IQVIA Holdings Inc. (IQV) Earnings Call Transcript & Summary

Hi, everyone. Welcome to today's live broadcast, Centralized Data Flow and Connected Workflow Intelligence to Enable Data Science and Clinical Trials: A Day in the Life of an IQVIA data manager. I'm Andy Studna, the Associate Editor of Applied Clinical Trials and I'll be your moderator for today's event. We are pleased to bring you this webcast presented by Applied Clinical Trials and sponsored by IQVIA. I would like to share a statement from our sponsor. IQVIA is a leading global provider of advanced analytics, technology solutions and clinical research services to the life sciences industry. IQVIA creates intelligent connections to deliver powerful insights with speed and agility, enabling customers to accelerate the clinical development and commercialization of innovative medical treatments that improve health care outcomes for patients. With approximately 74,000 employees, IQVIA conducts operations in more than 100 countries. Learn more at www.iqvia.com. We have a few important announcements before we begin today. This webcast is designed to be interactive, and we encourage you to ask questions during the event. You could submit questions by typing them in the Q&A box, which can be found on the right-hand side of your screen. You can enlarge the slide window by clicking on the small square icon in the upper right-hand corner of the slide window, or by hovering your mouse over the lower right-hand corner and dragging the window to the desired size. The slides will advance automatically during the event. And if you have any technical problems viewing or hearing this presentation please click on the question mark Help widget in the dock at the bottom of your presentation window. I would now like to introduce today's speakers. We are pleased to be joined today by Christina Lentz Larsen, Diana Enid and Parama Sil. Christina Lentz Larsen serves as a Director in the innovation team of IQVIA's Data Sciences, Safety and Regulatory division. In this role, Christina leads the standardization, simplification and digitization of data management, biostatistics and medical processes to transform the business, providing insight and guidance into IQVIA's next-generation unified data and process platforms. She has over 20 years' experience in the industry across programming, data management, statistics and risk-based monitoring and has worked both domestically and internationally within the large pharma, CRO and technology spaces. Christina graduated with a Bachelor of Arts degree in Mathematics, Cum Laude from Boston University and is a certified IQVIA Lean practitioner. Diana Enid serves as an Associate Director in Clinical data management of IQVIA's Data Sciences, Safety and Regulatory division. In this role, Diana serves as data management, customer liaison and provides management support across global data management projects. She has 15 years' experience in clinical data management and has worked on developing strategies for new processes and technology within IQVIA and partnered in driving innovations for large pharma. Diana graduated with a Master of Science degree in Clinical Research, ICRI, Cranfield University and is a certified IQVIA Lean practitioner. Parama Sil serves as a senior manager in clinical data management of IQVIA's Data Sciences Safety and Regulatory division. In this role, Parama manages a team of professional data management staff, manages key customer accounts and leads discussions on project requirements and strategic planning. She has 15 years' experience in the industry across multiple EDC platforms, various therapeutic areas and serves as data management subject matter expert. Parama graduated with a Master of Pharmacy degree in pharmacology from Manipal University and is a certified IQVIA Lean practitioner. Thank you to all of our speakers for joining today. And if you would, Christina, please get us started.

