Health Data Analytics & SNOMED CT | Prof. Sarbadhikari
Unlock the power of coding data analytics in healthcare. Discover how Health facts management (HIM) professionals are leveraging data to improve productivity and accuracy in coding. Explore the crucial role of SNOMED CT in enhancing patient care thru information retrieval and analysis of clinical data. Learn about the capabilities of this clinical terminology system, including its use in querying records, supporting clinical queries, and facilitating logic-based inferencing.Understand how SNOMED CT enhances analytics tasks for individual patients, population health, and clinical research. Data architecture options and integrated queries are also discussed. News Directory 3 provides timely insights for healthcare professionals. Interested in unlocking more insights? Discover what’s next in the world of healthcare data.
Health facts management (HIM) professionals are increasingly using coding data analytics to monitor coding teams and demonstrate the value of ongoing training. This commitment aims to improve coding performance in terms of both productivity and accuracy.
While electronic medical records (EMRs) collect meaningful data, only a fraction may be relevant to current medical practices and analytics. New applications in genomics and the Internet of Medical Things (IoMT) are driving the need for a big data approach in healthcare.
Healthcare analytics encompasses data from claims, costs, pharmaceutical research, clinical records and patient behavior. Data literacy is crucial for HIM professionals to interpret and abstract data for analysis. This data supports reimbursement, claims, quality reporting, disease management and best practices.
Coding productivity is affected by the type of EHR used, the number of systems accessed, clinical documentation enhancement initiatives, physician query turnaround time and non-coding tasks assigned to teams.
Accuracy, however, should not be sacrificed for productivity, as it can lead to claim denials and financial repercussions. A balance between productivity and accuracy is essential, with coding audits and denial analysis being common methods for assessing accuracy.
The World Health Institution’s fully electronic 11th edition of the International Statistical Classification of Diseases (ICD-11) contains 55,000 codes, a significant increase from the 14,400 in ICD-10.
SNOMED CT, a clinical terminology system, contains 311,000 clinical concepts with descriptions and relationships. It supports effective clinical information representation and retrieval. SNOMED CT is available for use in India at no cost.
According to SNOMED CT, analytics involves discovering meaningful information from healthcare data to describe, predict or improve clinical and business performance. Using SNOMED CT for analytics enhances patient care through information retrieval, guideline integration and retrospective searches. It also improves population health through epidemiology monitoring, research and identification of patient groups. Cost-effective care is supported by minimizing errors,reducing duplication and auditing clinical services.
SNOMED CT enables clinical records to be queried by grouping concepts, using formal meanings, testing for membership in subsets and using local dialects. It also facilitates clinical queries over heterogeneous data, analysis of records without original SNOMED CT content, logic-based inferencing, linking to guidelines and mapping to classifications like ICD-10.
Analytics tasks enhanced by SNOMED CT include point-of-care analytics for individual patients,population-based analytics for public health and clinical research to improve assessment and treatment guidelines.
Data architecture options for using SNOMED CT in analytics include direct analysis over patient records, data warehouses, Virtual Health Records (VHR), distributed storage and processing, or a combination of these.
Integrated queries over SNOMED CT-enabled data are essential for maximizing benefits. SNOMED International is developing languages to support various uses of SNOMED CT,and user interfaces are being designed to make clinical querying more accessible. Data visualization and analysis tools are becoming more prevalent as SNOMED CT content is increasingly utilized.
Predictive analysis tools are providing billing and coding companies with higher returns on data mining. HIM professionals should advocate for the adoption of SNOMED-CT enabled systems to improve outcomes through better-informed analysis.
References:
- https://www.healthcatalyst.com/big-data-in-healthcare-made-simple
- https://www.healthcareittoday.com/2017/11/15/opening-the-door-to-data-analytics-in-medical-coding-him-scene/
- https://www.osplabs.com/insights/data-mining-in-medical-coding-and-billing/
- https://bok.ahima.org/doc?oid=302591#.Xj-2xvkzbDc
- https://confluence.ihtsdotools.org/display/DOCANLYT/data+Analytics+with+SNOMED+CT
- https://confluence.ihtsdotools.org/display/DOC
- https://jbcr.net.in/JBCR-VOL-6-issue-1-2019-20/current-issues-volume-VI-issue-1-1.html