MMI HIT Integration, Interoperability, & Standards Group 1 Project SONMED CT November 27, 2012

MMI 405 - HIT Integration, Interoperability, & Standards Group 1 Project SONMED CT November 27, 2012 Group 1 Members - Cooper, Jenna - Murray, James - Danford, Jeff - Nowak, Mike - Gowda, Ramesh - Prokic, Michael - Kaplan, Lindsay - Thanawala, Ruchi

Table of Contents 1. Abstract 3 2. Introduction 3 a. Granularity 3 b. Interoperability 3 c. Mapping 3 3. Development Process 4 a. Stage 1: Start-up and Initiation 4 b. Stage 2: Terminology Design 4 c. Stage 3: Production 4 d. Stage 4: Alpha Test 5 e. Stage 5: Beta Test 5 f. Stage 6: Release Process 5 4. Mapping of other terminologies to SNOMED CT 5 5. Implementation Challenges and Benefits 6 a. Quality Challenges 6 b. Implementation Challenges 7 c. Other Challenges 7 d. Benefits 7 6. Cost of Implementation and Meaningful Use 7 7. Implementation - Best Practices 8 8. Use Cases 9 9. Novel Techniques 10 a. Semantic Enhanced Auto-completion 10 b. Improving Mapping of User-specified terms to SNOMED CT 11 through Semantic Structure c. Pruning of Large Ontologies based on Use 12 10. Conclusions 13 11. References 14 12. Appendix 16 MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 2

1. Abstract United States Healthcare industry is undergoing tremendous transformation due to the Affordable Care Act (ACA) and Health Information Technology for Economic and Clinical Health Act (HITECH Act). Health information technology (HIT) has become a key enabler in implementing concepts such as pay-forperformance, coordinated care models (e.g. ACO, PCMH), and for improving quality of care, and patient safety, while achieving efficiencies. Consistent clinical data capture and sharing across care settings is of core significance in achieving some of the ACA objectives. Terminology standards such as SNOMED CT have shown promise with a comprehensive vocabulary and adequate granularity to capture clinical information clearly and consistently. This research paper explores SNOMED CT implementation issues, mapping to other terminology standards, how the standard is currently deployed, novel techniques at user-level data capturing, and future potential applications of SNOMED CT. 2. Introduction SNOMED CT (Systematized Nomenclature of Medicine -- Clinical Terms) is a comprehensive, standardized, multilingual vocabulary of clinical terminology that is used by physicians and other healthcare providers for the electronic exchange of clinical health information. The system includes signs and symptoms of diseases, diagnoses and procedures to represent the full integration of medical information in an electronic medical record. SNOMED CT is both a coding scheme, identifying concepts and terms, and a multidimensional classification, enabling concepts to be related to each other, grouped, and analyzed according to different criteria. Numeric codes (the SNOMED CT identifier SCTID) identify every instance of the three core building blocks: concepts, descriptions, and relationships. Each concept represents a single specific meaning; each description associates a single term with a concept and each relationship represent a logical relationship between two concepts (T. Benson, 2010). The International Health Terminology Standards Development Organization (IHTSDO) releases updates to the International Release of SNOMED CT twice per year (January and July) to ensure that the terminology reflects current clinical knowledge and evolving user needs. The College of American Pathologists (CAP) develops and maintains SNOMED CT for the IHTSDO (CAP, 2012). The National Library of Medicine (NLM) has the rights and responsibilities to maintain and distribute SNOMED CT in U.S, which is available to anyone including EHR vendors, to use free of cost. NLM pays an annual fee of about $6 million to IHTSDO. Most countries are using SNOMED as the clinical terminology of choice. For example, in Canada, SNOMED CT is a mandatory for all clinical documentation. In US, adoption of SNOMED CT is required of all health care providers by 2015 in order to qualify for meaningful use certification. Granularity: In January 2009, the terminology contained over 310,000 active concepts, 990,000 English descriptions, and 1.38 million relationships (Search Health IT, 2012). The American College of Physicians (ACP) prefers SNOMED CT to International Statistical Classification of Diseases and Related Health Problems 10th revision (ICD-10) because of its clinical usefulness. "While it is clear that coding with a classification system such as ICD-10 has benefits when it comes to compiling data for secondary purposes, it is generally acknowledged that a reference terminology such as SNOMED CT is much more effective for accurately capturing the nuances of health conditions and clinical care," the ACP letter notes (ACP, 2012) Interoperability: By using numeric codes to represent medical concepts, SNOMED CT provides a standard by which medical conditions and symptoms can be referred, eliminating the confusion that may result from the use of regional or colloquial terms. The numerical scheme facilitates indexed retrieval of clinical information at the point of care, as well as for management, surveillance, and research activities (T. Benson, 2010). It also facilitates the exchange of clinical information among disparate healthcare providers and electronic health records (EHR) systems. The Health Information Technology (IT) Standards Committee (HITSC) has recommended SNOMED CT as one of the vocabulary standards to report quality measure data. Current quality measures are being updated and new measures developed to the emeasure format and include reference to SNOMED CT value sets MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 3

