I am Mark Elsley, a Senior Clinical Research / Data Management Executive with 30 years’ experience working within the pharmaceutical sector worldwide for companies including IQVIA, Boehringer Ingelheim, Novo Nordisk and GSK Vaccines. I am skilled in leading multi-disciplinary teams on projects through full lifecycles to conduct a breadth of clinical studies including Real World Evidence (RWE) research. My specialist area of expertise is in clinical data management, and I have published a book on this topic called “A Guide to GCP for Clinical Data Management” which is published by Brookwood Global.
Data quality is a passion of mine and now receives a lot of focus from the regulators, especially since the updated requirements for source data in the latest revision of ICH-GCP. It is a concept which is often ill-understood, leading to organisations continuing to collect poor quality data whilst risking their potential rejection by the regulators.
White and Gonzalez1 created a data quality equation which I think is a really good definition: They suggested that Data Quality = Data Integrity + Data Management. Data integrity is made up of many components. In the new version of ICH-GCP it states that source data should be attributable, legible, contemporaneous, original, accurate, and complete. The Data Management part of the equation refers to the people who work with the data, the systems they use and the processes they follow. Put simply, staff working with clinical data must be qualified and trained on the systems and processes, processes must be clearly documented in SOPs and systems must be validated. Everyone working in clinical research must have a data focus… Data management is not just for data managers!
By adopting effective strategies to maximise data quality, the variability of the data are reduced. This means study teams will need to enrol fewer patients because of sufficient statistical power (which also has a knock-on impact on the cost of managing trials).2 Fewer participants also leads to quicker conclusions being drawn, which ultimately allows new therapies to reach patients sooner.
I believe that clinical trials data are vitally important. These assets are the sole attribute that regulators use to decide whether to approve a marketing authorization application or not, which ultimately allows us to improve patient outcomes by getting new, effective drugs to market faster. For a pharmaceutical company, the success of clinical trial data can influence the stock price and hence the value of a pharmaceutical company3 by billions of dollars. On average, positive trials will lead to a 9.4% increase while negative trials contribute to a 4.5% decrease. The cost of managing clinical trials amounts to a median cost per patient of US$ 41,4134 or US$ 69 per data point (based on 599 data points per patient).5. In short, clinical data have a huge impact on the economics of the pharmaceutical industry.
Healthcare organizations generate and use immense amounts of data, and use of good study data can go on to significantly reduce healthcare costs 6, 7. Capturing, sharing, and storing vast amounts of healthcare data and transactions, as well as the expeditious processing of big data tools, have transformed the healthcare industry by improving patient outcomes while reducing costs. Data quality is not just a nice-to-have – the prioritization of high-quality data should be the emphasis for any healthcare organization.
However, when data quality is not seen as a top priority in health organizations, subsequently large negative impacts can be seen. For example, Public Health England recently reported that nearly 16,000 coronavirus cases went unreported in England. When outputs such as this are unreliable, guesswork and risk in decision making are heightened. This exemplifies that the better the data quality, the more confidence users will have in the outputs they produce, lowering risk in the outcomes, and increasing efficiency.
ICH-GCP8 for interventional studies and GPP9 for non-interventional studies contain many requirements with respect to clinical data so a thorough understanding of those is essential. It is impossible to achieve 100% data quality so a risk-based approach will help you decide which areas to focus on. The most important data in a clinical trial are patient safety and primary end point data so the study team should consider the risks to these data in detail. For example, for adverse event data, one of the risks to consider could include the recall period of the patient if they visit the site infrequently. A patient is unlikely to have a detailed recollection of a minor event that happened a month ago. Collection of symptoms via an electronic diary could significantly decrease the risk and improve the data quality in this example. Risks should be routinely reviewed and updated as needed. By following the guidelines and adopting a risk-based approach to data collection and management, you can be sure that analysis of the key parameters of the study is robust and trust-worthy.