Great. Thanks, Andy. I'm going to skip right to the next slide. More than 4 years ago, IQVIA data management recognized the need to change to meet the industry challenges of ever increasing data volume, pressures to deliver faster while maintaining high-quality without increasing our costs. The obvious answer for us was automation, but how could we do that? IQVIA solution is intelligent data cleaning. It's a process leveraging an end-to-end ecosystem of data standards and process digitization, supported by technology, which centralizes both clinical and operational data to facilitate automation. By automating the flow of all data sources into a centralized location, we eliminate the need for human processing during conduct. We established a single source of truth for all data sources on the study, and we enabled all of our team members' visibility into the same data at the same time. By leveraging connected standards from eCRF design and edit checks through to SDTM, ADaM and TLF and tracking compliance to those standards, we accelerate study startup activity to be completed closer to [ SPI ]. And lastly, by digitizing our processes, we're able to transform key information that's previously locked in documents like Word and Excel into operational data that's used electronically to drive, process and help our teams manage workload as we move through conduct. All of this enables IQVIA to clean data in real time as data sources are refreshed. Meeting the data volume challenges, delivering faster with high quality without increasing costs. So regarding our digitized processes, we developed a workflow navigator. Internally, it's known as GDMPM, and this supports all of our digitized process. At the top of the operational processes, that contain the key information, which is used throughout the system to automate tasks, control access and rights and prevent content. At the bottom of the processes, which are the key components of IQVIA's intelligence data cleaning process. By digitizing all of these processes and then intelligently connecting them within the technology, the Workflow Navigator guides users through task to be completed and manages those tasks automatically for them. This has enabled our staff to focus on the more value-added tasks requiring their expertise. So what is real-time data cleaning, and how do we enable it? So first, we set up -- we need to set up our data sources. We're defining those data sources, all of the sources on the study and the data acquisition module. Our data strategists are a specialized group that works specifically with the vendors and the sponsors to identify the data sources and the metadata that will be provided to us when those files start to be ingested. Once the state of metadata -- the data source metadata is defined, that metadata is made available in our data programming specification module. And here is where we define the reviews and the checks that our data management team are going to perform during conduct. These are those reviews and checks that can't be programmed as automated edit checks within the EDC system. The metadata that we're using directly connects those checks to the source files through the data. We program and validate those checks and those fit on our clinical data repository waiting for conduct to start and the data flows to be established. Now during conduct, as the data is refreshed -- the data -- the metadata that was defined in the data acquisition modules is automatically used to ingest that data and run performance checks against that data to ensure that the data that's flowing in meets the specifications that were defined in the data acquisition module. Any issues found with the formatting of the data are automatically sent back to the vendor for resolution right then and there. Then as that data flows into our clinical data repository, the checks that were programmed against that data fire and run, and those issues are -- the issues that are identified appear automatically in our data review modules for our data management team to review and action. Those issues, if they need to be sent as a query to the site, the data management team select -- simply selects site as the resolution owner. And the system takes the information that was captured in the issues and transforms that into an auto query directly in the EDC system. That auto query retains a linkage to the issue and to the data that generated that issue. In addition, if an issue needs to go out, say, to a vendor as part of vendor reconciliation, or perhaps it needs to go as an escalation to a clinical team member to help resolve something, the data management team simply checks who needs to go to, and those issues are routed directly to those resolution owners. These resolution owners don't need to get an e-mail or a log, they log into a portal and they can see immediately the issues that are there for them. They address those issues directly in the portal and our data management team has immediate access to the status of those issues. In addition, now that all data, both clinical and operational data is available essentially, we have new ways of reporting on that data. And including one of these ways is the Clean Patient Tracker Dashboard, which Diana will get into in a few minutes. So what has this meant to our IQVIA and our sponsors? Well, essentially reduced cycle times. By automating ingestion and triggering checks, as the data refresh, we're able to cut down significantly the amount of time that we used to see in the traditional method of cadence-based cleaning and put the data cleaning closer to the time that the data is collected, making it easier for teams to identify issues and trends and mitigate those issues and trends before they occur more frequently in other -- for other sites and other patients. In conclusion, RTDC not only saves time and decreases costs, but also accelerates data access and increases the visibility for both clinical and operational data. This allows teams to collaborate more effectively, resulting in efficient operations, the ability to maintain high quality in the face of those increasing data volumes and ultimately helping to improve patient lives. And with that, I'm going to hand it over to Andy.

Thank you, Christina. At this time, I would like to ask the audience to participate in a few polling questions. You can record your answers by clicking on your answer directly on screen. And here is our first question. How do you manage projects in your organization? Choices are: Excel trackers; systemized, but not visible to all team members; systemized, but fully visible to all team members; and other. While we give the audience a moment, I'll repeat the choices: Excel trackers; systemized, but not visible to all team members; systemized, but fully visible to all team members; and other. [Voting]

And our results -- it looks like a tie at the top between Excel trackers and systemized, but not visible to all team members. Thank you for participating. Now on to our second polling question. As a reminder, you can record your answers directly on the screen once again. What is the frequency of sending or receiving EDC status metrics in your projects just prior to study locks? The choices are: Monthly; weekly; daily; and other. Once again, what is the frequency of sending or receiving EDC status metrics in your projects just prior to study locks? Choices are: Monthly; weekly; daily; and other. [Voting]