(IHTSDO, 2012). Mapping: Despite emerging and promising cooperative efforts, there remain many overlapping and competing standards addressing all aspects of healthcare systems. The most common approach has been to allow the coexistence of overlapping standards by supporting mapping efforts between the standards. The Unified Medical Language System (UMLS) in the U.S. has facilitated the mapping of various terminologies and coding systems, in order to normalize data within an electronic system. An example is SNOMED CT to ICD cross maps which are able to reuse the data that was captured via SNOMED CT to generate ICD codes for billing and statistical reports (Dennis Lee, et. al, 2012). Tools like I-MAGIC (Interactive Map-Assisted Generation of ICD Codes) generate interactively ICD-10-CM codes from SNOMED CT terms. 3. Development Process and Methodology of SNOMED CT A large number of healthcare organizations and professionals were involved in the initial development of SNOMED CT. It is an open process that guides the development of the content. As a clinical terminology will never be perfect, SNOMED CT recognizes that the design and development must be agile and flexible to continue to function. SNOMED is based on six main points: 1. Evolution without pre-ordained design 2. Accumulation of design 3. Heterogeneity 4. Participatory consensus-based approach 5. Semantics-based concurrency control 6. Configuration management (K. Spackman and G. Reynoso, 2004) The development consists of six stages: Stage 1: Start-up and Initiation This first stage involved the U.S. and U.K. becoming active on the SNOMED International Authority, the Editorial Board and the design team. This stage produced a prototype design for mapping between SNOMED Reference Terminology (SNOMED RT) and Clinical Terms Version 3 (CTV3). A tool was developed for this process. Stage 2: Terminology Design Stage 2 saw the development of multiple consultation documents from a technical working group to describe the functionality and structure of SNOMED CT. Documents were created describing goals, core structure, subset mechanisms, analysis of requirements, cross mapping tables, and history mechanisms. Once approved by the editorial board, these documents were made public for review and comment. Comments from users were taken into consideration and then used to refine the documents. These documents are still available for public review and comment on the IHTSDO's SNOMED CT website (SNOMED, 2012).This phase was also a merger of the upper level hierarchies of CTV3 and SNOMED RT, an important step to identify any legacy issues. Part of this process was to review existing as well as proposed attributes in both sources. Those attributes that did not merge easily were transformed. A consensus process was also developed to further refine attributes and evaluate changes. An advanced quality assurance plan was utilized to leverage past experiences (Schulz E, Barrett J, Price C, 1997). Stage 3: Production The actual merger of SNOMED RT and CTV3, a complex process, happened in Stage 3, which was divided into three phases. The first phase, focused on Description Mapping, tried to identify equivalent concepts and create maps between them. Concept conflicts were flagged to be resolved and specific software was created to allow editors to review the issues. General editors and senior editors worked to complete mapping with domain specialists. A content working group worked to resolve further conflicts that may exist between the original description and the mapping. Once the description mapping was complete and all conflicts were resolved, the phase of Concept Modeling began. Each SNOMED CT concept emerges from the earlier phase with concept MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 4

interrelationships from one or more sources. This phase involved the removal of redundant relationships and then moved to an editorial review of each concept for attributes. The final phase of the production stage was Terminology Refinement. In this phase concepts were prepared for implementation in a clinical system. This phase included assigning fully specified names and preferred names, adding non-definitional attributes, developing subsets, adding new concepts, and overall quality control. At the end of this stage the content of SNOMED CT is ready for implementation. Stage 4: Alpha Test An alpha test stage was authorized for prototypes in six clinical domains, orbital region procedures, orbital region disorders, urinary system disorders, urinary system procedures, respiratory system disorders, and breast neoplasms. The concepts in these domains were reviewed and evaluated. Developers used this information to further refine the content of SNOMED CT. Stage 5: Beta Test The beta test phase expanded the content that was available to developers to fully populated tables. This allowed for more complete feedback from the users. Information from this phase led to the initial release version of SNOMED CT. Stage 6: Release Process The final stage of development is the release. The tables of fully populated data went through a final quality control procedure for any corrections or revisions needed for data integrity. The data was then made available for download. The development and maintenance of SNOMED was committed to several basic principles. These were commitments to (1) clinical integrity and quality, (2) usefulness for support of patient care, patient safety, audit, research, analysis, and planning, (3) scientific validation, (4) sustainability, with direct input from stakeholders, (5) widespread adoption, (6) protection of legacy data, and (7) accommodation of local needs (K. Spackman and G. Reynoso, 2004). The merged hierarchy and open access to the information provides both flexibility and the ability to continue to refine based on the user base feedback. While the development process can never necessarily meet perfection and at times the principles mentioned can create some challenges, the development process remains flexible and can adapt to the changing needs. 4. Mapping of other terminologies to SNOMED CT Hospitals have been using computers to support operations for over 40 years. These systems were often proprietary or home grown. The primary use of the computer systems was to support financial operations and billing. With the move to the electronic medical record (EMR) hospitals have placed a greater importance on clinical data. This move has intensified with the implementation of Meaningful Use and the need for interoperability. While the major task of a hospital computer system is billing, there is greater desire to use information for clinical decisions. Healthcare organizations want to use their information systems for quality initiatives, clinical decision, epidemiological studies and research. They will also need to export their data to insurance companies, Medicare and data exchanges. In order to accomplish these tasks an industry wide standard is needed. The key to this standard is the need to satisfy both billing and clinical management. SNOMED CT has become the international standard clinical vocabulary. Therefore, mapping systems to SNOMED CT is important for the interpretability for both internal hospital