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Life Sciences Trends for 2020

Life Science Trends For 2020

The life sciences industry is changing at unprecedented rates. In 1999, life sciences companies spent 1-2% of revenue on information technology relative to other high-tech industries because FDA regulations were too restrictive. We are seeing growth in technology investments of 4-7% across our life sciences customers. With the FDA’s more modern approach to Computer System Assurance on the horizon, medical device manufacturers will more rapidly embrace cloud and emerging technologies to be able to take advantage of functionality like automated testing and automated CSV tools to get products to market faster.

Globally we also see significant changes in data management and the use of advanced analytics. With EUDAMED and other international agencies changing how they will ingest product data, data integrity and the ability to manage supersets of data is going to be of critical importance. And as the use of artificial intelligence, machine learning, and data lakes becomes more commonplace, we are going to see game-changing advances in areas like clinical trials, disease management, and drug discovery. The power of advanced analytics is going to transform entire organizations’ – people, process, and technology – and disrupt convention as we know it.

This disruption is requiring life sciences companies to more carefully consider their technology vendors and form strategic partnership with best of breed tech companies specialized in advanced technologies such as AI, robotics and cognitive automation, and cloud computing to ensure they have the right talent and the right technology to sustain them for the next three to five years. Life science companies need to start thinking and operating more like agile tech companies as competition increases, and tech giants like Amazon and Google diversify into health care and life sciences.

Further, as regulatory agencies catch up with technology, we are seeing new players enter the market like Apple with sophisticated heath monitoring devices, Bose developing hearing aids, or Cannabusiness evolving in Canada with the first federally regulated CBD laws that will propel new medical therapies. At USDM, we have never been more excited about these opportunities and are at the forefront of this thinking in both regulatory and technological expertise. I hope you enjoy our latest white paper on the trends we are most excited about in 2020.

With gratitude,

Kevin Brown
CEO, USDM Life Sciences

#1 – Increasing Complexity of Global UDI Regulations

Regulatory agencies around the globe are concerned about the authenticity, traceability, and cost-control of medical devices. As a result, the number of regulatory agencies adopting unique device identification requirements is growing, which is creating a huge task for medical device manufacturers who must comply with these varying requirements. The International Medical Device Regulators Forum (IMDRF) set out basic UDI requirements, including recommended “shared” data elements, in their guidance document “Unique Device Identification (UDI) of Medical Devices” to promote the global harmonization of UDI. As more global regulatory agencies begin to develop and implement country or regulator-specific UDI or UDI-like systems, they aim to further resolve specific national or local concerns.

These new regulatory, commercial, and patient-safety use cases create complex challenges for device manufacturers, who now must establish and maintain robust global systems and processes that allow them to develop, maintain, and submit the ever-growing, ever-changing information to these various UDI systems and stakeholders. This daunting burden requires that device manufacturers be able to create, maintain, and associate a broad set of global device attribute information, with real-world clinical information to support its indications, continuous receipt and analysis of quality, performance, and safety data, as well as information related to its distribution and costs. Understanding what is happening globally – and why – will help prepare manufacturers to develop and support these global initiatives.


On 5 May 2017, the EU Medical Device Regulation (MDR) and the In Vitro Diagnostic Medical Device Regulation (IVDR) were published in the Official Journal of the European Union. The MDR applies from 26 May 2020 and the IVDR from 26 May 2022. Among the many changes are the introduction of the UDI system requirements for almost all medical devices and IVDs (and even some products that have not been previously regulated as medical devices, i.e., MDR Annex XVI). The MDR/IVDR UDI system requirements are drawn heavily from the IMDRF UDI guidance document. Like the US rule, these regulations require that;

  • the label and packaging of all medical devices be assigned and marked with a unique device identifier,
  • that devices intended to be reused and reprocessed have the UDI directly on the device itself,
  • that a set of data about the device be submitted to Europe’s EUDAMED database,
  • and that the device’s UDI be used in various other regulatory activities.

These requirements will also be phased in over several years, starting in 2021 and finishing up in 2029. Below are the top challenges we have seen our customers have to overcome as they start to prepare for the EU MDR/IVDR.

  1. Ensuring that the UDI implementation is correct and extensible to the EU requirements
  2. Developing a “global” approach to device attribute data
  3. Understanding the differences between the US and EU requirements
  4. The application and use of the Basic UDI-DI
  5. The incorporation of UDI into other regulatory requirements (e.g., postmarket surveillance, supply chain traceability, field safety corrective actions)

Saudi Arabia

On 11 December 2019, the Saudi Food and Drug Authority (SFDA) published version 2.0 of MDS-G34 “Guidance on Requirements for Unique Device Identification (UDI) for Medical Devices.” Compliance is based on the availability of the Saudi Arabia UDI Database (SAUDI-D), which has not yet been made publicly available, but we expect in early 2020. And though the SFDA UDI requirements are based on the GHTF/IMDRF guidance documents, and are similar to the US and EU requirements, there are some significant differences.

