Clinical Terminology and Clinical Classification Systems: A Critique Using AHIMA’s Data Quality Management Model

by Zahraa M. Alakrawi, MS

Abstract

Clinical coding constitutes one of the fundamental functions in the field of health information management. Clinical classification systems and clinical terminologies represent two distinct sets of coding schemes that are used in healthcare. In this context, it is critical to distinguish between clinical terminologies and clinical classification systems, identify how both sets of systems are utilized in healthcare settings, and acknowledge individual contributions of each system to providing data infrastructure for clinical as well as administrative data uses in the healthcare delivery system. The two sets of systems were designed to serve different purposes and therefore are intended to satisfy different user requirements. However, essential elements distinguish a clinical terminology from a classification system. Rather than concluding which system is “best” to accommodate healthcare needs and data structure, a critique of both systems will be presented in this article using AHIMA’s Data Quality Management Model. SNOMED CT and ICD-10-CM/PCS will be utilized as examples of clinical terminologies and clinical classification systems, respectively.

Keywords: clinical terminology, clinical classification systems, coding, SNOMED CT, ICD-10-CM/PCS, data quality management, electronic health record

Introduction

Clinical coding constitutes one of the fundamental functions in the field of health information management (HIM).1, 2 It can be defined as “designating descriptions of diseases, injuries, and procedures into numeric or alphanumeric designations. It involves the use of a health record as the source for determining code assignment..”3 Clinical classification systems and clinical terminologies represent two distinct sets of coding schemes that are used in healthcare. In reality, these concepts—clinical terminology and classification-—are often used incorrectly and interchangeably. The purpose of this article is distinguish between clinical terminologies and clinical classification systems, identify how both sets of systems are utilized in healthcare settings, and acknowledge individual contributions of each system to providing data infrastructure for clinical as well as administrative data uses in the healthcare delivery system.

Clinical Terminology

A reference terminology can be defined as “a set of concepts and relationships that provide a common reference point for comparisons and aggregation of data about the entire health care process, recorded by multiple different individuals, systems, or institutions.”4 Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) represents an example of clinical terminologies used in healthcare. SNOMED CT is a standardized healthcare terminology that was originally developed from a pathology-specific nomenclature called Systematized Nomenclature of Pathology. SNOMED CT is a controlled medical terminology that encompasses diseases, clinical findings, etiologies, procedures, and health outcomes.5, 6 It can be used by physicians, nurses, allied health professionals, veterinarians, and researchers.

SNOMED CT is defined by the International Health Terminology Standards Development Organisation (IHTSDO) as “SNOMED CT is a comprehensive clinical terminology that provides clinical content and expressivity for clinical documentation and reporting. SNOMED CT contains concepts for both human and non-human medicine.”7 SNOMED CT is basically comprised of concepts, descriptions, and relationships in order to accurately represent clinical information in healthcare. 7

The ownership, maintenance, and distribution of SNOMED CT was originally the responsibility of the College of American Pathologists, but this responsibility was transferred to the IHTSDO in 2007.8 The current version of SNOMED CT is available at no charge through the National Library of Medicine (NLM). The US license for SNOMED CT was obtained by the NLM through the Unified Medical Language System project.9 SNOMED CT can be used to support direct patient care, clinical audit, research, epidemiology, and service planning. Furthermore, “the global scope of SNOMED CT reduces geographical boundary effects arising from the use of different terminologies or coding systems in different organizations and countries.”10

Clinical Classification Systems

A classification is “a system that arranges or organizes like or related entities.”11 Classification systems are intended for classification of clinical conditions and procedures to support statistical data analysis across the healthcare system. Classification systems can provide standards for comparisons of health statistics at national and international levels. Also, classification systems can be used to support other applications in healthcare, including reimbursement, public health reporting, quality of care assessment, education, research, and performance monitoring.12, 13 The International Classification of Diseases, Tenth Revision, Clinical Modification and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-CM/PCS) represents an example of the clinical classification systems. It is the US clinical modification of the World Health Organization (WHO) International Classification of Diseases, Tenth Revision (ICD-10). ICD-10-CM/PCS replaced ICD-9-CM on October 1, 2015, in the United States.

