Running Head: Report
The module name: BM565 Digital Business & New Technologies
Course work reference (CW1)
Name and student ID Laura Iacob
Total word count: 1992
Importance of topic
Blood draw devices used in clinical trials are of utmost importance for ensuring the accuracy and reliability of data collected during the trial (Inan et al., 2020). It enables professionals to collect high-quality blood samples from patients, which is an essential part for the success of clinical trials. Similarly, the development of blood draw devices has made the process less invasive and less painful for patients giving blood for the tests (Solheim et al., 2021). As a result, patient experiences have improved during clinical trials and increased patient participation rates. Likewise, the use of blood draw devices can also reduce the costs associated with clinical trials by minimising the need for repeated sampling, which is both time-consuming and expensive. Hence, blood draw devices are essential for improving the quality, efficiency and effectiveness of clinical trials, making it a crucial topic of discussion.
Development of topic
The use of blood draw devices in clinical trials has developed significantly in the recent years (Liu et al., 2019). However, before the blood draw devices emerged, the most common method of blood collection involved a simple needle and syringe, which was often painful and difficult to use. Whereas many patients were hesitant of giving blood for samples due to fear and painful experience. Hence, Bolla and Priefer (2020) stated with the advent of new technologies blood collection has become much easier and less invasive allowing professionals and patients to give blood samples in less time and with convenience.
Vacuum tubes, for example, were first developed in the 1940s and have become one of the most commonly used methods for blood collection (Gros et al., 2023). In addition, the development of butterfly needles, which are designed to be less painful and easier to use, has also been significant. Likewise, in recent years, smart blood draw devices have been developed to provide real-time monitoring and feedback during blood collection. Hence, revolutionising traditional practices with advanced and reliable clinical practices.
As clinical trials continue to grow in complexity and scale, the development of new and improved blood draw devices is expected to continue, further improving the accuracy, efficiency and comfort of blood collection for both patients and practitioners.
Description of technology used and what it is used for
Blood draw devices are used to collect blood samples from patients advised to give blood samples in clinical trials (Boyarsky et al., 2021). These devices vary in size, design and function depending on the specific application. Some of the most common types of blood draw devices include vacuum tubes, butterfly needles and smart devices.
Ozkan and Polat (2020) stated that vacuum tubes are among the most common types of blood draw devices used in clinical trials. These tubes contain an additive that helps preserve blood samples for analysis. Butterfly needles are another type of blood draw device commonly used in clinical trials (Venturella et al., 2019). These needles are designed to be less painful and more comfortable for patients, making them ideal for use in clinical trials involving multiple blood draws.
However, recently smart devices have emerged as a new type of blood draws device in clinical trials (Coravos et al., 2019). These devices employ sensors and other technologies to provide real-time monitoring and feedback during blood collection. While it makes the process efficient and accurate, whereas reducing the potential for errors. Likewise, smart devices are particularly useful for trials involving home-based blood collection, where patients can collect blood samples on their own with minimal training.
d. Technological platforms and software used
In clinical trials, blood draw devices use a variety of technological platforms and software to improve the process of blood collection and analysis. In this regard it was found, one such platform is the Laboratory Information Management System (LIMS), used to track and manage laboratory samples and data (Tan et al., 2020). While making it easier for the professionals to generate reports in real-time. Similarly, LIMS has the ability to automate the process of creating reports and maintaining quality control data. Electronic Data Capture (EDC) is another extensively used software that collects data directly from clinical trial participants and stores it in a centralised database.
As per Jimenez-Maggiora et al. (2022) EDC also allows for real-time monitoring of the clinical trial’s progress and triggers alerts for safety issues and protocol violations. Additionally, image analysis software is used to analyse blood samples and identify cell populations, which supports in disease diagnosis and treatment. In addition, machine learning and artificial intelligence tools are being developed to analyse large amounts of clinical data and improve patient outcomes.
e. Types of business and organisations use the technology
The blood draws devices technology is used across a range of business and organizations, including clinical research organisations (CROs), pharmaceutical companies, hospitals, diagnostic labs and blood banks (Inan et al., 2020). CROs are a significant users of this technology since they conduct clinical trials on behalf of pharmaceutical companies. The devices are used to collect blood samples from patients during various phases of the trial.