systems and larger external data repositories. Mapping with SNOMED CT falls into three main categories 1) legacy systems, 2) current systems and 3) future systems. Legacy systems still make up a considerable part of the industry s electronic systems. Most of these systems involve ancillary systems involving laboratory, pharmacy and radiological data. Historically, these systems use a diverse set of terminologies. It is therefore important to create mapping solutions so that an EMR based base on SNOMED CT can still communicate with clinical data from these legacy systems. As mentioned earlier, billing systems are very important to hospital operations. The major current vocabulary used today in hospital billing is the ICD-9. The first ICD classification was established in 1949 by The International Classification of Diseases. This version was named ICD-6. It was followed by ICD-7 (1959), ICD-8 (1965) and ICD-9 (1975). The ICD classification has been the standard disease classification in the world for many years. With the emerging presence of SNOMED CT it is extremely important to map both MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 5

vocabularies. The two vocabularies were successfully mapped in 2005. This mapping included over 93,000 concepts. ICD-10 is the newest version of this vocabulary. It is currently being used in several countries and set to become the standard in the United States by the year 2014. The goal of ICD-10 mapping is to support semiautomated generation of ICD-10 codes from clinical data encoded in SNOMED CT for reimbursement and statistical purposes. UMLS (2012) There are a number of issues that need to be addressed when mapping vocabularies. Mapping is based on a large set of rules. These rules can get very complex due to the nature of disease states. There has to be agreement on mapping rules and source domains. It is important to involve Subject Matter Experts (SMEs) during mapping. Mapping is time consuming and validating mapping is very critical. Organizations need an educated viewer to validate the mapping to ensure semantic mapping. It is expected that there will many functionality gaps. It is estimated that initially up to 40% will not be mapped in a 1-to-1ratio. These domains include clinical findings such as disorders, context dependent categories such as family history and reason for visit. There are a number of questions that will need to be addressed when matching vocabularies. What do you do when they do not match? In this case which vocabulary become the primary driver, SNOMED CT or the mapped vocabulary (ICD-10)? What happens when incorrect information from mapping ends up in the patient s record? Is the source of data proprietary or public? Does there need to be a reorganization of SNOMED CT codes to support reimbursement? These questions will need to be addressed in order for there to be a successful match between the vocabularies. The move to a matched SNOMED CT and ICD-10 vocabulary can have a major impact on the creation of a universal standard for the EMR. By having a mapped standard you will have a system that can meet billing requirements, clinical decision support and will be interchangeable with national systems. In addition, a move to the new SNOMED CT and ICD-10 mapping solution can create an interaction vocabulary. The key to an interactive mapped vocabulary is the use of SNOMED CT embedded maps. These maps can be encoded in problem lists. Since the maps are embedded in the program clinicians can have access to the lists in real time. These maps can also support coding by suggesting ICD codes based on SNOMED CT encoded problem lists. The incorporation of real time mapping would be an important step forward for clinical decision making. The next version of ICD codes (ICD-11 in the year 2015) structures resemble SNOMED CT and are expected to provide better mapping between SNOMED CT terms and ICD-11. 5. Implementation Challenges and Benefits Several challenges exist in the implementation of SNOMED CT. Despite these, there have been many benefits cited in relation to its implementation. Looking at the implementation of SNOMED CT in clinical settings was the focus of a recently published survey by the Journal of Biomedical Informatics. Rather than focusing on topics such as the technical aspects and content coverage of SNOMED CT, this survey focused specifically on SNOMED CT implementation issues such as design, use, and maintenance to better understand its implementation in clinical settings (Lee, et al., 2012). The survey included interviews and results from thirteen different organizations in the implementation of SNOMED CT. While the number of interviews was small, the questions used in the interviews were thorough and provided valuable information into how SNOMED CT is being implemented in a variety of clinical settings, including: academic, government, vendor, and healthcare enterprises. A variety of clinical domains were also included among the different settings of those interviewed, including: hospital-wide, intensive care, primary care, ambulatory care, interdisciplinary practice, palliative care, and personal health records. The use of SNOMED CT ranged from being in the developmental stages to being in the midst of a pilot program to new implementations to production of several years or more. Through the interviews in the study, challenges and benefits of using SNOMED CT were highlighted which not only provide valuable information about current implementation practices but also help guide future implementations of SNOMED CT in clinical settings. Challenges: Challenges related to implementation of SNOMED CT were categorized into three areas: quality challenges; design, use, and maintenance challenges; and other challenges. Quality Challenges: Content coverage was not a problem incurred by most of the organizations, however some of the interviewees MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 6

