Interoperability is one of the leading goals in the healthcare industry, but how can we get there? In spite of decades of experience with electronic health records, the lack of semantic interoperability in healthcare has prevented sharing of healthcare data. Often, health data is not comparable, cannot be aggregated, and cannot be used to accurately automate or augment clinical decision making. The Health Information Technology Standards Committee has recommended Logical Observations, Identifiers, Names and Codes (LOINC) as the standard for structured coded assessment instruments and Systemized Nomenclature of Medicine Clinical Terms (SNOMED CT) for appropriate responses (“answers”). This implies that point-of-care measures should be codified using LOINC and SNOMED CT.
Unfortunately, these standard terminologies are only words, and without a sharable sentence structure, or information model, the use of the words will not result in interoperability. For example, a systolic blood pressure (SBP) can be taken on the left arm with a manual cuff. This clinical observation can be sent between systems in a message using one distinct LOINC code. Additionally, it can be sent as a different code for SBP, with additional codes for qualifying observations of body side (left), body location (arm), and blood pressure measurement method (cuff). As humans, you and I know that these are the same measurement, but the computer does not. The solution is to have a detailed clinical model (DCM) that defines the structure of the systolic blood pressure observation.
DCMs provide an approach to structure medical information. A DCM specifies clinical knowledge about small items of information, usually single observations or actions, or small clusters of observations that belong together. They are developed through a combination of expert knowledge, data specification, and standard terminologies. The DCM concept was developed by Stan Huff of Intermountain Healthcare in Salt Lake City, UT. The objective is to build a range of information models so medical data can be used in a consistent manner throughout healthcare settings and across disparate applications. The goal of DCMs is to provide sharing of data, information, decision support, reports, and knowledge to support evidence-based practice and ultimately translates into a higher level of quality care.
There is an international initiative in progress to develop guidelines for the creation of DCMs. The Clinical Information Modeling Initiative (CIMI) is an international collaboration that is dedicated to providing a common format for detailed specifications for the representation of clinical content so that semantically interoperable information may be created and shared. CIMI has been holding meetings in various locations around the world since July 2011. All funding and resources for these meetings have been provided by the participants. The goal of CIMI is to provide freely available models presented in a number of formats, such as Unified Modeling Language (UML), with bindings to the coded terminology. It is a lofty goal, but the collaboration is meeting regularly and making progress.
Technology plays a growing role in clinical documentation. As we move toward electronic documentation, it is important to incorporate structured data capture and modeling into clinical workflow using DCMs and standard terminologies. This provides opportunities to have consistent data and ultimately improve outcomes for patients.
Susan Matney is a Medical Informaticist with 3M Health Information Systems.