For example, certain information for home-use devices or those used by lay persons will need to be provided in Arabic. Also, only single barcodes will be allowed (either concatenated linear or 2D). More importantly, to support the goals of UDI – the UDI will be used to control devices entering the Kingdom (import control). It is also important to note that the UDI data attributes will exist in multiple systems (e.g., MDMA and MDNR).

China and Other Countries

Many other countries and regulators like China, Korea, India, Singapore, Taiwan, and Australia are introducing their own UDI or UDI-like requirements – with deadlines that will likely be very compressed. At this stage, we have very limited visibility into the specific requirements – and the extent to which many of the concepts embedded in the GHTF/ IMDRF guidance documents (and further implemented in the US and EU regulations) will be leveraged. For example, the US, EU and SFDA regulations all leverage global standards organizations for UDI assignment (e.g., GS1, HIBCC). It is not clear if this model will apply in all regions.

More importantly, it is clear that each country/ regulator will develop and implement its own “UDI” (or UDI-like) database – with different data attributes, different ways in which the attributes are represented, and different submission methods. This presents several challenges for manufacturers in developing and maintaining a “global” data set for global products. To date, each of the regulators that have developed or are in the process of developing a UDI or UDI-like system, have followed the GHTF/IMDRF guidance. This means that, to the extent practicable, the foundation of these UDI systems is similar. This includes leveraging global issuing agencies (i.e., GS1 HIBCC, ICCBBA), similar application to UDI assignment, and the making of labels and packages, similar approach to exceptions (e.g., for single-use devices), and similar phased implementation.

This means that it is possible that a single UDI can be applied to a device label (and its packaging) – and that UDI used globally. There are, however, many reasons why manufacturers can’t or don’t do this – including language issues, differences in devices distributed in different regions, and distribution practices. It is also essential to recognize that each regulator has a different set of problems that it is trying to resolve with the introduction of UDI. Therefore, it is likely that each regulator will have a distinct database to support its use cases. Including unique data requirements, definitions, a list of values, and change rules. Moreover, the way these databases are structured and operate is likely going to differ.

How USDM Can Help

USDM is the industry leader in understanding UDI regulation and is at the forefront of these changes. We have worked with many of our medical device customers to take the critical first step in auditing and assessing their EU MDR readiness. Then we develop an overarching, cross-organization, data-centric framework to manage the data and disrupt traditional silos.

USDM EU MDR / IVDR Execution Framework

  1. May 2020 readiness
  2. Audit/Gap assessment and product portfolio rationalization
  3. Devices with valid MDD/AIMD certificates that will be distributed > 2020
  4. Devices that will be (re)certified to the MDR
  5. Class I devices that must be MDR compliant by 2020
  6. Economic Operators: Assess AR, importer, distributor compliance (enhanced responsibilities – including traceability
  7. QMS (ISO 13485:2016 standard) identifying additional or changes to existing SOPs required to manage EU MDR including UDI

At USDM, we believe that a single global superset of data will address many of the differences and similarities in data requirements worldwide. As the preliminary EUDAMED implementation specs are available – several of the data elements have been implemented differently than in the GUDID. We understand differences in how the data will be collected and have identified the commonalities and unraveled the differences in the known datasets to create a superset of data elements for manufacturers to manage. We believe it will support the current regulatory agency UDI submission requirements in addition to being extensible to evolving UDI regulations in other countries.

#2 – Enhancing Medical Device Quality and Patient Safety

The FDA believes the medical device manufacturing industry lags in implementation of automated systems and new technologies due to a lack of clarity, outdated compliance approaches, and perceived regulatory burden. The FDA also says companies often struggle to understand the root cause of issues in order to improve product quality. As a result, the FDA is expected to release a new guidance document, “Computer Software Assurance for Manufacturing, Operations and Quality System Software,” within the next few months.

This new guidance is highly anticipated because it will actually streamline some of your computer software systems. The FDA is leaning towards a Case for Quality (CfQ) approach with less emphasis on a compliance approach allowing device manufacturers to focus on enhancing device quality and patient safety. The Center for Devices and Radiological Health (CDRH) discovered that one of the reasons medical device manufacturers were not seeking quality improvements by adopting automation and new digital technologies was the perceived compliance burden and regulatory risk of such innovation. In particular, the validation of computerized systems was a significant barrier to the adoption of new technologies. Companies are no longer willing to automate for automation’s sake due to the cost of implementation of those systems.

