Data integrity has been one of the top global issues reported in the life science media over the past two years, particularly within the pharmaceutical industry with high profile cases such as Sun Pharma and Wockhardt.
It is a subject that should be talked about as a single integrity issue that can call into question all of the data produced by a company and auditors are already citing an increasing number of manufacturers for data integrity failures. In 2014, 13 of 18 GMP warning letters focused on data integrity violations.
Research on this subject has revealed that many data integrity issues arise from:
- Tacit internal pressure to achieve positive outcomes for powerful customers
- The temptation to select results to prove a hypothesis
- Shortcuts through an overly bureaucratic process
- Confusion in a fragmented system
- Professional ignorance and lack of awareness of SOPs and compliance related requirements
When you take a closer look at data integrity deficiencies and violations cited by the FDA, MHRA and other European regulatory authorities, the majority of issues frequently relate to bad practice, fragmented business processes, poor organisational behaviour and weak Quality Management Systems.
This analysis dispels the general misconception that data integrity failures only result from acts of deliberate fraud, as well as highlighting the detrimental factors to data integrity that need to be addressed.
However, there is way for companies to navigate the troubled waters of data integrity deficiencies by taking some basic behavioural, procedural and technical steps. These steps can allow organisations to significantly improve their systems and reduce the risk to data integrity.
Agencies such as MHRA and FDA increasingly take a carrot and stick approach to inspection and enforcement. The 'sticks' include such enforcements as import alerts, interdicts and injunctions and, of course, criminal prosecution. Post-approval 'carrots' include reduced frequency of subsequent inspections, regulatory flexibility where there is confidence that a firm or facility has low risk of manufacturing problems, and acceptance of information provided by trusted overseas regulatory partners.
The industry has responded positively to the scandals and the increased regulatory scrutiny. Diligent firms take data integrity very seriously and seek to address it through improved inspection regimes, quality management systems and their management structures.
As a supplier of quality, compliance and risk management software to the pharmaceutical industry, we see that this increased level of diligence has had the effect of making companies aware of shortcomings in business processes rather than dishonest behaviour by individuals. The data integrity challenge for the vast majority of businesses is not rooting out malpractice, but rather modernising obsolete, fragmented and poorly controlled practices and systems including document management, internal audit, training and competence and third party management.
Our message is that the right solution can eliminate negative operational and cultural practices and drive a culture of openness and integrity: turning negative practices and processes into positive ones.
Some of the ways to do this are:
- Modernising your operational processes
- Strengthening internal controls
- Eliminating obsolete systems based on paper files, spreadsheets and email
- Addressing professional ignorance and managing competence in research staff
- Modelling and managing risk
The challenge of data integrity should be looked at in the context of good governance and the management of risk and compliance. This is because it then drives businesses towards a system-level response to a system-based problem.
Our latest whitepaper 'A Nudge in the Right Direction: assuring data integrity in the Life Sciences industry'discusses the implementation of effective behavioural, procedural and technical steps. These steps, supported by appropriate systems, will encourage the right behaviours, improve compliance, provide greater assurance of product quality and maintain integrity throughout the data lifecycle.