Recommender Systems

The Affordable Health Care Insurance Marketplaces (Exchanges) are massive interconnected governmental systems that might be destine to not only complicate the health insurance process, but confuse and bewilder individuals seeking personal and coverage information. But this destiny is not certain, especially if federal and state governments leverage the collective intelligence presence in these highly distributed exchanges through a governmental variant of the Machine Learning Recommender System.

As envisioned, these marketplaces are a state run set of government-regulated and quasi-standardized health care plans in the United States, from which individuals may purchase health insurance eligible for federal subsidies. Each marketplace will interconnect to and serviced by the Federal Exchange Program System Data Services Hub (AKA Fed Data Hub), which will relay individual, program, and meta information between the exchanges and through several federal agencies (e.g., IRS, Citizenship and Immigration Services, Department of Homeland Security). It is in these hubs where vast amounts of information on users’ preferences, activities, and behaviors (AKA Psychographics) are located and can be the basis for predicting what health care users would like or need, based on their similarity to other users in the system.

Recommender systems are a type of information filtering system that seek to predict certain psychographic characteristics that user would give to an item (e.g., insurance policy, source of information, desire inquiry response, etc.) or social element (e.g. people or groups) they had not yet considered, using a model built from the characteristics of an item itself (content-based approaches) or the user’s social environment (collaborative filtering approaches). These systems can be used promote (market) items that are highly likely to be interest or value by end users. In the vast world of interrelated clusters of information governed by highly complex sets of rules (e.g., exchanges, fed hub, etc.), recommender systems can be oracle through which valuable information can be disseminated to the masses.


While the uses of machine learning recommender systems is only bounded by imagination, here are just a few use cases that could be uniquely in the early days of building out this insurance ecosystem:

  • Educate users about valuable health plan benefits and minimize costs based on what other people with similar economic, lifestyle, and geographic characteristics have selected.
  • Present additional information and resources that are relative to particular situations faced by others in similar positions.
  • Identification of buying patterns and how they might be under or over exposing the users to risk (e.g., lack of particular medical coverage for correlative care items).

The beauty of machine learning recommender systems in the Affordable Health Care Insurance Marketplace is that they improve with time. They learn from successful and unsuccessful recommendations that are either acted or not acted upon by the users. Their strength is naturally derived from the weakness that often pelages most enterprise system – unbounded growth. They will grow through the changes that will naturally occur as the uncertainty rules are resolved its adolescence.

In the end, as our needs for healthcare related information grows, so will the collective ability of a machine learning recommender system. Their use in the evolving insurance exchanges could be the intellectual catalyst that provides users with enough information stability needed to rid out any potential confusion or bewilderment that could result for the certainty arising out of any new governmental program.



  1. Hi Dr. Smith,
    Thanks for writing an interesting blog on using machine learning to identify the insurance products. Well, i am curious is there any data about users activities, preferences and behaviors is available publicly? Are there any available datasets for academic experimentation with deidentified fields of basic information?

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