The FDA noted that other industries utilizing automation have shown a substantial benefit in enhancing product quality and safety, thereby reducing risk as compared with nonautomation. Instead of relying on humans, more and more companies are improving the quality of their products by the utilization of AI tools for example. The FDA engaged with industry stakeholders to learn about barriers and best practices for high-quality medical devices. It revealed that most validation time was spent on documentation rather than testing, thereby not helping to improve product quality. This was never the FDA’s intent. To assist companies in moving towards automation, the new guidance will focus on using risk-based methodologies to justify the amount and types of testing required as well as leveraging testing completed during the Software Development Life Cycle (SDLC) process.

In reality, a risk-based approach has been encouraged for quite some time, but companies have struggled to balance risk when it comes to computer software. The new FDA guidance will more clearly identify computer software risks and applies a validation approach based on that risk and focuses on assurance, not validation. While the guidance is new and provides examples understandable by the industry, most of the principles and practices are already acceptable to the FDA and can be implemented today in your business. This means you can start realizing savings in time and money right away!

Why this is Important

Well, if you use a computer software application that does not directly impact product safety or product quality, your validation burden can be significantly reduced. For non-direct impact software, the FDA is recommending the leveraging of existing activities such as supplier qualifications as well as process controls to mitigate risk. Additionally, the FDA intends to focus only on direct impact systems and does not intend to review the validation of support tools. This means inspectors will no longer ask to see the validation of your indirect impact systems.

Prior to this guidance, companies focused on documentation and testing activities. The new paradigm will focus on critical thinking (risk based), assurance needs, testing activities, and documentation in that order. Critical thinking should focus on the following questions:

  • Does this software impact patient safety?
  • Does this software impact product quality?
  • How does the software impact your quality system integrity?

While not specifically called out, it might be wise to document your risk justifications for each software application. This doesn’t mean you need to be long-winded, simply document and justify your risk classification. The FDA is allowing for more flexibility when it comes to an assurance approach and acceptable records of results based upon the level of risk. At a high-level view, companies should identify the intended use of the software application, determine a risk-based approach (at the function level), leverage existing activities as appropriate for each function, and identify the required records.

Leveraging supplier qualifications sounds great, right? But… what comprises a good supplier qualification? How do you apply critical thinking to your vendor qualification activities so that you can rely on reduced testing activities? That’s a tough question because software is changing. Companies used to have a lot more control over it. Now, you’re ceding some of your control to data centers and Software-as-a-Service (SaaS) vendors.

A vendor that has been in the marketplace for only a couple of years and has an immature SDLC process, Capability Maturity Model Integration (CMMI) level, ISO certification, etc., would require a higher validation effort than a vendor with a long track record with a mature and transparent SDLC process. So, the selection of good vendors becomes much more important from both risk management and cost perspectives. We have seen many cases where our customers chose the cheaper vendor solution, only to have to invest more in their validation efforts down the road as the vendor lacked the GxP experience, knowledge, and documentation to leverage the necessary requirements.

How USDM Can Help

At USDM, we understand these variables can be confusing, which is why we offer to manage the vendor audit and validation maintenance for our customers through our Cloud Assurance service. The new guidance is intended to assure companies that they can apply less effort if they use the right techniques. Mainly, companies need to truly know and measure the risk and use vendor audits the way they are intended to be used. In the event that a solution does require validation, our Cloud Assurance service is established and equipped to assist companies for their targeted needs.

We understand risk; that’s what we’ve been preaching to software companies and life science companies from the start – look at the real, true risk. Is there a risk? If so, where are the risks in the application? All functions, some functions? USDM understands that not all functions have the same level of risk and can help companies navigate the waters to ensure the necessary effort is applied to the right functions for validation. This proposed guidance involves a paradigm shift to a less burdensome approach, where critical thinking drives minimal documentation effort associated with validation activities. Whether it is assisting in validation activities, providing updated procedures, or performing audits, USDM can assist companies in delivering meaningful compliance with this more modern guidance.

#3 – Prioritization of Digital Transformation

The life sciences industry continues to face unprecedented challenges amid growing regulatory scrutiny. Globalization, heightened transparency expectations, accelerated emphasis on cloud technologies, and the ever-evolving needs of customers are driving companies to re-examine their approach to quality and compliance. Digital transformation for life sciences companies is becoming a critical imperative to succeed in an ever changing business environment and deliver on customer expectations.

As demand and adoption of cloud technologies grow, choosing the right vendors, both Cloud Service Providers (CSP) and Software-as-a-Service (SaaS) applications, becomes a daunting task. The weight of what systems to pursue and the change management required to drive real digital transformation can leave decision-makers paralyzed on how to move forward. There is a delicate balance needed in transforming your approach from a focus on the cost of quality to the value of quality in your compliance strategy.

