Not long ago, traditional healthcare market research was the mainstay, if not the only method, to gain insights into the healthcare market. However, this has changed, with stakeholders in the healthcare industry increasingly trusting technology and data-driven market research. Several significant factors have contributed to this shift, such as technological innovations, changes in regulations, evolving patient and brand expectations, and the limitations of traditional methods. Given this scenario, the move towards modern techniques is inevitable, raising questions about the future efficacy of conventional research.
Over time, the research landscape has also changed, bringing certain clear requirements to the forefront across all segments of healthcare research :
- Infusion of technology
- Demand for customization
- From the obvious to the unknown
- Ready to listen and act
Limitations of Traditional Healthcare Market Research
Traditional research alone was unable to address the above, mainly due to:
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Time/Resource Intensive and Cost
Methods such as surveys, interviews, and focus groups often require significant time to gather and analyze data, which can delay the availability of insights. The process is expensive, considering participant recruitment, data collection, and analysis.
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Lack of Real-time Insights
Traditional approaches generally capture data at a single point of time/moment. As a result, it may not always reflect ongoing changes in healthcare market dynamics or patient behaviors.
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Small Sample Sizes and Risk of Bias
Traditional processes work with a limited sample size, which may not statistically represent the larger audience. Also, various biases, such as selection of data, external influences, personal choice, etc., may skew insights.
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Challenges with Longitudinal Data
Healthcare research data is ever-changing. So, tracking changes over time through traditional approaches can be challenging and costly. Additionally, insights produced from the fragmented data may not lead to any real actionable plans.
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Inflexibility
Traditional methods are not flexible enough, or only partially so. Once data is collected, it is difficult to modify, update or refine. As a result, the outcomes are often not aligned with emerging trends and changes.
Introduction of Technology in Healthcare Market Research
While the early part of the millennium saw the emergence of online panels, and digital research continued to spread its wings, it was around 2010 when researchers saw merit in joining the deep-tech bandwagon in search of real-time and passive data. Somewhere around 2009, when Fitbit was launched, a new era for the healthcare market research industry dawned in response to rapid advances in technology. AI, machine learning, and the cloud became ubiquitous, and the literal explosion of mobile/portable devices such as smartphones, tablets, and wearable devices created an opportunity that never existed earlier.
From traditional techniques such as data collection through interviews, journals, interview transcripts, and medical literature, healthcare market research started moving to online telehealth platforms to conduct interviews and discussions virtually, collect large volumes of data from different sources such as social media platforms and wearable devices, and use artificial intelligence for data analytics.
It's Now or Never!
Most traditional methods would capture retrospective and stated data for research. For example, a patient’s heart rate during a treadmill test and its significance or a patient’s opinion via a survey on how a particular drug helped them. Data was hardly real-time, and hence, decision-making had to account for such scenarios, which were often inconclusive.
Enter wearables and IoT!
Eyeglasses were the very first wearable device to be developed in 1286, Fitbit in 2009, Apple Watch in 2014, and Oculus Rift in 2015. Wearable technology has grown significantly. The gradual adoption of wearables in research seemed par for the course, with researchers thoroughly encouraged by prospects of direct access to real-time data and lots of it!
Smart glasses and watches, health trackers, smart clothing, virtual reality, etc., all have immense potential to convincingly alter the way real-world data is collected and interpreted. Just the way mobile/handheld devices helped evolve qualitative research, the integration of wearables offers the ability to capture in-the-moment, in-depth data with relative ease. With applications from remote patient monitoring to chronic disease management, adherence to medication, physiological monitoring, and smart implants, IoT is a game changer in healthcare research through continuous, real-time data that enhances patient care and drives medical advancements.
How Big is Actually Big?
With the proliferation of unstructured data from various sources such as journals, biometric data, electronic medical records (EMR), Internet of Things (IoT), social media, payer claims and records, and data banks, there's an abundance of real-time data (big data) readily available, uncovering patterns and trends previously inaccessible due to limitation of sample size. When data elements are readily scalable, results are also equally accurate and reliable. The transition from volume to value-based care is driving the demand for big data analytics, as healthcare providers are seeking new innovations to improve patient outcomes while parallelly managing costs.
Precision medicine is a key area where the application of big data has been critical, enabling medical professionals to customize treatments and procedures to improve outcomes. In addition, big data has also been effective in identifying disease risk and providing early detection systems applicable to large population segments, including predicting patient admission rates and managing available resources effectively. For example, SOPHiA GENETICS’ platform analyses and generates insights from genomic data, helping researchers create drugs tailored to patients’ needs and clinicians to offer more effective treatments.
Tempus and Flatiron Health are making good utilization of big data in oncology research. Tempus is creating diverse groups of molecular and clinical data, which will help physicians with customized case scenarios. Flatiron Health uses billions of cancer patients' data points to discover new insights and raise standards of care.
