- Email: [email protected]
The development of an anesthesia lexicon
The Development of an Anesthesia Lexicon Terri G. Monk, MD, MS, and Iain Sanderson, BM, BCh, MA, MSc, FRCA
T
he Institute of Medicine (IOM) was established in 1970 by the National Academy of Sciences to advise the federal government on issues of medical care, research, and education.1 In November 1999, the IOM issued a landmark report titled To Err Is Human: Building a Safer Health System. This document discussed the problem of medical errors and estimated that between 44,000 and 98,000 Americans die each year as a result of medical errors. Even if the lowest estimate is correct, it implies that more people die from medical errors each year than from motor vehicle accidents (43,458) or breast cancer (42,297).2 The authors of this report recognized that the problem of medical error is not a problem of the individual health care provider but is instead a systems problem. They also concluded that health care is at least a decade behind other high-risk industries, such as aviation, in ensuring basic safety.1 The stated goals of this report were to question the status quo in medicine and to break the cycle of inaction in dealing with medical errors. The IOM report recognized that several groups in the United States were already working on methods to improve patient safety and named the Anesthesia Patient Safety Foundation (APSF) as a leader in this field.1 For years, the APSF has been an advocate of outcomes research in the specialty and has recognized the need for aggregate databases in outcomes research. In 2001, the APSF endorsed the use of automated information systems as a means of collecting data for this purpose. The first attempt to create a national database for anesthesia outcomes occurred in the late 1990s. The National Center for Clinical Outcomes Research (NCCOR) was organized for the purpose of developing a data warehouse to which participants would send their perioperative records. In return, NCCOR would deliver comparative derivatives of their data. This effort failed for a variety of reasons. One of the major obstacles to its success was the lack of a standardized medical terminology. Even if participating hospitals had an information system, they faced the daunting task of reformatting and normalizing their data into a standard data
file for submission. In addition, the lack of a standard terminology for medicine meant that participating hospitals could not be sure that their data collection was comparable with that of other hospitals collecting data with slightly different terms and semantic meanings. THE CURRENT STATUS OF AIMS SYSTEMS
Anesthesia information management systems (AIMS) are installed in less than 3% of US hospitals, yet the value of these systems is apparent in terms of legibility and accessibility of the patient’s medical record (Fig 1A and B) as well as for research and practice analysis. According to the Institutes of Medicine report Crossing the Quality Chasm, “automated clinical and administrative data enable many types of health service research applications, such as assessment of clinical outcomes associated with alternative treatment options and care processes; identification of best practices; and evaluation of the effects of different methods of financing, organizing, and delivering services.”3 One of the most significant barriers to adoption of information systems is the complexity and length of the product installation process. Complete product installations in excess of one year are not uncommon; this situation is unduly burdensome to vendor and customer alike. The result is a delay or barrier to the adoption of technology that at the least solves the problem of legibility and accessibility of the anesthesia record (Fig 2). The installation of an anesthesia information management system typically features the develFrom the Department of Anesthesiology, Duke University Hospital, Durham, North Carolina. This work is supported by the Anesthesia Patient Safety Foundation Address reprint requests to Iain Sanderson, Department of Anesthesiology, Box 3094 DUMC, Duke University Medical Center, Durham, NC 27710. © 2004 Elsevier Inc. All rights reserved. 0277-0326/04/2302-0004$30.00/0 doi:10.1053/j.sane.2004.01.003
Seminars in Anesthesia, Perioperative Medicine and Pain, Vol 23, No 2 (June), 2004: pp 93-98
93
94
MONK AND SANDERSON
Fig 1. Manual data collection. (Color version of figure is available online.)
