Rule Engine: The next leap towards healthcare transformation

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We clearly discussed in our last blog that the healthcare industry is experiencing a period of unprecedented change. Rising above all the chaos, Rule Engine is trying to bring lasting improvements in healthcare while minimizing expenditure. It might not be immediate, but a Rule Engine can play a pivotal part in improving operational flexibility for organizations. So what exactly is an ideal Rule Engine?

The Ideal Rule Engine

The term Rule Engine may sometimes sound confusing, as it can be any system that uses rules, in any form that can be applied to data to produce outcomes. This includes simple systems like form validation and dynamic expression engines. Many tools and technologies are used when it comes to engineering a rule engine such as Drools, JBoss, jBPM. They use expressions and delegates in its decision nodes, which control the transitions in a Workflow. It becomes confusing as to which rule engine suffices the need of the business.

An ideal Rule Engine should collate data from multiple sources, ensure data quality and allow the user to create a rule as per business needs. Business rule management gives an additional advantage over a general-purpose Rule Engine, as it provides an advantage for the custom rule creation, analysis, data collaboration, multi-facet collaboration, management, visualization of charts and graphs. For instance, what if a rule engine suggests to the user which rules can be created when multiple data sets have collaborated. Does this scenario sound helpful when it comes to regulatory reporting such as MACRA-MIPS and HEDIS reporting?

While a lot of Engines in market claim to be an ideal Rule Engine, following are some important factors that need to be taken into contention by CIO’s.

Factors to consider when selecting a rule-engine

Some of the very popular Rule Engines in the healthcare IT world does not provide flexibility to create own rules easily, and visualization has its own limitations as it is not in the user’s control. It also lacks the recommendation or suggestion part wherein a user can know which of the rules are feasible at the time of creation.

Important factors influencing CIO’s decision to choose a rule engine depends on how fast a rule engine can adopt to the existing workflow. An ideal Rule Engine allows to collate data from multiple sources, provides flexibility to create own customized rules.

You should explore the following capabilities in particular:

  • Is it easy for non-technical users to author rules?
  • Can authors use tools in collaboration with each other, or do they each have to work separately using their own individual tools?
  • Does it suggest what rules can be applied and does it provide visualization as per the choice of the user?
  • Can the engine quickly determine which rules or rulesets to apply based on the nature of the data?
  • How well the Rule Engine works when it comes to the data security?

The most important point to keep in mind – no single rule engine allows regulatory reporting and helps create rules of choice as per business need. It means that organization must have more than one Rule Engine and should keep different workflows in mind. Performance is also a big issue in some of the popular rule engine since it does not foresee that data could become so large.

What if we told you that there is an ideal rule engine that can take care of regulatory reporting and custom rule creation. In addition, it can take care of data collaboration from multiple sources, data security and ensure that performance is not hampered at any given time.

In future, technologies such as machine learning, artificial intelligence (AI) & robotic process automation will play an important role in Rule Engine – as it evolves from the current state. Furthermore, workflows will become more automated with minimum manual intervention. Nitor’s futuristic Rule Engine known as Health Pivot is one of the forerunners in applying AI-ML & RPA based concepts.

Health Pivot is a new generation ideal rule engine framework – which gives flexibility even for a non-technical person to create own rules and has the pre-defined rule to choose from a list of for regulatory reporting. It can easily collate data from multiple sources and ensure data quality as well.

In our next blog, we will discuss Health Pivot in detail, till the time stay tuned.

About Ravi Agrawal

Senior Lead Business Analyst

  • Healthcare
  • FHIR
  • Blockchain
A self-confessed healthcare warrior, an expert in Medicare, Medicaid, ACO, and Integration projects, Ravi speaks HL7 as a language. A doctor, doubling up as a Healthcare consultant, he is always a ‘patient’ person (pun intended) with a business mind. He says that technology never ceases to amaze him, and he is a student forever.