Article updated June 2024
Business intelligence can provide valuable insight that uncovers trends, opportunities, and potential breakthroughs key to successful healthcare and life science operations, including pharmaceutical innovation. Organizations are challenged to differentiate themselves in a competitive market by innovating ahead of the competition. To ensure a competitive edge, many industries rely on business intelligence (BI) solutions, and the healthcare and pharma industries are no exception. Let’s explore some of the ways companies use healthcare business intelligence.
In 2024, business intelligence in healthcare and pharma has become inextricably linked to the potential power of advanced technologies like AI. Enterprises, including pharmaceutical companies, are becoming acutely aware that ignoring AI’s potential can cede competitive advantage or market share to forward-looking competitors.
“The playing field is poised to become a lot more competitive, and businesses that don’t deploy AI and data to help them innovate in everything they do will be at a disadvantage.” – Paul Daugherty, Chief Technology and Innovation Officer, Accenture
Business intelligence in healthcare and the new world of AI
Beginning in the 1960s, the term business intelligence was used to describe a system of sharing information across organizations. This concept evolved in the 1980s with the introduction of computer modeling for decision-making and strategic planning. BI, as we know it today, is a specific type of technology that combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations identify any actionable insight to make more data-driven decisions.
According to Research and Markets, the healthcare business intelligence market is growing steadily, valued at $3.4 billion in 2022 and estimated to reach $7.4 billion by 2032. Factors driving the market’s growth include an industry-wide interest in controlling costs and the ongoing shift to value-based and data-driven medical decisions.
The breakneck pace at which AI is disrupting traditional workflows can unearth valuable business intelligence in pharma and help teams act upon it as well. However, “AI is not a strategy. It’s how you do things,” says Lance Hill, Within3 CEO. “The strategy is to affect some business result or change that you are dealing with depending on where you are within the organization and understanding the latest ways that companies are trying to solve that. This is probably going to involve AI in some way.”
Developing and bringing a new drug to market is labor-intensive, costly, and risky. Studies estimate that launching a new drug can take a decade and cost between $314 million and $2.8 billion in R&D costs. Even after all that work and investment to develop a quality product approved for clinical utility or prescription, there’s still no guarantee it will reach the desired adoption rates for patient outcomes and commercial success.
To survive this competitive space, pharmaceutical companies must engage in a well-planned drug commercialization strategy that helps them enter the market, enhance patient access and education, prepare the supply chain, inform marketing activities, and drive revenue.
How does BI manage large quantities of complex healthcare data?
The business of healthcare – research, development, patient care, patient advocacy, clinical studies, and more – generates so much information that it defies traditional informational storage and data management systems. Fortunately, advances in medical technology, digitized records, and data analytics can handle the collection, storage, and processing of these large volumes of data.
Where does all this data come from? It’s generated from a variety of sources and is sorted into several primary types:
- Electronic health records include information obtained at the point of care, such as a medical facility, hospital, or clinic.
- Administrative data, including information that might be reported to government agencies or other entities.
- Claims data include billable interactions between insured patients and the healthcare delivery system.
- Patient/disease registries track critical data for chronic conditions like Alzheimer’s, cancer, diabetes, heart disease, and asthma.
- Health surveys that evaluate population health and establish disease prevalence estimates.
- Clinical trial data may be available to researchers or the general public.
- Scientific publications.
- Non-structured data from key opinion leaders in advisory board engagements or one-on-one interactions with MSLs or other medical affairs professionals, including field medical team members.
Each data type is subject to various regulations and privacy requirements and must be carefully protected and monitored. Therefore, healthcare organizations often have a complex and diverse application ecosystem, making surfacing insights difficult. With business intelligence, a data warehouse can centralize data from any data source so that managers and analysts can access all of it for use with BI software.
“I truly believe the biggest challenge is data quality and data availability,” says Neeraj Goel Mittal, Global Head of Data, Analytics & AI, GSK. “We forget that insights are based on data and the principles of data lineage in terms of data ingestion, data quality, data transformation and data to action still apply. Sometimes because of the hype, leadership expects the insights to be available [quickly], when the data on which those insights depend might not be available in the format we need it.”