And our results, a very large lead for weekly on that one. Now on to our third polling question. How much time do you spend weekly in identifying critical to-do tasks for your projects? More than 1 hour, less than 1 hour, no hours spent, it is immediate. Once again, how much time do you spend weekly in identifying critical to-do asks for -- tasks, excuse me, for your projects? More than 1 hour, less than 1 hour, no hours spent, it is immediate. [Voting]

And the results. The vast majority, more than 1 hour. Now moving on to our fourth polling question. On an average, how often do you clean data when your projects are in conduct? daily, weekly, monthly, as and when data gets refreshed. Once again, on an average, how often do you clean data when your projects are in conduct? Daily, weekly, monthly, as and when data gets refreshed. [Voting]

And the results for our fourth question, 41.2% as and when data gets refreshed, the top response. Now on to our fifth and final polling question. What do you see as the biggest challenge enabling faster cleaning cycles? And for this one, you may select more than 1 response: not using a standard library for CRF design; too many manual activities requiring more effort and slowing the progress; delayed identification of external data providers; unavailability of proper customer reports to track subject cleanliness; and late identification of data issues and inadequate mitigation plans. Once again, what do you see as the biggest challenge enabling faster cleaning cycles? You may select more than 1 response to this: Not using a standard library for CRF design; too many manual activities requiring more effort and slowing the progress; delayed identification of external data providers; unavailability of proper custom reports to track subject cleanliness; and late identification of data issues and inadequate mitigation plans. [Voting]

And our results for our fifth and final full question, the majority at 72.2%, too many manual activities requiring more effort and slowing the progress. Once again, thank you for participating in all of our polling questions today. I would now like to hand it over to our speakers, excuse me for any comments on the results.

Yes. Thanks, Andy. I think the polling questions were interesting because it gives a sense of -- I think what was surprising for me was the cleaning frequency, as and when data gets refreshed. That one stepped up to me a lot because it's really -- I think it's very manual in most cases. Unless you have an automated data flow to be able to clean data as and when it's refreshed. There's a lot of manual proxies that go into receiving files, processing those files, checking those files and then running checks against those files. So it's interesting to see that, that was one of the top responding -- responses, excuse me. I think the rest of the responses, in my opinion, it's typical of our industry for sure that things that are tracked in Excel are not visible centrally, and we tend to be on -- in general, on a cadence schedule. Our administrative task take a long time. I saw more than 1 hour, which is not surprising. And so I think when we get into the presentations and demos, Diana and Parama will show -- will show some of the ways that our systems and our ecosystem and real-time data cleaning has helped our staff reduce those identification of tasks and really made things more centrally visible. And with that, I'll hand it back to you, Andy.

Thanks, Christina. I'd now like to hand it over to Diana and Parama for the next portion of our webcast today.