Read more: Importance of Data Analytics in the Healthcare Industry
Move Aside Millennials! It’s the age of Gen AI!
With the advent of Artificial Intelligence (AI) & Machine Learning (ML) technologies, vast datasets can now be processed in real-time to predict market shifts, patient behaviors, and the development of new products and services. New AI/ML methods would capture real-time data that would be more credible and scalable. It can be used to answer the pertinent questions of “Where Should You Go”, “Who Should You Target?” and “How and When Should You Target?”
AI makes the identification of potential markets much faster, analyzing large data sets in real-time from research papers, industry reports, and social networks and providing the analyst with opportunities. Moreover, there are segments where using traditional segmentation tools is rarely possible, for instance, rare diseases or patients with hard-to-reach access to products, and where the application of AI is highly beneficial.
While AI has long been a valuable tool in market research, the current buzz centers around GenAI powered by large language models (LLMs). Unlike traditional analytical AI, these LLMs are trained on massive datasets, enabling them to perform language tasks with near-human accuracy at scale. This advancement fuels innovative GenAI applications, including chatbots and interactive survey experiences, revolutionizing healthcare market research.
For example – In big and global pharma companies, smaller markets were neglected as they were too costly to research manually. However, Gen AI can generate synthetic data from these markets, which are also free of bias and can be collected at significantly lower costs.
The Rise of the Patients
Patients played a reactive role in contributing to healthcare market research, and their roles would be restricted to interviews and discussions.
Read more: The Ethics of Healthcare Technology: Balancing Innovation and Patient Privacy
Now, in the new technology era, social media monitoring platforms and specialized health forums offer insights into patient sentiments and discussions in real-time. Being unsolicited and user-generated content, this data has tremendous value. Technology and social media have enabled patients to proactively provide inputs in the form of feedback, reviews, and ratings in various channels. For example, various social media and internet forums and websites collect feedback on hospitals, drugs, and health insurers. Such platforms are veritable goldmines for healthcare research agencies because, unlike in the past, they would find large volumes of readily available first-party patient data.
For example – Listening to social media conversations can help in identifying and tracking HCPs in a specific therapy area and identify oncologists in the field of immuno-oncology based on the number of posts and the topics they have covered.
Say Hello to Synthetic Data!
While still an extremely sensitive and debatable topic, synthetic data has, in the last few years, been the focus of discussions at top market research events. Its impact on research is still unverified; there seem to be quite a few areas to which it can contribute. Synthetic data has been used to simulate the spread of infectious diseases like COVID-19. This helps researchers understand potential outbreak scenarios and evaluate the effectiveness of public health interventions without compromising real patient data.
The generation and integration of synthetic datasets can potentially mimic the statistical properties of real-world data and address many access, privacy, and confidentiality barriers. For example, the Synthea project generated synthetic patient data that mirrors real-world healthcare scenarios, allowing developers to test new health IT solutions in a risk-free environment.
Apart from being fast and cost-effective, the accuracy of synthetic data in creating personas of interest and engaging in interactive dialogues with these is of active interest to the fraternity. Not only does it accelerate insight generation and, subsequently, product development, but it substantially reduces risks of failure.
One such excellent application is digital twin technology, creating virtual replicas of physical healthcare systems or processes. This technology is useful in simulating and predicting, in real-time, patients' behaviors for the design of patient-specific treatment plans and optimized operations. In that regard, digital twin models of hospital operations will improve efficiency, cut costs, and provide better care through better allocation and management of resources.
From Regulations to Revelations
Blockchain technology has the potential to revolutionize market research by enhancing data security and privacy, which can build trust and increase participation rates. This technology ensures that data is tamper-resistant and decentralized, making it more secure against attacks. Blockchain also encrypts and anonymizes data, ensuring personal information remains private. Providing transparent consent mechanisms and rewarding participants with blockchain tokens can encourage more honest and diverse responses, ultimately improving the quality of market research insights. However, challenges such as the complexity of the technology, regulatory compliance, and integration with existing systems need to be addressed for widespread adoption.
Read more: Prognosis 2024: Unveiling Healthcare Trends and Strategies
So, what’s ahead?
As per ESOMAR, 35% of market research is still being conducted through traditional online surveys and discussions, while over 40% of researchers admit to using at least some part of AI in their research processes. While the fundamental framework of healthcare market research remains largely undiluted despite the technology onslaught, we would probably see a steady increase in the adoption of agile and more incisive methodologies. Also, while the patient side of research has the potential to move completely deep tech, the physician insights process will continue to remain traditional for at least a while.
The below report from GlobalData probably summarizes the sentiment clearly and the fact that researchers will utilize this opportunity to deploy more such methods in the near future. It is all about finding the sweet spot where traditional methods can be augmented with passive learning to generate next-level analysis.
It is, however, the scale of adoption that will determine the efficacy of such agile methods and will only be of avid consequence when researchers train and equip themselves adequately.
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