Clinical quality measures are tools that help measure and track the quality of healthcare services provided by eligible professionals, eligible hospitals, and critical access hospitals (CAHs) within the healthcare system. These measures use data associated with providers’ ability to deliver high-quality care or relate to long-term goals for quality health care. Clinical Quality Measure is used to measure the following
- health outcomes
- clinical processes
- patient safety
- efficient use of healthcare resources
- care coordination
- patient engagements
- population and public health
- adherence to clinical guidelines
These measures require a lot of data and manpower and so quality measures and quality reporting programs have begun to shift to the use of Electronic Clinical Quality Measures. As this shift continues, healthcare providers and analysts should prepare by reviewing the integrity of their clinical data. If the data is not reviewed as the transition to electronic is being made, it could result in inaccurate reporting and create financial risk.
By tightly combining 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.
With all this talk of inaccuracies, it is worth wondering what are the reasons for all the chaos. Two main reasons for these inaccuracies have been identified.
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.
Electronic clinical quality measures measure inaccuracies that 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 electronic health record 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 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 reliefs from the reporting burdens and better program alignment. With the introduction of the Electronic Health Record Incentive Program also known as Meaningful Use which was yet another quality reporting requirement healthcare organizations continued to complain and called for an overhaul.
Each time a new quality reporting program is introduced, it is presented as the better option fixing all past problems in previous programs. However, there have been so many new introductions, health practitioners are becoming fed up and are starting to lose faith in these programs.