By Cheryl Mason, Health Language
In our previous blog, we discussed the importance of leveraging administrative data for better quality assessment. As the wrap-up to this standards blog series, I want to look back at Crossing the Quality Chasm as a foundational work for improving the quality of healthcare delivery. This paper called not only for better quality but also for a reduction in the cost of that care--this at a time when the population is aging, technology is evolving, and research is rapidly expanding evidenced-based medicine. At least eight of the 13 recommendations made by the authors of Crossing the Quality Chasm directly involve the collection, aggregation, and actionable use of healthcare data. The remaining five are supporting those objectives through the development of committees that address quality care and reimbursement models, and in training the workforce to meet the increasing needs of an industry that is becoming more and more reliant on data and analytics.
We introduced the major standard terminologies needed to help collect data in a structured way so that we can measure quality, meet meaningful use requirements, and provide accurate information at the point of care. We also touched on value-based care models and how standards such as SNOMED, LOINC, and RxNorm can help your organization aggregate and analyze the data that is found in disparate systems across your enterprise to meet the needs of better reporting and quality measures. We talked about billing and coding standards (ICD-9, ICD-10, and CPT), SNOMED for documenting problem lists in the EHR, LOINC to code laboratory data, RxNorm for pharmaceutical information, and the importance of administrative data like race and ethnicity.
By Cheryl Mason, Health Language
In our previous blog, we discussed the importance of leveraging administrative data for better quality assessment. As the wrap-up to this standards blog series, I want to look back at Crossing the Quality Chasm as a foundational work for improving the quality of healthcare delivery. This paper called not only for better quality but also for a reduction in the cost of that care--this at a time when the population is aging, technology is evolving, and research is rapidly expanding evidenced-based medicine. At least eight of the 13 recommendations made by the authors of Crossing the Quality Chasm directly involve the collection, aggregation, and actionable use of healthcare data. The remaining five are supporting those objectives through the development of committees that address quality care and reimbursement models, and in training the workforce to meet the increasing needs of an industry that is becoming more and more reliant on data and analytics.
We introduced the major standard terminologies needed to help collect data in a structured way so that we can measure quality, meet meaningful use requirements, and provide accurate information at the point of care. We also touched on value-based care models and how standards such as SNOMED, LOINC, and RxNorm can help your organization aggregate and analyze the data that is found in disparate systems across your enterprise to meet the needs of better reporting and quality measures. We talked about billing and coding standards (ICD-9, ICD-10, and CPT), SNOMED for documenting problem lists in the EHR, LOINC to code laboratory data, RxNorm for pharmaceutical information, and the importance of administrative data like race and ethnicity.
The standards (terminologies) necessary for better analytics are created and maintained by different standards bodies. To further complicate things, each terminology is released on a different cycle, in a different format, and with different licensing requirements.
So how are you managing all of this today? Is your data warehouse up to date with your EHR vendor? Is your laboratory information system normalized to LOINC? How many different systems do you have that utilize one of the many medication terminologies? Are you able to update all of your systems easily so that all providers are using valid codes when ICD-10 updates on October 1st of each year? Are your problem lists coded in SNOMED? Are they in alignment with billing information that is coded in ICD-10? (We maintain that map, by the way.)
These are some of the problems we help our clients solve every day. We collect the raw files from the standards bodies, analyze them for changes (and errors), format them into similar structures for ease of use in all of our applications, and release them to clients for use in their systems. We ensure that best practices in data governance are adhered to throughout this process so that clients can trust that they have the most current content and a single source of truth.
Ideally, good data governance and terminology management lead to more efficient and accurate collection of data for easier reporting and analytics. In a 2014 study by Garrindo et al, it was found that many EHRs can capture up to 65% of the elements needed for e-quality reporting. This alone can reduce the time it takes to manually abstract information from charts by as much as half. Much of the remaining information required for e-quality reporting can be found in ancillary applications and free-text fields.
Let Health Language help you capture all of the information found in your many disparate systems in order to lower your costs while increasing patient and provider satisfaction, create a single source of truth for accurate, up-to-date data, improve semantic interoperability, increase productivity, and improve population health and analytics initiatives.
About the Author
Cheryl Mason has over years 25 years of medical industry experience including 10 years of experience in medical informatics. Cheryl received her Master’s degree in Health Informatics from Walden University and has a particular interest in data normalization as it advances interoperability and data analytics in healthcare. Currently, she leads a team of content analysts and subject matter experts at Health Language that consult with clients across the health care spectrum regarding standardized terminologies, data governance, ICD-10 remediation, data normalization, and risk mitigation strategies.