did cite that they found SNOMED CT was missing terms, medications, and ingredients when used within their clinical information systems (Lee, et al., 2012). Additionally, hierarchical relationships and ambiguity of terms were other categories posing challenges. A specific example included different concepts for a vascular anomaly of the eye with difficulty distinguishing the disorders between one concept that referred to a congenital disorder with another referring to an acquired disorder. (Lee, et al., 2012). This will continue to be a challenge as new topics, concepts, and medications develop, however this should be helped by the fact that there are over 300,000 active concepts, nearly 1 million descriptions and over 1 million links or semantic relationships between SNOMED CT concepts as well as revisions of SNOMED CT released twice a year (Kostick, 2012). Syntactical inconsistencies as well as differing linguistic styles and acronym usage were also noted; but as they are discovered, these can be remedied with future revisions (Lee, et al., 2012). Implementation Challenges: Post-coordination, subsets, and data retrieval were three areas of concern related to implementation among the interviewees in the survey. The interviewees did not have a good strategy on how to design a post-coordination interface that was intuitive and unobtrusive (Lee, et al., 2012). It was found that at times the clinicians did not want to split a long input into separate terms that would be recognized by the SNOMED CT concepts. Creating subsets, especially for broad domains, such as a reason for admission, was also noted as a challenge. The organizations interviewed thought this would be helped by additional guidance by the International Health Terminology Standards Development Organization (IHTSDO) and by using a domain as restricted as possible and working towards more complex ones (Lee, et al., 2012). While biannual revisions of SNOMED CT are helpful in many ways, clinicians thought it was challenging at times for data retrieval. They stated that from the clinical perspective, answers to their queries were changing with new releases, and at times clinicians did not find their expected results when using updated versions of SNOMED CT. Other Challenges: In addition to quality and implementation challenges, other challenges included a resistance to change by some clinicians, coding granularity, and policies. Resistance to change could be overcome by providing clinicians with more information about SNOMED CT, including the benefits of using it as well as deficiencies of a clinician s current coding scheme (Lee, et al., 2012). Respondents pointed out that despite a substantial concept list provided by SNOMED CT, a codification of all clinical data is likely impossible and that free text still needs to exist, such as in narratives for consults or referral letters. Policies or lack thereof posed another challenge. Some clinicians were reluctant to implement SNOMED CT if there was not a government mandate to do so. Benefits: Despite the many challenges that came to the forefront in the survey about the implementation of SNOMED CT in clinical practice, there were also many benefits to the system that were recognized. Direct data entry, data reuse, content coverage and subset development, and legibility were four main areas noted to be benefits of implementing SNOMED CT (Lee, et al., 2012). Due to the large number of synonyms in SNOMED CT, clinicians were typically able to find an exact diagnosis with a corresponding code versus having to use a terminology analyst who reviews free text to determine an appropriate code. Additionally, mapping of SNOMED CT with ICD codes allowed for the reuse of data to help generate billing and statistical reports (Lee, et al., 2012). Data reuse also allowed for the identification of patients who could be recommended for clinical trials. Although one challenge with SNOMED CT was the number of concepts in existence with the constant need for new disorders, diagnoses, medications, and other concepts, the survey respondents noted that SNOMED CT did provide the best content coverage for their use cases compared to other terminologies ) (Lee, et al., 2012). While not as significant as some benefits, the legibility of a patient record when using SNOMED CT within a clinical information system was reported as being beneficial, especially for clinicians switching directly from paper-based documentation to a clinical information system with SNOMED CT. Another benefit to clinicians that was discussed with Deb Konicek (2012) is that SNOMED CT is designed to be a magic behind the scenes type of application; even though the terminology is used in clinical practice, it is used within an organization s clinical information system and the technicalities of SNOMED CT are basically invisible to the clinician. 6. Cost of Implementation and Meaningful Use MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 7

Understanding the significance of the use of standardized terminology to maintain interoperability and the terminology implications are essential within the new Meaningful Use reporting structure. The NLM has engineered and designated a subset of SNOMED CT specifically for use in problem lists. An educational set of coordinated work products formed a solid foundation for future standards development, such as adopting SNOMED CT as the standard vocabulary for documenting patient problems. According to the Journal of AHIMA(American Health Information Management Association), Reporting requirements for the problem list for the stage one Meaningful Use requirements state the provider must maintain an up to date problem list of current and active diagnoses based on ICD-9-CM or SNOMED-CT for 80% of patients (80% of what group if 80% of all patients just have to have at least one coded problem), and 80% of all patients have to have at least one coded problem as opposed to their entire problem list coded." (AHIMA, 2012). This requirement will force the providers to place all of their patients in a shared dictionary through the coding process. This standardization of terminology in the problem list will permit clinicians to accurately support medical decision making and aid interoperability of health information exchange with internal and external associates. According to HealthIT News which is published through HIMSS (Healthcare Information and Management Systems Society), the implementation of SNOMED CT will be a requirement by 2015 for receiving money from the recovery stimulus. The HIT Standards Committee endorsed recommendations to call for SNOMED CT for physician's clinical observations by 2015. In 2010, providers needed to use ICD-9 or SNOMED CT to qualify, and in 2013 they must use ICD-10 or SNOMED CT. (Health IT News, 2012). In the proposed Stage Two Regulations, SNOMED will be used not only for problem lists but will also include the coding of medications, lab data and immunizations. It has been acknowledged that SNOMED CT is much more effective for precisely identifying specific health conditions and monitoring clinical care. Many in the field of IT are pushing to replace ICD in its entirety with SNOMED CT. Realization of the potential benefits of SNOMED is dependent upon proper implementation, deployment and use. Support for multiple levels of granularity allows SNOMED CT to be used to represent clinical data at a level of detail that is suitable for a variety of different uses. Whether a practice is gearing up for stage one or stage two of Meaningful Use they will be utilizing a software application that can be used in conjunction with SNOMED. SNOMED CT will provide the standard for clinical information. The standard terminology will improve analysis and provide physicians, their patients, administration, software developers, payers and researchers with more complete information in regards to the healthcare process and clinical outcomes. 