IQVIA's response to intelligent data cleaning was it adopting the principles of real-time data cleaning, also referred to as RTDC, to drive data management operations consistently across the organization. RTDC empowers a data manager to work smartly through IQVIA's connected workflow manager, also called as Global Data Management Project Manager or the GDMPM. I am Parama Sil, and today I would like to share with you certain benefits that I have experienced while working on RTDC projects as a data manager. I would also like to describe a few unique features spread across several applications within the GDMPM. The first application that I would like to describe is the User Dashboard. Once I log into the GDMPM and reach the landing page of the User Dashboard, this is the screen that appears. At the moment, I have only 1 project in RTDC, so the top of the screen gives me the project information. How the User Dashboard helps me is, it helps me in all administrative tasks by providing a holistic view of my projects health, by displaying a summarized view of the various metrics for the different modules applicable to my project and my role in the project. When I scroll down, the different GDMPM modules of my study appear. I can verify the overall status of those GDMPM modules by the consistent color coding. A green check means these modules do not need my attention, while a red exclamation mark means I have to action them. Clicking on any of these modules would open its summarized status in the center of the page. For now, the Data Management Dashboard is clicked and the overall status from that module is coming on the screen. Again, User Dashboard provides a consistent red, amber, green color-coding system. Red are items that need my immediate attention, they are critical to action, where amber are items that needs attention, but not immediately. To conclude, this kind of color coding differences helps me to prioritize my work, putting more focus on critical action items, pulls the study upwards and forwards to our customers. Instead of searching through the entire GDMPM to identify the location where the updates are required, User Dashboard provides me with a hyperlink next to each of these metrics. I can click and directly go to the item and resolve it quickly. One great feature of the User Dashboard which I find brilliant is the fact that it gives the real-time status on filing of the various data management documents into the study trial master file. Within GDMPM, several data management documents are created and even go through a review and approval workflow. Once finalized, GDMPM auto loads that document into the study trial master file. If any document fails upload, the status appears in the User Dashboard as shown here. As GDMPM links with the study trial was still file, it helps me in taking contemporaneous and correct filing of all data management documents. Overall, the User Dashboard helps me to achieve higher project compliance scores and to maintain the overall quality of the data management deliverables. I cannot edit any of these summary tasks. I need to actually go into the items and actually make the corrections, submit them into the system, return to the User Dashboard and verify that either the red or the amber metrics have turned into green. This intelligence in the User Dashboard helps me by reducing manual effort and hours spent on project management. The summary metrics can also be downloaded in the form of a report, which helps in better tracking of the study. It promotes transparency and visibility of the study to the entire organization. So this is regarding -- some of the key features of the User Dashboard, which makes it so friendly for the data managers. The next module within the GDMPM application that I would like to review is the Data Management Dashboard. The hyperlinks are provided here, so once I go and click into this arrow, it would take me to the Data Management Dashboard of GDMPM. Unlike the User Dashboard, the Data Management Dashboard module within GDMPM is primarily used and completed by the data manager. This is the landing page, showing the studies I have been assigned to. Right now, I just have 1 study in RTDC, which is coming on the screen. I will load the study by clicking on this hyperlink. In Data Management Dashboard, I would be capturing important study management information across the several tabs that are present, as you can see on the top of the screen. The information that I feed into this module would be converted into systemized data, residing in a centralized and standardized repository. This data would then generate more intelligent data to efficiently power the downstream DM activities. For me, the best features of DMD are its capability of auto-generating study-specific SOP list based on the raw information included in the tabs like the summary tabs. A very important section of the Data Management Dashboard summary tab is the data management specific field section. In this section, I would be recording the EDC platform of the study, whether local labs are there for the study or not, whether there are data integration scope for the study or not, what is the library that is being used for the CRM design? Is it the IQVIA library or the customer liability? If it is the IQVIA library, what is the version of the library being used? So once all this information is fed into this section and the data is submitted in the system, all this data goes into auto creating the SOP list, which is specific for the study. The SOP list, thus created, appears under the controlled documents tab within the Data Management Dashboard. All I have to do as a data manager is to check the validity of the SOPs that has appeared, click all the SOPs that are actually valid for the study, go and submit it into the GDMPM with a click of the button. Not only does it make the SOP list final for the study within GDMPM, but it also auto loads into the trial master file, as I have described when describing the User Dashboard section. One more feature of the control documents tab is whenever there is any SOP that gets upgraded, it notifies the user that there is some action that needs to be taken, so I can go and update the division number of the particular SOP and again, submit it into the system, which again goes and feeds into the trial master file. So it is the User Dashboard that gives me the filing -- which gives me the alert that there is some action or some SOP that needs to be upgraded. And I just need to go check the validity of the instruction, take the actions and submit it in the system so that always the SOP remains current. Another very important feature of the Data Management Dashboard is I would be able to create and manage the project team assignments, both data management and non-data management, both IQVIA and external project team members. So under the tab, IQVIA project team, this is a section where I would be managing the IQVIA -- or the project team from IQVIA, both the data management roles as well as the non-data management roles. One good feature is it allows me to enter the name of the primary contact as well as the list of backup contact. So it helps me to -- it gives me the coverage to continue with the project even when their primary contacts are out of office or on a long leave of absence. Under the vendor -- under the vendor external project team, it is the place where all the external project team members are assigned. Because I have this contact list within GDMPM, as and when needed, I would push the data management documents into the review and the approval workflow based on these assignments. GDMPM would direct to the correct team members without me referring to any external contact list or manually sending additional e-mails or reminders. In Data Management Dashboard, this is the domain where the different data sources along with their respective vendors are listed, including the e-mail domains and the e-mail addresses of these different vendors. As GDMPM modules are interconnected, I would liaise with a data strategist to align these different data sources into a single defined data stream, thus reducing duplication of efforts across team members, often leading to inconsistencies while reviewing the same data. The information that is being captured here would be important for me to help in consideration of the external data types. Any actions that are to be taken by the vendor teams during the data review process are also directed to the correct people easily. This feature will be discussed in more details while reviewing the next GDMPM modules. [indiscernible] capabilities on centralizing data means it is able to aggregate all data sources into a single data stream to power operational processes consistently across IQVIA. There are 3 key components of modules that are interconnected and work in unity with IQVIA's clinical data repository in order to enable the real-time data cleaning. These modules are: Data acquisition; data