Maximize your insights: What happens when analytics algorithms are interconnected?

Data is arguably a company’s greatest asset. But that data is insignificant if you can’t glean insights from it. Being able to interpret findings and trends can lead to greater effectiveness of services and efficiency of costs. However, analytic challenges can arise when the data is disconnected, and definitions are independent of one another. For example, health plans are faced with a growing collection of claims, clinical, and operational data from a variety of sources, making it difficult to extract the insights needed to drive business transformation and value.

An analytics strategy that uses a suite of interconnected algorithms – analytic methods that are preconfigured to build upon and enhance one another – can help payers generate consistent, reliable information more efficiently and maximize value from their enterprise data.

The significance of an interconnected algorithm strategy for payers is immense. Aggregating claims data through a suite of methodologies and groupers can enhance enterprise datasets by generating consistent and reliable information. Rather than attempting to piece together disparate methodologies, interconnected algorithms allow payers to report and analyze across sources to provide analytic continuity to deliver trusted insights.

A singular health care event such as an outpatient encounter, inpatient admission, and related pharmacy scripts can be aggregated within the data model to assess outcomes by translating claims into meaningful units of analysis. For example, a patient diagnosed with a specific disease can be categorized by combining their initial and subsequent office visits, hospital admissions, and all related prescriptions to assess the care coordination for that specific disease. This collection of events comprises an episode of care.

With linked methodologies, the outpatient events, admissions, and gaps in care can be evaluated in context of the episode of care and its severity. The connection between methodologies allows users to analyze data from a global patient view, through various grouping methodologies to a claim or record view, all while maintaining analytic integrity and consistency.

Interconnected analytic algorithms also introduce business value and can be achieved in the following use cases:

Streamline analytic specifications and mitigate reporting disparities

Consistency is key. Having standard specifications across the entire analytic package will ensure results are uniform. If a payer employs several models or methodologies from different vendors, analytic outputs could be different and conflicting.

A payer would not want a patient to be identified for a particular disease and stage in one model, yet remain undetected in a care management algorithm for the same disease due to differences in coding. With interconnected algorithms, methodologies utilize the same logic to create consistent criteria in each case and each method can enhance one another for maximum analytic value.

Differing outputs can affect reporting and make business decisions more difficult if reports across disparate methods produce conflicting results. With interconnected algorithms, payers can move from an aggregate view to the underlying individual claims quickly and transparently, accelerating the time to insights and increasing confidence in the information.

Optimize analytic and IT resources

Not only does deploying an all-inclusive suite help streamline the analytic process, but this will also ease the burden on data managers and developers. Standardized layouts for interconnected algorithms with consistent formatting for input and output files will not require a labor-intensive process to merge etdata from multiple methodologies. This can remove the burden of manually attempting to join disparate data through common identifiers or producing multiple layouts for different methodologies. The same can be said for dealing with different formats of inputs such as claim IDs, value size limits, and many other ETL procedures that could be inconsistent across individual, manually connected methods from multiple vendors versus interconnected algorithms. IT resources can focus on higher-value tasks, such as complex data implementations.

Simplify vendor management

Utilizing one best-in-class vendor has enormous benefits versus working with numerous, disjointed vendors. There are several complexities that can arise when dealing with separate products – increased relationships to manage, multiple licensing agreements to track, and the need for frequent contact to understand the details of each methodology upgrade and software update.

A simplified approach with one vendor will ease the procurement and implementation process, streamline the receipt of product information and updates, and allow payers to scale analytic solutions more easily as business needs adjust. Interconnected algorithms sourced from a single enterprise can allow users to evaluate episodes of care, inpatient admissions, outpatient events, provider attribution, quality measures, member risk of hospitalization, rising cost, and many more healthcare models and methodologies conveniently in a single shop.


Interconnected algorithms offer payers an integrated and efficient approach to enterprise analytics. With Merative, payers can license methodologies that are designed to build upon and enhance each other. These analytic methods will help deliver cohesive, consistent, and trusted insights across any organization.

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