SaaS Analytics and Community Hospitals

This is a welcome change from the historical approach, where analytics projects in healthcare were “start from scratch.” These projects almost always were time consuming and expensive, relying on a data warehouse implementation. End users, especially at the operational level of smaller healthcare organization such as community hospitals, had to wait for the IT team to catch up with their analytics requirements.

Thankfully, today with third generation analytics tools delivered via SaaS, IT can be an enabler of analytics and provide users with self-serve analytics that leverages established IT infrastructure. This reduces the overhead and time usually devoted to creating and maintaining the data warehouse, and puts analytics ready insights into the hands of frontline workers much faster. This also enables community hospitals to strategically deploy analytics on their timeframe, scale and budget towards their most pressing issues, e.g. bed-to-patient ratios or staffing-plan-to-cost ratios. The flexibility of the SaaS model allows community hospitals to truly maximize the value of their data without impacting profitability or inhibiting business efficiencies.

There are certainly roadblocks, though. The more data there is to be standardized, the more departmental views there are to be resolved. And, healthcare data spans multiple systems across the continuum of care — clinical data, billing data, purchasing and payroll, just to name a few. Hospitals of all sizes, including community hospitals, are no different; they have similar complexities and multiple systems of record. It’s vital to have a clear picture of the issues you’re hoping analytics can help solve, build a strategy that can scale with your budget and deploy a technology like Qlik Sense that can bring multiple and disparate data sources together without the need of a data warehouse.

So, what holds community hospitals back from deploying SaaS analytics? One concern is that self-service tools and solutions do not always answer the data governance questions of IT, especially when it comes to sensitive patient data. Having an organization-wide data governance model that manages data assets throughout their lifecycle will ensure the organization’s quality and integrity standards are met regardless of the analytics tool in use. (We’ll dive more in depth into governance in a following post.)

A best practice community hospitals should aim for is to have solid enterprise-wide data governance policies and practices that facilitate achieving the Institute for Healthcare Improvement’s Triple Aim:

• Improving the patient experience of care (including quality and satisfaction);
• Improving the health of populations; and
• Reducing the per capita cost of healthcare

My advice?

• Start small with a few simple data sources and an easily defined business need
• Keep IT in the loop to avoid any data governance concerns
• Recruit a few data champions to help drive initial adoption and use, creating successes to build on for a larger rollout, pending budget and need
• Look at it as an agile development process since SaaS analytics allows you to pivot quickly as needed
• Fail or win fast — then move on

Most of all, don’t get stuck; keep pushing forward until you’re leveraging a solution that scales to your operations and needs and yields valuable insights. You and your community hospital colleagues — and not to mention your IT department — will be more than happy to reap the benefits of having and deploying a robust data analytics strategy across the care continuum.