programming specification; and the data review module. Let us first go into the data acquisition module. The landing page gives the project information, so I need to click the project I would like to go into. And once clicked, this is the landing page that I see. So what is this module? The information about the vendor data and the different data sources that would be used in a clinical study are created and maintained by the data manager in the Data Management Dashboard. In the data acquisition module, the data entered from that tab flows into this tab, and later on the creation and the ongoing maintenance of the information within the data acquisition module is done by the data strategist. So during database build on the process set up, the data strategist and I would work together to define all of the sources and document the metadata information for each of these data sources that will be collected on the study. For example in the screen, for these particular projects, the different types of data or the different sources of data, the different types of data as well as different vendors from where this data would be provided are all listed down. The metadata information regarding each of these data that is flowing into the study would be maintained within the data acquisition specifications, or the DAS. The data strategist would work directly with the vendors to identify and establish daily automated ingestion of data sources, eliminating the need for manual processes throughout the conduct of the study and establishing a real time data flow centrally. As a data manager, I would log in to check the status of the DAS, how far they have progressed in the review and the approval workflow. Once the data is -- once the DAS is approved, its configurations trigger automated ingestion of the vendor data, already posted to the SFTP by the vendor teams into the IQVIA's clinical data repository. During this automated ingestion, the data file gets auto verified with the metadata information contained within the DAS. Any errors in the file structure are caught in real time and followed up with the vendor teams until resolution. As ingestions are daily, I find it easier to detect issues early on, giving me enough time for risk mitigation. The next module that we would like to review is the data programming specifications. So what is the information that is contained within the data review programming specifications? Similar to how the information within the data management board is created and maintained by the study manager, by the study data manager and how the data acquisition module is built up by the data strategist, the data review programming specifications module is created and maintained on an ongoing basis by the technical designers with the data manager of the study playing a very prominent and a critical role. It is in this section that the technical designer will be defining the different specifications for the data review process. The specification checks would also include the vendor reconciliation checks at the [ assay ] reconciliation checks. The intelligent feature of this module is that it is linked both with the data acquisition model as well as the standard library of checks. So once the technical designer is building the data review checks, they have the metadata information of the DAS coming from the data acquisition module, as well as the ready-made checks that they can reference coming to them from the standard library of checks. Having this data with the technical designer while building this section helps to standardize the process. As a data manager, I would make sure to confirm that all the checks are in the approved status and ready for configuration. One thing to note is, when we use a check as it is from the standard library of checks, one can bypass the programming and the testing effort, as all the checks within the standard library are already pre-validated. So we just pick up those checks from the standard library if they are applicable to the project. And once it is approved within the workflow, it directly gets configured to the study without going through another exhaustive rounds of programming and testing. This has helped us to expedite study setup to a very great extent. Once these checks are programmed and configured to the data review module, these checks would run on the study data on a daily basis. When I set up checks within the DRPs, it means that I have shifted to a digitized ecosystem from a manner of working where specifications were created on physical documents. And thus, change into a digitized ecosystem has enabled automated efficiencies. The best feature in the GDMPM DRPs module is the presence of an audit log. So if I want to check how the specifications have got modified after they were initially set up in the study, I can log into the audit, presenting this AVI here, check the auditor report and understand and track the information of all the modifications done on the checks during the life cycle of the project. The next module that I would like to take you through is a data review module. The data review module is a tool for the reviewers to review the discrepancies and to take the appropriate actions of either raising queries in EDC or route the issues to an external vendor via the external portal. So what is the role of a data manager in this particular module? Before I start or before the data review process starts for a study, I would need to set up some predefined criteria by using the admin that I find -- that you'll defined here. In the case of the study, the different preset criteria were not defined, and that is why it is coming as an amber alert on the screen. What are the different types of criteria that I can go for and predefine? I can -- for example, I can manage the frequency in which the checks are being run on the refreshed data. I would also be able to set up the different study specific user roles who can work in the DR module. For example, at the moment, there is this red alert that is coming at the -- below the data reviewer's name. And this is coming because I have not yet set up the different data reviewer's name in the IQVIA project team tab on the Data Management Dashboard, who can actually work as data reviewers in the study. I can also set up a data aging criteria. So based on how the defense queries are given a particular red, amber, green status, that would help to generate those metrics under the DR dashboard. Until I set up the aging criteria, all the queries will come as 0. I would also be able to set up some alerts for all the reviewers so that the data review module is able to send automated alerts, asking them to log in and to take actions. One thing to note is that the data cleaning checks run continuously as the data is refreshed in the clinical data repository daily. That means that as data manager, I have to request my data management team to log into the data review model and to check the summary of the status and to take actions wherever necessary in order to keep data processing in real time. Data Review Dashboard also gives an overall status of the discrepancies within this application. So not even as a data manager, if there are other project team members or even the senior management who would like to track the status of the different discrepancies, they can just log in with the data review module, Go to the Data Review Dashboard, as you can see here, and get an idea of how the data cleaning is going in the study. Once I walk into the module, all the queries for my action comes under review. So once I review the query and allocate a resolution owner group to a particular query, it will go from the status of under review to awaiting response. So once other project team members also log in into the external portal and they navigate to this particular module, they can see all the queries that are under their action under review for them. And once they action the query and send it back to the request that is me, I will again get it under review section. So this is the DR dashboard, which is giving me the overall count of what it is -- of the different status of the queries. And if I want to check the individual queries, I have to just go to the listing view. And there, I will be getting with the details of each of the queries. In the listing new, I will see all of the issues assigned to me for review and action issues will flow in daily. So it will be important to check the DR regularly do. Usually, I review this module daily. This enables early detection of data issues, often lies with vendors, clinical and the medical team and even the biostatisticians to create a risk mitigation plan after checking the live data trends. Keeping data up cleaning real time and expediting study blocks without impacting the overall data quality. The next information of how the reported is also connected to various reporting and analytics system, for example, the Spotfire will be taken care of by my colleague, Diana Enid.