Aside from the risk-based approach which I mentioned before, another area which I feel is important is to only collect the data you need; anything more is a waste of money, and results in delays getting drugs to patients. If you over-burden sites and clinical research teams with huge volumes of data this increases the risks of mistakes. I still see many studies where data are collected but are never analysed. It is better to only collect the data you need and dedicate the time saved towards increasing the quality of that smaller dataset.
Did you know that:
In 2016, the FDA published guidance12 for late stage/post approval studies, stating that excessive safety data collection may discourage the conduct of these types of trials by increasing the resources needed to perform them and could be a disincentive to investigator and patient participation in clinical trials.
The guidance also stated that selective safety data collection may facilitate the conduct of larger trials without compromising the integrity and the validity of trial results. It also has the potential to facilitate investigators and patients’ participation in clinical trials and help contain costs by making more-efficient use of clinical trial resources.
Technology, such as Electronic Health Records (HER) and electronic patient reported outcomes (ePRO), drug safety systems and other digital-based emerging technologies are currently being used in many areas of healthcare. Technology such as these can increase data quality but simultaneously increase the number of factors involved. It impacts costs, involves the management of vendors and adds to the compliance burden, especially in the areas of vendor qualification, system validation, and transfer validation.
I may be biased as my job title includes the word ‘Data’ but I firmly believe that data are the most important assets in clinical research, and I have data to prove it!
Scimcon is proud to support clients around the globe with managing data at its highest quality. For more information, contact us.
1White, Christopher H., and Lizzandra Rivrea González. “The Data Quality Equation—A Pragmatic Approach to Data Integrity.” Www.Ivtnetwork.Com, 17 Aug. 2015, www.ivtnetwork.com/article/data-quality-equation%E2%80%94-pragmatic-approach-data-integrity#:~:text=Data%20quality%20may%20be%20explained. Accessed 25 Sept. 2020.
2Alsumidaie, Moe, and Artem Andrianov. “How Do We Define Clinical Trial Data Quality If No Guidelines Exist?” Applied Clinical Trials Online, 19 May 2015, www.appliedclinicaltrialsonline.com/view/how-do-we-define-clinical-trial-data-quality-if-no-guidelines-exist. Accessed 26 Sept. 2020.
3Rothenstein, Jeffrey & Tomlinson, George & Tannock, Ian & Detsky, Allan. (2011). Company Stock Prices Before and After Public Announcements Related to Oncology Drugs. Journal of the National Cancer Institute. 103. 1507-12. 10.1093/jnci/djr338.
4Moore, T. J., Heyward, J., Anderson, G., & Alexander, G. C. (2020). Variation in the estimated costs of pivotal clinical benefit trials supporting the US approval of new therapeutic agents, 2015-2017: a cross-sectional study. BMJ open, 10(6), e038863. https://doi.org/10.1136/bmjopen-2020-038863
5O’Leary E, Seow H, Julian J, Levine M, Pond GR. Data collection in cancer clinical trials: Too much of a good thing? Clin Trials. 2013 Aug;10(4):624-32. doi: 10.1177/1740774513491337. PMID: 23785066.
6Khunti K, Alsifri S, Aronson R, et al. Rates and predictors of hypoglycaemia in 27 585 people from 24 countries with insulin-treated type 1 and type 2 diabetes: the global HAT study. Diabetes Obes Metab. 2016;18(9):907-915. doi:10.1111/dom.12689
7Evans M, Moes RGJ, Pedersen KS, Gundgaard J, Pieber TR. Cost-Effectiveness of Insulin Degludec Versus Insulin Glargine U300 in the Netherlands: Evidence From a Randomised Controlled Trial. Adv Ther. 2020;37(5):2413-2426. doi:10.1007/s12325-020-01332-y
8Ema.europa.eu. 2016. Guideline for good clinical practice E6(R2). [online] Available at: https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-6-r2-guideline-good-clinical-practice-step-5_en.pdf [Accessed 10 May 2021].