Digital Transformation in Life Sciences

Digital technologies like Cloud Computing, Platform-as-a-Service, Purpose Built Applications, Mobile Platform, and Artificial Intelligence are helping modernize life sciences enterprises to drive high-impact business outcomes at a lower cost. Here are some of the current trends we see in our digital transformation work at USDM Life Sciences.

  • An integrated, single enterprise-wide view of compliance risk to create greater transparency and minimize risk across the organization.
  • Building deeper data analytics capabilities to predict critical events, and associated risks to ensure compliance, profitable growth, and better customer experience.
  • Outsourcing of global compliance expertise to help supplement the lack of internal knowledge and resources to stay well-informed of global regulatory changes and maintain continuous compliance with IT systems.
  • Many organizations have started to engage regulators as part of their innovation model by putting their compliance strategy at the forefront of their product roadmap.
  • More sophisticated enterprises have built a seamless quality and compliance process and workflow integrated on a single platform for a complete ecosystem view of the enterprise.

For organizations to better future-proof their digital transformation strategy, it’s important to take a holistic view of the organization’s needs and define what technologies will truly transform the businesses rather than just adding bells and whistles to existing processes. It’s critical to understand where teams are collaborating across GxP functional areas and identify value-generating processes to optimize the customer experience. Identifying multiple areas of the business that would benefit from more innovative technologies, starts to build a technology roadmap to allow the business to deploy data, applications, technology, and capabilities.

Start-ups and small companies can take a more agile approach to unlock value quickly by adopting cloud solutions that enable the business to start small and iterate and scale as the business grows. Large enterprises can take a more structured 36-to-48 months value-driven roadmap to deliver on its goals. Life science companies have historically lagged in digital transformation but are now thinking beyond incremental technology upgrades and taking on an organizational ecosystem and information management infrastructure approach including process specific applications (e.g., electronic data capture, predictive modeling, clinical trials, etc.), business applications (e.g., ERP, CRM, project management, etc.) and general nonGxP business applications.

Ecosystems Approach

One example of a sustainable ecosystem approach to building a seamless quality and compliance process and workflow on a single platform is to use a GxP enabled platform like Salesforce. Salesforce integrates seamlessly with many compliance-ready partner solutions like ComplianceQuest (EQMS), ServiceMax (Field Service Management), Rootstock (ERP for manufacturing) and more. This type of single integrated platform allows life science companies to bring core ERP processes, quality management, and field service activities together as in one harmonized, seamless workflow. All while reducing IT costs without compromising flexibility, scalability, performance, and GxP functionality.

Another more scalable example would be combining the power of best-of-breed platforms like DocuSign, Box, Salesforce, and ComplianceQuest to build a modern, cloud based EDMS (content, learning, and change management solution in one platform). This approach of bringing fragmented content systems into a single platform with enterprise grade security and electronic signature capabilities can be achieved with a strategic IT roadmap that considers the business needs for three to five years down the road.

IT Roadmap

It is important that your digital transformation strategy include a value-driven IT roadmap broken down by initiatives and projects that allow your enterprise to deploy data, applications, technology, and capabilities. Start by assessing your business needs and documented and informal processes and procedures. Then identify critical systems needed and document business requirements. Then prioritize critical systems, develop timelines for implementation, and finally, you can start to think about vendor selection.

Real digital transformation requires a significant change across an entire company, not just in the technology, but also in operations, business models, skill sets of the workforce, and the culture. It is equally important to think though how you will manage change across your organization, control change, and train employees to drive adoption and transformation. A perfect strategy without the execution and focus on change management can lead to wasted IT investments.

How USDM Can Help

At USDM, we help our customers to define digital transformation strategies that drive growth by combining business strategy, technology, digital skills, and cultural adoption for successful execution. We work with industry-leading partners to create value for our customers, help achieve measurable results with new technologies, and keep systems and applications in compliance. USDM’s digital transformation expertise has helped hundreds of life sciences clients embrace the value generating benefits of cloud-based quality and compliance systems and achieve superior implementations based on industry best practices.

#4 – The Use of Advanced Analytics

Most of us are familiar Alexa and Siri which use chatbots, a type of artificial intelligence (AI), which are software robots that interact with us by using Natural Language Processing (NLP). NLP is defined as the ability of a computer program to understand human language as it is spoken. A more sophisticated type of AI is Machine Learning (ML) which uses algorithms to learn from and make predictions on data. ML overlaps with computational statistics, predicative analytics, and data mining. Another aspect of ML and Deep Learning is Pattern Recognition, which focuses on the recognition of data patterns; Facebook uses this capability. All these types of advanced analytics are used today by life sciences companies, and some are supporting GxP functions.

Advanced analytics present a valuable opportunity for life sciences companies to profoundly transform their businesses at all levels of the organization – people, process, and technology. It may very well be the most innovative opportunity we have seen in life sciences for decades given its power to literally save lives, but advanced analytics are not a technology, it’s the computer systems that process the data that generate the value. To make use of advanced analytics it is critical your process for data generation and data quality is trustworthy and compliant.

