By Beverly Holley, Health Language
The introduction of industry standards such as SNOMED CT, ICD-10, LOINC, and RxNorm is an important step toward achieving the goals of interoperability and information sharing. Yet healthcare organizations still face notable challenges to laying the best frameworks for normalizing data to these standards. Since there is no one standard that addresses all healthcare information, clinical and financial data must be “cleaned” and appropriately mapped to a single source of truth to remove semantic ambiguity.
Data used in the behavioral health field from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) is no exception. As the universal authority for psychiatric diagnosis in the United States, DSM-5 information is critical to populating problem lists, enhancing clinical decision support, and supporting population health initiatives. Problem lists in particular have been a focal point of regulatory initiatives to improve electronic exchange of critical patient data by aligning multi-disciplinary treatment efforts as patients are triaged from unit to unit or provider to provider.
The introduction of industry standards such as SNOMED CT, ICD-10, LOINC, and RxNorm is an important step toward achieving the goals of interoperability and information sharing. Yet healthcare organizations still face notable challenges to laying the best frameworks for normalizing data to these standards. Since there is no one standard that addresses all healthcare information, clinical and financial data must be “cleaned” and appropriately mapped to a single source of truth to remove semantic ambiguity.
Data used in the behavioral health field from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) is no exception. As the universal authority for psychiatric diagnosis in the United States, DSM-5 information is critical to populating problem lists, enhancing clinical decision support, and supporting population health initiatives. Problem lists in particular have been a focal point of regulatory initiatives to improve electronic exchange of critical patient data by aligning multi-disciplinary treatment efforts as patients are triaged from unit to unit or provider to provider.
As such, DSM-5 codes must be accurately mapped to SNOMED CT to ensure accuracy and completeness of problem list information. Here is a look at the top three reasons DSM-5 mapping strategies matter to your organization:
- Clarity and consistency
Diagnoses form the basis for care planning, yet clinicians often describe patient conditions in different ways, which can limit effective capture of data in health IT systems. When problem lists are inaccurate or incomplete, multi-disciplinary clinical teams lack the information needed to optimize treatment. For instance, a patient suffering from diabetes may also have a mental health diagnosis such as depression or anxiety that can impact engagement in care and medication adherence. If the latter is not appropriately captured on the problem list, care teams lack important insights. Mapping DSM-5 appropriately to SNOMED CT ensures a uniform method of expressing clinical conditions, and improving problem list completeness and accuracy.
- Collaborative care
New care delivery models increasingly promote collaborative care across the continuum. When clinical systems accurately map DSM-5 to SNOMED CT, information sharing is enhanced, and healthcare organizations are better positioned to achieve a longitudinal health record. SNOMED CT bridges the communications gap for problems and diagnosis and helps overcome fragmented care delivery.
- Enhanced analytics
Value-based care demands that providers and payers elevate care management strategies to better address chronic disease and population health. Accurate mapping of DSM-5 data ensures analytics initiatives are more accurate and complete. The use of a standard clinical terminology across the enterprise and care continuum simplifies queries and resulting reports. In addition, the increased specificity of SNOMED CT compared to the ICD billing code sets improves the granularity of the analysis. For instance, healthcare organizations achieve capabilities that allow for more sophisticated queries such as identifying all patients who have both heart disease and anxiety.
Increasingly, health system IT departments find that manual efforts to address mapping initiatives like DSM-5 exhaust and consume resources such that it is impossible to create a sustainable strategy. That’s why many turn to the automation of Health Language solutions to establish crosswalks that ensure enterprise data standardization and normalization around a single source of truth.