9Pharmacoepi.org. 2020. Guidelines For Good Pharmacoepidemiology Practices (GPP) – International Society For Pharmacoepidemiology. [online] Available at: https://www.pharmacoepi.org/resources/policies/guidelines-08027/ [Accessed 31 October 2020].
10Medical Device Innovation Consortium. Medical Device Innovation Consortium Project Report: Excessive Data Collection in Medical Device Clinical Trials. 19 Aug. 2016. https://mdic.org/wp-content/uploads/2016/06/MDIC-Excessive-Data-Collection-in-Clinical-Trials-report.pdf
11O’Leary E, Seow H, Julian J, Levine M, Pond GR. Data collection in cancer clinical trials: Too much of a good thing? Clin Trials. 2013 Aug;10(4):624-32. doi: 10.1177/1740774513491337. PMID: 23785066.
12FDA. Determining the Extent of Safety Data Collection Needed in Late-Stage Premarket and Postapproval Clinical Investigations Guidance for Industry. Feb. 2016.The challenges of implementing ePRO – part two?
Clearly good cell network or WIFI connectivity is of primary importance when thinking about using ePRO in a clinical study. Sites should conduct a feasibility activity in their local vicinity during the study initiation phase to understand if cell network strengths are good enough for ePRO and if not, if there is likely to be a large population of subjects that have home WIFI. For remote locations with possible issues with either the cell network coverage or even with the availability of electricity, these sites should be reconsidered before a decision is made to use ePRO.
However, even with bad cell network coverage, it is possible to conduct a study with ePRO by ensuring the correct messaging appears to the subjects on the device. All ePRO systems allow the subjects to access and complete their diaries on a daily basis without cell or WIFI connectivity. The data entered by the subject is stored on the device until connectivity is established. There are circumstances where the site may need to be made aware of certain responses in real time in order to maintain subject safety. If connectivity is established and the data is sent in real time, the site staff may receive an alert from the system asking them to contact the participant. In order to ensure participant safety is maintained it is good practice to alert the participant to contact the sites / study doctor if certain responses in the diary are provided, such as if the participant required emergency medical attention.
As mentioned earlier in an earlier instalment, translation is an aspect not typically associated with clinical systems such as EDC. Additionally, because the ePRO devices are in the hands of the participants, the screens, in their local languages, must be submitted to the local ethics committees within the countries that the study is to take place in.
It should not be forgotten that paper PRO is also subject to a translation process. However, the additional complexity with ePRO is the need to apply the translated text to the software in order to generate the questionnaire screens. This involves multiple review and update rounds between the ePRO vendor and the translation vendor which increases time, effort and therefore cost. Once the screens are generated there may be further review rounds during the in-country review between the local sponsor representatives, ePRO vendor and translation vendor. All of these review rounds increase the timescales for an activity that is already time critical.
Often the date for submission to the ethics committee is set during the planning phase of the study by the sponsor study team. If the submission date is missed it could result in a delay to the study start. Consequently, it is important to ensure clear timelines are in place between agreeing the ePRO requirements, which will decide what is displayed on each of the screens, and submitting the screen report to the ethics committees. The ePRO vendor will need to be made aware of these timelines at the earliest opportunity. It can take more than 12 weeks for the completion of the translations resulting in the creation of the finalized screen reports. These timelines are difficult to manage if the process includes an in-country review round which allows for local country representatives from the sponsor to review the translations. It is important to make clear to the reviewers what it is that these stakeholders are reviewing. Translation is not an exact science. There are many ways of writing the same sentence. As the translator is the trained expert in translations it should be left to them to choose the most appropriate wording in the local language which most faithfully represents the English version. The in-country review, if indeed one is required, should only be conducted to ensure the correct screens are displayed in the screen reports. Allowing the in-country reviewer to make suggestions on preferred wording risks multiple back and forth review rounds, increasing timelines and jeopardizing study start dates.
