- Category: Clinical Data Quality Measures
- Published: Monday, 19 November 2018 20:19
- Written by Clinical Data
- Hits: 20
With the rapid changes to the healthcare industry's handling of clinical data, electronic quality measures are being implemented more frequently. While using electronic measures (e-measures) to handle clinical data can provide a range of important benefits, healthcare providers must also take steps to improve the quality of their clinical data; to improve the clinical data quality measures that are implemented in hospitals and other healthcare facilities.
Otherwise, healthcare providers open themselves up to avoidable risks financially. Forward-thinking healthcare providers will combine an emphasis on quality and accurate data reporting with effective data governance to take full advantage of electronic quality measures.
This is much more simply stated than it ever would be to implement and govern. However, this is not a reason to not move forward, especially because government regulations are also being ramped up to be put in place. No matter what reasoning or justifications might come up to not improve healthcare data and the measuring of it should not be considered. The long-term outcomes for the organization as well as patient care will only suffer the longer that this isn't in place.
Querying Clinical Data
Given the fact that healthcare data quality measures are designed to track and measure the quality of a healthcare provider's services, it is essential to get the data correct. Not only is this important from a practical perspective, it is also necessary due to government agency requests.
The Centers for Medicare and Medicaid services (CMS), along with other state agencies, often requested healthcare providers to take part in clinical quality reporting programs in recent years. These programs, however, were inordinately burdensome to many healthcare providers, thanks to the amount of time, hours and work that went into these reporting programs.
The healthcare providers and professionals ultimately advocated for better reporting processes and programs that would be less burdensome while delivering better results. These calls for change reached a fevered pitch after the introduction of the 2011 Electronic Health Record Incentive Program.
The duplication of quality measures outlined by the Meaningful Use standard and other reporting programs led to confusion, and different CMS agencies would use differing sets of definitions. At the core of this issue there is a key takeaway for medical professionals: the importance of improving clinical data quality and accuracy.
Querying Data Accuracy
Using e-measures, data should be managed correctly from the time it is gathered to the time it is analyzed. As such, data must be recorded accurately from the outset. Measurement errors or inefficient data recording techniques will corrupt the quality of the data at the start.
E-measures are typically calculated using data that is collected in a certified EHR technology (CEHRT). If an important element of e-measure data is not included in the CEHRT, the accuracy of the e-measures will be negatively impacted due to the missing data.
Ensuring that all pertinent clinical quality measure information is included in the CEHRT from the outset will allow the EHR to accurately calculate the data. To achieve this, healthcare providers might wish to consider updating their interfaces between the CEHRT and any department-specific modules.
At any rate, whether this tactic is implemented or an enterprise data warehouse is used to create complete sets of data, ensuring that no important data is missing will go a long way towards improving clinical data quality.
Querying Data Integrity
While having a complete set of e-measure data is essential, e-measure inaccuracies can also arise from issues caused by a lack of data integrity. A common cause of data issues pertaining to integrity involves the variation of workflow on the part of healthcare providers.
E-measures should account for variations in how a patient is processed, for example, to truly have accurate data. An EHR that is running on autopilot must take into account the various possibilities of how a patient is processed and treated to ensure that the data's integrity is maintained. Without this, accuracy is affected and the reliability of the data is compromised.
The Importance of Improving Clinical Data Quality
Inaccurate e-measures prevent healthcare providers from accurately assessing their performance, and in turn, this can have a deleterious effect on the services provided to patients. Effective healthcare services depend on accurate data and statistics that can be used to improve provider performance.
Moreover, it is essential that healthcare providers use accurate data since inaccurate data can compromise the health and treatment of patients. By improving data accuracy and integrity, healthcare providers will improve their practices and the lives of their patients in equal measure and will cut the overall costs associated with providing healthcare. Do not underestimate the importance of data in the health and healthcare, nor the possibilities that are constantly coming up as improvements are made.
You are welcome to research this topic further and talk to healthcare analytics specialists to learn more and see what is being used on the market.