The National Center for Health Statistics and the Centers for Medicare and Medicaid Services (CMS) are the US governmental agencies responsible for overseeing all changes and modifications to the ICD-10-CM/PCS.14

Coding Clinical Expressions

The two sets of systems were designed to serve different purposes and therefore are intended to satisfy different user requirements. SNOMED CT is designed for input into electronic health record (EHR) systems and other clinical applications, while ICD-10-CM/PCS is basically designed for providing outputs in terms of reports and statistics. Therefore, each system has a unique hierarchical structure to serve the purposes for which it was originally intended.15–19 Table 1 provides a brief description of how to code the clinical expression “pain in right leg” using a clinical terminology (SNOMED CT) and a classification system (ICD-10-CM). Additional examples can be found in Table 2.

However, coding in SNOMED CT is different from conventional coding using ICD-10-CM/PCS. Coding using SNOMED CT is always automated: end users cannot view the codes assigned by the system. For this reason, SNOMED CT is being used by software developers and EHR vendors in order to facilitate communication between different applications through creating a standard language. In fact, we can think of SNOMED CT as a programing language; users utilize applications that apply SNOMED CT without knowing what is at work in the background. For example, SNOMED CT has been combined with natural language processing (NLP) to improve EHR capabilities. In this case, SNOMED CT could identify where a condition exist or not or when it should be ruled out because of the set of concepts and attributes that could further clarify a certain case. If such capabilities are enabled, SNOMED CT could be used for generating alerts and reminders or as part of the decision-support system to identify contradictory notes and improve the quality of patient care.

In contrast, ICD-10-CM/PCS coding is performed by professional coders, who used to manually assign codes to patients’ diagnoses and procedures. With the advancement of technology, coders have been using special encoders or computer-assisted coding (CAC) applications. CAC applications can facilitate accurate and efficient coding by automatically suggesting codes based on the clinical documentation in the EHR system. Thus, ICD-10-CM/PCS coding is semi-automated at best and requires human intervention to either assign or validate selected codes.

However, essential elements distinguish a clinical terminology from a classification system. Before concluding which system is “best” to accommodate healthcare needs and data structure, a critique of both systems will be presented in the following section using the American Health Information Management Association (AHIMA) Data Quality Management (DQM) model. The AHIMA DQM model was chosen as a framework for assessment for the following reasons:

  1. AHIMA’s DQM model can provide a standard for comparison as well as an objective assessment of totally different systems with varying scopes and applications.
  2. AHIMA’s DQM model was developed to accommodate complexity of healthcare data by providing a way to quantify the quality of healthcare data and the attributes of the data.
  3. No other relevant models can replace the AHIMA’s DQM model in this capacity, making it a long-established health information standard.

SNOMED CT and ICD-10-CM/PCS will be utilized as examples of clinical terminologies and clinical classification systems, respectively.

 

AHIMA’s DQM Model

DQM is defined in AHIMA’s DQM Practice Brief (2015) as “the business processes that ensure the integrity of an organization’s data during collection, application (including aggregation), warehousing, and analysis.” 20–22 The purpose of DQM is continuous improvement of health data quality. DQM model consists of 10 characteristics to monitor data quality in four different domains: data application, collection, warehousing, and analysis. Table 3 provides a description of the four domains that constitute the AHIMA’s DQM model and the characteristics of data integrity that should be applied in each domain.

Accessibility: SNOMED CT contributes to semantic interoperability across a wide range of clinical applications between healthcare providers in different clinical settings and therefore can improve the capabilities of health information exchange.23, 24 Semantic interoperability can be defined as “ensuring that precise meaning of exchanged information is understandable by any other system or application not initially developed for this purpose.”25 However, such high-level of information exchange is not quite feasible utilizing a classification system like ICD-10-CM/PCS that is too general to serve this purpose.26 Therefore, SNOMED CT can greatly improve data accessibility as opposed to ICD-10-CM/PCS. In addition, applications that use SNOMED CT make the data accessible at the point of care, while ICD-10-CM/PCS data are accessible only after codes are assigned by the coders.