As briefly mentioned in part one of this series, possibly the most crucial aspect when introducing a new system or process to a user base is change management. If you do not bring your users along the journey with you, you are less likely to gain their acceptance.
How does this manifest itself? With ePRO there is plenty of opportunity for issues to arise, from delays in getting devices to sites, to errors in the software, to usability and connectivity issues. All of which can affect the investigators ability to get on with their daily work. If you have not put in place a good change management process you will find the investigators very quickly become disenchanted with the system and even the smallest of issues will become magnified, resulting in escalations to the sponsors senior management team.
It is important from the outset to set the stage with the investigators. Why are we using ePRO? What benefits does it bring to the sponsor? It may require more work on behalf of the investigator which will need to be compensated for. It is also about setting expectations. Things will go wrong, issues will need to be resolved, backup processes may need to be utilized, but the investigator must be aware that they have the support when needed and how they can access that support.
When implementing ePRO thought should go into understanding how to improve the investigators experience. For example, investigators can be working on multiple studies at the same time for different sponsors all using ePRO, so adding labels onto the packaging of shipped devices so that investigators can quickly store them together is a quick win.
As a sponsor it may also be necessary to implement an additional layer of support for the study team. When issues arise the investigator will contact the vendor helpdesk. Often the helpdesk are unable to provide an immediate remedy so the investigator will then contact the sponsor’s study team representatives. The additional layer of support sits between the study team and the vendor, collating issues, communicating technical information back and forth in a manner that is easily digestible and holding the ePRO vendor to account. This relieves the frustration experienced within the sponsor’s study team and reduces the likelihood of escalation.
It is important to remember that in accordance with ICH GCP guidance section 5.2.1  the sponsor may transfer trial-related duties to a vendor, however the ultimate responsibility for the quality and data integrity of the trial data always resides with the sponsor. In order to ensure data is collected, stored and transferred in accordance with ICH-GCP guidance the sponsor will need a suitable oversight strategy of the vendor’s processes. This may, at a minimum, include a qualification activity which involves auditing the ePRO vendor on a regular basis. It may also invoke some internal validation work to ensure, as the sponsor, your own organisation’s technical and study specific requirements for clinical systems are met.
It is not recommended for the sponsor to leave the responsibility of vendor oversight to the individual study teams. The study teams may not have the experience or technical know-how necessary to understand the challenges and considerations discussed earlier in this blog.
We would recommend a two stage process.
Stage one is the qualification of the vendors system by a centralized team including stakeholders from the data management and computer system quality departments. Once qualified, the vendor’s system can be classed as a platform that can subsequently be used on multiple studies.
Stage two is to have experienced stakeholders conduct study specific User Acceptance Testing (UAT) in order to test the many scenarios and nuances associated with ePRO and to ensure the system meets the requirements of the study Protocol. Once implemented and running live on a study it is far more difficult to update the system than finding and fixing the issue during the implementation phase.
However, UAT is the very least the Sponsor must do as part of the overall study specific validation activity (Stage two). The Sponsor’s quality management system (QMS) may also mandate that the implementation of any clinical system (which would include ePRO) requires internal validation documentation to supplement the vendor’s validation package. This can include documents such as a sponsor Validation Plan, Requirements Specification, Risk Assessment, Traceability Matrix and Validation Report.
ePRO vendors will often offer to provide a service to create UAT script on behalf of the Sponsor, however this is not recommended as a misunderstanding of requirements by the vendor may also manifest itself in the scripting. This can result in the script passing when executed when in fact it does not meet the requirements of the Sponsor.
For these reasons, having experienced validation professionals generating the deliverables and executing the testing is recommended.
The two parts of this blog have detailed, at a high level, many of the challenges that can be experienced during the implementation and use of ePRO. These challenges are summarized below:
These challenges, and others, often create frustration for the investigators and study teams, and in the worst case scenarios, studies can be put on hold or stopped completely, costing millions of dollars and preventing a product from going to market.