AI’s potential impact: breaking down silos, accelerating insights and potential new indications
Mittal points out that the biggest benefit is the amount of data AI and gen AI can handle. “Not just the visible data for a patient, which is on paper, but also invisible data in terms of social demographic and economic conditions where AI can combine data and give personalized insights.”
Within3’s Hill says that AI’s capabilities make it a good partner for human expertise. “AI tools are now capable of examining data and/or observations from the field, one-on-one interactions, information from congresses, online, social listening, advisory boards, medical information and so on…They can then generate a view of it all in a way humans can understand. The humans then apply their own knowledge and expertise to impact strategy and outcomes.”
How does healthcare business intelligence impact patient care?
When healthcare organizations use analytics and reporting to drive data-driven insights and improve patient care, it’s known as clinical business intelligence or healthcare business intelligence. Clinical organizations use business intelligence to store data, keep patient data secure, and conduct analysis and reporting.
Healthcare organizations generate a huge amount of data from a variety of sources, including:
- Electronic health records (EHRs)
- Patient feedback
- Operational data
- Financial data
In organizations that are less data-mature, this information is likely stored and analyzed in multiple, disconnected systems. A single, centralized solution provides access to all data, along with the ability to track key performance indicators (KPIs) and patient outcomes. Here are some examples of how healthcare organizations use clinical BI to improve patient care:
- Symptom-based treatment planning
- Scheduling staff based on patient volumes
- Facility supply chain management
- Identifying patients for follow-up care
- Tracking readmission rates
Clinical BI can integrate a patient’s medical history into the patient care plan and alert providers and caregivers if a newly prescribed medication interacts with another medication. Technology can be used to support patient portals and dashboards that allow patients to schedule appointments, fill out forms online, and view or pay bills. This change leads to improved patient processing, patient experience, and greater patient satisfaction.
Patients can also be reassured of their data privacy because these platforms are designed to keep data secure and accessible. When healthcare is convenient for patients, they have positive experiences, keep their appointments, and have better outcomes. – Tableau
AI’s potential impact on patient care
Ultimately, business intelligence in healthcare should serve value provision and patient outcomes first. Those outcomes increasingly depend on pharmaceutical therapies across the clinical and therapeutic spectrums, which, in turn, are increasingly under pressure to prove their value relative to price.
AI is helping clinicians spend more time on patient care and less time on reporting or chart generation. It translates clinical guidance into plain language appropriate to their age, education, or experience level. It’s helping HCPs make the most of their advisory board engagement for pharma and engaging with patients and their caregivers in ways that clarify communication and recommendations.
But what’s the point of innovation to improve patient care if prescriber education is ineffective and therapy awareness levels are low?
For one medical affairs team, Within3 helped determine that slower-than-anticipated adoption was attributed to misunderstood HCP education and engagement efficacy. Contrary to internal assumptions, 22% of relevant HCPs did not know about the client’s therapy, despite its superior clinical results.
By applying AI-supported insights management to this problem, the team acted on recommendations to increase HCP education about the therapy, potentially extending market share opportunity by >40% based on increased awareness.
What’s the difference between business intelligence and insights management?
While there are numerous applications for business intelligence in pharma, these global organizations may use other solutions, such as insights management, to drive processes and decisions specific to drug and device development. Whereas BI tools act as a combination data warehouse, visualization, and analytics platform for information generated throughout the enterprise, an insights management platform for life science is designed to capture and interpret observations and other data obtained during the product lifecycle. Insights management solutions may also include the ability to present data on a specific disease community, provide insight into online conversations through the use of social media listening, and collect and analyze insights from discussions with healthcare providers.
Traditionally, there were a handful of information streams that life science companies used to inform scientific narratives, including advisory board meetings, large in-person events like medical conferences, and one-on-one meetings with key opinion leaders. Some organizations used pandemic-related travel restrictions as a time to learn how to run a successful virtual conference of their own – providing yet another data stream. Now, these channels have multiplied and include virtual advisory boards, post-conference huddles in healthcare, social channels, and other types of digital interaction. All of this information can be used to streamline, shorten, or improve processes that take place from research and development through launch and post-market monitoring – but it must be processed through a single platform to be most effective.
Life science teams are turning to insights management for a variety of reasons: the proliferation of data generated via multiple channels, an increased appetite for digital transformation, and the need for faster, more precise decisions. Learn more about the trends driving the adoption of insights management in our blog post.