opment of a customized set of terms and phrases to be created specific to each institution. There are few guidelines available and minimal standardization of the terms used (Fig 3). This lack of standardization inhibits the sharing of data between information systems from the same vendor, let alone across institutions with different vendor systems. This data interoperability issue did not arise with the advent of automated
systems. It exists for paper-based anesthesia records and continues to stifle comparative research in the specialty. Attempts have been made to standardize subsets of terms pertinent to the specialty, such as the American Association of Clinical Director’s (AACD) “Glossary of Times Used for Scheduling and Monitoring of Diagnostic and Therapeutic Procedures.”4 However, these lexicons have not been widely adopted and, to date,
AN ANESTHESIA LEXICON
95
Fig 2. Automated data collection. (Color version of figure is available online.)
no group has attempted a more complete terminology for the specialty. THE PROPOSED SOLUTION: A STANDARDIZED ANESTHESIA LEXICON
Table 1 provides relevant definitions in this linguistic arena. For example, a lexicon is the organized vocabulary of a terminology (ie, the collection of words, but not the relationships in a terminology). A data dictionary is a lookup table of terms and their definitions. THE APSF SOLUTION: CREATION OF THE DATA DICTIONARY TASK FORCE (DDTF)
The APSF approached the problem of developing meaningful outcomes research from a new perspective. This organization recognized that the primary problem preventing meaningful outcomes research was the lack of a standard anesthesia terminology. In 2000, the APSF executive committee commissioned a task force to create a standard-
ized terminology for AIMS. Dr. Terri Monk of the University of Florida was appointed chairperson and given the responsibility of organizing this task force and recruiting individuals in academic medicine, private practice, and industry to serve on this committee. Dr. Iain Sanderson from Duke University Hospital was appointed the technical expert for the terminology group. Together, they established the DDTF, recruited members, and outlined the following two objectives for this group: 1. To establish a data dictionary for the collection of perioperative information; and 2. To identify the specific perioperative outcomes to be investigated. The technical arm of the DDTF was given the task of creating a tool for organizing a lexicon or reference set of anesthesia terms. Existing standard terminology were to be used wherever possible. A deliverable of this process would be a set of anes-
96
MONK AND SANDERSON
Fig 3. The problem associated with a lack of standard medical terms. (Color version of figure is available online.)
thesia terms that could be pre-loaded into an information system during an initial installation, providing a considerable service to the vendors of these systems and institutions investing in them. This baseline set of terms would not preclude customization, but could reduce the need or desire for it. Table 1. Definitions of Commonly Used Linguistic Terms A TERM is a word or phrase used to describe a definite concept. A TERMINOLOGY is the collection of terms and their relationships describing a branch of knowledge. A DATA DICTIONARY is a lookup table of terms and their definitions. A LEXICON is the organized vocabulary of a terminology (ie, the collection of words, but not the relationships). An ONTOLOGY is a representation of a body of knowledge using terms, logical relationships, and rules such as to allow reasoning and inference. This implies a level of semantic representation in the knowledge beyond that of a terminology. New knowledge can be inferred from a properly defined ontology.
Originally, the DDTF was charged with developing a data dictionary for the practice of anesthesia in the United States. However, it members realized that this effort would create no more than another endorsed list and that the specialty really needed an anesthesia terminology, a set of terms and the relationships between them. The value of a terminology lies in the analysis and classification of data, which should not simply be governed by searches of text strings, a method that might miss some partially matching (but relevant) data. The members of the DDTF also investigated the possibility of creating a formal ontology to describe the practice of anesthesia. However, ontologies are difficult to build and implement and there are few widely accepted ontologies in complex domains such as medicine. Communities of practice with well-established research, like anesthesia, usually find it easier to deal with a narrower kind of conceptual representation than a formal ontology. Although the daunting task of creating an anesthetic ontology would have huge value, for
AN ANESTHESIA LEXICON practical purposes it would be beyond the scope and capability of existing AIMS systems to be practically applied. For this reason, the DDTF decided to focus on the creation of a terminology for anesthesia. WHAT IS A TERMINOLOGY?
