Smart Ways to Collect Real-World Data for Device Trials
Why would a payer cover an insulin injector that costs patients about $850 more per year than vials of insulin and syringes? (1). Because, compared to a normal syringe, an insulin injector is significantly safer and easier to use, and therefore leads to fewer complications and better clinical outcomes. This ends up saving patients and physicians money in the long run.
Based on clinical data alone, manufacturers can only speculate on a device’s cost-savings in the real world. The only way to prove the true value of a medical device is to collect real-world data (RWD) on its safety, efficacy and cost outcomes outside of the clinic.
Manufacturers who already collect RWD know the process can become time-consuming and expensive. But, as we discussed in a previous blog, smart RWD collection can lead to reduced post-market costs. Here, we will explore smarter, more cost-effective ways to collect RWD.
Collecting Data on Physical Activity
Many device manufacturers use data on the physical activity of patients as an additional measure of a product’s effects. For decades, manufacturers used patient and observer-reported survey instruments to assess mobility. Yet these instruments are subject to interpretation and depends on the respondent’s memory, rendering them inconsistent (2,3,4).
Accelerometers, on the other hand, measure physical activity objectively, making them a more reliable source for RWD. For instance, in an article published by ICON, Bill Byrom, Senior Director of Product Innovation, and his colleagues describe how tri-axial accelerometers, such as ActivPAL, can measure movement and posture while residing unobtrusively on a patient’s thigh (5). This device can detect minor changes in gait, infer a patient’s sleep patterns, and count the number of times a patient trips or falls, without having to rely on the patient’s memory or awareness (2,6). Using these devices lessens the demand on patients and trial staff by saving them time and easing data collection.
To capitalize on the utility of accelerometer data, while retaining low equipment costs, a “bring your own device” (BYOD) trial may be a manufacturer’s best option. In a BYOD trial, patients collect accelerometry data on their own devices, such as their smartphones. In fact, many manufacturers are already considering the validity of BYOD trials for their own products.
Some in the medical device industry have expressed concerns regarding the validity and reliability of RWD collected on a patient’s own device. But in a survey conducted on manufacturers who do engage in BYOD trials, they reported that data reliability has generally not been a problem.7 When properly validated, a patient’s own device may be an inexpensive and effective option for collecting accelerometry data.
Electronic Health Records
Some device manufacturers are already tapping into electronic health records (EHR) to characterise eligible patients at particular sites for their device trials (8,9).
Yet, the challenge moving forward is to use RWD from these records to also help manufacturers characterise patients from a wider subject pool to strengthen their clinical trial designs. One way to accomplish this is for device manufacturers to expand their access to EHRs from single sites to larger patient networks.
ICON, for example, has gained access to millions of deidentified patient records by partnering with EHR4CR, a consortium that includes 11 sites in Europe, and by acquiring PMG Research, an integrated network of clinical research sites operating from 12 metropolitan areas throughout the US. Also, ICON uses TriNetX, a research network and technology platform that connects them to healthcare organisations that represent a further 57 million patients worldwide. ICON can use deidentified patient records from these sources to make their device trials more efficient based on the individual characteristics of their target patient cohort (8,9).
Partnering with a CRO, who has access to such a large, diverse collection of EHRs, will improve trial efficacy in two ways:
It will enable the CRO to advise sponsors and manufacturers about the number of patients who are eligible for their studies in a particular geographical region. From these records, a manufacturer can find out where these patients are located and which sites can reach them in a timely manner (9,10).
It will allow us to help manufacturers model the effects of specific modifications to a trial protocol on recruitment feasibility and timing, thus optimising the study’s performance (10).
Overall, RWD from large EHR networks give manufacturers the potential to recruit the right patients for their trials faster, which in turn enables them to take time and cost from their development programmes.
Patient Registries for Observational Studies
Establishing a registry by recruiting new patients is typically time- and cost-intensive. Providers must perform some up-front work, including identifying and enrolling eligible patients into the study, and this could increase study costs. In fact, the delays and expenses associated with recruiting patients, compensating investigators and support staff, and following patients over the long term can amount to millions of dollars each year (11).
Tapping pre-existing EHR data to conduct observational studies, on the other hand, requires far fewer resources than does building and maintaining a registry. Consequently, it is far less expensive to accomplish.11 There are fewer costs associated with recruiting, consenting, and enrolling patients into an EHR-driven observational study. And there are no ongoing payments to providers for data entry as there are with registries, depending on the specific outcome measures of interest. EHR data are captured automatically as part of the healthcare delivery system and, once acquired, are ready for study. The FDA Center for Devices and Radiological Health is actively pushing this approach in their strategic plan to streamline post-market observational research (12).
The cost savings associated with an EHR-driven observational study means that it is more practical to observe patients for longer periods of time with EHR data than via traditional registries (11). Also, the data yield answers very quickly, which can be used to build a case for swift adoption by payers, rendering additional post-market research unnecessary.
The strategies described here will enable device manufacturers to collect valuable RWD more efficiently, thereby reducing overall costs. Using them will ensure that products reach the market faster so development does not eat away a significant portion of the exclusivity period for new devices.
To learn more about these tools as well as others that can help deliver more productive device trials, you can contact us to consult with ICON’s Medical Device and Real World Evidence experts, or visit: http://www.iconplc.com/services/late-phase/real-world-Intelligence/.
(1) Ayyagari R, Wei W, Cheng D, Pan C, Signorovitch J, Wu EQ. Effect of adherence and insulin delivery system on clinical and economic outcomes among patients with type 2 diabetes initiating insulin treatment. Value Health. 2015;18(2):198-205.
(2) de Morton NA, Berlowitz DJ, Keating JL. A systematic review of mobility instruments and their measurement properties for older acute medical patients. Health Qual Life Outcomes. 2008;6:44.
(3) Meijer GA, Westerterp KR, Verhoeven FM, Koper HB, Ten hoor F. Methods to assess physical activity with special reference to motion sensors and accelerometers. IEEE Trans Biomed Eng. 1991;38(3):221
(4) Yang CC, Hsu YL. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors (Basel). 2010;10(8):7772-88.
(5) Byrom B, Stratton G, McCarthy M, Muehlhausen W. Objective measurement of sedentary behaviour using accelerometers. Int J Obes (Lond). 2016;40(11):1809-1812.
(6) Nam Y, Kim Y, Lee J. Sleep Monitoring Based on a Tri-Axial Accelerometer and a Pressure Sensor. Sensors (Basel). 2016;16(5).
(7) Muehlhausen W, Doll H, Quadri N, et al. Equivalence of electronic and paper administration of patient-reported outcome measures: a systematic review and meta-analysis of studies conducted between 2007 and 2013. Health Qual Life Outcomes. 2015;13:167.
(8) Mccowan C, Thomson E, Szmigielski A, et al. Using Electronic Health Records to Support Clinical Trials: A Report on Stakeholder Engagement for EHR4CR. Biomed Res Int. 2015;2015:707891.
(9) ICON Further Enhances Clinical Trial Feasibility, Protocol Optimisation and Patient Recruitment Capabilities with TriNetX. Retrieved January 27, 2017.
(10) De moor G, Sundgren M, Kalra D, et al. Using electronic health records for clinical research: the case of the EHR4CR project. J Biomed Inform. 2015;53:162-73.
(11) Carroll J, Sambrook R, Spector I. ICON Plc. Meeting Evidentiary Needs with Electronic Health Records. Dublin, Ireland: ICON Plc. Retrieved January 27, 2017.
(12) FDA to Shift Clinical Evidence for Medical Devices toward Postmarket. (2016, April 1). Retrieved February 3, 2017.