Hello, I'm Diana Enid, and I will be presenting the Clean Patient Tracker and Reports Transfer Automation. In clinical trial data management, tracking project metrics aligns people and process with the project's objectives. It provides data insights, enabling data managers and study team with directional data to drive the strategies. It also helps make decisions and drive performance. Today, I'm going to demonstrate the Clean Patient Tracker Dashboard, a tool used in IQVIA for real-time metrics collection and status reporting. So Clean Patient Tracker Dashboard is a reporting tool built on typical software, data visualization and analytics software. It is linked with the EDC platform and the non-EDC data review platform, providing data managers, both EDC and non-EDC, that is the vendor data flow, and data quality status in a consolidated view. What we are looking at right now is the landing page of the CPT dashboard. It comes as of navigation buttons, which allows data managers and study teams to navigate across data entry status like missing visits, missing forms, incomplete forms, related information, and this also includes query status information, along with query aging details and other information, like the SDV, freeze, sign and lock. This includes the Clean Patient Tracker as well, which gives data at a subject level. I will be navigating into a few important tabs here to give an idea of how the data is reflected within the dashboard. Firstly, the missing visits report. The missing visits report, this metric is programmed to generate all visits that are overdue as per the protocol requirement on the day of report generation. Thus, it saves time from manually and eliminating undesirable visits from the report. And it is programmed to identify visits that not -- that are not currently overdue and visits that are truly not expected due to early termination. This saves a lot of time for the study teams because it gives focused information. and helps teams to connect with sites and ensure that the right support is provided to the sites to enter the data into the EDC system. Then we have the missing visits and incomplete forms report. These provide an insightful information into the forms that are overdue or incomplete for the visits that have occurred, again, allowing the study teams to follow up with the relevant cross-functional teams and influence timely data flow into EDC. Next, we have the query-detailed report. It is a very interesting report because this just doesn't just give query information, but it also helps segregate the query information based on the marking groups. So we know who has raised the queries, or if the queries have been raised to the site or to the CRAs, we know who needs to be looking into these queries. So therefore, the segregation by marking groups, it's very useful for the study teams. And then we also have the days answered, days closed information, which gives us a good idea about how many days these queries have been overdue, and we can follow off with the relevant teams to go ahead and action on these queries. Then we have the pending SDV. This section is designed to enable both 100% SDV status tracking and also targeted SDV reporting. Then we have the freeze, sign and lock. These provide us the information of any forms that are pending freeze, sign and lock that gets generated into the report. Moving on, we have the Clean Patient Tracker. This is a very useful information because Clean Patient Tracker, this section is integrated with the data review module and other vendor review status logs. Therefore, this provides data entry and cleaning status of EDC and non-EDC data at a patient level. And this also includes a risk criteria, like we see here. It has red, amber and green, which allows teams to focus on patients or sites that require attention. And these red, amber and green are -- it appears automatically based on the total number of outstanding issues, and it can be customized at project level. Similarly, we have a summary tab, which provides summary at the site level. And this is mostly beneficial for an overall status reporting and also to provide project during the project health reviews. Clean Patient Tracker has a very unique feature, this is called as the study level filters with support teams to perform targeted monitoring of data entry and cleaning status enabling faster metrics generation, especially for interim analysis and other interim milestones that require analysis of targeted subjects or critical data items. Clean Patient Tracker is also -- the Clean Patient Dashboard is also linked with reports transfer automation, or RTA. That's a process that automatically downloads reports from various sources without any manual intervention. Therefore, as a data manager, I don't have to log into the EDC system or any other tools to generate the reports because the programs -- the reports would be automatically generated by the RTA process. So in summary, by using various GDMPM tools, like the User Dashboard, Data Management Dashboard, the data review modules, data acquisition and Clean Patient Tracker Dashboard, I have seen time saving and improved quality across project deliverables. In addition to this, the sponsors, clinical teams and cross-functional teams have also benefited by the real-time and informative metrics provided by data management. As a data manager using IQVIA solution to intelligent data cleaning, I have personally experienced faster turnaround time and have been able to monitor multiple processes during setup, maintenance and closeout of a project seamlessly. And it has helped me shift focus from manual and administrative tasks to value-added, smart data management. After the evolution of data management on paper to EDC, this is the next big evolution experienced by data managers in IQVIA.