7. Implementation Best Practices Due to its unparalleled ability to represent clinical data in a longitudinal context, SNOMED CT is the preferred clinical reference terminology and is currently reported to be in use in over fifty countries around the world. To date however there have been no guides published recommending best practices for effective SNOMED implementations and, until recently, very few published implementation details. This situation changed just this year with the survey of SNOMED implementations performed by Lee, et al. (2012). In their study the authors reviewed the experiences reported by fourteen different SNOMED implementations by a variety of users (academic institutions, healthcare enterprises, government and vendors) from around the world. The survey documents the specific challenges noted by the respondents and concludes with a list of recommended factors for a successful implementation. The most important best-practice recommendation is the need to completely understand the technical details of SNOMED CT, including its limitations. A strong multi-disciplinary team of clinical and technical experts is required to achieve this understanding and to design a system that will leverage the strengths of SNOMED CT while minimizing and accounting for its shortcomings. Regarding accounting for shortcomings, it is suggested that the implementation include creating extensions to the SNOMED CT code set, and that these extensions be formally submitted for review and potential inclusion in later SNOMED CT releases. While the recommendation to include clinical and IT professionals is common to any HIT project, the reason for recommending it here is new. One of the primary challenges identified in the survey was a resistance to change, so here the desired goal is to allow the clinicians to understand the value of the transition to SNOMED and to gain their support going forward post-design. MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 8

Another best-practice recommendation is the need to provide a simple interface for the clinical users so that the relevant clinical data can be found quickly and easily. Ironically, the depth and breadth of the SNOMED code set that makes it uniquely qualified to improve clinical documentation also makes it exceedingly complicated to use, particularly for users unfamiliar with it. Best-practice recommendations for simplifying the interface included using Google-like autocomplete functionality for characters entered by the user and free-text searches on complete phrases, and to create subsets to limit search responses to values considered valid in the implementation. Drop-down lists and hierarchical searches are not recommended due to the time required to drill down to the desired term. Further, it is recommended that the interface be created using search terms and concepts drawn from the current system. The goal here is to again make the system easy to understand for the users while simultaneously masking the complexity of SNOMED CT. Using an interface that is not dependent on SNOMED terminology also has the benefit of making maintenance easier. Updated versions of SNOMED CT can be implemented via mapping and will not require changes to what the users see. The final best-practice recommendations address storing and retrieving clinical data. It is recommended that data be stored using the terminology from the interface instead of using SNOMED CT terms. The idea behind this recommendation is that it will allow for backward compatibility with clinical data entered preimplementation and eliminates the need for a data conversion, and it again leverages the ability to update the SNOMED CT version without impacting the user interface. Consideration should also be given to how the data can be retrieved, and the recommendations include incorporating cross-maps to other terminologies, such as ICD 10, and including mechanisms for performing patient queries, clinical decision support and interfacing with third-party applications to make the data more usable. Cloud-based technology to serve terminology standards and cross-mapping to other terminologies has potential to facilitate clinical data consistency and semantic interoperability across participating organizations. 8. Use Cases Although the terminology coding is standard, the success of implementing SNOMED CT differs by enterprise and area of healthcare. The best practices recommended for implementation may not fit the specific needs of a multi-specialty physician group nor does it guarantee that best practices are scalable for large healthcare entity. Additionally, from an information exchange perspective, the work effort that s needed to manage such an implementation will differ depending on the size and the number of external systems that feed into your Health Information Exchange (HIE). By examining use cases from multiple areas of healthcare, we can gain insight into the current success of SNOMED CT adoption within the healthcare arena. For the purpose of this section, we ll discuss and analyze use cases of SNOMED CT adoption ranging from a private physician group to a large hospital. Additionally, we will discuss the current speed bumps to implementation for HIEs with hopes of getting a breadth of information on the current state of the three institutions. Northwestern Memorial Physicians Group (NMPG), a large multi-specialty group of physicians, affiliated with Northwestern Memorial hospital in Illinois has embraced the SNOMED CT standard within their Electronic Medical Record (EMR). Within their EMR they have mapped multiple coding standards to help providers identify