Accuracy: SNOMED CT is an automated clinical terminology scheme in which clinical representations are automatically encoded using a variety of coding applications that utilize Natural Language Processing NLP.27, 28 In fact, SNOMED CT is agnostic, that is, it can capture all codes regardless of context. Therefore, incorrect data resulting from human errors are unlikely, in contrast to ICD-10-CM/PCS coding systems, in which human judgement is an important element of the coding process. However, clinical applications have a higher risk of systematic errors as opposed to human errors, which tend to be randomly distributed in most cases.29–32 The human judgment component of coding has also contributed to coding variations and issues with the accuracy of coded data. Complexity of resource grouping schemes as well as unclear documentation can lead to inaccurate coding.33 Furthermore, accuracy requires familiarity with medical terminology, surgical techniques, and complex coding systems.34

For example, coding accuracy can vary greatly across medical specialties. Some specialties, such as otolaryngology, encompass a wide range of procedures that are performed in “close anatomical proximity,” which ultimately affects coding accuracy.35 Similar results have been found in other medical specialties, such as urology,36 neurosurgery,37 and surgery.38

Comprehensiveness: SNOMED CT has better clinical coverage than ICD-10-CM/PCS. The number of codes representing concepts in clinical findings alone is 100,000 concepts, compared with the 68,000 diagnosis codes in ICD-10-CM.39–41 Thus, more than one ICD-10-CM code may be needed to represent one concept in SNOMED CT (see Table 4). New concepts in SNOMED CT (post-coordinated expressions) can be created, which contributes to the extensibility of the system extensibility to cover all concepts related to the medical domain.42 On the other hand, ICD-10-CM/PCS is updated periodically to revise or add new diagnosis or procedure codes.

Consistency: Concepts in SNOMED CT are consistent among different users and across all clinical applications.43 In contrast, studies have shown issues of coding reliability that contribute to inconsistent code assignments among coders and across medical specilaities.44–46 In addition, ICD systems in general are influenced by coding conventions that are subject to interpretation by coders and can vary across settings (e.g., inpatient vs. outpatient clinical context).47–49 For examples, coding symptoms and signs such as “shortness of breath” can have different guidelines in acute-care hospitals and ambulatory care settings.

Currency: SNOMED CT in its current form was developed in 2007,50 while ICD-10 was first introduced in 1990s and has been used to collect mortality statistics in the United States. However, the first field test of ICD-10-CM was conducted in 2003. Both systems are updated biannually to reflect contemporary medical knowledge and medical technology.51, 52

Definition: Because of its logical structure, SNOMED CT makes more sense and is easier for clinicians to understand.53–56 However, ICD-10-CM can be impeded with coding conventions and sometimes clinically irrelevant details needed for reimbursement of healthcare services (initial encounter, delayed healing, NOS [not otherwise specified], NEC [not elsewhere classifiable]). These instructions are designed for professional coders and therefore make it hard for clinicians to adopt the system for direct care purposes.57–59 (See Table 5.)

Granularity: SNOMED CT is in general is more specific than ICD-10-CM/PCS.60 Furthermore, SNOMED CT has a unique characteristic that enables extensibility and creation of new concepts (post-coordinated expressions) by end users.61 In contrast, less common diseases in ICD-10-CM are grouped together in “catchall” categories (e.g., J15.8 Pneumonia due to other specified bacteria), which can lead to loss of information.62, 63

Precision: Concepts have the same values in SNOMED CT; studies have shown up to 93 percent precision of SNOMED CT for identifying clinical expressions.64, 65 However, the presence of some codes with unspecified (not specified in documentation) and other specified (present in medical record but not enough details to code it) can affect the ability of the ICD system to collect data related to certain conditions, such as rare conditions. Therefore, caution is advised when administrative data are utilized for less common conditions, such as Down syndrome, eosinophilic esophagitis, congenital heart disease, genetic blood disorders, and surgery.66–70