Thank you to all our speakers for such an informative presentation today. Before we get started on our Q&A section, I would like to remind our audience how to submit questions. [Operator Instructions] So we'll give the audience a brief moment to put their questions in. And our first question is going to be for Christina. You are utilizing standards throughout your process. Do you find study teams willing to comply to the standards?

That's a great question. I think our industry has had standard forever in a day. Everybody has a set of standards for something, maybe not all components. But the key here is that we have standards and then we let people deviate from those standards. And so we need -- in order to automate, which is really where the industry wants to get to, we have the technology, we have the capabilities to automate, standardization is extremely important. You cannot automate without standardizing what you do. And so there has to be a renewed effort on all of our parts, both from IQVIA side, where we are actually touting the benefits of standardization and how that enables us to be more efficient across the board. But our sponsors and our vendors need to also get on board with standardizing and adhering, complying to those standards. Otherwise, we have no chance of becoming as efficient as we could be.

Thank you, Christina. Our next question that we have is, we heard that there is a lot of emphasis on real-time data cleaning in IQVIA. Considering this, what is the time frame and quality gates defined for the rollout of metrics report?

I can take that, this is Diana. That's a great question. Definitely a lot of emphasis on real-time data cleaning. During the poll question, we did observe that the biggest challenge enabling faster data cleaning is manual work, and that's the response that we got. It's definitely not surprising because that's the area that slows down and doesn't really meet the objective of real-time data cleaning. So both data cleaning and metrics go hand-in-hand. Therefore, what -- the time frame that we internally have defined is to ensure that the development of the metrics initiated at a very early stage, that is as soon as a stable ECR design is available, the metrics development is initiated, that is the creation of the Spotfire CPT. And this is rolled out at least 1 month after FPI. So this helps the study teams to have metrics at a very early stage and track compliance with data flow, data cleaning and so on.

Thank you. Our next question is what kind of DM study documents get authored and auto-loaded into the eTMF?