Terminologies work in the following way: In a study of postoperative complications of anesthesia, you could define that “Postoperative Myocardial Infarction” IS-A “Postoperative cardiac event”, and that “Postoperative cardiac event” IS-A “Postoperative complication of anesthesia.” You could further define that “Postoperative complication of anesthesia” HAS-A “Timestamp” and HAS-A “Causative factor” and that “Hypotension during surgery” IS-A “Causative factor.” What is created is an extraction of the underlying knowledge into a format that computers can manipulate with some sense of the underlying meaning of the terms themselves. It means that it is now possible to query a database for cases with events that are descendents of “Postoperative complications of anesthesia” and be reasonably sure that the result will be a meaningful and complete set, provided they have been documented correctly. The beauty of creating the semantic network of a terminology is that it deals mainly with the meaning of terms, called “concepts”. Different words used to describe the same concept are “synonyms”. Putting terms into the right slot in a terminology is a process called “modeling”. As the terminology grows, it becomes easier to see how it all fits together and to model new terms. If the modeling is done well, it doesn’t need to be done again. Another advantage is that the concepts or meanings should be equivalent in all languages, so internationalization becomes the process of translating a new synonym for each language; however, the structure stays the same. PREVIOUS WORK IN ANESTHESIA TERMINOLOGIES
Early on it its discovery phase, the DDTF learned of considerable expertise and experience in creating anesthesia terminologies in the United Kingdom (UK). Our anesthesia colleagues in the National Health Service in the UK had been working independently on an anesthesia terminology under the auspices of the Society for Computing and Technology in Anaesthesia (SCATA) for over 10 years. Members of SCATA were submitting
97 their anesthesia terms to UK’s Clinical Terms Version 3 initiative (CTV3), an extension of the wellrespected Read Codes. Concurrently, the DDTF became aware that the US government was considering the adoption of a major medical terminology called SNOMED (Systematized NOmenclature of MEDicine). SNOMED was originally developed by the College of American Pathologists (CAP). SNOMED International is a non-profit organization that oversees the strategic direction and scientific maintenance of SNOMED. Their goal is to develop a complete dictionary of medical terms. The latest SNOMED product, SNOMED CT, incorporates the CTV3 terms from the National Health Service (NHS) and is now licensed to be used throughout the NHS in the UK. SNOMED is extensively mapped to other lexicons, such as the ICD9-CM diagnosis coding system. One of the advantages of a well founded terminology is that terms only need to be modeled once; in theory there is only one correct way of describing the semantic relationship between terms. In July 2003, the National Library for Medicine purchased a national license for SNOMED CT in the US, making it free for all US medical entities. In September 2003, the DDTF was formally adopted by SNOMED as an official extension group that would establish the terminology for anesthesia in the US. With the adoption of SNOMED CT by the NHS and the US government, it was clearly synergistic for the DDTF to join forces with the UK experts in this area and incorporate the prior work by SCATA in their anesthesia terminology. Although there had been considerable effort toward developing an anesthesia terminology in the UK, this work was limited in scope by the lack of a method of organizing their work. Fortunately, the DDTF had made an advance in this area: early in our effort, we had created a highly visual term modeling tool that made the intricate relationships of a terminology understandable. Our modeling tool was adopted by the UK group as the means of extracting the knowledge of clinical and domain experts and a working pattern developed in which the semantics of anesthesia concepts were gradually broken down and reconstructed. The DDTF is now working with members of SCATA in the UK and the Canadian Anesthesiologists Society to develop a standard anesthesia terminology. The work method used by this collaborative group consists of
98
MONK AND SANDERSON
