With pressure from affordability care, consumerization of healthcare, consumers and physicians aware of technology advancements in delivery of information in their daily lives, the opportunity for significant impact to cost and quality of medicine through better data analysis is a very achievable goal. Yet, where are we? Can consumers see current stats around re-admission rates for asthma to increase their child’s daily asthma meds? Are physicians sitting with their staff to analyze real-time patient data, understanding re-admission rates and asthma comorbidities? Are researchers easily able to analyze trends in geographic population health? The answer to all these questions is an astounding no.

So the real question is, why not?   Is the technology capable of delivering this data? Is the market a willing adopter? Is the data available? Yes, yes and yes! Then what is the holdup…?

Although there are some vendors or hospitals that are close to or are delivering this type of data analysis, the majority are not. So again the question is why not and how can health providers meet the demand of their clinical providers and consumers?

There are three primary areas that are impacting the adoption of readily available technology to advance and facilitate how providers and consumers analyze patient data.

Close the communication gap between clinical users and IT staff

The divide between physician and IT is massive compared to say, the financial services industry where traders and analysts are typically arms length from their technology staff. This matters because IT cannot understand the needs of their physicians unless they are communicating and collaborating. Too often, and especially in healthcare, IT staff believes that their clinical users will not use and cannot understand the data analytics tools and  platforms. However, it’s impractical to expect a report developer who sits in IT to analyze and identify trends in data they are not trained in. Take the financial services industry for example. The analysts, portfolio managers, and traders have real-time access to all their data. They are not asking someone in IT to build them custom reports on data delivered days or weeks later. Yet, this is how the majority of healthcare data analysis is performed today.

Expand the trust circle beyond the Electronic Health Record Providers

Health providers need to shed the support shadow of fear and uncertainty that their Electronic Health Record (EHR) vendors place on them. Specifically the notion that an EHR vendor, whose expertise is clinical workflow/case management, can seamlessly extend that domain into the data warehousing and analytics world. What I mean by that is maybe the EHR provider is better kept in their swim lane of case management/electronic record provider and really shouldn’t be the domain owner of all data sources. For example, should an EHR provider be in the best position to build data models or determine technology platforms for the data warehouse?  Perhaps it’s better they act as a provider of information to the health data warehouse versus the designer, owner and technology architect of the data warehouse. To some extent, the healthcare provider has an obligation to ensure that health data is protected, but also that its extensible and available. Openness and extensibility isn’t a theme that the EHRs have easily adopted.

Embrace newer technology such as In-Memory Platforms and Data Visualization tools built for clinical users

Healthcare providers need to begin to explore some of the newer technologies hitting the market that can help them improve on delivery of self-service analytics to their clinical users and consumers. To do this there are a few key items that must have consideration:

Item 1: Ensure a strong use case driven by clinical/functional users

Many times, we are seeing IT partnered with an technology provider, carrying a significant hardware, software, and services bill of material, without a strong use case such as tracking chronic disease cormorbities, or identifying trends in RVU/CPT codes for example. This is problematic and likely to result in failure and goes back to the gap between IT and the clinical side in healthcare. It would make more sense to have a specific use case of the need for a healthcare provider who is moving to a new EHR, but needs to maintain access to their legacy or data. In this instance, the use case is clear, and adoption of a in-memory platform such as SAP HANA could make more sense and be far more cost effective than an investment in traditional database and legacy infrastructure which can struggle at serving the massive amount of data that is encountered on patient billing. As well, SAP Business Objects is the preferred analytics platform for many of the large EHRs, Epic, Cerner, McKesson etc. Thus, the interoperability between the analytics layer and the in-memory platform has already been established and proven.

In this scenario we have a clear business case, neutral third party platform provider, and more importantly IT can partner with the functional side to validate the EDW.

Item 2: Provide the same budgeting and executive guidance to your analytics/EDW as your EHR implementation

Too often I see significant capital, planning, and executive support behind the implementation of organizational EHR onboarding. This is the way to go. However, the analytics budget and support process is not given the same stature. Think of it this way, the EHR is all about collecting the data, the analytics platform is all about delivering the data. They live hand in hand and co-dependent. As my good friend and associate Angel Davila always states, “You can’t do BI/Analytics on a shoe string!” So please, don’t have your board and executive staff focused on the success of your EHR implementation but your Crystal Reports developer determining budget and success for your health analytics platform.

Item 3: Ensure the selection process for the analytics platform includes functional users as well as IT and consider a return on investment and build vs buy analysis in the selection criteria

The same approach is also critical when selecting the technology provider of the Analytics and Data Warehouse platform. Sometimes the technology department is leading the selection of the analytics and EDW platform but missing key criteria in their selection process. Namely the build vs buy mentality. There are a lot of analytics/BI players that claim integration with the big EHRs. But what customers tend to miss is that they are paying significant annual maintenance to their EHR provider who is building content designed to improve the time to value, simplicity, and integration of a particular analytics vendor. For example, as stated earlier many of the large electronic health record providers build integration with SAP Business Objects. In that instance, if IT decides to select an alternate platform, they are guaranteeing the organization additional development, integration, and support cost. What’s worse, I’ve seen many customers, supporting multiple analytics platforms, one for batch reporting, one for visualization, a third for ad-hoc reporting. This never works well. And often it leads to poor implementation and weak security models that can pass HIPAA audits. Point being, if your EHR provider is developing out-of-the-box integration with a particular analytics platform, there has to be a very strong business case with hard cost savings detailing why the additional development and integration expense is merited.

In conclusion, the market, government, consumer, and physicians are demanding better access to health data. The technology to provide health data is available and easy to adopt, and new in-memory data warehousing platforms such as SAP HANA offer tremendous flexibility and time to value. All that is required is the correct alignment and lessons learned between executive leadership, IT, technology providers, and the right level of consultative expertise for success in the health market.