Challenges in data management - from breaking data silos to data democratization

By Liza Laws

- Last updated on GMT

© Getty Images
© Getty Images

Related tags Data management Artificial intelligence Research machine learning Clinical trials

WNS supports the research and brand teams of more than 30 life sciences companies. The company believes its comprehensive pharma market research capabilities help clients in assimilating information from various data sources.

The company says that if business partners are able to harness health date with strong partnerships with the healthcare industry, new use cases will emerge as artifical intelligence (AI) is used correctly and effectively. 

We put a number of questions to Doctor Gauri Puri who leads the business for life science projects within the healthcare business unit of WNS.

What are the current flaws in managing health data?

We would refrain from using the term 'flaws' since we have progressed significantly in leveraging health data to provide better outcomes to patients while reducing waste in healthcare spending.

However, there are some challenges that we need to mitigate. Key among them are breaking data silos by integrating information from across healthcare and treatment systems; this includes aligning data from pharmaceutical and institutional research platforms, provider networks, diagnostics, omics studies, claims, and sales channels, among others.

Another is ensuring data democratization and interoperability by harmonizing data in a common format for seamless exchange. Furthermore, overcoming regulatory hurdles, augmenting data quality and privacy, ensuring accountability on the costs, and tacking the increasing variation in data types as more and more data sources gain prominence are all areas to keep a close eye on.

Meeting the demand for increased data frequency and real-time data, fueled by wearable devices and personalization trends is another area on our radar.

When, in your opinion, did data management and AI start to walk hand in hand?

AI and data have been evolving side by side; there is no AI without data. While AI officially began in the mid-20th century, it was first applied in healthcare in the 1980s and its commercial significance in the field has increased in the last five years. Most pharmaceutical organizations now have dedicated leadership teams and budgets to drive AI and Machine Learning (ML) benefits in their data operations.

The recent pandemic prompted a big shift as the healthcare sector was forced to deploy digital transformation to manage business operations. Through this disruption, enterprises realized the big gaps in data availability as per user need, and thus the necessity of bridging data siloes.

What have been the hurdles between transitioning from dated systems to what is on offer now?

The biggest hurdle is the cost of the systems upgrade/transition combined with the high cost of AI implementation and maintenance. The other hurdles would include a lack of knowledge and best practices, a lack of regulatory clarity in all areas, change management (of mindsets, processes, and skillsets), and dependency on third-party partnerships.

Additional hurdles include data integration challenges due to data variability and too many technological choices.  

What potential can well-harnessed data management and strong partnerships have on the industry?

The industry is headed toward increased personalization, faster drug development, and narrower therapy areas. Data and data-driven insights will be crucial to meet these new market needs.

Well-harnessed data management and strong partnerships can profoundly impact the industry in several ways.

Accelerated Drug Discovery is one of the main areas. Drug discovery typically takes a long time owing to the extensive analyses conducted on diverse and large datasets to identify potential drug targets. Efficient data management means accelerated data integration, analysis, and target identification, speeding up the entire drug discovery process. Furthermore, strong partnerships between pharmaceutical companies, research institutions, and tech firms increase access to a broader range of data sources and expertise, fostering innovation.

Precision medicine advancements - the future of healthcare lies in personalized care, which requires analyzing patient data (genomic, clinical, and lifestyle) and developing treatments tailored to individual profiles. This improves drug efficacy and reduces the chance/impact of adverse effects. Developments in personalized medicine will be driven by partnerships across government, genomic data platforms, life science companies, device developers, providers, and payers. Such partnerships will give rise to 360-degree views of patients, thereby advancing the creation of precision medicine and customized care.

Optimized clinical trials - well-managed data and collaborations can optimize clinical-trial designs by identifying suitable patient populations, streamlining recruitment processes, and enhancing the understanding of drug responses, ultimately leading to more successful trials and faster approvals. Insights from the analyses of various stakeholder datasets can also enable patient continuity over the trial and impact efficacy.

Enhanced overall healthcare ecosystems - effective data management and collaborations contribute to the creation of comprehensive healthcare ecosystems where information flows seamlessly between different stakeholders, improving patient care, outcomes, and cost-effectiveness. This will enhance interoperability and provide a seamless experience for patients as each stakeholder will have access to the same patient information.

Data-driven decision-making - access to well-managed, high-quality data combined with partnerships that offer analytics and AI expertise enables more informed and data-driven decision-making across all aspects of the industry, from research and development to marketing and distribution.

Omnichannel and opti-channel - in pharmaceutical marketing, an omni-channel strategy is necessary for a consistent brand experience. For commercial life-science operations specifically, the omnichannel strategy requires a Next Best Engagement (NBE) or Next Best Action (NBA) platform at the core, with real-time data push and pull from digital partners, an interoperable creative platform to deliver the right message to the right channel, and real-time measurement against pharma-specific Key Performance Indicators (like NRx, NBRx and TRx). A true omnichannel strategy ensures that every engagement with the target audience builds on the previous messaging and adds value to encourage the target down the decision funnel.

Ultimately, the combination of effective data management practices and strategic partnerships can lead to groundbreaking advances, increased efficiency, and better outcomes across the pharmaceutical and healthcare industries, benefiting patients, researchers, healthcare providers, and payers alike.

Could you give some examples of new use cases that could emerge if AI and data management are used as efficiently and effectively as they can be?

Smart diagnostics: AI algorithms have been developed to assist in diagnosis by analyzing medical images (like X-rays, MRIs, and CT scans) and identifying patterns that might indicate conditions such as tumors or fractures.

Personalized treatment: AI has helped analyze genetic data and patient histories for tailored treatments and medications based on an individual’s genetic makeup and other factors.

Drug discovery: AI has been employed in drug development, speeding up the process of identifying potential drug candidates by analyzing biological data and predicting the effectiveness of compounds.

Virtual health assistants: AI-powered chatbots and virtual assistants have begun providing basic medical advice, scheduling appointments, and answering patient queries, improving access to healthcare information.

Predictive analysis: AI can predict a range of factors related to disease progression and patient outcomes. For instance, AI can help identify patients who face higher risks of contracting certain diseases, complications, or adverse events during treatment. AI can also predict the effectiveness of specific treatments for individuals based on medical history, genetics, and lifestyle. Furthermore, it can help medical device companies predict device failures/malfunctions. 

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