the appropriate patient problem from a search window. Physicians have the ability to select the terminology library they wish to pull from (ICD-9 or SNOMED CT) and select the appropriate problem for the patient. The physician can add additional information where appropriate, such as free-text comments or date-ofonset. The NMPG reports this as a useful tool because the provider can select familiar, yet appropriate terms from a list and have it pull in as discrete data into the record. The NMPG have embraced the coding standards and reported that using the SNOMED CT nomenclature provides a treasure trove of synonyms that helps them choose their patient s problems faster and more accurately (Bowman, 2005). Additionally, the NMPG report a more precise communication after adopting this standard. They expect the initiative to increase the maturity of the data captured discretely in the EMR database and expect that using these standard terminologies will lead them to identify patients that would serve as candidates for disease management programs. This is one use case in a multi-specialty, outpatient setting where SNOMED CT has been integrated within the current EMR to better patient outcomes, while adopting a universal standard that will help aggregate and trend data (Bowman, 2005). MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 9

Leeds Teaching Hospitals employ over 14, 000 staff members across six different campuses in West Yorkshire, England. Each year they treat over 1 million patients within a number of different specialties. Leeds Hospitals took on the initiative to incorporate SNOMED CT terminology in their emergency department with the goal of mapping the SNOMED codes to Commissioning Data Set (CDS) codes. These codes are an industry standard and help the hospital meet a mandated initiative. Prior to the implementation of SNOMED terminology it was felt that the data returned to CDS was not optimal. A 2011 case study reports that other diagnosis was often documented for the visit. Leeds reports having an employed clinician do a custom mapping from SNOMED CT to the appropriate CDS code. Leeds teaching hospital reported an increase of 75% in documenting diagnosis accuracy for their patients since the SNOMED CT implementation. Additionally, Leeds Hospitals did an audit six months after the implementation and found a reduction in lack of diagnosis entered for a patient visit. This was reduced from 30% to 7% only six months after the implementation. Finally, as a lessons learned from their implementation, Leeds Hospitals found that all the required diagnoses were not entered with the initial mapping and therefore had to be audited frequently to incorporate all the possible use cases for their providers. Ultimately, Leeds Teaching Hospitals improved the quality of its data in- line with a mandated standard as a result of their SNOMED CT initiative (Leeds Teaching Hospitals, 2011). From an information exchange perspective, Meaningful Use stage two is driving the initiative to exchange data in a standard format and create clinical integration that will allow entities to better patient outcomes. Although an HIE serves as central data repository, vendors are in the process of incorporating third party mapping solutions or crosswalks, to compensate for EMRs that are not feeding up data in a unified standard. For instance, if multiple EMRs feed into a state HIE, one of which has adopted SNOMED CT terminology and another is using ICD-9, the need for a crosswalk exists, so that two distinct codes can be mapped to a single patient problem. The information can then be aggregated to identify health disparities within the population. HIEs have been integration focused and very few vendors have begun to incorporate mapping technology at the HIE level. John Hatem, Director of Healthcare Strategy at Oracle, focuses on a couple initiatives that would help HIEs adopt SNOMED CT terminology to help providers meet Meaningful Use stage two initiatives. First, the ability to codify free-text problems at the HIE level and then incorporate that in a Continuity of Care Document (CCD) or clinical note. Second, he notes that a public library of who is using SNOMED CT terminology and in what ways they are using it would be beneficial. Finally, he identifies the need for a mapping solution that would take custom codes and map them to current SNOMED codes. These three initiatives will help HIEs incorporate SNOMED coding standards and share data in a unified way (Hatem, 2012). Whether SNOMED CT terminology is being implemented at a multi-specialty physician practice or a large health organization, the technology has proven to be beneficial. From a workflow perspective, NMPG states that SNOMED CT allows their physician group to document a patient problem faster than without the terminology. Additionally, Leeds Teaching Hospitals have increased the integrity of their patient records by capturing a diagnosis for an Emergency Room visit more frequently than before. We acknowledge that most HIEs have yet to embrace a SNOMED CT crosswalk that would allow multiple EMRs to map their patient problems to a single code. However, stage two Meaningful Use will be driving this initiative in the near future. Ultimately, SNOMED CT terminology has proven to be an effective solution in the use cases we studied. 9. Novel Techniques Three types of novel techniques have been developed to enhance the mapping of terms provided by clinicians in clinical documentation to SNOMED CT concepts. These algorithms differ in their approaches with one focusing on user-level term entry, another utilizing post-processing techniques to match user-entered terms to concepts, and yet another which modularizes the SNOMED CT ontology to improve the likelihood of correct mapping. Semantic Enhanced Autocompletion In an article by Sevenster et al., the addition of semantic enhancement is used to improve matching of the term to a SNOMED CT concept which would then be presented to the user as he types in the clinical term. Semantic enhancement is defined by contextual information semantically similar to the target term. Autocompletion is a functionality known to most clinical users in the setting of Web browsers such as Google Chrome and FireFox MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 10