Relevancy: A clinical terminology such as SNOMED CT could be more useful in clinical applications, information retrieval, and research. SNOMED CT is regarded as a global standard because of its wide acceptance and application worldwide, which makes it a safe and accurate alternative for clinical communication among healthcare providers.71–74 In contrast, classification systems such as ICD-9-CM or ICD-10-CM/PCS are intended for classification of clinical conditions and procedures for use in other applications, including statistical reporting and reimbursement.75–78 Both systems are relevant with respect to the purposes for which they were originally designed.

Timeliness: SNOMED CT is designed to be used at the point of care by clinicians, while ICD-10-CM/PCS codes are usually assigned by professional coders after the patient’s episode of care is complete.79–84

Table 6 presents a model that was developed based on AHIMA’s DQM to illustrate the fundamental differences between clinical terminologies (represented by SNOMED CT) and clinical classification systems (represented by ICD-10-CM).

Discussion

Users and Applications

Healthcare terminology and classification systems can be used by consumers, healthcare providers, quality and utilization management personnel, researchers, and other administrative staff (accounting, billing, and coding personnel). They are also used to facilitate communication between healthcare providers and consumers at the point of care for data collection purposes. A more organized system of data collection and retrieval can be provided by utilizing healthcare terminology. This system can promote quality of care by providing a link between published research and clinical care. Furthermore, such systems can support integration of care by allowing effective exchange of clinical information among healthcare providers in different settings. Although terminologies such as SNOMED CT can be utilized to support real-time decision making and retrospective reporting for research and management, such utilization can hindered by complexity of these systems. Classification systems are utilized by wider spectrum of users in healthcare. They can be used to provide data to consumers on costs, treatment options, and outcomes. Also, classification systems provide a less complex system for data collection and reporting that can be further used for research purposes. Information provided by such systems can be used to improve clinical, financial, and administrative performance by enabling effective payment systems, identifying potential fraud and abuse, and ensuring accurate reporting.

ICD-10-CM/PCS

The ICD coding system was originally created to code death certificates, but its use has expanded to encompass a wide range of statistical reporting. In fact, ICD-10 has been used since the 1990s to collect mortality statistics around the globe. The WHO defines coding as “the translation of diagnoses, procedures, co-morbidities and complications that occur over the course of a patient’s encounter from medical terminology to an internationally coded syntax.”85 In this definition, the WHO acknowledges the capability of the ICD system that is used for clinical coding and classification to enable international comparisons with respect to mortality as well as morbidity statistics.

ICD-9-CM had been used since 1978 as the foundation of the reimbursement system in the United States and is used by the Center for Medicare and Medicaid Services for inpatient and ambulatory resource grouping. The Medicare Severity Diagnosis Related Group (MS-DRG) system constitutes the foundation of Medicare’s Inpatient Prospective Payment System (IPPS), which is used to reimburse acute-care and short-term hospitals for services rendered to Medicare beneficiaries. ICD-9-CM was replaced by ICD-10-CM/PCS in October 1, 2015, and it will continue to serve as a base for healthcare reimbursement. For outpatient encounters, reporting of diagnosis codes in ICD-10-CM is required to establish medical necessity.

Also, ICD-10-CM is now used in place of ICD-9-CM for public health reporting (i.e., reporting the leading cause of death and morbidity on the national level). ICD-10-CM/PCS can also be used to assess clinical outcomes and improve quality of care provided for individual patients. For example, ICD-10-CM/PCS data are utilized for clinical documentation improvement initiatives to educate physicians on effective clinical documentation in EHR systems.

