free everyone to post some questions if you have them, and we'll get them answered.

Chris Detzel: Thank you.

Harneet Kapil: Yeah. So thank you, Chris. So now I love, Azim is going to take us through this variety of data sources, speak to some of the new ends, and then also take us take you through our [00:23:00] solution, the end to end solution, MDM, and the 360, and the enablement of the outcomes through analytics, Azim.

Azeem Ahmed: Thank you. Honey talked about it also, but it was still going through the patient journey. I was talking about the patient 360. there's a variety of data sources. This is a a collection of not exhaustive, but a representative list of the data sources, the 1st party, 2nd party, 3rd party data sets.

Azeem Ahmed: The 1st part is they essentially the pharma systems actually are collecting the data. So it could be patient portals, like a brand website. So they actually have set up and they can set up the patient communities over there and they actually collecting data where their patient support services. They actually interacted directly with the customers.

Azeem Ahmed: They have the patient level data and clinical trials. Variables and some of the variables of key data, which would feed into the clinical trials. And that's where actually the farmer would have the direct access to variable data and only in the clinical context. The majority [00:24:00] of other variable data is going to come in through the clinical EMR data systems that becomes like a 3rd party data sets data.

Azeem Ahmed: For second party they actually companies are doing business like especially pharmacy, where actually servicing patient dispensing products to the to the patients for the for the therapy management, hobby copy. Care management, all this in marketing, all it's basically doing as a second party.

Azeem Ahmed: It says, and many of these companies have data contracts with the pharma companies, and they actually share the data with the pharma companies. Many times it is identified, and many times it is de identified. It depends on the mix of both identified and de identified what Harneet was talking about, you have this mix of data sets coming in and then we have to resolve the identity of all these patients.

Azeem Ahmed: Similar thing things about the third party data sets so the claims, whether it's a prescription, the the medical diagnostics. Or what have you all the [00:25:00] claims that would be coming through the third party data sets clinical like electronic health records, medical records, those kinds of things will be coming in and most of the time, all these are identified essentially data sources and also with the gene therapy, the genomics that becomes very important as well.

Azeem Ahmed: This is like the overall. Overview of the different types of 1st party and 2nd party data. It's not a loss of list, but actually can tells us how actually we would be managing that. And this is actually a solution architecture and that actually shows that different data sources that we just talked about 1st, 2nd and 3rd party data sets are represented on the left side.

Azeem Ahmed: And then all the data patient level, whether it's a patient personally, identifiable information, or the token information along with the demographics, we actually pulling into the identity resolution platform. That's based on MDM platform relative and we are using the [00:26:00] extra data max platform and to a house and make it like the the main payment platform.

Azeem Ahmed: Patient master index. This is becomes the where we have all the data about the patient P I. I there. And then we we do the pay. And defied matching and merging using relative and the identified tokenization actually using any of the tokenization engines, whether it's health ready, I'm going to say, or what have you, any of those solutions kind of agnostic to the tokenization engine.

Azeem Ahmed: We do that and then we ma do the patient match and merge over there. And all the deidentified data once it's been deidentified, actually gets sent to the patient 360 data hub below. This is again built on top of the exterior data max platform, and we do the typical data warehousing steps like data quality management, enrichment integration.

Azeem Ahmed: Create a star schema and then publish it for the analytics to use. We also generate the reports, ready data sets, analytics, ready data sets, and GI [00:27:00] Gen AI, ready data sets. All of this actually helps to quickly enable the different BI of choice. So essentially, whatever the choice is for the BI tool, we can enable the persona based insights and I also enable like the advanced analytics use cases that were there.

Azeem Ahmed: So all in all, essentially, this is the overall solution architecture that actually takes the data from multiple sources performs the identity resolution that hurry that I talked about earlier and then makes it available as part of the integrated cohesive data sets so that the formal companies can drive the insights from the actual insights from the data sets.

Azeem Ahmed: Thank you.