Andy, that's a good question, and I would like to take that up. So like already touched upon, some of the documents that are authored and also go through a review approval workflow within GDMPM and they include some of the administrative documents like the SOP list. And then there is another module between GDMPM, which is the action management module where all the projects or statistic meetings are tracked. So with the capabilities of GDMPM not only of the meetings tracked, even the interconnected activities like communication log, the meeting minutes are published, based on how the project in assignments have been done by the clinical data managers, the right people also get just the notification of the project meetings being maintained within the GDMPM. Another quick picture is based on how the completion dates, the due dates of each of the reaction submission are included in the GDMPM, the rights would also get the notification, that there is an action that is very -- that is coming -- that is coming close with the completion date, so that gives them another chance to prioritize the completion of the task. In addition to the administrative document, we also have some of the data management plans, very specific for the study that are also reviewed and approved within GDMPM and thus it is auto-loaded within the trial master file and that improves the data acquisition specifications for each vendor, for each data file of each vendor. And then we have the review programming specifications as well where all data review checks are listed. With regards some of the most common documents, which are both administrative as well as study specific, that are authored and move from draft to version within the GDMPM and this gets auto loaded in the study trial master file. Thank you, Andy.

Our next question from the audience is what challenges do your teams face when trying to improve the quality of the data?

Andy, I can take that one. This is Christina. So I think one of the biggest challenges that we see is that everyone wants to move faster and identify -- the best way to move faster and also improve the quality of the data is to ensure that all team members are moving at the same speed. We already clean the data. Everyone in our industry cleans the data to its fullest, and we all strive for the highest quality. But really, it's about speed and quality in this case. And so we ask -- IQVIA's prepared to receive data more frequently to perform cleaning activities as the data is flowing in. And so really, we would like the industry to get on board with getting data to us faster, getting -- making data available as quickly as possible and being able to respond to issues when they're -- when they are identified. And I think this is a paradigm shift for our industry. We're used to working cadence based. And so everything is scheduled on a monthly cadence. And our cleaning processes really need to speed up, and we really need to get those resolutions to the issues that are being identified in more real time faster, so that those issues and trends can be mitigated sooner. And that ultimately leads to a higher quality data, higher-quality experience for both the team members and the patients. So I think that's really the key, just trying to improve quality of the data as we move into the next entry of data cleaning.

Thank you. Our next question is going to be -- your solution is able to ingest vendor data daily, but I still see many third-party vendors contracting to transfer data monthly. How do you handle this?

Yes, I'll take that one, too, because it's kind of a follow-on to the last one. So I think while we're asking, we know that the industry might not be ready. There are certain vendors that can provide data very frequently. In fact, we can establish direct connections to their vendor sources. But other vendors aren't ready to do this, and that's okay. Baby steps. So if we can get data instead of monthly, maybe we get it bi-weekly or maybe we get it weekly. Then that just means that, that particular data source is cleaned at that frequency. The issues will still trigger as soon as the data source is flowing in or refreshed. But if we could get that data daily, we could identify the issues on a daily basis rather than on a weekly or biweekly basis. So really, it's -- it's -- we're trying to work with each and every vendor, each and every sponsors to get data as close to collection as possible, as frequently as possible, and work with them to get issues resolved as quickly as possible.

Thank you, Christina. Our next question that we will go into is, how do you deal with different ontologies across the various data sources? How do you map in between?

Yes. This is all about how do we make the data sources work together. It's one thing to actually centralize the raw data, but then how do we become more efficient using that raw data. And so at IQVIA, what we do, our data strategy is not only ingest the raw data, but then they are mastering that data. So each data source as it comes in, is mastered to a set of standards that we can use downstream, so that we don't need to potentially reprogram, revalidate and change, or customize the downstream activities to a particular data source. So you can imagine there's quite a bit of effort to get that data mastered, but the idea is that the mastering will enable us to streamline and become more efficient downstream. This doesn't just include column headers, but it also includes the content of the column. So things like codeless, which might not be the same from study to study. While we ask and we try to get standards in place upfront with each direct design, it's oftentimes not possible. And so we work on the back end or in the intermediary stage to master that data and enable those connections to be made downstream.

Thank you, again, Christina. Unfortunately, we are out of time for today. I want to thank the audience for attending and for participating in today's event. I would also like to thank our sponsor, IQVIA, for making today's educational webcast possible. We would like to ask everyone in the audience to participate in a brief survey. And you can see the survey to the right of your screen. You will receive an e-mail alerting you when this webcast will be available for replay. We invite you to forward them also to your colleagues who may have missed today's live event. Thanks to all for joining today, and we will see you next time. Goodbye.