When Does Hospital Quality Data Make A Difference?

By Warren Strauss, director of health and analytics, Battelle

Hospitals and healthcare providers generate more patient data than ever before. But how much of this data is really being used to make a difference in healthcare quality?

Most healthcare organizations have systems in place to collect and report data for key measures such as adverse events and hospital readmissions. For many hospitals, data collection and reporting is driven by Centers for Medicare and Medicaid Services (CMS) requirements for reimbursement. While addressing requirements for CMS and other payers is certainly critical, many healthcare providers are missing opportunities to use the data they are collecting to drive internal quality improvement initiatives.

The Gap Between Quality Measure Data And Quality Improvement
Quality measures for healthcare providers have proliferated rapidly over the past decade. Healthcare providers often find themselves faced with data collection and reporting for multiple measure systems, including CMS and Agency for Healthcare Research and Quality (AHRQ), Centers for Disease Control and Prevention (CDC), National Healthcare Safety Network (NHSN), and National Database of Nursing Quality Indicators (NDNQI).

In an ideal world, hospitals are not just passively reporting this data as required, but also using it internally to make operational decisions that can improve patient outcomes and reduce healthcare costs. To make that happen, data must not only be collected but actually be used for day-to-day decision making.

However, for far too many healthcare organizations, quality measure reporting is not effectively connected to quality improvement initiatives. There are several reasons for this disconnect:

• Reporting lag: Many hospitals rely on CMS/AHRQ data for benchmarking purposes. However, reports from these Federal programs typically lag behind data collection by nearly two years. Effective change management requires near real-time reporting so decision makers can quickly determine whether quality improvements are having the intended effect and implement course corrections if they are not.
• Poor accessibility: Hospital leaders need to be able to access data when, where, and how they need it in order to make effective decisions. However, too often data is not accessible to the people who need it most. The need for HIPPA compliance and other technical barriers can limit the ability of quality improvement professionals to pull and analyze important data points. Data that could shed light on hospital performance may be behind a digital privacy wall or otherwise siloed from other relevant data sources, making it difficult to compile data for analytical purposes.
• Data disconnect: Often, quality measure data is presented in a vacuum, making it hard to connect actions (in the form of quality initiatives and other operational decisions) to outcomes (as measured by the quality indicators). To make effective decisions, administrators need to be able to identify the factors that contribute to adverse events and analyze the risk factors that are correlated with quality measure results.
• Lack of appropriate benchmarks: Individual performance data tells healthcare organizations where they stand on various metrics — but how do they know where they should be? Benchmarking against comparable peers can help hospitals and healthcare providers set realistic and appropriate goals. For benchmarks to be meaningful, they need to be set against organizations with similar characteristics. This can be especially challenging for specialized facilities with few comparable peers.

Moving To A Data-Driven Quality Improvement Model
Healthcare organizations need quality data that meets four critical characteristics: data must be timely, accessible, actionable, and comparable.

When data meets these criteria, it can be used not just to meet reporting requirements, but also to drive quality improvement initiatives. Platforms that provide near real-time analysis and reporting can help hospitals move from simply collecting and reporting required data to actually using it to drive results.

A data-driven decision making model uses data to continually evaluate the outcomes of decisions and make adjustments to programs and processes if desired outcomes have not been met. Quality improvement professionals should be able to use their data to answer critical questions such as:

• How have different metrics of patient safety and quality changed over time?
• What factors are related to those changes?
• Are quality improvement initiatives reflected in associated outcomes such as reduced morbidity and mortality, improved patient experiences, cost savings for the patient and/or provider, and reduced days of care?
• Are the changes we are observing statistically significant and meaningful?
• How do our trends compare to appropriately established benchmark comparison groups?

Analytical tools for data-driven decision making can help healthcare organizations make sense of all of data they are collecting. These tools use quality measure data combined with other data sources to help healthcare organizations analyze trends, monitor performance, and identify areas for improvement. By putting these data to work, providers can drive decisions that lead to better outcomes for patients, payers, and their own organizations.