Digital Transformation Is Integral to Success in Healthcare

By Nutan B, Vice President of Consulting at Gramener


The capacity to process large volumes of data has made great strides thanks to advanced analytics, sensors, and internet of things (IoT) devices. They can vastly improve the responsiveness, quality, and efficiency of healthcare business processes.  

While bootstrapped startups, innovators, and disruptors are already adopting these technologies, legacy pharmaceutical companies have been slow to jump on the bandwagon. This is because, unlike other industries, healthcare operators do not view digital transformation as an organizational prerogative but as individual project elements.  

Pharmaceutical organizations have a more conventional approach to business than other industries, resulting in separate business and digital teams. This creates barriers to entry for digital transformation initiatives that require constant buy-ins from the business teams to move forward.  

Furthermore, technological companies driving organization-wide digital transformation often fail to address the core business model and proposition of healthcare providers, concentrating instead on automating peripheral patient onboarding and improving the experience.  

The global healthcare industry is undergoing a paradigm shift, and digital transformation is at the heart of this change.  

Digital Transformation Is Helping Pharma Companies Tackle Regulations 

The McKinsey Global Institute estimates that big-data strategies could help customize new tools for regulators, insurers, consumers, and physicians. It could also improve the efficiency of clinical trials and optimize innovation, generating up to $100 Bn annual value in US healthcare.  

Over 50% of healthcare industry executives who participated in a 2019 study predict widespread AI adoption by 2025.  

To further research, the pharmaceutical community must share clinical trial data. International regulatory standards like Health Canada and EMA 0070 have strict guidelines to anonymize clinical trial data, protecting patient privacy.  

Healthcare providers constantly race against time to disclose clinical documents cost-effectively without compromising patient confidentiality. Manually, this process can take weeks or even months. This is because clinical records comprise large amounts of unstructured data.  

Manual processes are also error-prone, increasing the risks of patient information re-identification and data breaches that can lead to hefty fines.  

Unstructured medical datasets like medical research records, videos, images, and medical documentation are more difficult to work with. Traditional methods to track personal information, such as processing data in tabular form, do not apply to unstructured data since these methods are specifically designed for structured data.  

Artificial intelligence uses computer vision and Natural Language Processing (NLP) techniques to help computers “understand” unstructured data like videos, images, and human language. This automates the redaction and detection of personal information and helps anonymize the data.  

All personally identifiable information (PII) or protected health information (PHI) must be removed to share health data related to medical research. This includes protecting a person’s identity by blurring videos or images. When the subject in the data can no longer be identified, it can be described as anonymous data.  

It can also involve removing information that can help identify a person associated with a record, such as addresses, dates of birth, and names. The data anonymization process comprises encryption, generalization, and removal of identifiable markers.  

Following is a tabular representation of anonymization challenges faced by healthcare operators and how AI solutions can help overcome them.

Advanced Analytics for high-accuracy PII identification  The manual process of identifying PII information by auditing thousands of clinical summaries is exceedingly tedious. Furthermore, it also requires considerable domain expertise.High-accuracy analytics algorithms that are domain specific can reduce the time it takes to parse and identify PII entities from weeks to just minutes – a drop-in turnaround time of a staggering 95%!
Optimize risks in line with regulatory authorities  Data anonymization and risk calculation are required to ensure that CSR documents maintain enough data utility and transparency for meaningful consumption down the line while adhering to stringent regulatory mandates. Risk optimization algorithms can help seamlessly align the justification process and risk calculation with regulatory authorities. Optimization algorithm automation approaches can help strike the right balance between privacy and transparency.  
Digital elements led user-centered process redesign  Anonymization is a considerable undertaking. The review and approval processes integral to making the anonymized clinical summary report reliable and trustworthy follow rigorous TATs and involve multiple stakeholders.  Well-planned change management processes, stakeholder involvement at an early stage, analytics accuracy reviews that are well designed, and collaborative development of solutions that are user-interface driven help eliminate the monotony of the review and handoff processes.  


The scope of digital transformation in healthcare is limited only by the ingenuity of industry insiders. Cloud technologies, analytics, artificial intelligence, and machine learning are already redefining legacy processes, changing the value propositions and, in some cases, even the business models.  

Earlier, digital transformation was restricted to peripheral services like patient management, customer service, experience, etc. Thanks to the advances in domain-specific analytics, digital transformation can now address the fundamentals of the healthcare business, impacting it at a granular level.  

Digital transformation presents a unique opportunity for healthcare leaders looking to elevate their business proposition. To ensure that they do not miss the train, pharmaceutical operators must unleash the full potential of digitalization, implementing it in every facet of their business functions and strategy.

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