However, the process of clinical classification itself is prone to variation because of the complex coding schemes and conventions that are subjected to interpretation by coders, which makes it difficult for clinicians to assign the codes by themselves. Thus, ICD-10 in general and ICD-10-CM/PCS in particular lacks the standardization needed for electronic communication and clinical documentation.

SNOMED CT

SNOMED CT provides a unified language that can be used as a standard for communication among healthcare providers and across clinical applications. SNOMED CT can contribute greatly to semantic interoperability in healthcare applications.86–88 Its standardized logical structure as well as its wide acceptance makes it more suitable than other terminologies or classification systems for high-level information sharing and information retrieval.89–91 Thus, SNOMED CT can be used for health information exchange and clinical documentation in EHRs. SNOMED CT is an automated system, which makes it convenient to be used at the point of care for generating clinical alerts and reminders, serve as a part of a clinical decision-support system, and link providers to medical knowledge and current publications that can be used for outcome measurement. Furthermore, because of its fully automated scheme, SNOMED CT can be used for healthcare research, and it can be used for automated identification of patients for clinical trials because of its extensive granularity and content coverage.92–96 In addition to its higher specificity, SNOMED CT has a unique feature that enables extension of concepts by end users, which can foster reliable communication among healthcare providers and across medical specialties and can facilitate health information exchange at national as well as international levels.97 SNOMED CT has become one of the federal requirements for health information technology; CMS mandates the use of SNOMED CT to code the problem list for Meaningful Use stage 2.98, 99

Clinical Documentation in the EHR

However, the information provided above should not be take to suggest that SNOMED CT is superior to ICD-10-CM/PCS, as both coding schemes provide the necessary data structure needed to support healthcare clinical and administrative processes. Clinical terminology systems as well as clinical classification systems were originally designed to serve different purposes and different users’ requirements. ICD-10-CM/PCS is an output system that was designed for general reporting purposes, public health surveillance, administrative performance monitoring, and reimbursement of healthcare services. In contrast, SNOMED CT was developed to serve as a standard data infrastructure for clinical application, which requires a greater degree of specificity. A classification system can be less detailed than a clinical terminology.100 Therefore, the lower specificity of ICD-10-CM/PCS is an intrinsic feature rather than a shortcoming; SNOMED CT is too detailed to replace ICD-10-CM/PCS in this context. In fact, the systems complement each other and contribute to providing quality data for different domains of the healthcare system. For example, “If a researcher wants to know how many patients died with a diagnosis of heart attack last year, ICD-10 (WHO’s) is enough. If they want more detail, such as what muscle of the heart was involved, they will need SNOMED CT.”101 Therefore, both systems can be used in research and education depending on which degree of specificity is required by circumstances: SNOMED is a better choice for identifying rare diseases, while ICD-10-CM/PCS is more efficient for general reporting, such as collecting the top causes of mortality and morbidity at the national level. Furthermore, ICD-10-CM/PCS will be needed to constitute the foundation of reimbursement in the United States.102

Mapping SNOMED CT to ICD-10-CM/PCS

The NLM, with participation of the National Center for Health Statistics, is working on a project to map SNOMED CT concepts to ICD-10-CM codes, called I-MAGIC (Interactive Map-Assisted Generation of ICD Codes). According to NLM, the purpose of mapping is to “is to support semi-automated generation of ICD-10-CM codes from clinical data encoded in SNOMED CT”103 in order to fulfill the requirements of healthcare. Therefore, SNOMED CT cannot replace ICD-10-CM/PCS; both systems complement each other and equally contribute to quality data structure for the entire healthcare system. In fact, the WHO, together with the IHTSDO, has been working on similar projects that will enable mapping between SNOMED CT and ICD-10 (the WHO version) as well as ICD-11. However, because of the substantial differences between these coding schemes, it is not always possible to have one-to-one mapping. However, these mapping projects further emphasize the importance of future data infrastructure that encompasses characteristics of both systems to achieve the maximum benefits of information technology in healthcare.

