Improving Quality Measures Can Lead to Better Outcomes

Summary

Current quality measures are expensive and time consuming to report, and they don’t necessarily improve care. Many health systems are looking for better ways to measure the quality of their care, and they are using data analytics to achieve this goal. Data analytics can be helpful with quality improvement. There are four key considerations to evaluate quality measures:

1. Organizations must develop measures that are more clinically relevant and better represent the care provided.
2. Clinician buy-in is critical. Without it, quality improvement initiatives are less likely to succeed.
3. Investment in tools and effort surrounding improvement work must increase. Tools should include data analytics.
4. Measure improvement must translate to improvement in the care being measured.

When the right measures are in place to drive healthcare improvement, patient care and outcomes can and do improve.

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Editor’s Note: This report is based on a 2018 Healthcare Analytics Summit presentation given by Christian Dankers, MD, MBA, Associate Chief Quality Officer, Partners HealthCare, Harvard Medical School Faculty, entitled, “Real Quality: A Recipe for Healthier Patients and Happier Doctors.”

In the United States, each year physician practices spend $15.4 billion and individual physicians spend 785 hours to report quality measures, according to a March 2016 study in Health Affairs. It’s clear there is a lot of emphasis on quality measurement, yet current quality measures aren’t doing enough to drive care and outcome improvement. With this in mind, health systems are examining measures and how to improve them so they more accurately reflect the quality of care, have clinician buy-in, and lead to better care.

Shortcomings of Current Quality Measures

Quality measurement in its current state is used in four general ways:

  • To inform clinical care (quality improvement).
  • To ensure meeting a minimum standard (regulatory).
  • To inform decision making (transparency).
  • To incentivize behaviors (pay for performance and transparency).

This approach presents several limitations. It is both expensive and time consuming, as noted in the Health Affairs study cited above. Additionally, according to that same 2016 study, most physicians don’t believe that the measures represent true quality of care and don’t use those measures to inform care practices.

Measures also fall short in capturing the scope of care that needs to be measured and improved, focusing largely on hospitals and primary care practices. Ambulatory care and patient-reported outcome measures (PROMs) aren’t included in current quality measures, which leaves patients and consumers with an incomplete picture of the care provided by different physicians at various healthcare facilities.

Translating Better Quality Measures to Better Care

Many healthcare organizations are rethinking quality measurement. With the goal of improving measures and, ultimately, improving care, organizations are looking at new ways to measure care provided that will increase the positive effects of measurement.

The Triple Aim Plus One can inform stronger measure development that leads to meaningful care improvement. Based on the IHI Triple Aim, this system expands to include both patient and clinician-oriented experiences. Look at the Triple Aim Plus One framework:

Triple Aim

  • Population health (mortality rate, disease burden, and health/functional status).
  • Per capita cost (total cost per member per month).
  • Experience of care (patient experiences, patient safety, and healthcare effectiveness).

Plus One

  • Clinician satisfaction (because all other parts of the triple aim depend upon an engaged clinician workforce).

But simply developing new measures is not enough. The best measurements in the world are meaningless without a way to translate those measures into better care. Four aspects are critical to the process of moving from improved measurement to improved care and outcomes:

  • More clinically relevant quality measures.
  • Increased buy-in from clinicians.
  • Increased investment in tools and the effort around the improvement work.
  • Demonstrated improvement in clinically relevant measures.

More Clinically Relevant Quality Measures

Clinically relevant quality measures should follow a set of standards:

  • Unifying – Measures must have buy-in from stakeholders and represent the core mission.
  • Motivating – Poor performance against the measures should concern stakeholders.
  • Accurate – Measures should accurately represent the delivery of care and related outcomes.
  • Available – Data for the measures must be accessible and not excessively costly to gather.
  • Actionable – Stakeholders should be able to easily use measures to guide discussion and initiate change. In other words, clinicians must be able to do something about the data being measured.
  • Balanced and parsimonious – There should be a limited number of measures to convey what clinicians (and patients) need to know.

Data for these types of measures will come from multiple sources, including the EHR, as they must be clinically based.

Increased Buy-In from Clinicians

Having buy-in from clinicians is key to quality measures that improve care. Measures that are more clinically relevant and that providers and stakeholders agree better represent the care provided may help spark buy-in from clinicians.

The Importance of Meaningful Measurement

One way to get clinicians to see value in quality measurement is to have meaningful measures. Using the EHR to gather data and using data analytics and electronic tools to demonstrate value can make measures more relevant and actionable.

Using the EHR for data collection tends to lead to more accurate, actionable measures than claims-based, HEDIS measures. EHRs are ripe with actionable data just waiting to be analyzed and used for improvement.

Electronic tools are also important:

  • Data analysis tools have several benefits for quality measurement. For example, they allow benchmarking and evaluation of both individual physicians and entire practices. Data analysis tools can give health systems the ability to leverage data to improve care and operational efficiency while reducing costs.
  • Predictive modeling tools allow teams to understand the likelihood that measures will yield benefits. For example, using predictive modeling, an organization can look at a specific population to see whether smoking cessation activities are predicted to reduce risk of heart attack or mortality.
  • Shared decision making tools allow patients and physicians to look together at data about risks and benefits of treatment options, which can inform development of an individualized treatment plan. And this ultimately can help improve outcomes.

Increased Investment in Quality Measurement Tools and Effort

Sustained, meaningful change in quality measurement takes investment both in tools and in an effort. Investing in the tools to help meet quality improvement goals is critical. Investing in effort is important as well. Effort must come in many forms: effort to engage leaders, effort to increase transparency, effort to design incentives that work, and effort to ensure policies, measures, training and education, and tools are focused on quality improvement and better patient care.

Transparency is an important part of quality improvement initiatives because it tends to build trust with consumers and hold providers accountable. Investing in tools that aid in transparency can help translate new measures into better care. There are several tools that can improve transparency:

  • Webpages that publish quality measure results, such as Press Ganey patient rating information can improve transparency by providing information about patient experiences throughout the organization.
  • Open notes that allow patients to see their own medical records give patients a quick and easy way to see what’s in their health records. This not only improves transparency, but it can help patients become more invested in their own care, which tends to improve outcomes.
  • PROMs publication tools give patients an insider’s view of how other patients report their care outcomes. When patients know their healthcare providers are being transparent, it helps improve trust and the patient/provider relationship.

Knowledge is empowering. Training leaders in the work and culture of improvement and making sure they have the skills and tools to support change and improve is a first step toward meaningful change. When leaders are on board and prepared, offering training throughout the organization helps create a culture of improvement.

Once organizations have the right tools, making measurable improvements becomes easier.

Demonstrated Improvement on Clinically Relevant Quality Measures

Investing in quality improvement efforts is not optional for health systems. The push for improving care while cutting costs is ongoing, and health systems are under great pressure to perform. Developing measures that are clinically relevant and representative will help get clinicians on board. Getting clinicians on board will help improve outcomes because they will be more likely to use data to inform care. When patients and clinicians can see how data relates to outcomes, they tend to be more informed and invested in care.

A culture focused on quality improvement must continually reevaluate measures and approach to ensure the measures are doing what they set out to do – drive improvement in care. By continually re-evaluating measures and the approach to quality measurement, measures can lead to better patient care. When measures are clinically relevant, clinicians are engaged, and everyone has the tools they need to drive improvement, outcomes and patient care can and do improve.

Additional Reading

Would you like to learn more about this topic? Here is an article we suggest:

A Guide to Applying Quality Improvement to Healthcare: Five Principles

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