Hospitals and diagnostic labs also use blood draw devices to perform various medical tests such as blood tests, glucose tests and lipid profile tests (Nowak et al., 2019). Likewise, blood banks also use these devices to collect and store blood samples for transfusion. In recent years, the use of blood draw devices has expanded to include at-home sample collection kits for patients, which was increasingly popular due to the ongoing COVID-19 pandemic. It allowed patients to collect blood samples from the comfort of their own homes, which reduces the need for in-person medical visits and protected patients.
f. Use as a business tools and its effectiveness
Blood draw devices are commonly used in the healthcare industry for clinical trials, patient care and disease diagnosis (Haleem et al., 2021). Whereas these devices are used as an essential tool to improve patient care, reduce healthcare costs and increase the efficiency of healthcare professionals. Similarly, the healthcare sector is the primary industry using blood draw devices. These devices are also used in pharmaceutical companies, research institutes and academic institutions to collect blood samples and conduct clinical trials to provide evidence-based interventions. As a result, blood draw devices are reducing the time for trials and allowing healthcare professionals to work effectively and efficiently.
Moreover, Khan and Algarni (2020) found blood draw devices are effective in reducing patient discomfort, speeding up the collection process and increasing accuracy. Meanwhile, minimising the risk of infection and reduce the need for multiple venipunctures. Whereas blood draws devices provide a comprehensive overview of a patient’s health, resulting in improving treatment outcomes. Hence, the use of blood draw devices in the healthcare industry is a vital tool that has proved to be effective in improving patient care and outcomes.
The healthcare sector has witnessed a notable paradigm shift in recent times with the widespread adoption of digital technologies, such as blood draw devices (Awad et al., 2021). It is imperative to comprehensively grasp the potential advantages of employing blood draw devices, as these devices have the capability to optimise the blood collection process, minimise the risk of contamination and enhance patient outcomes. This, in turn, leads to more efficient use of time and effort for both healthcare professionals and patients alike. Nonetheless, Azodo et al. (2020) stated the effective integration and utilisation of blood draw devices necessitate healthcare organisations to acknowledge and proactively tackle potential challenges.
One theoretical model that can be used to evaluate the adoption of digital technologies is the “Technology Acceptance Model (TAM)”. The TAM suggests that the perceived usefulness and ease of use of a technology are key determinants of its adoption (Diop et al., 2019). In the case of blood draw devices, healthcare organisations have to recognise the potential benefits of the technology and ensure that it is easy for healthcare professionals to use.
According to Reyes et al. (2021) studies have shown that healthcare organisations have been quick to recognise the potential benefits of blood draw devices. Similarly, Ferrario et al. (2021) found that hospitals that adopted these blood draw devices experienced a reduction in needlestick injuries, which otherwise lead to the transmission of infectious diseases. In addition, Baron et al. (2020) found that the use of blood draw devices resulted in a reduction in the number of blood samples needed per patient.
Nevertheless, the implementation of blood draw devices has not been devoid of challenges. One prominent obstacle is the potential resistance to change exhibited by healthcare professionals, which may necessitate additional training to proficiently utilise these devices. Furthermore, concerns regarding the financial implications of acquiring and integrating these devices into existing workflows may also arise.
To counter these challenges, healthcare organisations have employed various strategies including comprehensive training programs for healthcare professionals to ensure their proficiency in operating the devices. As a result, reducing work stress and unnecessary delays in professional practice. Additionally, efforts have been made to seamlessly integrate the devices into existing workflows, minimising the potential for disruptions or delays in the system and professional practices. Meanwhile, it is clear that healthcare organisations have recognised the potential benefits of blood draw devices and are taking several steps to ensure their effective adoption. While there have been challenges associated with the adoption of blood draw devices and their processes, the use of theoretical models such as the TAM has the ability to help organisations of the healthcare industry to identify and address these challenges.
