Smoother clinical workflows. Powerful decision support. Faster R&D. AI holds lots of promise for healthcare orgs – and the last few years have seen a surge of investment as leaders race to train and implement new models.
But healthcare AI initiatives are only as good as the data they’re built on. Put differently, they rely on good data governance.
Here are two of the biggest barriers to effective governance – and how healthcare orgs can overcome them to achieve their AI ambitions.
Healthcare AI Depends on Data Quality and Patient Trust
Generally speaking, effective data governance relies on three essential pillars:
- Data curation and quality management to ensure accuracy, consistency, and completeness.
- Accessibility and security protocols that permit data use without compromising safety.
- Regulatory compliance (e.g., with HIPAA, GDPR, etc.).
Why are these pillars so crucial in the context of healthcare AI? For starters, the technology depends on massive datasets (often structured and unstructured) that must be cleaned and integrated in order to be useful. After all, high-quality data is likely to yield helpful insights – but low-quality data is bound to produce inaccuracies or worse down the line.
The sensitive nature of healthcare data also makes data governance a necessity. Patients are already cautious about sharing their data, especially in the age of AI. With strict access controls, security measures, compliance checks, and an active human in the loop, healthcare orgs can go beyond the bare minimum to lower the risk of data breaches and misuse. The result: more patient confidence in healthcare AI.
But Good Governance Has Two Common Roadblocks
Despite the clear importance for healthcare AI, many healthcare orgs find it challenging to implement a data governance structure. Two of the most common roadblocks: data silos and ownership confusion.
Many healthcare orgs have data siloed in various systems across multiple departments. These silos can increase the risk of duplicate entries and make it difficult to create the unified datasets that healthcare AI needs to yield powerful insights. What’s more, they can isolate data that could otherwise prove valuable to the whole organization – leaving countless opportunities untapped. It’s important to consolidate data across departments to better ensure consistency and accessibility.
Of course, as with any large-scale initiative, there are often questions around who’s responsible for making data governance a reality. In many cases, this work is often treated as a matter for compliance or data teams. IT, compliance, and data groups cannot be solely in charge of data governance. In reality, good governance requires universal buy-in and ownership – with clear representatives to establish who will use the data and who will manage the data.
One way to secure that buy-in? Assign department-specific data stewards. Together, these stewards can collaborate on a governance council to support and oversee a cohesive data governance strategy.
To Implement a Governance Strategy, Start Small and Scale
With so many moving parts, it’s easy for leaders to succumb to analysis paralysis – a frozen state of indecision when faced with a seemingly insurmountable task. But a data governance strategy doesn’t have to be implemented overnight. By taking a phased approach, healthcare orgs can make meaningful progress and gradually build momentum.
Our recommendation? First, identify parts of your organization that could benefit most from improved data governance to support healthcare AI. Then, start small by implementing one governance pillar at a time – ideally on a single department or team. For example, you might appoint a data steward to oversee data curation and quality management in the oncology department.
This is a lot more manageable than, say, standing up a hospital-wide data governance council all at once. And with a targeted pilot initiative, you can demonstrate the value of good governance early on to gain broader buy-in over time.
Pave the Way for Effective AI – and Operational Excellence
So far, we’ve largely discussed data governance as a sort of groundwork for healthcare AI. But its importance goes much deeper.
The reality is the way an org handles data is a reflection of its operating model. Good governance signals an organization that values privacy, quality, collaboration, and trust. By improving your data governance strategy, you won’t just see payoff when it comes to AI – you’ll likely see benefits from the clinic to the back office.
The bottom line? By nurturing a data culture committed to good governance, healthcare orgs can pave the way for far-reaching success.
About Daniel Vieira Viveiros
Daniel Vieira Viveiros is the SVP, Data and Analytics at CI&T. A global technology transformation specialist for 100+ large enterprises and fast growth clients, CI&T helps retailers engage customers, increase sales, and drive greater operational efficiencies.