A mechanism to help mitigate, or at the very least support the investigators and study teams through, many of these challenges is for the sponsor to put in place a centralized ePRO team within their organization. This team, consisting of internal sponsor stakeholders or perhaps supplementing Scimcon resources into the team, would be responsible for the qualification of the ePRO vendor, the strategic implementation of libraries and standards and for supporting the implementation of ePRO study-specifically, providing help with creating validation deliverables and executing UAT, and being a second level of support during the conduct phase of a study. ePRO is fast becoming the standard and it is worthwhile investing early on in an internal team to ensure success.
To discuss how Scimcon can support your organization with the implementation of ePRO, please contact one of our consultants for an informal conversation.
 ICH harmonised guideline integrated addendum to ICH E6(R1): Guideline for Good Clinical Practice ICH E6(R2) ICH Consensus Guideline, https://ichgcp.netThe challenges of implementing ePRO – part one?
When it comes to documenting the advantages of using ePRO over paper in clinical trials, the benefits are clear.
With all the advantages to using ePRO over paper it seems to be a no-brainer to use ePRO whenever possible. However, it’s important to be mindful of certain considerations and challenges that come with the implementation of ePRO within your organization before jumping in.
Historically the implementation of electronic clinical systems in general has been challenging. In the majority of cases it requires the move from a paper-based process to an electronic system in an environment where the reliance has always been on paper, hindering the adoption of computer systems that are seen as alien. Taking EDC as an example, the response to an international survey cited that 46% of respondents identified inertia or concern with changing current process, and 40% identified resistance from investigative sites as the major causes for adoption delays .
ePRO is not immune to these challenges. In fact, it could be argued that ePRO is even more susceptible. While ePRO suffers from the traditional technical issues and user acceptance that EDC experiences, ePRO is also placed in the hands of potentially thousands of study participants many of whom may have little technical understanding. Additionally, ePRO relies on hardware (mobile device or tablet), cell network or WIFI connectivity, translation into the participant’s local language, multiple userbase (study teams, investigators and participants) and local helpdesk support, all of which comes with their own set of challenges and associated costs and few, if any, of which are encountered with EDC.
ePRO is one of the few electronic systems that directly collects source data and as a result comes under increased scrutiny from a data integrity and quality perspective, especially when used for primary or secondary end point data collection. The system must always be available in order to allow subjects to be activated on ePRO devices. If a participant leaves a clinical site without an active device, this can result in missed data which can be construed as a serious quality issue and perhaps put subject safety at risk.
Before I go into the more detailed challenges associated with ePRO, let’s first consider the financial costs.
On the surface of it, it would appear that implementing ePRO is significantly more costly than paper. The expense of the devices, associated logistics and data usage (monthly SIM costs), the licenses, helpdesk and translations all contribute to costs that range from hundreds of thousands of dollars to multi-million-dollar contracts per study.
When making a business case for ePRO it is important to take into account the hidden costs associated with paper in order to compare the two.
When conducting a full assessment, the gap between the cost of implementing ePRO vs paper reduces significantly. ePRO vendors have attempted to provide examples which result in paper diaries actually contributing more cost to a study budget than ePRO.
The business case for implementing ePRO should not be solely based on raw cost. This will likely result in failure to get agreement at the leadership level. You will find it easier to get acceptance if you can prove that ePRO costs are comparable to paper while also concentrating on the non-tangible benefits as, in the case of ePRO, these are the real reasons for its consideration. Increasing the quality of your data collection results in more confidence in that data, which in turn reduces the likelihood of rejection when submitted to the regulators (predominantly for primary and secondary end point data). Receiving the data in real time and reducing the need for data cleaning can aid the ability to get a product to market quicker by shortening the timelines to close the study, which in turn results in cost avoidance.