Harneet Kapil: All right, so guys, we are this is really our last slide. Really, the point here is, it seems a lot, but it's a journey with any other programs when it comes about patient. And patient data management and a variety of use cases. It's a journey usually starts with one use case, and then it works [00:28:00] to a number of use cases.

Harneet Kapil: This is our point of view on the foundational capabilities, how we start, right? It's about collecting patient data from various sources. That's the first thing the whole governance frameworks to ensure a responsible use of data, which, if you notice, it was on our the last slide, as you had, had an underlying horizontal for data governance ensure compliance.

Harneet Kapil: We spoke about that, then all of the data security policies are it's part of, it's part of data governance, right? But everything that's needed is from the platform compliance perspective. So put that foundation, then the whole matching algorithms, it's fine tuned to create that longitude on record that was.

Harneet Kapil: Spoke about, and in the MDM language, it's the golden record, right? So it's a golden profile of patient with a crosswalk, right? So the crosswalk is what's letting you stitch all of the integration in the bottom section there on the last slide that Azeem had, right? We spoke about patient consent, so that gets consolidated.

Harneet Kapil: So the patient consent management is happening upstream from the digital channels. [00:29:00] It's flowing into MDM where it gets consolidated. We are staying compliant with the latest consent that has come from patient and really making sure that the data is utilized as per the consent. The enable use, initial use case we spoke about patient segments.

Harneet Kapil: That's a very common use case, from commercial perspective is a starting point. So you need, we need 1 use case at least to start with. So we have that and then when we get to intermediate is all about. Really realizing more value, which is really getting more use cases, patient support services and other so commercial.

Harneet Kapil: We spoke about patient segments for all of the outreach and proactively managing. Patient engagement and patient experience and then patient support is another big area of patient assistance programs, patient assistance, patient support is another big area a use case, or I would say the stakeholders or the sponsors of patient MEM, right?

Harneet Kapil: And then enhancing [00:30:00] something like pharmacovigilance capabilities. And then we can get to advance, right? So this is where. As we all know we start with certain data sources, and there's so much data coming in. There's always new data sources we want to bring in to enrich to augment, right? And there could be new needs of data analytics that require a new data set.

Harneet Kapil: So that's really bringing more data. So you're really advancing your whole data analytics Right and this is where we talk about incorporating AI and predictive analytics. Nothing you can't have or leave any conversation today without a mention of Jenny idea. It is core. The reason we have any advances, because we need to be.

Harneet Kapil: Focusing on the data, right? So we have to make our data is ready to provide those AI based analytics, right? It has to be ready ready. So that's really where why we have time to advance. And then strategic is really again, is around companies improvement. It is around [00:31:00] really going to the enterprise level, right?

Harneet Kapil: You'll probably start with a function or a group of functions. And then now you're taking it, commercial and R and D are usually 2 different companies within a company. So this is about really taking it to, more enterprise level and compliance regulatory requirements.

Harneet Kapil: Staying very proactive, making sure. And make sure that we are continuously complying with the new requirements that are coming in. So, that really brings us to the end. So we're happy to take any questions. You may have

Chris Detzel: right looks like a Zima answered one question, but one question, I think you touched upon this, but can you share like an example of how patient 360 is improved patient outcomes and experiences.

Azeem Ahmed: Yeah, I can take that question if you want. So in terms of the using the patient 360 data view 1 example, I can just talk about it from the patient perspective is that using the 360 data all the data, different points about [00:32:00] the patient, the pharma companies can actually use the machine learning algorithms to identify adverse patients.

Azeem Ahmed: The patients are at risk of not getting on a therapy. A patient was at risk of dropping out of therapy. And that actually, when you. Combine the different data sets that we talked about the patient interaction, the clinical and also social health. One of the things actually, that has been observed that the patients who have don't have a family connection.

Azeem Ahmed: They don't they live by themselves, right? They don't have any relatives, family, any of that. Actually, they're not really socially connected with other in the society. Those patients tend to actually have less. Desire a motivation to stay healthy and the use of the patients actually initially they may start a therapy because they keep feeling worse and then they will therapy starts to feel better than they stopped the therapy.