According to Kazanskiy et al. (2022) the healthcare industry has seen the emergence of new organisations that are built around digital technology, such as blood draw devices and challenging old-style organisations that have failed to grasp the significance of digital technology. The phenomenon is known as digital disruption, referred to the transformation that occurs when new digital technologies and business models which change the way an industry leverages its functions to remain adaptive with technological advancements.
The advent of digital disruption has presented organisations with opportunities to challenge established players in various industries. In the healthcare sector, Zahra et al. (2023) found digital disruption has given rise to new organisations that capitalise on digital technologies to offer innovative products and services aimed at improving the quality of healthcare services. These organisations often exhibit characteristics such as agility, efficiency and customer-centricity, setting them apart from their traditional counterparts.
One theory that can help in explain this phenomenon is Christensen’s theory of disruptive innovation. According to this theory, disruptive innovation happens when a new technology or business model enters a market and initially deals with the niche segment of the market that is underserved or ignored by existing players (Ganguly et al., 2022). As the new entrant continues to develop its capabilities, it gradually moves upmarket, eventually displacing established players in the market. Therefore, healthcare organisations, using Blood draw devices against the traditional practices are likely to enjoy long-term benefits due to enhanced services and quality.
Similarly, the resource-based view (RBV) theory can also be applied to understand digital disruption in organisations. According to the RBV, a firm’s resources and capabilities are key determinants of its success in the marketplace (Lee and Yoo, 2021). New organisations that enter the healthcare industry and successfully disrupt traditional players often have unique resources and capabilities, such as a deep understanding of digital technologies or a customer-centric approach to innovation.
Hence, the emergence of new organisations built around digital technology, such as blood draw devices, has disrupted the healthcare industry and challenged old-style organisations (Charulatha and Sujatha, 2020). The theories of disruptive innovation and the resource-based view of the firm help in explaining why these new entrants have been successful in disrupting established players. It is important for traditional organisations in the healthcare industry to understand the significance of digital technology and adapt their strategies to remain competitive in the face of digital disruption. Hence, digital and technological transformation in healthcare organisations is essential to reduce medical errors and enhance service quality.
Awad, A., Trenfield, S. J., Pollard, T. D., Ong, J. J., Elbadawi, M., McCoubrey, L. E., … and Basit, A. W. (2021). Connected healthcare: Improving patient care using digital health technologies. Advanced Drug Delivery Reviews, 178, pp.113958.
Azodo, I., Williams, R., Sheikh, A., and Cresswell, K. (2020). Opportunities and challenges surrounding the use of data from wearable sensor devices in health care: qualitative interview study. Journal of medical Internet research, 22(10), p.e19542.
Baron, D. M., Franchini, M., Goobie, S. M., Javidroozi, M., Klein, A. A., Lasocki, S., … and Meybohm, P. (2020). Patient blood management during the COVID–19 pandemic: a narrative review. Anaesthesia, 75(8), pp.1105-1113.
Bolla, A. S., and Priefer, R. (2020). Blood glucose monitoring-an overview of current and future non-invasive devices. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(5), pp.739-751.
Boyarsky, B. J., Werbel, W. A., Avery, R. K., Tobian, A. A., Massie, A. B., Segev, D. L., and Garonzik-Wang, J. M. (2021). Immunogenicity of a single dose of SARS-CoV-2 messenger RNA vaccine in solid organ transplant recipients. Jama, 325(17), pp.1784-1786.
Charulatha, A. R., and Sujatha, R. (2020). Smart healthcare use cases and applications. Internet of Things Use Cases for the Healthcare Industry, pp.185-203.
Coravos, A., Khozin, S., and Mandl, K. D. (2019). Developing and adopting safe and effective digital biomarkers to improve patient outcomes. NPJ digital medicine, 2(1), p.14.
Diop, E. B., Zhao, S., and Duy, T. V. (2019). An extension of the technology acceptance model for understanding travelers’ adoption of variable message signs. PLoS one, 14(4), p.e0216007.