Conclusion

Clinical classification systems and clinical terminologies represent two distinct coding schemes that are used in healthcare. Both sets of systems are utilized in healthcare settings and contribute to providing data infrastructure for clinical and administrative data uses in the healthcare delivery system. A critique of both systems was presented in this article using AHIMA’s DQM model, using SNOMED CT and ICD-10-CM/PCS as examples of clinical terminologies and clinical classification systems, respectively. Each system is used for distinct clinical and administrative applications and has its own benefits and potential limitations. Classification systems such as ICD-10-CM/PCS and reference terminologies such as SNOMED CT are two complementary systems that are needed to provide data infrastructure in healthcare.

 

Zahraa M. Alakrawi, MS, is a PhD candidate in the Department of Health Information Management at the University of Pittsburgh School of Health and Rehabilitation Sciences in Pittsburgh, PA.

 

Notes

  1. American Health Information Management Association (AHIMA). “Assessing and Improving EHR Data Quality (Updated).” Journal of AHIMA 84, no. 2 (March 2013): 48–53 [expanded online version]. http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_050085.hcsp?dDocName=bok1_050085.
  2.  Setting the Facts Straight About ICD-10: What Physicians Need to Know About the Transition. 2014. Available at http://bok.ahima.org/PdfView?oid=300625.
  3. Brouch, Kathy. “AHIMA Project Offers Insights into SNOMED, ICD-9-CM Mapping Process.” Journal of AHIMA 74, no.7 (July/August 2003): 52-55.
  4. Imel, M., and J. R. Campbell. “Mapping from a Clinical Terminology to a Classification.” AHIMA’s 75th Anniversary National Convention and Exhibit Proceedings, October 2003. Available at http://bok.ahima.org/doc?oid=61537.
  5. Cornet, R., and N. Keizer. “Forty Years of SNOMED: A Literature Review.”BMC Medical Informatics and Decision Making 8 (2008): 1–6.
  6. International Health Standards Development Organisation. SNOMED CT Starter Guide. 2014. Available at http://ihtsdo.org/fileadmin/user_upload/doc/download/doc_StarterGuide_Current-en-US_INT_20140222.pdf.
  7. International Health Standards Development Organisation. SNOMED CT® Frequently Asked Questions. Available at http://ihtsdo.org/fileadmin/user_upload/doc/download/doc_FAQ_Current-en-US_INT_20130731.pdf.
  8. Cornet, R., and N. Keizer. “Forty Years of SNOMED: A Literature Review.”
  9. Giannangelo, K. Healthcare Code Sets, Clinical Terminologies, and Classification Systems. 2nd ed. Chicago, IL: AHIMA, 2009.
  10. International Health Standards Development Organisation. SNOMED CT Starter Guide. 2014. Available at http://ihtsdo.org/fileadmin/user_upload/doc/download/doc_StarterGuide_Current-en-US_INT_20140222.pdf.
  11. Giannangelo, K. Healthcare code sets, clinical terminologies, and classification systems. (2nd ed.). Chicago: American Health Information Management Association (AHIMA).
  12. AHIMA. Setting the Facts Straight About ICD-10: What Physicians Need to Know About the Transition.
  13. Giannangelo, K. Healthcare Code Sets, Clinical Terminologies, and Classification Systems. 2nd ed.
  14. Ibid.
  15. Ibid.
  16. AHIMA Work Group. “Taking Coding to the Next Level through Clinical Validation.” Journal of AHIMA88, no. 1 (January 2014): web extra. Available at http://library.ahima.org/doc?oid=300246#.V0Xk5ZerKUk.
  17. AHIMA Practice Brief. “Automated Coding Workflow and CAC Practice Guidance (2013 Update).” Journal of AHIMA 84, no. 11 (2013). Available at http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_050535.hcsp?dDocName=bok1_050535.