searching for anesthesia terms in SNOMED CT, and where they did not exist, modeling them in our tool for submission to SNOMED CT. The work was in effect creating the “Anesthesia subset” of terms for SNOMED CT. IOTA: THE EVOLUTION OF THE DDTF
The scope of the DDTF has clearly become more than a data dictionary for US anesthesia. In 2003, the DDTF evolved into the International Organization for Terminology in Anesthesia (IOTA) with the mission to create a standardized terminology for the global anesthesia community. IOTA presently contains members from the DDTF, the Canadian Anesthesiologists Society, and the Society for Computing and Technology in Anaesthesia (SCATA) in the United Kingdom. The American Society of Anesthesiologists (ASA) has also appointed a member of the Committee on Performance and Outcomes Measurement (CPOM) to represent them at all meetings and to lead the effort to establish meaningful outcomes research questions for the specialty of anesthesia. RELEVANCE OF AN ANESTHESIA LEXICON TO FUTURE OUTCOMES RESEARCH
In summary, the terminology developed by the IOTA will have the following elements: a) A list of terms (the lexicon); b) A list of terms and their relationships (the terminology, a database); and c) Concordance of this terminology with the Anesthesia Subset in SNOMED. The development of a standardized anesthesia lexicon will greatly facilitate outcomes research in the future even if an AIMS system is not in place. The use of a standardized anesthesia terminology will allow outcomes comparisons between hospitals and within departments in a single institution. Meaningful outcomes research will be an important step toward improving patient safety in the future. INTERNATIONAL ORGANIZATION FOR TERMINOLOGY IN ANESTHESIA (IOTA)
USA: 1. Dr. Terri G. Monk, Professor, Department of Anesthesiology, Duke University Hospi-
tal, Durham, NC: International Organization for Terminology in Anesthesia (IOTA) 2. Dr. Iain Sanderson, Associate Clinical Professor, Department of Anesthesiology, Duke University Medical Center, Durham, NC 3. Dr. Ronald A. Gabel, Committee on Performance and Outcomes Measurements, American Society of Anesthesiologists, Professor Emeritus of Anesthesiology, University of Rochester, Rochester, New York 4. Dr. David L. Reich, Professor, Department of Anesthesiology, Mount Sinai School of Medicine, New York, New York UK: 1. Dr. Andrew Norton, Consultant Anaesthetist, Pilgrim Hospital, Lincolnshire, Chairman, Clinical Standards Group, Society for Computing and Technology in Anaesthesia (SCATA) 2. Dr. Roger Tackley, Consultant Anesthesiologist, Member of SNOMED International Editorial Board, Chairman, Society for Computing and Technology in Anaesthesia (SCATA), South Devon Healthcare, National Health Service Trust, Torbay Hospital, Devon, UK 3. Dr. Martin Hurrell, Member, Clinical Standards Group, Society for Computing and Technology in Anaesthesia (SCATA), Chief Executive Officer, Clinical Informatics, Ltd., Glasglow, Scotland, UK CANADA: 1. Dr. Homer Yang, Canadian Anesthesiologists Society, Professor and Chair, Department of Anesthesiology, University of Ottawa, Ottawa, Canada 2. Dr John Muir, Residency Program Director, Dalhousie University, Department of Anesthesia, Halifax, Nova Scotia, Canada REFERENCES 1. Committee on Quality of Health Care in America, Institute of Medicine: To err is human building a safer health system. Washington DC, National Academy Press, 1999 2. Centers for Disease Control and Prevention (National Center for Health Statistics): Births and deaths: preliminary data for 1998. National Vital Statistics Reports 47:6, 1999 3. Committee on Quality of Health Care in America, Institute of Medicine: Crossing the quality chasm. Washington DC, National Academy Press, 2001 4. Donham RT, et al: Am J Anesthesiol Suppl. September, 1996
T
he Institute of Medicine (IOM) was established in 1970 by the National Academy of Sciences to advise the federal government on issues of medical care, research, and education.1 In November 1999, the IOM issued a landmark report titled To Err Is Human: Building a Safer Health System. This document discussed the problem of medical errors and estimated that between 44,000 and 98,000 Americans die each year as a result of medical errors. Even if the lowest estimate is correct, it implies that more people die from medical errors each year than from motor vehicle accidents (43,458) or breast cancer (42,297).2 The authors of this report recognized that the problem of medical error is not a problem of the individual health care provider but is instead a