From molecule to market the life sciences ecosystem has a value stream of services from R&D, medical, manufacturing, supply chain, sales, and marketing that is dependent on, not just, compliant global support functions and information technologies, but also advanced analytics. We see the rapid adoption of advanced analytics in each of the value chain services. AI/ML is used to enable compliance of legal, security and risks, environmental health and safety; global functions include communications, HR, finance, procurement; IT includes Robotic Process Automation (RPA), help desks, enterprise content management, etc. Not to be overlooked is the customer, i.e., physicians, health authorities, pharmacies, whole-sellers, distributors, patients, payers, hospitals. Here are a few use cases in life sciences we believe will have continued promise and progress in 2020:

Drug Discovery – Data Insights, Drug Adherence

The pharmaceutical industry continues to experience challenges sustaining drug development programs due to the increase in R&D expenses and reduced efficiency. AI, Machine Learning, and Natural Language Processing are saving scientists a tremendous amount of time in their research efforts by feeding data from sources such as research papers, patents, clinical trials, and patient records into a cloud-based system to serve up relevant data insights through visualization.

One of the fastest-growing pharmaceutical companies in the world used AI to analyze the performance of its flagship drug in a tightly regulated environment surrounding drug adherence. Data from patients, prescribers, and payers was used to feed an anomaly detection tool, that recognized and learned from the sequential pattern data of patients going through a workflow and identified those at risk of lapsing from drug adherence.

Electronic Health Records (EHRs) and Real-World Evidence (RWE)

A survey and analysis of 98 articles revealed that data analytics of EHRs focused on the following tasks: disease detection/ classification, sequential prediction of clinical events, concept embedding, data augmentation, and EHR data privacy. The data analytics of EHRs is critical preprocessing for feeding of RWE data lakes life sciences companies are expanding with more sources of data and more advanced use of AI/ML.(1)

A major consumer of EHRs are medical affairs teams who are using advanced analytics to uncover patient insights and advance the use and sophistication of RWE with AI/ML fed with large and frequent intake of patient information from various sources, i.e., trail outcomes, electronic medical records. As business users become more familiar with the benefits and capabilities of AI tools and techniques their appetite for more constant, responsive and proactive capabilities. As EHRs become more important to RealWorld Data (RWD) and RWE, we will more than likely see EHR interoperability solved and patients carrying their EHR on their smart phone or stored in a cloud. It is doubtful that we’ll experience implanted EHRs anytime soon.

Radiotherapy and Radiology

AI and Deep Learning have become more accurate at identifying diseases from medical images versus that of healthcare professionals and has become a more feasible source of diagnostic information. As this use of deep learning grows, it is possible it becomes a more efficient way to diagnose diseases.

Here are two examples:

  • The U.S. Food and Drug Administration has approved a new artificial intelligence (AI) algorithm that works with portable X-rays to rapidly screen for collapsed lung. The technology was developed by GE Healthcare and UC San Francisco researchers.(2) The AI has been validated achieving more than 96 percent accuracy. When the AI detects a pneumothorax, the image prioritized for radiologist review.
  • Another recent example of an AI diagnostic is “IDx-DR, a device that is capable of diagnosing diabetic retinopathy without human intervention. The system is the first FDA-approved autonomous artificial intelligence (AI), using its software to analyze images from a retinal camera for evidence of lesions.”(3) AI powered diagnostics is moving rapidly from identifying glaucoma and macular degeneration to exploring how to detect ear infections, Alzheimer’s disease, etc. The key ingredient to success in AI supported applications is consensus among experts and scientist on the definition of the disease and precise information to build and validate the AI algorithm.

Artificial Intelligence in the Life Sciences Market was valued at USD 902.1 million and is expected to grow at a CAGR of over 21.1% during the forecast period (2019-2024)(4)

Regulatory Challenges

While AI, Machine Learning, and advanced analytics will no doubt advance the life sciences and health care industries, there are still many strict regulatory hurdles to overcome. Data privacy and the use of personal data is a critical issue and regulatory agencies need transparency to algorithms and better understand of how Machine Learning works.

Further GxP processes need to be validated, data integrity safeguarded, and digital trust in AI to become commonplace as the innerworkings and benefits become clearer. Finally, finding the right talent that has both life sciences experience and big data expertise is very difficult. There is great demand for these skills and simply not enough talent available in the market today. It’s a long road but USDM has already helped several customers on their journey to unlock the power of advanced analytics in regulated organizations.