Azeem Ahmed: Having that kind of viewpoint about the patient, social determinants of health, as we can identify the at risk patients, and then the, pharmacompanies using the patient support programs [00:33:00] can device and actually reach out to these patients and get them into the patient communities, essentially have get them into like the different social settings.

Azeem Ahmed: Actually, they would feel connected. They would fully useful about that. And if you're good about actually the, their health is improving. Essentially, all these things like. Getting data from multiple sources and being able to analyze and actually interconnect the data points to be able to figure out the adverse patients, whether not able to get on the therapy and the same therapy, and then to be able to actually match them, those adverse patients with the interventions that are actually really helping patients.

Azeem Ahmed: So there's another aspect of that. We talked about the vision support programs. Being able to analyze the data that what interventions are really effective once that is is known, those effective interventions can be matched with the adverse patients. And that actually becomes a very powerful messaging that the way that the the pharma companies can interact with these patients and get their lives better.

Azeem Ahmed: Essentially.

Chris Detzel: That's great. Can you talk a little [00:34:00] bit about the data max platform? Is it specific to like patient or is it generic to all mdm systems?

Azeem Ahmed: It is a generic, basically, it is not specific. So is it essentially any of these data scenarios actually that we can use it for? We actually has been focused on life sciences consulting.

Azeem Ahmed: Actually, we focus more on life sciences, but we have built this data, the data max platform that actually has a lot of life sciences, specific items. For example, the in addition to we talk with MDM, we have do customer MDM, which is the nothing, but it should be MDM, a mastering solution. Thank you.

Azeem Ahmed: Also the other items. So we have specific things that are specific to life sciences. For example, the we use the NPA data uh, database to verify the NPIs. So we have all these built-in capabilities, actually are specific to the life sciences that actually, but having said that, any of the life sciences functions can be used and be uh, for this data MX platform.

Harneet Kapil: Yes, so just to add some [00:35:00] more color, right? It's a product. As she is so we are leveraging our own product in combination with radio. Augmenting for the capabilities to enable the patient 360 that we took you through. We have a similar for customer 360. so we have a lot of commercial clients that are bringing a variety of commercial data sources.

Harneet Kapil: We, we are very licensed workers as same sets. We actually work only with life sciences, right? So we know the data, we know the process processes, right? We know the sources. So all that comes. Over the platform, right? So it's not something we have to understand, right? We have, we know the variety of data sources.

Harneet Kapil: You can think about hundreds of connectors that are coming with the platform at this point. And then the from patient perspective, the another key differentiator that we have is really the integration with tokenizers. So we've already pre built that acceleration with some of the top tokenizer vendors.

Harneet Kapil: To enable what we need for. For patient 60.[00:36:00]

Chris Detzel: Are there any other questions? If there are, feel free to take yourself off mute and or put them in the chat, but for now, Arneet and Azeem, wow, that was really great, a lot of great thought leadership there and getting to know more about you guys. That was really good. Before we go please make sure you Take the survey at the end.

Chris Detzel: We always want to know how you how we did. And then, look, we have a couple more shows coming up over the next few weeks. Next week, we're taking off relative is off the whole week. So we're taking a recharge a week. But then after that, we'll have a few more shows, but If there are no other questions, any last words or need to resume?

Harneet Kapil: No I guess you will have our contact information. Please reach us. Feel free to reach us. If you want to, if you have any questions, or you want to start talk about the subject.

Chris Detzel: Yeah, great. So what I'll do is this has been recorded. I'll share this out. Hopefully later today or tomorrow with all the [00:37:00] information and with their contact information, if that's okay, or need to seem.

Chris Detzel: Okay, great. And so from there, thank you everyone for coming to the show. We'll see you next time. And again, please take the survey at the end whenever you leave. Thank you so much. Thank you. Chris. Thank you

 


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