By: David F. Zirkle, PhD
[box]“It is a capital mistake to theorize before one has data.” Sherlock Holmes (Sir Arthur Conan Doyle)[/box]
Big data is a broad term for information sets so large or complex that traditional analytical techniques are inadequate. While big data has been common in many retail and financial sectors for years, it is now beginning to receive more attention in healthcare.
Health systems have always had a great deal of data from a variety of sources such as medical records, claims data, CRM systems, patient surveys, social media, and other sources. However, the information has been historically stored in data silos, limiting an integrated and meaningful analysis of the data.
Big data in and of itself is not of value. But, by applying advanced analytical techniques, healthcare organizations can discover patterns and predictive relationships that can guide clinical, operational and business decisions.
From a marketing perspective, big data can help improve how we market and communicate with key customer groups. Some examples include:
Micro-targeting: Household demographics can be combined with clinical encounter data to predict future healthcare needs. The data includes disease-specific information, pharmaceutical usage and behavioral and lifestyle factors. The models are then used to select current patients with the highest propensity to respond to a disease-specific marketing campaign. Similar methods, without the clinical data, can also be used to identify consumers with the greatest potential of becoming new patients.
Message Development: There are many factors to consider when designing a message campaign. Everything from age and gender to income level and geographical location can affect which types of message will have the greatest impact on customers. Instead of a one-size-fits-all message, organizations are using big data to segment customers and produce specific, targeted content tailored to each group.
Channel Optimization: Big data can be used to predict how individuals are most likely to interact with the organization in the future. Behavioral data from online and offline sources such as browser data, social media, email activity, call center contacts and shopping and purchase patterns can help the organization better align marketing efforts with customer preferences.
Physician Relationship Management: Big data can be used to analyze physician referral patterns, focusing on loyalty and leakage. Claims data is integrated with financial information and outreach activities to identify those physicians with the greatest opportunity for revenue growth by improving patient choice and referral patterns to the organization. The loop is then closed on the back-end by measuring the impact sales and marketing efforts have on referral patterns, volume and revenue.
ROI Measurement: Data from several sources can be aggregated, including both offline and online metrics, to calculate marketing ROI. The fundamental data elements include campaign budgets, tactics and channels, timing, service lines and outcomes – measured as leads, encounters and contribution margin. Once the data is aggregated, the influence of each campaign on consumer behavior can be compared to the marketing costs to calculate ROI.
The cost of acquiring data can be significant and the size of the files can be enormous, requiring a significant investment in data warehouses by the organization. Analytics and business intelligence tools can also be complicated and costly. Regardless, big data is here to stay and has the potential to transform healthcare as we know it.
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