More mature life sciences organizations are beginning to utilize AI in Quality Assurance (QA) and testing requirements for software validation. There is not a widely accepted approach to this methodology yet, but many organizations are experimenting with how to use AI in their IT systems and GxP processes. Currently USDM is leading a top 5 pharmaceutical company’s IT team in developing a validation approach to use their Azure DevOps framework and Natural Language Processing (NLP) tools for Intelligent Assistants, also known as chatbots, in support of clinical trials. USDM will ensure the framework is aligned with the regulatory guidelines to be compliant with applicable laws and regulations. This is just one example of how AI can be used to address clinical trial expense.

Clinical trials are a target rich environment for the application of AI to reduce costs and speed up trials. Top targets include patient enrollment, competition for patients and doctors, and clinical trial site fatigue. AI technology can help clinical trial coordinators to identify centers likely to have untapped patient populations and key leaders interested in clinical trial research by connecting data point to speed up the trials.

How USDM Can Help

USDM is assisting clients with AI empowerment to experiment, innovate, and deploy cloud-based solutions. But before you can implement a game-changing AI solution, your process for data generation and data quality is critical to the success of your AI outputs. Our support addresses data quality, people, risk management, and process.

  • People – Providing governance, validation, audits, and outsourcing or augmenting of system owners, infrastructure, cybersecurity, master data management, and data scientists.
  • Data Quality – Data integrity assessments, defining, measuring and automating data quality checks, and validating data in Data Lakes.
  • Process – Identify lean short-term prototype projects and develop a roadmap of where you will apply AI capabilities and business functions and well as implementation and deployment.
  • Risk Management – Assessments, validation, and regulation and risk-based trainings.

For a more guidance on how to think about utilizing advanced analytics in your organization read our white paper on Data Lakes, AI and Machine Learning: Why predictive analytics in life sciences requires teamwork and software assurance.

#5 – The Paradigm Shift from Vendors to Strategic Partners

Regulated life sciences organizations have typically been slow to adopt and implement digital technologies. Many have relied on manual processes and on-premise systems that no longer meet business requirements. Many of these companies that have operated IT systems have done so in silos, without integrations, collaboration, and many times without access to critical data. Regulatory changes, confusion, and the “expense” of compliance have been the rationale for not keeping up with technology, but those days are over. Life sciences companies must now keep up with the times, which means embracing the right technology and services partners to accelerate their digital transformation and maximize digital technologies to create value-generating capabilities that support innovation and more modern cloud-compliant solutions.

At USDM Life Sciences, we have seen many pharmaceutical, biotech, and medical device companies that commit to implementing more modern digital solutions struggle to build teams capable of delivering and maintaining compliant systems and processes. They have many times opted to outsource IT and compliance functions due to a lack of expertise and limited budgets. The desire to decrease IT spending as well as minimize the internal effort required to stay compliant resulted in and hiring “bodies” to handle the burden and headache of learning new technologies and addressing compliance. The outsourcing of bodies, or staffing approach, is no longer enough. Customers, patients, and the overall industry now demand more advanced cloud technologies, artificial intelligence, Machine Learning, and IoT, to meet their needs. Compliance and the maintenance of a complaint-state cannot be a deterrent to digital transformation, innovation, and, most importantly – patient care.

Why this is Important

The life sciences industry is constantly evolving. In 1999, life science companies spent 1-2% of revenue on IT relative to other ‘high-tech’ industries because the FDA regulations were restrictive. We have seen 2-3x growth in IT spending with 4-7% IT spending across life sciences. Here are some other significant trends we are seeing;

  • The FDA is going to announce its computer Software Assurance guidance that will likely allow for more flexibility with the cloud and automated testing.
  • EUDAMED and other international agencies are changing how they will ingest data and managing global supersets of data is going to be critical.
  • Manufacturing and selling products in Europe and across the globe will continue to require additional regulatory and compliance scrutiny.(5)
  • Six of the top 10 tech giants like Amazon, Google, and Apple are diversifying into health care and life sciences, disrupting regulatory compliance.
  • AI in life sciences will grow over 20% in the next five years.(6)
  • Compound annual growth rate (CAGR) for deep learning in life sciences will increase by nearly 50% in the next 5 years.(7)

It’s difficult to successfully innovate, get quality products to market fast enough, control costs, keep up with competitors, and, most importantly, improve patient outcomes without the right IT partners.

Where to Start

Pharmaceutical, biotech, and medical device companies want to be as innovative as non-regulated industries. They want to capitalize on IoT and Machine Learning and capitalize on the vast amounts of data they must produce better outcomes. So, where do you start? You need to be clear on where your company is before you can select the best partner.

