Information is expensive. The costs of acquiring, handling, and understanding information to achieve actionable, business value directly affects the bottom line. Not investing in information can be even more expensive.
Understanding the return on investment that information can provide is a key aspect of sound business strategy. In many cases, businesses arrive at data’s value based on the outcomes predicted from the utility of the data investment. For example, if a company wanted to determine the soundness of a decision to buy a list of leads, that is a simple unit economics calculation (is the cost of a lead divided by the rate of closing deals less than the average profit per sale?).
Most organizations find deriving the value of data far more complex than that. Value extends beyond a simple calculation, requiring understanding of the competitive advantages a set of data can bring beyond direct revenue. Exploring this impact is where the concept of asymmetric information becomes imperative.
Information asymmetry is when one party has more relevant information than the other party, usually in the context of a transaction or competition. Optimally, that imbalance would be used to gain an advantage.
An example of where information asymmetry is commonly observed is in the sale of a car. Sellers, especially dealers, start the transaction with an advantage because they know how much they paid for the vehicle, are familiar with the vehicle’s features, and are aware of any issues the vehicle may have. Buyers are advised to “do their homework” which can mean looking up a vehicle’s estimated value and reported history through third-party services such as CarFax and Kelley Blue Book. Even where buyers have access to data and tools, they are limited to what can be collected and reported, often in aggregate, unlike the sellers’ inherent and specific knowledge of the individual vehicle. Through education and effort, buyers can attempt to close the information asymmetry gap, but the availability of data remains in favor of the seller.
(Read more about this example in the “lemon problem” by George A. Akerlof, describing defective vehicles being resold by unscrupulous dealers to unsuspecting customers).
Exponentially increasing amounts of data are impacting results in almost every, modern decision. This requires understanding the concept of information asymmetry now more than ever - how to identify it, how to assess its role, and how to quantify its value. There are three, primary categories in the healthcare and life sciences industries where information asymmetry affects transactions - business-to-business, business-to-consumer, and internal asymmetry.
Michael E. Porter was one of the first to describe the challenges and leverage created by information asymmetry within healthcare, pointing out that healthcare organizations using data as an asset can realize both commercial and clinical advantages over their peers. This was recently proven in a webinar presented by Healthgrades and Kythera where it was illustrated that health systems using data to understand COVID-era market dynamics affecting elective orthopedic procedures saw growth in patient and procedure volumes far greater than their competitors.
Armed with information, successful health systems discovered cohorts of patients using demographics, disease burden, and payer dynamics based on real-world data analyzed in nearly real-time and engaged those cohorts to deliver care. This is one of many examples where organizations can gain market advantages by turning data into usable metrics to guide strategy, create tactics, and achieve differentiation through information asymmetry.
In the insurance industry, actuaries build and use statistical models from massive troves of data to analyze the risk of insuring people so that insurance plans are priced for profitability. Consumers may understand that certain risk factors like smoking or speeding tickets contribute to the price of insurance, however, consumers are unlikely to know the depth or specificity of data which is being collected and used to arrive at the price they pay. Interestingly, this can go both ways, as a medical condition or behavior undiscovered by the insurance company could advantage the consumer.
Healthcare providers and insurance companies have proprietary pricing models which are complex and unknown to the consumer. Healthcare providers have critical information about their true costs, charges, and payer reimbursements. The patient is disadvantaged by a trifecta of information asymmetry – patients lack the basic information on cost, quality, and competition required to be a smart consumer, do not know where to find that information, and face a complex, dense system requiring expertise to interpret information.
Price transparency is one way to help reduce this information asymmetry. A new law went into effect in January 2021 that requires hospitals to display standard charges for all items and services provided, as well as list at least 300 “shoppable services” which a healthcare consumer can schedule in advance. This law is an incremental and important step to help consumers make better choices, but implementation has been slow and inconsistent.
Internal asymmetry of information occurs when different departments or units within the same organization do not have access to the same information. This can occur when data resides in silos that can be accessed only by the possessors of that data, when data is only partially shared, or when data only flows in one direction.
Information asymmetry is a particular problem as the size and complexity of organizations grow. Pharmaceutical manufacturers are often structured so that their research and development teams operate separately from their commercial teams. Because of this division, each team may purchase different data sets and use different technology. A lack of internal collaboration reduces the ability to maximize their knowledge and investments.
For example, having accurate, well-integrated, payer data is critical to market access functions, however, sales teams often do not have access to the same high-quality insights used by market access teams, even when those teams have aligned incentives and are promoting the same products. This type of information asymmetry dynamic has been shown to limit organizational functionality from drug development and manufacturing through sales, marketing, and distribution.
The perils and advantages of information asymmetry are real and ever increasing as the information economy continues to grow and evolve. This is the first part of a series exploring the ways information asymmetry impacts the health and life sciences industries. Next time, we will discuss examples of how to reduce information asymmetry and extract more value from data. If you have any questions about this topic in the meantime, please be in touch.
Kythera Labs was an early adopter of Databricks, and we are a founding member of their Data Lake Technology Council. After evaluating Snowflake, we knew Databricks was the right solution for us. We were so convinced of its benefits, we became the first Databricks OEM for health providers.
Datavant and Kythera recently hosted a webinar that covered several topics related to patient privacy and identity resolution, ranging from the important difference between identifying and knowing to covering real-world applications using patient-level information, such as patient engagement campaigns, patient hub operations, and rebates auditing.
Data is an asset which businesses and industries can not ignore. If used properly, data provides its users a competitive advantage, however, without a sound approach, data may fall short of its full potential. To help ensure success, organizations should start with the critical first step of identifying what questions need to be answered and what problems need to be solved, then quickly mobilize to identify what data is needed to answer those questions. Data needed for strategic purposes almost always comes from different sources, in different formats, and in varying quality that can complicate utilization, especially for machine learning applications. As data and analytics are evolving, so to are data technology platforms - and keeping up is critical.