Healthcare Analytics Simplified: It is no more Build vs Buy, but Build and Buy

Dr. Ravi Agrawal	Dr. Girish Shinde
Senior Lead Business Analyst	Healthcare Specialist

An analytics solution for the healthcare industry today is expected incorporate extensive native industry knowledge with deep tech-domain expertise to actually provide value. A stellar amalgamation of both disciplines will help leverage the teeming amount of data that is created for each patient every second.

The major building blocks of any Healthcare Analytics solution are:

• Source Systems
• Business Rules
• Reporting Module based on the business needs

The components that unify the above three building blocks are:

• Data Ingestion/Extraction
• Data Warehouse/Mart/Lake
• Analytical Model

When any enterprise wants to adopt such a solution, they should collaborate with various vendors to gather their perspectives and experiences regarding ‘Pure configuration in the Buy approach’ vs. ‘From scratch development in the Build approach’.

The “Buy” approach will let you pick up your requirement right off the self, albeit with limited flexibility to serve your end-goal. Whereas the “Build” approach will serve cater to the exact organizational requirements, but with various caveats. Having said that, we can now take a look at the Build Vs Buy argument.

For better understanding, let us list the top 4 considerations when building or buying a healthcare analytics solution.

1. Control over product roadmap
2. Cost
3. Maintenance
4. Opportunity cost

If we look at the above points and think really hard, there is no clear choice between Build and Buy. The very next thing which comes to our mind is ‘Can we have a hybrid between Build and Buy’?

Healthcare data analytics, including Big Data in healthcare, is a growing business that calls for a need for providers to bulk up their infrastructure to supplement their EHRs with sophisticated tools and solutions for Clinical Analytics, Payer/Provider Analytics and Predictive Insights. The number of new Healthcare Analytics vendors is growing every day as providers have recognized the need to harness data for predictive insights into their growing business. Picking a winner in such cases isn’t always easy.

For organizations wishing to implement predictive analytics and Big Data analytics in healthcare, there are primarily four options available to healthcare organizations today:

1. Going with the out-of-the-box analytics products from vendors

In this case, organizations that deal with population health analytics and medical data analytics generally outsource their analytics to analytics service providers. These service providers have a set of reports and dashboards as part of their solution offering. These integrate very quickly with the organizations’ data. Such an approach calls for a very low upfront investment on hardware and software and does not require building in-house expertise. Organizations that opt for such an approach generally have very limited analytics requirements. Any enhancements desired are generally tied down by the limited functionality/reporting capabilities offered by the vendor. Although the upfront investment is less, the organization ends up spending heavily on subscription and maintenance costs.

2. Buying a specific product/solution which solves an immediate problem

Another option for organizations that deal in Big Data health, healthcare informatics and advances population health data analytics is to opt for a top-notch point solution. Solutions like these are target-specific analytics opportunities and have a very short implementation time. Such analytics solutions can prove to be very helpful to organizations that specialize in a particular business area. However, the very explicitness of these solutions also makes this approach disadvantageous. The solution may provide a very specific fix to a given domain but would fail to integrate with other source systems while providing a holistic view of the business. Maintaining a separate point solution for every analytics need would make the entire system more expensive and difficult to maintain.

3. Purchasing auxiliary products/services from an existing EMR vendor

Organizations undertaking clinical data analytics usually have an EMR system in place. There is something else that is also possible – having an EMR that also doubles up as an analytics solution. These solutions are very specific to the modules and data that the EMR system has. But the only two challenges with getting your EMR vendor to supply you with an analytics solution are the limited flexibility it offers and the finite number of EMR systems that are capable of performing advanced analytics.

4. Build and Buy tailored solution which scales for future needs and reduces total cost

Having a pre-built analytics solution from a data warehouse platform vendor is the best way to tackle the Build vs. Buy dilemma. These solutions offer the highest degree of scalability, extensibility, and adaptability. The key here is to understand and execute the organization’s strategy and deliver a robust analytic application that delivers the information and analysis that the users need. For this approach to be successful, a healthcare organization must have a data-driven culture with high aspirations that view analytics as a clear business differentiator. The approach is also best suited for a culture with a commitment to a higher degree of data literacy and data management skills throughout the organization.

Nitor Infotech has built NHAF (Nitor Infotech Healthcare Analytics Functional framework) which blends healthcare subject matter expertise and technology best practices currently available for:

• Providers – Patient Experience, Population health management, Eligibility & Enrolment, Patient satisfaction, Value based care (P4P)
• Payers – Claims & Case Analytics, Health Plan performance, Claims denials management, Fraud analytics
• RCM / Medical billing – RCM operations management, AR management
• Pharma / Life Sciences – Pharma claims and benefits management
• Healthcare Quality measures (CQM) – HEDIS and PQRS metrics

The NHAF platform, on similar lines, is built to deliver a fully tailored analytics experience to empower customers through:

• Flexibility of a late binding data model which can be configurable
• Healthcare domain experience updated with industry know-how
• Agile approach to build BI Solutions
• Reduced time to market
• Experience of successful past implementations

This analytics framework comes with the following components which are tool agonistic, fully configurable, extensible, and without any licensing frills. Both new products can be built from scratch existing products can be enhanced with the analytics module. These accelerator frameworks are scalable for future needs and align with the trends in the healthcare industry.

To know more about our services, or how to better mould data to elevate your healthcare analytics platform capabilities to a whole new level, drop us an email.