  1. Assess where you are now. Where do you want to be in three years? Do you have the leadership to take you to your three-year vision?
  2. Prioritize technology platforms that will get you to the next level. Talk to software vendors that you see in your future. Do they have a Cloud Assurance offering? Do you feel confident in their ability to support you with ever-changing global regulations?
  3. Take a hard look at your team. Do you have expertise in AI, IoT, Machine Learning, or do you need to hire talent for those areas?
  4. Make a plan. Understand that the plan needs to be fluid and will require revisions along the way. You need to have leadership, the right IT partner, AND a sustainable compliance strategy and process that will keep up.

How to Select the Right Partner

Life sciences companies need to align with the very best technology platforms and form true strategic partnerships with experts in process, technology, and compliance. But how do you know who the best partner is or who the right partner is for your company? Here are some things to consider when exploring new vendors or partnerships.

  • Expect white-glove service: Strategic partnerships with companies that provide white-glove service to execute compliant digital transformation will enable real, meaningful change, for your company and your customers. Your needs should be their top priority. Continuing to rely on staffing organizations to supplement your internal IT and quality teams without proper management and strategic vision will leave you behind.
  • Ensure vision and culture alignment: Strategic partners should be more than just time and materials type project-based engagements. They should help you execute your vision, planning, processes, and management of on-going execution of digital initiatives to see success.
  • Drive change, instead of reacting to it: True strategic partners will be ahead of market shifts, technological innovations, and global compliance changes, so you can focus on product innovation and improve patient outcomes.

How USDM Can Help

USDM Life Sciences has developed a suite of solutions with the best of breed technology platforms and vendors with quality, innovation, and compliance built in. IT vendors that provide single platforms to share, integrate, collaborate and make decisions are critical for success. Cloud, AI, and IoT vendors can’t offer guidance on how to build and manage a well-developed cloud compliance approach, which is where USDM comes in, and solutions are jointly developed. IT vendors that partner with regulatory experts to ensure compliance is critical.

Life sciences companies need to think more like tech companies. It is not just about selecting the right vendor, the right SaaS partner, or application; it is thinking about your future ecosystem needs and what platform enables growth that scales with technology. For example, one of the reasons USDM partners with Salesforce is because the Salesforce platform powers that eco-system (ComplianceQuest for Quality Management, Rootstock for ERP, and Propel for PLM).

Will your current vendors ensure your regulatory compliance and IT requirements are met for the next three to five years and drive your company’s growth and innovation?  USDM is currently working with a global pharmaceutical company to develop chatbots that will be used to provide GxP content such as clinical trial support information to their clinical sites. This type of AI work will help speed up their clinical trials, better share data across their global clinical sites, and ultimately get their products to market faster.

#6 – The Growth of Cannabusiness in Life Sciences

With the explosive growth of the cannabis, cannabis-derived products, and marijuana industries, the FDA understands that there is significant interest in the development of therapies and other consumer products derived from cannabis and its components, including cannabidiol (CBD). The FDA recognizes the opportunities that cannabis and cannabis-derived compounds may offer, including neuropathic and inflammatory pain relief, addiction treatment and more. Via the drug review and approval process, the FDA supports sound, scientifically-based research into the medicinal uses of drug products containing cannabis and cannabis-derived compounds.

To date, the FDA has authorized only a single cannabis-derived drug product. It is important to keep in mind that the FDA has not approved any other cannabis, cannabis-derived, or cannabidiol (CBD) products currently available on the market. Interestingly, Canada is taking the lead on the regulatory front and announced new regulations that took effect in October 2019 for cannabis edibles, extracts, and topicals. The Canadian regulations are divided into two sets, Cannabis and Industrial Hemp (the difference is Hemp is classified as having  <0.3% tetrahydrocannabinol, or THC. These regulations cover such things as physical security, limits on contamination (residual solvents), and supply chain requirements. Not surprisingly, most regulatory bodies are setting up compliance regulations to regulate cannabis as a drug product. The FDA, as well as most other regulatory bodies, are following Canada closely.

What does this mean for the industry today? Regulatory health agencies have the jurisdiction to regulate the cannabis industry for safety and efficacy. Compliance with the regulations is necessary, especially for quality, clinical research, validation, manufacturing, and supply chain. It is wise to implement cGxP controls such as Good Laboratory Practices (GLP), Good Clinical Practices (GCP), and Good Manufacturing Practices (GMP). This will put you ahead of competitors and ease the transition to a fully regulated environment. Secondary benefits of implementing GxPs are increased consumer safety, brand identity, and increasing profits through efficiencies that GxP brings by helping to minimize mistakes and rework.

There are many challenges as this industry develops. For example, there are complications around obtaining research-grade marijuana due to its continued federal classification as a Schedule I drug. This classification makes it difficult to obtain medical grade material for research. There are also technical challenges such as the lack of approved test methods for laboratory analysis, and restrictive licensing requirements that have been put in place on the state level.