Ferrario, M. M., Veronesi, G., Borchini, R., Cavicchiolo, M., Dashi, O., Dalla Gasperina, D., … and Gianfagna, F. (2021). Time trends of percutaneous injuries in hospital nurses: evidence of the interference between effects of adoption of safety devices and organizational factors. International Journal of Environmental Research and Public Health, 18(8), p.4371.
Ganguly, A., Talukdar, A., and Kumar, C. (2022). Absorptive capacity and disruptive innovation: The mediating role of organizational agility. IEEE Transactions on Engineering Management.
Gros, N., Klobučar, T., and Gaber, K. (2023). Accuracy of Citrate Anticoagulant Amount, Volume, and Concentration in Evacuated Blood Collection Tubes Evaluated with UV Molecular Absorption Spectrometry on a Purified Water Model. Molecules, 28(2), p.486.
Haleem, A., Javaid, M., Singh, R. P., Suman, R., and Rab, S. (2021). Biosensors applications in medical field: A brief review. Sensors International, 2, p.100100.
Inan, O. T., Tenaerts, P., Prindiville, S. A., Reynolds, H. R., Dizon, D. S., Cooper-Arnold, K., … and Califf, R. M. (2020). Digitizing clinical trials. NPJ digital medicine, 3(1), p.101.
Jimenez-Maggiora, G. A., Bruschi, S., Qiu, H., So, J. S., and Aisen, P. S. (2022). ATRI EDC: a novel cloud-native remote data capture system for large multicenter Alzheimer’s disease and Alzheimer’s disease-related dementias clinical trials. JAMIA open, 5(1), ooab119.
Kazanskiy, N. L., Butt, M. A., and Khonina, S. N. (2022). Recent advances in wearable optical sensor automation powered by battery versus skin-like battery-free devices for personal healthcare—A review. Nanomaterials, 12(3), p.334.
Khan, M. A., and Algarni, F. (2020). A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access, 8, pp.122259-122269.
Lee, S., and Yoo, J. (2021). Determinants of a firm’s sustainable competitive advantages: Focused on Korean small enterprises. Sustainability, 13(1), p.346.
Liu, J., Geng, Z., Fan, Z., Liu, J., and Chen, H. (2019). Point-of-care testing based on smartphone: The current state-of-the-art (2017–2018). Biosensors and Bioelectronics, 132, pp.17-37.
Nowak, E. S., Reyes, D. P., Bryant, B. J., Cap, A. P., Kerstman, E. L., and Antonsen, E. L. (2019). Blood transfusion for deep space exploration. Transfusion, 59(10), pp.3077-3083.
Özkan, T. K., and Polat, F. (2020). The effect of virtual reality and kaleidoscope on pain and anxiety levels during venipuncture in children. Journal of PeriAnesthesia Nursing, 35(2), pp.206-211.
Reyes, D. R., van Heeren, H., Guha, S., Herbertson, L., Tzannis, A. P., Ducrée, J., … and Becker, H. (2021). Accelerating innovation and commercialization through standardization of microfluidic-based medical devices. Lab on a Chip, 21(1), pp.9-21.
Solheim, S. A., Ringsted, T. K., Nordsborg, N. B., Dehnes, Y., Levernæs, M. C. S., and Mørkeberg, J. (2021). No pain, just gain: painless, easy, and fast dried blood spot collection from fingertip and upper arm in doping control. Drug Testing and Analysis, 13(10), pp.1783-1790.
Tan, W. C. C., Nerurkar, S. N., Cai, H. Y., Ng, H. H. M., Wu, D., Wee, Y. T. F., … and Lim, T. K. H. (2020). Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy. Cancer Communications, 40(4), pp.135-153.
Venturella, M., Carpi, F. M., and Zocco, D. (2019). Standardization of blood collection and processing for the diagnostic use of extracellular vesicles. Current Pathobiology Reports, 7, pp.1-8.
Zahra, S. A., Liu, W., and Si, S. (2023). How digital technology promotes entrepreneurship in ecosystems. Technovation, 119, p.102457.