This article was originally published in September 2019, and updated in May 2022
Welcome to the final installment of our four-part blog series dedicated to the importance of data mapping. If you are just now tuning in, be sure to check out the last three blogs discussing the importance of lab, medication, and allergy mapping.
The topic of this fourth and final blog post is the importance of mapping problem lists and diagnoses across your electronic health records (EHR) system and other health IT systems, specifically the following:
- The clinical and financial benefits of accurately mapping problems and diagnoses
- Essential elements of a successful mapping strategy
- How Health Language solutions can help overcome these challenges
- Real client uses cases and successful outcomes
Why is mapping patient problem lists and diagnoses important?
Accurate documentation of a patient’s problem list is critical for improving clinical outcomes and optimizing reimbursement in today’s healthcare environment. Accurately capturing this information at the point of care is especially crucial as this information has the power to impact the safety of patients and multiple downstream healthcare operations, from financial planning and billing to care coordination and population health initiatives.
When compiled accurately and thoroughly, problem lists have the potential to immediately align multi-disciplinary treatment efforts as patients are triaged from unit to unit or provider to provider. Because of this, problem lists have been a primary focus area for establishing regulatory requirements around improving electronic exchange of critical patient data.
Clinical and financial leaders must prioritize proper documentation to ensure accuracy of patient problem lists, to avoid negative downstream impacts related to analytics, clinical decision support, and healthcare compliance and reimbursement. Incomplete and inaccurate documentation puts healthcare organizations at risk of experiencing:
- Inaccurate diagnoses due to lack of specificity
- Incomplete data related to patient conditions for analytics initiatives
- Ineffective patient safety alerts
- Decrease in productivity with increased number of coder queries
- Inability to accurately report quality measures
The benefit of mapping SNOMED to ICD-10
To establish a single source of terminology truth and promote semantic interoperability, diagnoses and problems must be mapped to an industry standard. Standardizing the way health IT systems read and recognize conditions enables consistency of documentation within electronic health records and increases the likelihood of accurate code capture and data sharing.
As the industry health IT standard for coding problems and diagnoses, SNOMED CT® (Systematized Nomenclature of Medicine Clinical Terms), is the world’s most comprehensive multilingual clinical terminology, encompassing more than 300,000 codes, terms, synonyms, and definitions for human and non-human concepts. As with its well-known coding counterpart, ICD-10, a key benefit of SNOMED CT is the granularity of its concepts, allowing for greater specificity in the documentation of patient issues.
When mapped to other international standards such as ICD-10, SNOMED CT can promote revenue cycle efficiency and accuracy, eliminating the need for identifying the correct code from ICD-10’s expansive library of more than 90,000 codes.
How Health Language solutions help overcome data challenges
The Health Language Data Normalization solution combines the efficiency of machine learning with the deep clinical knowledge of our industry experts to help organizations address the burdensome, error-prone processes often managed across numerous spreadsheets and departments. Clinical auto-mapping ensures disparate problem, diagnosis, and procedure data is mapped to industry standards such as SNOMED CT, ICD-10, and CPT®, ensuring the highest map rates and accuracy. In addition to standard and proprietary maps, the Wolters Kluwer Health Language clinical content team is available to help design custom maps based on an organization’s specific needs.
Complementing mapping and data normalization solutions, the Health Language Clinical Interface Terminology solution is uniquely positioned in the marketplace to streamline problem and diagnosis searches for clinicians. Our content database makes it easy for clinicians to record problems and diagnoses by providing a comprehensive library of clinicians’ commonly used synonyms and abbreviations, such as afib, ankfx, and elevated BP, each recognized and mapped to an ICD-10 code. In addition, the solution leverages coding and clinical attributes to ensure clinicians are aware of additional specificity needed to code at the highest level of reimbursement.
Best-in-class examples of clinical data mapping
Mapping data to help clinicians determine care plans:
A leading health IT vendor providing educational and point-of-care resources for physicians, nurses, and other allied health professionals is currently using Health Language mapping solutions to map disease and problem terms to both ICD-10 and SNOMED. Additionally, this client is leveraging the HL solution to auto-map Hospital Acquired Conditions to ICD-9, ICD-10, and SNOMED, and signs and symptoms to SNOMED. This data normalization framework maps diagnoses to appropriate concepts, which helps clinicians determine the appropriate treatment and care plan for the patient.
Tagging and mapping knowledge library documents to improve end-user experience:
A leading patient engagement platform vendor uses the Health Language Data Normalization Solution to make sure each document within their own knowledge library is tagged with and mapped to medical terminologies such as, ICD-9, CPT, HCPCS, SNOMED, and MS-DRG. With these maps, the client can efficiently and accurately provision the appropriate patient reference materials and educational information based on the specific diagnosis.
Enabling clinical decision support tools:
Another health IT vendor is leveraging Health Language Solutions to enable semantic interoperability through mapping their own clients’ disparate data and custom terminologies to industry recognized standards such as ICD-9, ICD-10, and SNOMED. By integrating the Health Language data normalization functionality, the organization can reduce duplicate terms, accurately map clinical quality measures, and power optimal clinical decision support tools.
Thank you for reading our four-part data mapping blog series! To learn more about how the Health Language Data Normalization Solution can help your organization, contact a Health Language solutions expert today.
SNOMED CT® is a registered trademark of the International Health Terminology Standards Development Organisation (IHTSDO).
CPT® is a registered trademark of the American Medical Association (AMA).