  18. AHIMA. “Data Quality Management Model (2012 Update).” Journal of AHIMA83, no. 7 (July 2012): 62–67. Available at http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_049664.hcsp?dDocName=bok1_049664.
  19. Mitchell, Glenn. “Synergizing ICD-10: Integrating an ICD-10 Implementation into Other Compliance Programs Will Reduce Costs, Maximize Investments.” Journal of AHIMA84, no. 2 (March 2013): 34–38.
  20. Davoudi, Sion; Dooling, Julie A; Glondys, Barbara; Jones, Theresa D.; Kadlec, Lesley; Overgaard, Shauna M; Ruben, Kerry; Wendicke, Annemarie. “Data Quality Management Model (2015 Update)” Journal of AHIMA 86, no.10 (October 2015): expanded web version.
  21. Giannangelo, K. Healthcare Code Sets, Clinical Terminologies, and Classification Systems. 2nd ed.
  22. Abdelhak, M,. S. Grostick, M. A. Hanken, and E. Jacobs. Health Information: Management of a Strategic Resource. 3rd ed. St. Louis, MO: Saunders/Elsevier, 2007.
  23. Duarte, J., S. Castro, M. Santos, A. Abelha, and J. Machado. “Improving Quality of Electronic Health Records with SNOMED.” Procedia Technology 16 (2014): 1342–50.
  24. Gøeg, K. R., R. Chen, A. R. Højen, and P. Elberg. “Content Analysis of Physical Examination Templates in Electronic Health Records Using SNOMED CT.” International Journal of Medical Informatics 83, no. 10 (2014): 736–49.
  25. Ibid.
  26. Jensen, P. B., L. J. Jensen, and S. Brunak. “Mining Electronic Health Records: Towards Better Research Applications and Clinical Care.” Nature Reviews: Genetics 13, no. 6 (2012): 395–405.
  27. Duarte, J., S. Castro, M. Santos, A. Abelha, and J. Machado. “Improving Quality of Electronic Health Records with SNOMED.”
  28. Stanfill, M., M. Williams, S. Fenton, R. Jenders, and W. Hersh. “A Systematic Literature Review of Automated Clinical Coding and Classification Systems.” Journal of the American Medical Informatics Association17 (2010): 646–51.
  29. American Health Information Management Association (AHIMA). “Assessing and Improving EHR Data Quality (Updated).”
  30. AHIMA. Setting the Facts Straight About ICD-10: What Physicians Need to Know About the Transition.
  31. AHIMA Work Group. “Taking Coding to the Next Level through Clinical Validation.”
  32. AHIMA Practice Brief. “Automated Coding Workflow and CAC Practice Guidance (2013 Update).”
  33. Nouraei, S., A. Hudovsky, J. Virk, P. Chatrath, and G. Sandhu. “An Audit of the Nature and Impact of Clinical Coding Subjectivity Variability and Error in Otolaryngology.” Clinical Otolaryngology 38 (2013): 512–24.
  34. Moar, K., and S. Rogers. “Impact of Coding Errors on Departmental Income: An Audit of Coding of Microvascular Free Tissue Transfer Cases Using OPCS-4 in UK.” British Journal of Oral and Maxillofacial Surgery 50 (2012): 85–87.
  35. Nouraei, S., A. Hudovsky, J. Virk, P. Chatrath, and G. Sandhu. “An Audit of the Nature and Impact of Clinical Coding Subjectivity Variability and Error in Otolaryngology.”
  36. Moar, K., and S. Rogers. “Impact of Coding Errors on Departmental Income: An Audit of Coding of Microvascular Free Tissue Transfer Cases Using OPCS-4 in UK.”
  37. Beckley, I. C., et al. “Payment by Results: Financial Implications of Clinical Coding Errors in Urology.” BJU International 104, no. 8 (2009): 1043–46.
  38. Naran, S., A. Hudovsky, J. Antscherl, S. Howells, and S. Nouraei. “Audit of Accuracy of Clinical Coding in Oral Surgery.” British Journal of Oral and Maxillofacial Surgery 52 (2014): 735–39.
  39. AHIMA. Setting the Facts Straight About ICD-10: What Physicians Need to Know About the Transition.
