If you’ve had issues with reporting quality measures using your hospital’s electronic health records (EHR) system, you aren’t alone. According to the results from a pilot project conducted by the Centers for Medicare & Medicaid Services (CMS), not all EHRs are effective at accurately reporting quality measures – and the reasons why are common across multiple systems and facilities.
The project, which took place in 2015, involved 29 hospitals. According to a summary of the project found on QualityNet, CMS’ goal was to assess how well EHRs reported the required data for the Hospital Inpatient Quality Reporting Program (Hospital IQR), as well as evaluate how ready hospitals are to continue submitting data about quality via their EHRs.
Challenges uncovered
CMS examined the quality data recorded in each hospital’s EHR by using a contractor to remotely extract the information from medical records. The agency then compared that data to the quality measures the hospitals reported.
The comparison showed that while details about clinical quality could be pulled from the medical record fairly easily, reporting on quality measures wasn’t so streamlined.
In fact, many participating facilities had difficulty with creating complete files that gave an accurate picture of their clinical quality, especially when comparing them to the raw data directly from their EHRs.
Hospitals identified two big challenges that contributed to this situation:
- Data mapping. Data mapping is the process that allows data fields in an EHR to be matched with the information required for reporting quality measures. Ideally, this process is automatic in an EHR, but many systems didn’t have these capabilities. To make matters worse, certain medical information wasn’t recorded in a specific data field so it could be found easily. Instead, it was entered using various formats, such as through dictation, scanned PDFs and free text notes. These factors meant that hospitals were required to manually pull some of the data needed to report on clinical quality.
- Workflows. The administrative requirements for reporting clinical quality measures don’t mesh well with the average hospital’s workflow. Besides the need to manually pull data to completely report certain measures, other issues added confusion to the reporting process. Example: One quality measure needed the time when a patient left the emergency department. But multiple data fields in the EHR recorded time periods related to a patient’s ED visit, such as the time when an order was generated for a patient, the time an order was signed, the time the patient was placed in a bed and the time a patient left the facility entirely. Facilities didn’t know which time period best aligned with the intent of the measure – and the guesswork slowed down their workflows.
What CMS will do
As a way to address these problems, CMS plans to boost its education and outreach efforts targeted toward hospitals and their efforts with reporting quality measures.
The agency has also modified aspects of existing ED measures, including the Decision to Admit to Hospital Inpatient Grouping Value Set, to make reporting a bit easier for hospitals. In addition, it’s updated the timing for ED encounters to be based solely on a patient’s admission and discharge times, rather than the times when patients first arrive at and leave the facility.
Action steps for hospitals
Per CMS, the biggest takeaway for hospitals from this project is the importance of collaboration and communication when reporting quality measures. Hospitals need to keep an open relationship with their EHR vendors so they can more easily pinpoint barriers within the system that prevent the submission of accurate quality measures – and come up with solutions ASAP.
It’s also key for hospitals to examine staff workflows and address any problems that may bog down the submission process from a user standpoint, such as having to enter redundant information in multiple data fields of an EHR, or the excessive use of free notes where fields aren’t available to capture key health information. Streamlining these issues can help make data mapping more effective when reporting quality measures.