and Web-based search engines such as Google and Bing. The authors describe three ways in which autocompletion is beneficial to user experience and conformance to standardized terminology: (1) the evolving suggested terms offered by the software make the user feel as if he is being understood, (2) over time, the user may become more trained to utilize standardized terminology when documenting, and (3) the suggested terms will ensure the usage of terms which can be mapped to SNOMED CT concepts at the time of documentation (Sevenster & Aleksovski, 2010). Sevenster et al. hypothesize that, in addition to using the string input from the user and user-specific information (e.g. previous queries, location information, user profile and trends), semantic contextual information can improve autocompletion suggestions. The hypothesis was tested by establishing a set of terms as the target, which represent the terms that the user wants to produce, and the context is represented by terms semantically related to the target. Both groups are SNOMED CT terms. The context group was gathered from a significant corpus of MEDLINE abstracts. Three dimensions, (1) four various degrees of freedom in the context, MEDLINE abstract-level, journal-level, static (all body structure (BS), finding (FDG), and disorder (DIS) SNOMED CT terms), random (n randomly selected BS, FDG, DIS terms), (2) the number of terms in the context set, (3) the semantic categories (e.g. BS, FDG, DIS) in the set, represent the variables in the system (Sevenster & Aleksovski, 2010). Two autocompletion algorithms, horizon expansion (HE) ( optic n results in optic nerve, optic neuritis, optic nerve injury ) and multi-word matching (MWM) ( op ne results in optic nerve and nerve operation ), were created by the authors. Each algorithm was run in two modes, the syntactic criteria only and syntactic plus semantic modus. Performance comparisons occurred between each algorithm and each mode in a set algorithm. The number of keystrokes was established as the quantifiable metric used to assess the influence of semantic context on efficiency of mapping. Both HE and MWM save 50% of keystrokes in the syntactic mode over the manual entry of each character in the target term. The addition of semantic enhancement was found to decrease keystrokes by an additional 5 to 18%, with the MWM algorithm, in static contexts, performing the best with an additional 18% keystroke savings (Sevenster & Aleksovski, 2010). This study demonstrates a novel method of using available semantic data to improve mapping of clinical terms supplied by users to SNOMED CT terms in real-time and to facilitate learning on usage of standardized SNOMED CT terms on the user's behalf. Autocompletion represents a viable option for documents such as forms or narrative free-text aspects of clinical documents such as the chief complaint, past medical history, past surgical history, and medications. Improving Mapping of User-specified terms to SNOMED CT through Semantic Structure Term diversity is defined by different users selecting varied and multiple terms (e.g. abdomen represented by abdomen, ab, abd, and belly) to describe the same concept. Term diversity creates challenges to the integration of clinical databases and subsequent analysis because of challenges to reliable, computable mapping of these terms to the appropriate concept. Current mapping is based solely on syntactic string similarity, without the use of semantic contextual information. Techniques utilizing syntactic information alone are adept at mapping terms to SNOMED CT concepts that are similar linguistically but potentially inaccurate semantically (Khare, An, Li, Song, & Hu, 2012). Most current SNOMED CT browsers, such as Snoflake, either map user-entered terms to the entire bank of SNOMED concepts or offer suggestions based on the semantic category of the entered term. Algorithmically each term is treated as a context-independent entity (Dataline, 2009; Khare et al., 2012). The best concept fits are weighted based on the overlap ratio between the user-specified term and a SNOMED CT concept's fully specified name (Khare et al., 2012). In a study by Khare et al., 900 user-specified clinical terms, originating from encounter forms, were mapped to their SNOMED CT concept by semantic plus linguistic matching and compared to traditional linguistic matching alone, which is known to improve recall (n terms correctly mappedrelevant terms in SNOMED set), and not impact precision (n correctly mapped termsterms mapped manually). A form tree data representation, where each form field is mapped to its SNOMED CT semantic category (e.g. Field: Patient Name Category: Person, Male vs Female Qualifier Category), was used to statistically model the semantic structure of the encounter form. Then a machine-learning classifier called sclassifier, based on Naїve Bayes Classifier, was used to assign a semantic category to the term (Khare et al., 2012). MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 11

The process was devised to be four stepped, with the first three steps exploiting the semantic structure of the form based on the location of the term within the form tree and the sclassifier. This assigned a statistical probability that the term belonged to a particular semantic category within SNOMED CT. The last step introduced the more classical linguistics mapping to assign the term to a final SNOMED CT concept, but only after, in the third step, the category ranked first by the previous steps was found to contain a SNOMED CT concept matching the term. This check was termed a concept presence test by the authors. This hybrid approach to mapping user-specified terms to SNOMED CT concepts resulted in precision equal to 0.89 and recall of 0.76, noted to be a 23% and 38% improvement, respectively, over the linguistic-only approach (Khare et al., 2012). The potential advances to the implementation and utilization of a standardized terminology such as SNOMED CT are far reaching with this academic work. As clinical forms are still a part of current medical documentation and represent a population of documents from legacy systems, the algorithms presented in this paper can facilitate reducing healthcare database heterogeneity through backend software processing and result in improved mapping of terms to concepts. Furthermore, this can serve as an intermediary step to more robust natural language processing algorithms within the medical context. Pruning of Large Ontologies based on Use Ontologies are useful for the breadth of concepts contained there within, but can become cumbersome due to their large size and complexity. The concept of ontology modularization or