systems problem. They also concluded that health care is at least a decade behind other high-risk industries, such as aviation, in ensuring basic safety.1 The stated goals of this report were to question the status quo in medicine and to break the cycle of inaction in dealing with medical errors. The IOM report recognized that several groups in the United States were already working on methods to improve patient safety and named the Anesthesia Patient Safety Foundation (APSF) as a leader in this field.1 For years, the APSF has been an advocate of outcomes research in the specialty and has recognized the need for aggregate databases in outcomes research. In 2001, the APSF endorsed the use of automated information systems as a means of collecting data for this purpose. The first attempt to create a national database for anesthesia outcomes occurred in the late 1990s. The National Center for Clinical Outcomes Research (NCCOR) was organized for the purpose of developing a data warehouse to which participants would send their perioperative records. In return, NCCOR would deliver comparative derivatives of their data. This effort failed for a variety of reasons. One of the major obstacles to its success was the lack of a standardized medical terminology. Even if participating hospitals had an information system, they faced the daunting task of reformatting and normalizing their data into a standard data
file for submission. In addition, the lack of a standard terminology for medicine meant that participating hospitals could not be sure that their data collection was comparable with that of other hospitals collecting data with slightly different terms and semantic meanings. THE CURRENT STATUS OF AIMS SYSTEMS
Anesthesia information management systems (AIMS) are installed in less than 3% of US hospitals, yet the value of these systems is apparent in terms of legibility and accessibility of the patient’s medical record (Fig 1A and B) as well as for research and practice analysis. According to the Institutes of Medicine report Crossing the Quality Chasm, “automated clinical and administrative data enable many types of health service research applications, such as assessment of clinical outcomes associated with alternative treatment options and care processes; identification of best practices; and evaluation of the effects of different methods of financing, organizing, and delivering services.”3 One of the most significant barriers to adoption of information systems is the complexity and length of the product installation process. Complete product installations in excess of one year are not uncommon; this situation is unduly burdensome to vendor and customer alike. The result is a delay or barrier to the adoption of technology that at the least solves the problem of legibility and accessibility of the anesthesia record (Fig 2). The installation of an anesthesia information management system typically features the develFrom the Department of Anesthesiology, Duke University Hospital, Durham, North Carolina. This work is supported by the Anesthesia Patient Safety Foundation Address reprint requests to Iain Sanderson, Department of Anesthesiology, Box 3094 DUMC, Duke University Medical Center, Durham, NC 27710. © 2004 Elsevier Inc. All rights reserved. 0277-0326/04/2302-0004$30.00/0 doi:10.1053/j.sane.2004.01.003
Seminars in Anesthesia, Perioperative Medicine and Pain, Vol 23, No 2 (June), 2004: pp 93-98
93
94
MONK AND SANDERSON
Fig 1. Manual data collection. (Color version of figure is available online.)
opment of a customized set of terms and phrases to be created specific to each institution. There are few guidelines available and minimal standardization of the terms used (Fig 3). This lack of standardization inhibits the sharing of data between information systems from the same vendor, let alone across institutions with different vendor systems. This data interoperability issue did not arise with the advent of automated
systems. It exists for paper-based anesthesia records and continues to stifle comparative research in the specialty. Attempts have been made to standardize subsets of terms pertinent to the specialty, such as the American Association of Clinical Director’s (AACD) “Glossary of Times Used for Scheduling and Monitoring of Diagnostic and Therapeutic Procedures.”4 However, these lexicons have not been widely adopted and, to date,
AN ANESTHESIA LEXICON
95
Fig 2. Automated data collection. (Color version of figure is available online.)
