*Disclaimer: This article written by Jane Wood is an abbreviated version of an article that appeared in the November 2018 Issue of The RQA Magazine with complete references. This version has been edited and approved exclusively for YourEncore’s Thought Leadership.
Audits: A Brave New World, Time for a Refresh?
Helping the world love Clinical Quality
In this article, let’s take a journey into the rapidly evolving world of clinical trials, where data and technology rule. Let’s look at how R&D Clinical and safety Quality are positioned and what new and existing strategies we need to consider to continue our growth as a critical and highly regarded function.
With the enormous developments in robotics, data analytics and Artificial Intelligence, why are we still doing audits mostly the same way we have been doing them all my career? All of us (including many Health Authorities) now perform system audits and inspections in one shape or other on a routine basis. I still believe audits are incredibly valuable, but they haven’t really changed a great deal in the last 40 years.
So, should we change? Can we throw down a challenge to bring about change for Pharmaceutical, Consumer and Medical Device auditing? Can we move with the times and embrace audits and data in a different way? Can we let go of some of the things we have been doing historically, just because we have always done them that way? Now don’t get me wrong, I am not saying we shouldn’t do all the great things we do now, but maybe it’s time to press the ‘refresh’ button.
The Changing Face of Clinical Trials
Let’s start by glimpsing into the ever rapidly evolving new world of clinical trials and gain a deeper understanding of our world as it is changing around us. We now have Big Data, Data Analytics, Central Statistical Monitoring, Structured vs Unstructured Data, Personalized Medicine Data, Risk managed Data, Data Warehouses and Data Lakes. Here’s a quick check point on a few to help us along the way:
- Big Data is the term used to describe massive amounts of exponentially growing data. The data however in itself, gives us very little until it is analyzed and processed.
- Data Analytics is the process of analyzing the data with the intent to infer useful conclusions or hypotheses. As you can imagine in our industry, we are generating huge amounts of protocol driven data and the clinical teams are intent on looking for broader and deeper insights that can help them make more informed decisions on clinical trial outcomes. In other words, Data Analytics rule.
- A Data Warehouse is a large repository of aggregated data that is specifically designed for analytical intervention, typically storing data from multiple sources.
- A Data Lake is similar to a Data Warehouse in that it stores data from various sources, but it has additional data attributes from the source systems at a level of detail that would normally be lost in a data warehouse. This allows for a much greater level of granularity to the analysis that can be performed.
In Pharmacovigilance especially, data analyses are of huge importance to determine safety profiles. Data is also reviewed from the many forms of social media as well as the traditional data sources of literature, regulatory and clinical sources. Moves are afoot to use AI and machine learning tools to quickly review large amounts of data and with great precision reducing the human time spent on processing this information.
Data is now an ever-present part of our world. Yet, in Quality do we really understand or know:
- How regulated data are being used?
- How the data is gathered, stored and how it flows across interface areas?
- How it is being analyzed?
- The tools or apps being used for analysis?
- Are we confident they are validated?
- Whether the outcomes of the analysis are correct?
- Can we in quality see the data anomalies?
- Are we even looking at this data at all?
Augmented Reality and Wearable technology
This is a very exciting area of development that I know some companies are piloting. Let me give you an example from the food industry where the technology is somewhat advanced. In February 2015 on the Google campus, a food and safety audit was conducted without the auditor leaving her office. Instead, a kit was shipped to the restaurant hundreds of miles away containing everything that was needed to conduct the audit. The manager of the facility was then guided through each step of the audit by the remote auditor using two-way video communication via Glass. The auditor filled in the inspection report each step of the way and emailed the audit report to the facility manager for review and discussion, as if they had been present in real time.
We can now imagine a time when we can conduct an audit alongside a Clinical Research Associate at an Investigator site, and they can review the clinical research Form (CRF), source data, and even pharmacy supplies before our very eyes. Directed by us, the auditor, we can electronically complete the audit report as we observe (taking live photos as evidence). The opportunities are endless, and at some point, surely clinical trials will be far more automated than they are now.
Forward Thinking Strategies
So, where does this leave us? I see the need for a visionary Quality audit. I also see a Quality function where audit professionals are not only experienced in the traditional elements of a clinical investigator / systems audit, but are also confident and strongly focused on data and IT and have the ability to look objectively and subjectively at large swathes of data. This is what I am going to call an “integrated audit and auditor.”
Let’s brainstorm some areas where we can consider new ideas and take actions:
- Research new audit technologies (wearables) and artificial intelligence audit tools.
- Understanding that the value of integrating big data and analytics into the audit will only happen when auditors can understand the scope, nature and extent of the audit. This requires the development of new skills, knowing what questions they need to ask of the data and the ability to use data analytical output to produce audit evidence to draw audit conclusions and derive meaningful insights.
- Understanding and reassuring our QA colleagues that the human role is essential when using the results of data audits to inform on critical decisions.
- If we don’t have the internal capability, maybe we should contemplate a world where we employ shared resources via centralized models, where specialized, external, independent audit groups perform data/ IT (other) audits of our vendors on our behalf, according to a consistent set of industry-accepted standards.
- Quality Assurance absolutely needs to be independent, but let’s make sure “Quality” is also owned by the business, at the end of the day everyone owns quality. Let’s not underestimate the value of culture, a culture where audit observations and quality are seen as value added and not a burden.
We live in a world of data – global, overwhelming, insightful amounts of data, and we have astonishing technology at our fingertips. We also have some Quality functions that haven’t changed much over the years. So, I encourage us all to embrace our new clinical trial world, let our excitement at new technological opportunities bubble up. Plan now for our brave new world, recruit new talent, nurture existing talent, develop integrated audits and auditors, use external vendors or consortiums, and help your business partners see our value.