The Unintended Consequences of Electronic Clinical Quality Measures

As quality reporting programs begin shifting to the use of electronic clinical quality measures (e-measures), healthcare providers should prepare by reviewing the integrity of their clinical data. Failure to do so could result in inaccurate reporting and create financial risk. By tightly combing quality reporting efforts with strong data governance practices, however, healthcare organizations will not just survive, but also benefit from, the move to electronic quality measure reporting.

The Growing Quality Reporting Burden

Over the years, healthcare providers have been confronted with numerous requests from the Centers for Medicare and Medicaid Services (CMS), state agencies, specialty groups, and others to participate in various clinical quality reporting programs. A decision to take part in these programs meant the healthcare providers had to implement time- and labor-intensive processes to collect, organize, and submit data per the specific requirements of each program.

As the quality reporting programs grew in number, so too did the calls coming from the healthcare community for both relief from the reporting burdens and better program alignment. With the introduction of the Electronic Health Record Incentive Program (Meaningful Use) in 2011 — and yet another quality reporting requirement — the volume of the calls for relief rose to a clamor.

This uproar is not surprising when you consider the apparent duplication between the list of quality measures required for Meaningful Use and other reporting programs. For example, hospitals participating in both the Core Measures Hospital Inpatient Quality Reporting Program (HIQRP) and the Meaningful Use program are required to report three entire measure sets — Stroke, VTE, and ED Throughput — to two different CMS agencies using two different sets of definitions and two different submission methods. But dig below the surface and for many organizations there will be a big difference between these seemingly duplicate measures: accuracy.

Meaningful Use Introduces E-Measures for Quality Reporting

When the Meaningful Use (MU) program introduced e-measures for quality reporting, it did so with no accuracy or performance requirements. Instead, eligible professionals and hospitals were asked only to demonstrate the ability to report e-measures. It’s not too surprising then that healthcare providers, faced with more immediate challenges, such as preparing for ICD-10 and MU stage 2, have not yet begun to review the accuracy of their e-measure reporting or address known issues. This concern is especially prevalent in hospitals where the meaningful use clinical quality measures are complex and involve large data sets.

What Causes Inaccurate e-Measures?

There are two main reasons for e-measure inaccuracies:

Missing data. E-measures are calculated using only the structured data collected in the certified EHR technology (CEHRT). Any e-measure data element not in the CEHRT can skew the accuracy of how the e-measure is calculated. For example, if the date and time a urinary catheter is inserted for an emergency department (ED) patient resides in the ED information system and not in the CEHRT, the EHR will be unable to accurately calculate the relevant Catheter Associated Urinary Tract Infection (CAUTI) e-measures. To address the problem, healthcare providers may need to create or update several interfaces between the CEHRT and department or specialty modules. Alternatively, organizations using an enterprise data warehouse (EDW) may be able to leverage this tool to create the complete data sets needed to improve e-measure reporting accuracy. Data integrity. Quality e-measure inaccuracies may also be the result of data integrity issues, often caused by documentation or workflow variation. Take, for example, a scenario in which the hospital EHR is set up to automatically capture a patient’s arrival time as they are being registered with the emergency department. This may seem like an efficient way to collect patient information from the registration workflow but what if the patient is triaged first and then registered? In this case, a change in the workflow produces an inaccurate ED arrival time, which affects the accuracy of any e-measures using this data.

The IPPS Proposed Rule Raises the Stakes for E-Measure Accuracy

While e-measure reporting has been mandatory for Meaningful Use, it has so far been voluntary for other quality reporting programs. This may soon change; in the Inpatient Prospective Payment System (IPPS) proposed rule published in April, CMS is proposing that e-measures be the required reporting method for the HIQRP program as of calendar year 2016.

If this proposal makes it to the final IPPS rule in August, hospitals will have a clear deadline by which e-measure accuracy issues must be found and resolved. Given that the HIQRP e-measures would be audited for accuracy, publically reported, and eligible for inclusion in the Value-Based Purchasing program, accurate e-measure reporting becomes of paramount importance. Hospitals will want to use the next several months to assess each e-measure data element for reliability and precision as part of a larger data governance program. Remember, inaccurate HIQRP reporting can result in the loss of the Medicare annual payment update hospitals receive for participating in this program.

Silver Linings

If the challenge of preparing for accurate electronic clinical quality measure reporting seems to be creating yet another grey cloud on the horizon, let me point out a few silver linings.

Using the e-measure model to report clinical quality measures will reduce the abstraction burden for physicians and hospitals, which is welcome news indeed! This would allow redeployment of valuable clinical resources from low-value data capture to high-value analysis and improvement activities.

With the move to e-measures, CMS and other agencies will be able to more quickly align and harmonize the quality reporting requirements of various programs and remove duplicative reporting burdens for hospitals and physicians.

Once data integrity issues are resolved, healthcare providers will be left with a far richer data set that can deliver value in a number of ways: robust clinical decision support rules, improved service line analytics, and improved readiness for future e-measure reporting requirements.

There can be little doubt that the use of electronic clinical quality measures will accelerate as EHR technologies and standards continue to mature. A robust data governance program will help you prepare for – and take advantage of – this transition in quality reporting.

Do you have an effective data governance program for the clinical quality reporting at your facility? Can you identify other causes of clinical quality measure inaccuracies? How will your healthcare analytics program benefit from the improved data set you’ll have once your e-measure issues are resolved?

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