no group has attempted a more complete terminology for the specialty. THE PROPOSED SOLUTION: A STANDARDIZED ANESTHESIA LEXICON
Table 1 provides relevant definitions in this linguistic arena. For example, a lexicon is the organized vocabulary of a terminology (ie, the collection of words, but not the relationships in a terminology). A data dictionary is a lookup table of terms and their definitions. THE APSF SOLUTION: CREATION OF THE DATA DICTIONARY TASK FORCE (DDTF)
The APSF approached the problem of developing meaningful outcomes research from a new perspective. This organization recognized that the primary problem preventing meaningful outcomes research was the lack of a standard anesthesia terminology. In 2000, the APSF executive committee commissioned a task force to create a standard-
ized terminology for AIMS. Dr. Terri Monk of the University of Florida was appointed chairperson and given the responsibility of organizing this task force and recruiting individuals in academic medicine, private practice, and industry to serve on this committee. Dr. Iain Sanderson from Duke University Hospital was appointed the technical expert for the terminology group. Together, they established the DDTF, recruited members, and outlined the following two objectives for this group: 1. To establish a data dictionary for the collection of perioperative information; and 2. To identify the specific perioperative outcomes to be investigated. The technical arm of the DDTF was given the task of creating a tool for organizing a lexicon or reference set of anesthesia terms. Existing standard terminology were to be used wherever possible. A deliverable of this process would be a set of anes-
96
MONK AND SANDERSON
Fig 3. The problem associated with a lack of standard medical terms. (Color version of figure is available online.)
thesia terms that could be pre-loaded into an information system during an initial installation, providing a considerable service to the vendors of these systems and institutions investing in them. This baseline set of terms would not preclude customization, but could reduce the need or desire for it. Table 1. Definitions of Commonly Used Linguistic Terms A TERM is a word or phrase used to describe a definite concept. A TERMINOLOGY is the collection of terms and their relationships describing a branch of knowledge. A DATA DICTIONARY is a lookup table of terms and their definitions. A LEXICON is the organized vocabulary of a terminology (ie, the collection of words, but not the relationships). An ONTOLOGY is a representation of a body of knowledge using terms, logical relationships, and rules such as to allow reasoning and inference. This implies a level of semantic representation in the knowledge beyond that of a terminology. New knowledge can be inferred from a properly defined ontology.
Originally, the DDTF was charged with developing a data dictionary for the practice of anesthesia in the United States. However, it members realized that this effort would create no more than another endorsed list and that the specialty really needed an anesthesia terminology, a set of terms and the relationships between them. The value of a terminology lies in the analysis and classification of data, which should not simply be governed by searches of text strings, a method that might miss some partially matching (but relevant) data. The members of the DDTF also investigated the possibility of creating a formal ontology to describe the practice of anesthesia. However, ontologies are difficult to build and implement and there are few widely accepted ontologies in complex domains such as medicine. Communities of practice with well-established research, like anesthesia, usually find it easier to deal with a narrower kind of conceptual representation than a formal ontology. Although the daunting task of creating an anesthetic ontology would have huge value, for
AN ANESTHESIA LEXICON practical purposes it would be beyond the scope and capability of existing AIMS systems to be practically applied. For this reason, the DDTF decided to focus on the creation of a terminology for anesthesia. WHAT IS A TERMINOLOGY?
Terminologies work in the following way: In a study of postoperative complications of anesthesia, you could define that “Postoperative Myocardial Infarction” IS-A “Postoperative cardiac event”, and that “Postoperative cardiac event” IS-A “Postoperative complication of anesthesia.” You could further define that “Postoperative complication of anesthesia” HAS-A “Timestamp” and HAS-A “Causative factor” and that “Hypotension during surgery” IS-A “Causative factor.” What is created is an extraction of the underlying knowledge into a format that computers can manipulate with some sense of the underlying meaning of the terms themselves. It means that it is now possible to query a database for cases with events that are descendents of “Postoperative complications of anesthesia” and be reasonably sure that the result will be a meaningful and complete set, provided they have been documented correctly. The beauty of creating the semantic network of a terminology is that it deals mainly with the meaning of terms, called “concepts”. Different words used to describe the same concept are “synonyms”. Putting terms into the right slot in a terminology is a process called “modeling”. As the terminology grows, it becomes easier to see how it all fits together and to model new terms. If the modeling is done well, it doesn’t need to be done again. Another advantage is that the concepts or meanings should be equivalent in all languages, so internationalization becomes the process of translating a new synonym for each language; however, the structure stays the same. PREVIOUS WORK IN ANESTHESIA TERMINOLOGIES
