Health Analytics vs. Business Intelligence

One of the questions I get asked frequently is the difference between business intelligence and health analytics.  And I struggle with a good answer; there is so much inconsistency in the use of terminology in this space, and to answer the question requires that you be able to cleanly define terms like “business intelligence.”  But I do think it is an important question, as how you answer the question implies quite a lot in terms of what you need to be doing operationally.

Business intelligence (BI) was a term first coined in the 1950’s, but many people would argue that our modern conceptualization of BI is based around Howard Dresner‘s 1989 definition as “concepts and methods to improve business decision making by using fact-based support systems.”  As the adoption of information technology has grown over the past two decades, the conceptualization of BI has evolved in parallel such that today, BI can be taken to mean just about anything.  This situation has led to the advent of the term “business analytics” (an equally ambiguous term) to characterize a more modern, advanced approach to insight development.

Of course, health analytics has an equally ambiguous history rooted in other overly generalized terms like “informatics.”  A few years ago, this terminology issue led me to explicitly define health analytics as a domain separately from health informatics and business intelligence:

Health analytics is the domain of advanced analytics focused on providing strategic insights into the inter-dependencies in health outcomes, profitability, and customer preferences and behaviors.  Health analytics target insights that support transformational programs and business growth opportunities, enabling organizations to improve medical care, strengthen financial performance, deepen customer relationships, and pursue medical innovations.

I’ve reflected many times since then whether this was a good definition or not, but I’ve not come up with a better alternative yet.  But in my mind, three key attributes that fairly clearly characterize health analytics are:

  • multi-market: health analytics intentionally blur the lines between payer, provider, and pharma.  A fundamental assumption in health analytics is that the insights needed to transform health require collaboration across these historically siloed market segments (i.e., convergence).

  • multi-dimensional: health analytics are designed to explore the relationships within and between clinical, financial, administrative/operation, and personal aspects of health.  As such, they cross many domains of business inquiry and data.

  • multi-method: health analytics provide both retrospective and prospective views of health through multiple channels.  So in addition to the typical descriptive health metrics (e.g., counts, averages, percentages), health analytics incorporates more sophisticated and powerful techniques such as predictive modeling, data mining, and optimization to help define what is going to happen in the future (not just what happened in the past).  And the insights it produces get deployed operationally in a broader set of channels within an organization (e.g., reports, alerts, rules, etc.).

So with that as backdrop, here is my attempt at describing the differences between business intelligence and health analytics.

Scope Usually domain specific: clinical, financial, administrative Domain specific or cross domain: designed to link, for example, clinical and financial information together into one model
Timeframe Mostly retrospective Both retrospective and prospective
Mathematical Concepts Descriptive statistics: sums, averages, means, medians, percentages, counts Descriptive statistics + inferential statistics: correlation strength, forecasting, prediction, simulation, optimization, data mining
Data Structures Standardized, usually in data marts Both standardized and emergent (based on research needs)
Hypotheses Implicit — included in the assumptions behind measure / metric Explicit — part of a formal iterative discipline of research, discovery, and validation
Project Delivery Linear: project scope can be well characterized before the project starts Iterative: project scope is designed to constantly evolve based on findings
Project Risks Mainly associated with data: completeness, accuracy, cleanliness, representativeness Data risks + Time risks + Finding risks: projects include research which by definition carries uncertainty
Insight Delivery Standardized paper reports and web pages that often include “dashboards” Standardized paper reports and web pages/dashboards. Also includes custom reports based on research, and direct integration of insights into operational systems and processes (e.g., alerts, rules engines, decision support)
Business Impacts Best suited for operational performance measures against clear standards; rarely competitively differentiated Operational + Transformative: best suited for strategic insights into changes, growth, investments, outcomes, etc.

In summary, the difference is really based on how broadly you define “business intelligence.”  In it’s broadest sense, health analytics could be considered a subcomponent of BI, taking many of the concepts of BI and applying them to an industry-specific set of questions and issues.  In it’s fairly popular technology-oriented characterization, BI is seen more as a reporting framework; in that sense, BI is a subcomponent of health analytics.  And if you prefer the term business analytics, health analytics could be an industry-specific implementation of BA.  But regardless of how we define it, I hope we can agree that it represents an under-served aspect of health information technology today.