How USDM Can Help

USDM works closely with emerging cannabis and cannabinoid companies, and we follow the global regulatory agencies to stay on top of the evolving regulations. We realize the marijuana and hemp industries will have many regional impacts and considerations. The earlier on your road to commercialization that you align your compliance and technology strategies, the more competitive market advantage you will have in the industry.

USDM can support and guide your journey to commercialization or compliance for your cannabis products. Some of the services we offer for emerging cannabis companies are below:

  • Regulatory education and training
  • Establishing risk-based, pragmatic, phase-appropriate quality and IT compliance programs
  • Developing a comprehensive IT strategy and 3-year cloud technology roadmaps for an accelerated regulated business model
  • Accelerating vendor selection, implementation, and validation of all regulated systems
  • Validation and qualification for CSV, process, and equipment
  • Compliance-as-a-Service
  • Internal and mock audits to prepare for regulatory submission

In Conclusion

Life science business processes are becoming increasingly complex and are evolving faster than ever before. Technology and intelligent systems are critical to keeping up the speed required to stay competitive and compliant in this cutting-edge industry. Being able to trust in the technology, data, and partnerships necessary to succeed requires a new level of digital trust that can create feelings of uncertainty within organizations. At USDM, we keep ourselves grounded in regulation while vetting and embracing new technology to be your trusted compliance and technology advisor. We simplify technology by bridging the gap between Quality and IT to enable our customers to get their products to market faster.

About the Authors

Jay Crowley (#1 – Increasing Complexity of Global UDI Regulations)
Jay Crowley is the Vice President of UDI Solutions at USDM. Jay is a recognized expert in the area of UDI and held a variety of positions over his 26 years at the FDA, including developing the requirements for the FDA’s UDI System. Most recently, Jay was the Senior Advisor for Patient Safety in the FDA’s Center for Devices and Radiological Health. His contributions to design control regulations to reduce the chance of human errors with medical devices, patient safety, and adverse event reporting are highly regarded.

Sandy Hedberg (#2 – Enhancing Medical Device Quality and Patient Safety)
Sandy Hedberg has over 20 years of professional experience in Quality and Regulatory Affairs in the medical device, pharmaceutical, and biologics industries. She has participated in assisting companies with responses to consent decrees and audit findings to the FDA. She has extensive experience in risk analysis, creation of quality procedures, computer system validation, auditing, and authoring regulatory submissions.

Vishal Sharma (#3 – Prioritization of Digital Transformation)
Vishal Sharma is the Vice President of Digital Trust and Transformation at USDM. Vishal has more than 20 years of experience in the life sciences industry providing consulting services in the areas of strategic planning, business process automation, quality and compliance management, content management, cloud adoption, data analytics (artificial intelligence, machine learning, and deep learning), project management, and application development.

John Petrakis (#4 – The Use of Advanced Anlaytics)
John Petrakis is the Vice President of Cloud Assurance at USDM. He is responsible for the strategy and implementation of USDM’s Cloud Assurance service, which is a managed subscription delivering end-to-end GxP cloud compliance from implementation through ongoing validation maintenance of new releases. John has 30 years of diverse global industry experience in business transformation, and delivering solutions that address regulatory and quality pressures.

Kim Hutchings (#5 – The Paradigm Shift from Vendors to Strategic Partners)
Kim Hutchings is the Head of Alliances and Digital Transformation Lead at USDM Life Sciences. Kim has more than 15 years of experience in the life science industry and has managed USDM’s Alliances program for over a decade, establishing partnerships and solutions with the best of breed technology vendors, including Oracle, Salesforce, DocuSign, Microsoft, and Box.

Joeseph Cassella (#6 – The Growth of Cannabusiness in Life Sciences)
Joseph Cassella is the Director of Regulatory Compliance at USDM Life Sciences. With over 20 years of experience in the pharmaceutical, biotech, and medical device industries, Joe’s background is both broad and deep in Information Technology, Laboratory and Analytical Applications, and Quality Systems. Joe has led many projects, including IT Infrastructure, Research & Development, QA/QC, Manufacturing, Compliance, and Sales and Marketing.


(1) Opportunities and challenges in developing deep learning models using electronic health records: A systematic review,” Journal of the American Medical Informatics Association
(2) Artificial Intelligence That Reads Chest X-Rays is Approved by FDA
(3) IDx-DR, the First Approved AI System, is Growing Rapidly
(4) Artificial Intelligence in Life Sciences Market – Growth, Trends, and Forecast
(5) Preparing for the future: The new European Union medical devices regulation, Deloitte
(6) Artificial Intelligence in Life Sciences Market – Growth, Trends and Forecast (2019 – 2024), Mordor Intelligence
(7) Artificial Intelligence Market to Reach $26.4 Billion by 2023, According to Beroe AI to Replace up to 50% of Unskilled Labor Over the Next Five to Seven Years, Beroe Inc.

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