Data and Analytics for Healthcare: 3 Essential Steps to Success

5 minute read

 

Healthcare organizations are being inundated with data from an unprecedented variety of sources, including their electronic health record (EHR) applications, clinical and diagnostic applications, back-office business applications, patient-reported data, and the explosive growth of the Internet of Medical Things (IoMT).

All of this health, research and business data has the potential to dramatically improve quality of care and population health, reduce costs, and even find cures. But for too many healthcare networks, the data just continues to pile up in databases, on workstations and devices, and on network storage—resulting in a vast pool of unrealized potential.

When you can capture data from a wide variety of sources, filter and correlate it to find meaningful trends, and apply accurate analysis to reach actionable results, you can make a significant and repeatable difference in patients’ lives while realizing efficiencies in the business.

The goals that all organizations must strive for are what the Institute for Healthcare Improvement (IHI) calls the Triple Aim: improving care for individuals, lowering cost, and improving the health of populations.

In recent years, many healthcare organizations have added a fourth goal, turning their Triple Aim into a quadruple: improving the experience of providers in an effort to reduce burnout, since all the stress of treating patients and dealing with information, device and application overload is coming at the expense of the providers themselves—which is especially relevant during the current pandemic crisis.

It is helpful for health systems to use these goals to target data and analytics opportunities and measure and report value from their data and analytics efforts.

Data and analytics success is bigger than one technology

Too many organizations are looking for a specific technology to address their data and analytics challenges. Technology is important, but it’s more important to take a holistic view of everything that contributes to a successful data and analytics program.

For example, too much reliance is often placed on EHR solutions alone, mainly because so much money is invested in their acquisition, implementation and maintenance. But EHRs are only one part of an overall data and analytics ecosystem of sources and technologies.

A successful program requires technology, strategy, leadership, data and analytics professionals, statisticians and data scientists trained to do the analysis, and then the organizational discipline to use the insights to drive systemic, structural, diagnostic and treatment improvements.

That discipline starts with the data literacy to ask the right questions that will lead to actionable insights and true data value. That means clinicians and managers who really understand what their job is, what they have accountability for, and the business or clinical processes that need analytic insights to help them make better decisions that will lead to better outcomes.

In a clinical setting, that could mean identifying a specific disease or condition and working with clinicians to understand the best practices for providing care, backed by data and insight to help them make the optimum decisions and achieve the best outcomes.

On the business side, it means identifying decision points where data can lead to better decisions and outcomes, practices and technologies that are more current and effective, policies that make the organization more responsive and resilient, and even personnel who are data literate and disciplined.

The importance of data governance

A critical component of a successful data and analytics program is establishing and implementing appropriate data governance capabilities. Data literacy is one of the outcomes of a well-functioning data governance program, and it requires both technical and business cooperation and collaboration on data-related efforts.

Data governance programs treat data as an asset, including managing, leveraging and protecting it accordingly. There are seven functional areas of successful data governance: data stewardship, data quality, metadata management, master and reference data management, data security management, and information lifecycle governance.

By focusing on implementing and maturing each of these seven areas, data in your organization can be appropriately understood, managed, protected and leveraged to its full extent. Having the ability to exercise authority and decision-making over your data assets improves your organization's data value and supports an overall data strategy. Applying data governance in an organization improves all areas of the business and aligns data needs across departments. 

Three steps to improve your data and analytics maturity

  • Step 1: Identify a champion within the organization who understands the value of analytics to be a champion for the cause.
  • Step 2: Take an inventory of what’s working and what's not. Talk to executives and their teams to find out who’s doing analytics throughout the organization. Take an inventory of technology investments that have already been made. Identify gaps and invest where necessary to mature your analytic capabilities.
  • Step 3: Identify one project that would be the best possible opportunity for improvement, one that you know would provide measurable value and where data and analytics can really shine. Successfully completing a project and applying what you’ve learned to the next will let you build momentum that will allow you to be successful in analytics.

Perhaps the most important thing to remember when you are looking to advance your data and analytics capabilities is that this is not just a project, it is a journey that will grow and evolve. But in terms of improving quality of care, enabling a healthier population and reducing healthcare costs, the journey is well worth the effort.

Develop a balanced data and analytics program

Since an optimal data and analytics solution can comprise products from several different vendors, it’s essential to choose a vendor-agnostic technology partner who can focus on your organization’s current capabilities, investments and gaps in order to identify the best combination.

If you work with a trusted advisor that leads with an independent perspective, you can develop a strong data analytics program that is custom-fit to your organization.

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