Healthcare enters the age of enterprise analytics

Healthcare’s analytics paradigm is ripe for progress. It is clunky, slow, and artisanal - a far cry from the big data infrastructure powering the banking and consumer industries. While companies like Amazon and JP Morgan benefit from automated algorithms and self-service platforms, payers, providers, and life sciences companies are trapped in a legacy model. We can now break free from expensive and dirty data, manual analyses with cumbersome customizations, and highly variable insights.

JD: The purpose of big data and analytics investments should be to generate more actionable insights in a better, faster, and cheaper manner. Yet, achieving these goals requires the arduous task of acquiring, ingesting, cleaning, refreshing, linking, and grouping massive amounts of data in relevant schemas. While these steps can be done manually, they become unmanageable when you have petabytes or exabytes of data, a quantity most suitable for machine learning. Our industry has been notoriously slow to adopt tools that automate data management. Additionally, we have become too complacent with piecemeal information, remaining blind to complete, sequenced patient journeys. With the rise of tokenization, linking patient-level data, at a scale required for machine learning, is more feasible than ever before.

Healthcare’s big data infrastructure must pave the way to enable predictive modeling, blazing-fast queries, and the delivery of precise insights. The new standard in advanced analytics includes automation and tokenization, opening the door for healthcare institutions to benefit from meaningful and actionable insights at an unprecedented scale.

JD: As healthcare shifts from volume to value, data, analytics, and business strategy are converging. If data is the foundational commodity, harnessing it for actionable insights is the critical capability our industry must master. Here are four criteria for the new standard in advanced analytics.

1. Automation and architecture – The latest big data standard is to combine external data sets from many sources, including claims, clinical, lab, prescription, and social determinants of health (SDOH) data, and link the data at the patient-level. Such a vast data set enables far more nimble benchmarking and more precise trending and predictive modeling.
   
   Automating the ingestion and cleaning of big and dirty datasets is a non-trivial task where the application of machine learning and AI is essential. It requires a secure, HIPAA-compliant infrastructure with the ability to rapidly ingest new data from external sources, including your own.  
    
2. Fast and precise intelligence – Closing the gap between analytics in healthcare and analytics in the consumer and banking industries does not stop with amassing vast, high-quality data sets. It requires an automated pipeline, capable of processing billions of claims and training hundreds of models on tens of millions of patient records in hours. Your insights are then always fresh and accessible, as the data is automatically ingested and cleaned, and the predictive models are refreshed in the background.
   
   A next-generation healthcare analytics stack should also have an advanced grouper, which flexibly organizes data, so it can be cut and analyzed to answer seemingly endless business questions. As business questions arise, teams want to quickly self-serve by querying the data in user-friendly software and having it return precise insights on-demand.
    
3. Adaptive delivery – Any insight generated by big data is worthless unless it leads to a better decision. This requires insights to be delivered into the workflow through a range of visualizations and formats. You will need to align the most appropriate format (data portal, cloud-based software or mobile application) to the recipient and workflow. 
    
4. Software applications – Ultimately, delivering analytics via configurable, user-friendly software applications is critical for team productivity. Enabling health plan teams to self-serve insights into referrals partners, network performance, and population risk, for example, can help them achieve value. Similarly, enabling an ACO team to self-serve insights into variations in care delivery or financial performance in value-based contracts can help them achieve success.
    

At Clarify, we have built an enterprise analytics platform that services payers, providers, and life sciences organizations with software that eliminates the need for substantial upfront investments, drives engagement, increases productivity, and delivers precise, reliable, and meaningful business insights.
 

To learn more, visit clarifyhealth.com.