decomposition is not unprecedented and has even been advocated to make SNOMED CT more usable in the clinical setting. This ontology, at present, contains the capabilities to build specific vocabularies, called reference sets, as defined by clinical users. An example is the subset of 2700 SNOMED concepts, developed by Patrick et al., derived from 44 million words in intensive care unit (ICU) patient progress notes. Various natural language processing techniques were used to create this reference set which provides concept coverage of 96% of the corpus represented by the ICU progress notes (Patrick et al., 2008). Garcìa et al. have sought to create a similar subset, utilizing graph-traversal (GTOM) and logic-based ontology modularization (LBOM), with and without frequency-based techniques, but excluding the use of a specific corpus to analyze, as used in the Patrick et al. study. GTOM involves viewing the ontology as a graph and utilizing link-traversal heuristics to determine terms and axioms related to the target term. LBOM defines a subset of terms based on ontologic logic, whereby concepts unrelated to the target term are discarded from a set. In the frequency-based technique, each SNOMED CT concept within the subsets derived from the GTOM and LBOM is given a score based on the number of times it appeared in a title or abstract in a subset of 206 484 English MEDLINE articles based on human research (Lopez-Garcia, Boeker, Illarramendi, & Schulz, 2012). The research corpus for the study consisted of twenty discharge summaries in which terms were manually mapped to SNOMED CT concepts. A single discharge summary was annotated by 17 to 64 concepts and 439 concepts were required to represent the entire set of discharge summaries. The four GTOM techniques resulted in subset coverage of the terms ranging from 71% to 95% and the average term module size was 17% to 51% of the size of SNOMED CT. The LBOM technique created a more refined subset at 1%, but the coverage was smaller than the preceding technique with only 55%. Precision for the correct target term to SNOMED CT concept mapped was very low at 1.18% or lower, with logic-based technique performing the best. The addition of frequency-based filtering improved precision for both GTOM and LBOM techniques, while coverage and size of the subset were decreased (Lopez-Garcia et al., 2012). Ontology modularization offers a method to make ontologies more tailored to specific use-cases, and therefore, more usable. The use of an external corpus as unspecific as the MEDLINE titles and abstracts have a measurable benefit on the precision of the terms contained in the reference set. The next step would be to evaluate the potential improvements to reference set precision, coverage, and size through frequency-based filtering in addition to GTOM and LBOM, using corpora conceptually similar to the clinical focus of the reference sets (Lopez-Garcia et al., 2012). MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 12

10. Conclusion As part of implementing electronic medical records (EMR), the SNOMED CT standard has become a centerpiece for capturing clinical information. Use of the SNOMED CT standard has been expedited due to the Meaningful Use mandate and the ability to map ICD codes for billing purposes. In addition, a combination of semantic contextually enhanced autocompletion at the user level and software level processing of terms using machine-learning, in the setting of appropriately created reference sets through ontological modularization, will lead to optimal mapping of user-specified terms to SNOMED CT concepts. All efforts drive towards the goal of semantic interoperability through the use of a standard terminology. MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 13

11. References Search Health IT (2012): SNOMED-CT definition Retrieved from: http://searchhealthit.techtarget.com/definition/snomed-ct T. Benson (2010), Principles of Health Interoperability HL7 and SNOMED, HI, Springer-Verlag London Limited 2010 Dennis Lee, et al (2012): A survey of SNOMED CT implementations, Journal of Biomedical Informatics ACP (2012): ICD-10 and SNOMED-CT: Better together? Retrieved from: http://www.fiercehealthit.com/story/diagnostic-coding-sets-snomedct-versus-icd10- /2012-05-23 IHTSDO (2012): Implementation of SNOMED CT in Quality Measures, 2012. Retrieved from: http://www.ihtsdo.org/fileadmin/user_upload/doc/showcase/ UMLS (2012): US Extension to SNOMED CT. Retrieved from: http://www.nlm.nih.gov/research/umls/snomed/us_extension.html CAP (2012): College of American Pathologists Snomed technology Solutions Retrieved from: http://www.cap.org/apps/cap.portal?_nfpb=true&_pagelabel=snomed_page K. Spackman and G. Reynoso (2004): Examining snomed from the perspective of formal ontological principles: Some preliminary analysis and observations. Whistler, Canada: Proc KR-MED-04, pages 81--7, 2004. UMLS (2012): SNOMED CT to ICD-9-CM Rule Based Mapping to Support Reimbursement Retrieved from: http://www.nlm.nih.gov/research/umls/mapping_projects/snomedct_to_icd9cm_reimburse.html SNOMED (2012): Clinical Terms technical implementation guide. Retrieved from: http://www.snomed.org Schulz E, Barrett J, Price C (1997): Semantic quality through semantic definition: Refining the Read Codes through intemal consistency. Proc AMIA- Symp 1997; 615-9. Kostick, K. (2012). SNOMED CT Integral Part of Quality EHR Documentation. Journal of AHIMA, 83 (10), 72-75. Lee, D., Cornet, R., Lau, F., & de Keizer, N. (2012). A Survey of SNOMED CT Implementations. Journal of Biomedical Informatics, 1-10 AHIMA (2012): Journal of AHIMA, Problem List Guidance in the EHR; http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_049241.hcsp?ddocname=bok1_ 049241 Health IT News (2012): SNOMED CT Will Be Required by 2015 for Bonuses under Economic Recovery LAW; 2012 Dataline. (2009). Snoflake Browser. Snoflake Browser. Retrieved November 4, 2012, from http://snomed.dataline.co.uk/account/logon?returnurl=%2f Khare, R., An, Y., Li, J., Song, I.-Y., & Hu, X. (2012). Exploiting semantic structure for mapping userspecified form terms to SNOMED CT concepts. IHI 12 (pp. 285 294). New York, NY, USA: ACM. doi:10.1145/2110363.2110397 Lopez-Garcia, P., Boeker, M., Illarramendi, A., & Schulz, S. (2012). Usability-driven pruning of large ontologies: the case of SNOMED CT. Journal of the American Medical Informatics Association, 19(e1), e102 e109. doi:10.1136/amiajnl-2011-000503 MMI 405-Group 1 Project SNOMED-CT Draft, November 13, 2012 P a g e 14