  40. International Health Standards Development Organisation. SNOMED CT Starter Guide.
  41. AHIMA Work Group. “Taking Coding to the Next Level through Clinical Validation.”
  42. International Health Standards Development Organisation. SNOMED CT Starter Guide.
  43. Duarte, J., S. Castro, M. Santos, A. Abelha, and J. Machado. “Improving Quality of Electronic Health Records with SNOMED.”
  44. Moar, K., and S. Rogers. “Impact of Coding Errors on Departmental Income: An Audit of Coding of Microvascular Free Tissue Transfer Cases Using OPCS-4 in UK.”
  45. Beckley, I. C., et al. “Payment by Results: Financial Implications of Clinical Coding Errors in Urology.”
  46. Naran, S., A. Hudovsky, J. Antscherl, S. Howells, and S. Nouraei. “Audit of Accuracy of Clinical Coding in Oral Surgery.”
  47. AHIMA. Setting the Facts Straight About ICD-10: What Physicians Need to Know About the Transition.
  48. AHIMA Work Group. “Taking Coding to the Next Level through Clinical Validation.”
  49. AHIMA Practice Brief. “Automated Coding Workflow and CAC Practice Guidance (2013 Update).”
  50. International Health Standards Development Organisation. SNOMED CT Starter Guide.
  51. Ibid.
  52. Centers for Disease Control and Prevention. “International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM).” 2015. Available at http://www.cdc.gov/nchs/icd/icd10cm.htm.
  53. Duarte, J., S. Castro, M. Santos, A. Abelha, and J. Machado. “Improving Quality of Electronic Health Records with SNOMED.”
  54. 54. El-Sappagh, S., M. Elmogy, A. M. Riad, H. Zaghloul, and B. Farid. “A Proposed SNOMED CT Ontology-based Encoding Methodology for Diabetes Diagnosis Case-Base.” 9th International Conference on Computer Engineering & Systems (ICCES) (2014): 184–91.
  55. Mikroyannidi, E., R. Stevens, L. Lannone, and A. Rector. “Analyzing Syntactic Regularities and Irregularities in SNOMED-CT.”Journal of Biomedical Semantics 3, no. 8 (2012).
  56. Kate, R. J. “Towards Converting Clinical Phrases into SNOMED CT Expressions.” Biomedical Informatics Insights 6 (2013): 29–37.
  57. AHIMA Work Group. “Taking Coding to the Next Level through Clinical Validation.”
  58. AHIMA. “Data Quality Management Model (2012 Update).”
  59. Stanfill, M., M. Williams, S. Fenton, R. Jenders, and W. Hersh. “A Systematic Literature Review of Automated Clinical Coding and Classification Systems.”
  60. Giannangelo, K. Healthcare code sets, clinical terminologies, and classification systems. (2nd ed.). Chicago: American Health Information Management Association (AHIMA).
  61. International Health Standards Development Organisation. SNOMED CT Starter Guide.
  62. AHIMA. “Data Quality Management Model (2012 Update).”
  63. Stanfill, M. H., K. L. Hsieh, K. Beal, and S. H. Fenton. “Preparing for ICD-10-CM/PCS Implementation: Impact on Productivity and Quality.”Perspectives in Health Information Management (Summer 2014).
  64. Lee, D., N. de Keizer, F. Lau, and R. Cornet. “Literature Review of SNOMED CT Use.” Journal of the American Medical Informatics Association 21 (2014): e11–e19.
  65. Skeppelstedt, M., and H. Dalianis. “Using SNOMED CT for High Precision Entity Recognition in Swedish Clinical Text.” 2011.
  66. Jensen, P. B., L. J. Jensen, and S. Brunak. “Mining Electronic Health Records: Towards Better Research Applications and Clinical Care.”
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Zahraa M. Alakrawi, MS. “Clinical Terminology and Clinical Classification Systems: A Critique Using AHIMA’s Data Quality Management Model.” Perspectives in Health Information Management (Summer 2016): 1-19.

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