Quality Metrics: Why Measuring Progress is Good for the Bottom Line

Reporting Quangela hunsberger croppedality Measures Has Never Been More Crucial

By Angela Hunsberger, Senior Healthcare Consultant, Hayes Management Consulting
Twitter: @HayesManagement

Understanding and reporting on quality measures has never been more crucial for healthcare organizations. Measuring quality is necessary to qualify for financial incentives such as Meaningful Use (MU), Patient Centered Medical Home (PCMH), as an Accountable Care Organization and other initiatives.

As organizations undergo optimization to improve patient care and enhance the revenue cycle, modifications are typically made to workflows, processes and technology. This is the perfect time to report on specific quality measures, both pre-optimization and post-optimization.

Once an optimization plan has been agreed to, there is an initial discovery process where opportunities are identified and future workflows are developed and implemented. There is a feeling of success and accomplishment when broken workflows are fixed and staff morale is high. However, how is success quantified? Meaningful metrics are mandatory.

Accessible data

EHR data paired with data from an increased selection of healthcare IT-related technology applications has provided organizations with an opportunity to examine in-house data that was either not available or too tedious to collect in the paper world. Data can now be more easily aggregated and leveraged to meet initiatives and goals. These tangible insights can be translated to measure an organization’s productivity, performance, and overall financial health.

Before commencing on an optimization project, a plan is needed to collect, monitor, and report relevant metrics. Metrics will be mandated in some cases such as MU. In other cases, organizational goals will drive which metrics are measured. Common metrics include those that show improvements in efficiency, staff and patient satisfaction, patient safety, and data quality. The results can be used to demonstrate quality outcomes, identify trends and spot problems before they develop. Furthermore, the ability to prove successful outcomes will help support funding for future initiatives.

Understanding your data sources

Collecting data can be challenging because the information available among all your systems can be overwhelming. Simply collecting discrete data to report upon is not productive in itself. Think strategically before implementation and determine whether the metric is meaningful and if the data collected is relevant to achieving your specific goals. When determining what to collect, refer to the initial plan and collect metrics that support it.

If the data you want to measure already exists in your system(s), find out how it is collected and verify its validity. Systems typically offer built-in tools such as inquiries, reports, or even data analytics. Understanding how the system collects and reports the data is vital to its interpretation. A sample audit should be conducted to prove data integrity. Changes to the data collection or process may need modified to obtain the desired end result.

Another approach to collecting data is by conducting staff surveys or interviews. This technique must be performed carefully; the question design should not sway a response. There are other methods such as time studies or even counting “clicks” or steps it takes to complete a task. Consider looking beyond primary software applications and into ancillary software systems. For example, you may find relevant reports on your patient portal or claims clearinghouse that are not available to you in your primary practice management (PM) or EHR software.

Pre-optimization and post-optimization metrics

Collecting initial metrics will provide a baseline for benchmarking and documenting progress. This process is especially important if the data has never been collected before. Keep in mind that baseline metrics only provide a snapshot in time and do not always portray the complete picture of performance. Metrics must be collected at various intervals to monitor and report on progress.

The next step is the interpretation of data and the presentation of results. Once the data is collected and analyzed, perform a gap analysis from current state to where you want to be. Identify best practices, and create future-state workflows. Now a solution can be implemented. Pre- and post-optimization metrics should be included in this process.

Small changes, big difference

We have seen firsthand what a difference a small change can make. During a recent optimization, we delivered a staff survey, and out of 200 staff members, 90% of the staff indicated the “School/Work Excuse Letter” was cumbersome to complete and time consuming. Pre-optimization metrics were collected showing that it took 49 seconds on average and 16 clicks for the staff to complete this one letter per patient. The staff also had to free-text much of the body of the letter.

During the optimization, the workflow was revised and the letter was transformed into a structured EHR form. Using EHR advanced technology and features, the clicks were reduced to five and it now takes only 15 seconds to complete the letter. Most of the body of the letter is no longer free-texted because the form pulls in defaulted values such as the patient name and address. The free-texting within the letter is only 10% typing on average. All of this data adds up to proven success from start to finish. Multiply the savings per the average school/work excuse letters per day, per doctor, over the course of a year and it adds up to big savings. Staff satisfaction also improved because it became easier and more efficient to perform this daily task.

In another engagement, we were hired to help an organization eliminate waste throughout their practice management system. Through years of practice mergers, the appointment types grew to a massive list of more than 780 appointment types. Some of them were redundant or irrelevant. Post optimization, the appointment types were reduced to just 130. Eliminating the excessive appointment types made it easier to train new staff and also provided consistent reporting among the providers. The measure was taken again after a few months and the number of appointment types did grow some, but it is nothing near the original mark. This is a great real-world example of how measuring results at different intervals unveils a more comprehensive picture of the end results.

Each organization is going to have its own specific goals, such as increasing the number of patients seen per day, decreasing no-show rates, or perhaps collecting and reporting patient outcome metrics. Regardless of the goal, it is important to build a roadmap ahead of time and decide what data to collect, the method of collection, and how to measure progress. Success in today’s data-driven world necessitates delivering results that are quantifiable, valuable and achievable.

This article was originally published on Hayes Management Consulting and is republished here with permission.