Many ePRO vendors will provide a cost calculator; a spreadsheet where the sponsor can plug in parameters associated with their study to provide an estimate of costs before engagement with the vendor. Only a small number of parameters are required to calculate a good estimate with the most important being the length of the study in months and the number of participants. The length of the study drives the helpdesk, data usage and PM costs, whereas the number of participants drives the device, logistics and shipping costs. There are other costs associated with the configuration of the system, translation, shipping, number of sites etc, however these are often negligible in comparison for larger studies.
In summary; it is important to build a business case for ePRO within your organization in order to assist in gaining acceptance at a leadership level. The business case should include areas of efficiency over paper together with examples of ePRO costs using the cost calculators provided by the vendors, as well as emphasizing the other benefits of ePRO, such as subject safety, compared to paper solutions.
In the past, ePRO implementations were customized pretty much from the ground up, coding the study specifics into the vendor’s study builder toolkit. This resulted in a huge effort required to validate the system to ensure errors and bugs were captured before studies went live. Inevitably despite all this testing some issues did make it through to the live study, causing frustration for the participants, investigators and study teams.
Over the past decade the systems have become more sophisticated. Less code is required during the implementation phase, which has been replaced with configuration. Vendors have also introduced library functionality which allow sponsors to define questionnaires up front that can be reused across studies. As the questionnaire is not rebuilt every time there is less opportunity to introduce errors. Additionally, the ability to reuse questionnaires from a library also results in less work by the vendor per study, less validation on behalf of the sponsor and can reduce the time and costs during the implementation phase.
It may also be possible to standardize other areas of functionality, perhaps the workflow as to when questionnaires are made available to the participants, or the alerting system, or the visit schedule. It may not be possible to standardize across therapeutic areas, but within a therapeutic area where multiple studies collect the same data this approach can result in substantially reduced timelines during the implementation phase, while reducing the risk of software errors on the studies.
ePRO can be implemented in a number of different modalities. In this blog, we are concentrating on provisioned devices; devices that are provided by the ePRO vendor at a cost to the study Sponsor, and “bring your own device” (BYOD); where a subject’s own device is used as the ePRO instrument. It should be noted that all studies require provisioned devices to a certain degree in order to cater for cases where a subject either does not own a compatible mobile device or does not own a mobile device at all.
When provisioning devices, ePRO vendors are responsible for the associated logistics such as software installation and shipping. Vendors are generally very knowledgeable when it comes to the customs regulations in many countries, including the average timelines required to get a shipment to a site.
In scenarios where competitive recruitment between sites is employed, it is particularly important to plan ahead. As it may not be possible to predict the number of subjects that will be recruited at a specific site and therefore the number of devices required at that site, it is necessary to purchase a sensible overage of provisioned devices. Although costly it ensures sites will not run out of devices.
With an increasing number of the world’s population now owning smart phones, BYOD was seen as the natural progression for ePRO. It reduces the costs and burden of acquiring devices and the associated logistics and also reduces the monthly costs for data usage. These costs do not completely disappear as a certain level of provisioning is required for those cases where participants don’t own a compatible smart phone. BYOD also reduces risks associated with not having enough devices on site, especially, as mentioned above, with competitive recruitment. However, BYOD does come with own set of unique challenges, mainly associated with data integrity and privacy. Some considerations might be:
There are clearly a lot of benefits of using BYOD over provisioned devices with more and more sponsors feeling comfortable moving into this space, however it is important to consider the implications before doing so.
In the next instalment of this blog, we will discuss some other challenges of implementing ePRO in your organisation, such as connectivity, translations, and end user acceptance testing. Keep an eye on our Opinion page for part two of the series, coming soon.
 ‘Electronic for Industry – Electronic Source Data in Clinical Investigations’, FDA 2013 http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm328691.pdf
 ‘Welker JA. Implementation of electronic data capture systems: barriers and solutions.’ Contemp Clin Trials. 2007 May;28(3):329-36. doi: 10.1016/j.cct.2007.01.001. Epub 2007 Jan 11. PMID: 17287151.