Why Measurement Matters

By Stacy Humphrey, director of product marketing, Dimensional Insight

Healthcare Business Intelligence (BI) & Analytics have been in the spotlight recently, and they will continue to be a top priority for healthcare organizations. But the focus of BI and analytics is changing – from implementation to leveraging data to drive improvements in the delivery of healthcare.

Why the shift? The reasons are numerous: regulatory reform pressures courtesy of the Affordable Patient Care and HITECH Acts; changing demographics yielding an aging and more chronic illness prevalent population; and seismic, risk-laden changes in care delivery and payment models.

Changes in how healthcare is paid for, in particular, will dramatically alter the landscape for healthcare providers. The transition to pay-for-performance models will focus providers on care delivery that keeps patients healthy and proactively improves the health of a population. Services delivered will be reimbursed based on value, determined by effectiveness and outcomes, as opposed to mere quantity.

Navigating this shift will rely heavily on data – digitalized information about the health of individual patients and populations of patients. As a result of electronic health records (EHR) adoption, clinical data is becoming increasingly available electronically, and it’s possible to combine it with administrative data (primarily claims and other financial data), as well as operational data (from the systems that run hospital processes).

BI & Analytics, long in use in other industries, can now be applied to integrate diverse healthcare data sources and to create meaningful measurements and metrics. Measurement provides the means to objectively evaluate how a healthcare organization is performing and drive care delivery improvement and cost reductions. You need to be able to measure what you’re trying to accomplish.

Understand Your Data
An important aspect of BI & Analytics is to understand your data, including its level of maturity:

• Administrative claims data is the bedrock of digital data maturity in today’s fee-for-service world. It not only serves a financial purpose, but historically has served as a proxy for clinical data, with claims-oriented diagnoses and procedures standing in for their clinical counterparts. The systems generating claims data are robust and reliable, data integrity and quality are good, and standards not only exist but are enforced through both reimbursement and quality reporting processes.
• Operational data is data generated from the systems that handle the business processes that make a healthcare organization work – laboratory, pharmacy, and the systems that help manage supplies, personnel, and payroll.  The quality of operational data varies by process, as do the standards that make it useful.  This type of information plays an important role in measuring productivity and operational effectiveness.
• Clinical data is the new kid on the block. Adoption of clinical systems has historically lagged behind other types of health information systems. But that is changing quickly, both out of necessity and with a boost from the federal government’s EHR incentive programs, which are helping to drive adoption. Standards and data completeness are less well developed, but are improving.

With an understanding of your data, you’re in position to building a measurement foundation.

The Building Blocks for Measurement
There are seven fundamental building blocks for measurement:

1. Measure the right things – start by identifying relevant performance metrics and measures that align with your organization’s strategic objectives. Measures fall into 4 main categories:

• Process measures – specific steps in providing care. Examples include aspirin administered to a heart attack patient at arrival (AMI-1).
• Outcome measures – actual results of care.
• Patient experience measures – patient’s assessment of their care, such as a HCAPS survey.
• Structural measures – capacity or resources related to the healthcare organization.

Not only do you need to measure the right things, but you also need to define good measures. The National Quality Forum’s The ABC’s of Measurement is an excellent reference resource for that.

2. Identify the data you need to collect, and determine how and where the data is stored in the EHR(s), other health information systems, and from external sources.
3. Define the measurements, determine the data elements for each measure, map the data sources to those elements, and establish the comparative basis for each measure.
4. Collect and integrate the data, no matter where it resides.
5. Apply the business rules that will transform and enhance the data to create uniform, consistent metrics and measures.
6. Compare measures to targets and benchmarks to provide a context for assessing your organization’s performance.
7. Communicate the resulting information in an appropriate and meaningful context, using summarization, visualization and comparison. Examples include dashboards and scorecards, reports, notification alerts, and analytics to uncover patterns and navigate the underlying details for insights.

Put Your Measures to Work
Once you have built effective measures, you need to put them to work. Among the questions your measures can help answer are: how does performance compare to a target or benchmark?  How is performance trending over time?  Can you stratify the measure to be more meaningful, say by disease, patient demographic group, or financial class? And, are your improvement initiatives having a measureable impact on performance? 

Here are some recommendations that can help you along your analytics journey:

• Go for the Quick Wins – providers just starting out with BI & Analytics can score early success by focusing on operational process measures. Popular applications include productivity, staffing, and physician practice profiling. Start working with the data you already have, know, and trust.  There’s a lot you can do with what you have, and it serves as a good way to begin building the foundations for both technology and competency in analytics. 

• Have a Clinical Champion – as BI & Analytics maturity grows within an organization, metrics expand to clinical outcomes. It’s important to have a clinician champion to help drive effective use and successful adoption.
• Incorporate Data into Process Workflows – to make a real impact, you need to leverage the data from underlying system workflows to make process changes, and then continuously monitor and refine to sustain performance improvements.
• Keep Your Initial Focus Simple – some of the most effective BI & Analytics have focused on specific domain areas, both disease (e.g. diabetes or sepsis) and regulatory (e.g. readmissions) related,  to deliver quantifiable before and after results.
• Make the Data Actionable – data needs to be immediately available and credible, and be presented in a context that highlights a problem or opportunity with access to detailed data to aid in determining the appropriate response.
• Remember the Culture – organizations need to foster a culture of measurement, analysis, and accountability. It begins with data transparency and metrics visibility both publicly and internally. It doesn’t happen overnight, so you need to plan for incremental progress.

The ultimate goal of measurement is to guide your organization’s actions and resource allocation decisions in ways that drive improvements in care, cost, and population health.

About the Author:
Stacy Humphrey is director of product marketing at Dimensional Insight (www.dimins.com), a four-time, Best in KLAS winning provider of business intelligence (BI) solutions to the healthcare industry and other industries worldwide.