Early on it its discovery phase, the DDTF learned of considerable expertise and experience in creating anesthesia terminologies in the United Kingdom (UK). Our anesthesia colleagues in the National Health Service in the UK had been working independently on an anesthesia terminology under the auspices of the Society for Computing and Technology in Anaesthesia (SCATA) for over 10 years. Members of SCATA were submitting
97 their anesthesia terms to UK’s Clinical Terms Version 3 initiative (CTV3), an extension of the wellrespected Read Codes. Concurrently, the DDTF became aware that the US government was considering the adoption of a major medical terminology called SNOMED (Systematized NOmenclature of MEDicine). SNOMED was originally developed by the College of American Pathologists (CAP). SNOMED International is a non-profit organization that oversees the strategic direction and scientific maintenance of SNOMED. Their goal is to develop a complete dictionary of medical terms. The latest SNOMED product, SNOMED CT, incorporates the CTV3 terms from the National Health Service (NHS) and is now licensed to be used throughout the NHS in the UK. SNOMED is extensively mapped to other lexicons, such as the ICD9-CM diagnosis coding system. One of the advantages of a well founded terminology is that terms only need to be modeled once; in theory there is only one correct way of describing the semantic relationship between terms. In July 2003, the National Library for Medicine purchased a national license for SNOMED CT in the US, making it free for all US medical entities. In September 2003, the DDTF was formally adopted by SNOMED as an official extension group that would establish the terminology for anesthesia in the US. With the adoption of SNOMED CT by the NHS and the US government, it was clearly synergistic for the DDTF to join forces with the UK experts in this area and incorporate the prior work by SCATA in their anesthesia terminology. Although there had been considerable effort toward developing an anesthesia terminology in the UK, this work was limited in scope by the lack of a method of organizing their work. Fortunately, the DDTF had made an advance in this area: early in our effort, we had created a highly visual term modeling tool that made the intricate relationships of a terminology understandable. Our modeling tool was adopted by the UK group as the means of extracting the knowledge of clinical and domain experts and a working pattern developed in which the semantics of anesthesia concepts were gradually broken down and reconstructed. The DDTF is now working with members of SCATA in the UK and the Canadian Anesthesiologists Society to develop a standard anesthesia terminology. The work method used by this collaborative group consists of
98
MONK AND SANDERSON
searching for anesthesia terms in SNOMED CT, and where they did not exist, modeling them in our tool for submission to SNOMED CT. The work was in effect creating the “Anesthesia subset” of terms for SNOMED CT. IOTA: THE EVOLUTION OF THE DDTF
The scope of the DDTF has clearly become more than a data dictionary for US anesthesia. In 2003, the DDTF evolved into the International Organization for Terminology in Anesthesia (IOTA) with the mission to create a standardized terminology for the global anesthesia community. IOTA presently contains members from the DDTF, the Canadian Anesthesiologists Society, and the Society for Computing and Technology in Anaesthesia (SCATA) in the United Kingdom. The American Society of Anesthesiologists (ASA) has also appointed a member of the Committee on Performance and Outcomes Measurement (CPOM) to represent them at all meetings and to lead the effort to establish meaningful outcomes research questions for the specialty of anesthesia. RELEVANCE OF AN ANESTHESIA LEXICON TO FUTURE OUTCOMES RESEARCH
In summary, the terminology developed by the IOTA will have the following elements: a) A list of terms (the lexicon); b) A list of terms and their relationships (the terminology, a database); and c) Concordance of this terminology with the Anesthesia Subset in SNOMED. The development of a standardized anesthesia lexicon will greatly facilitate outcomes research in the future even if an AIMS system is not in place. The use of a standardized anesthesia terminology will allow outcomes comparisons between hospitals and within departments in a single institution. Meaningful outcomes research will be an important step toward improving patient safety in the future. INTERNATIONAL ORGANIZATION FOR TERMINOLOGY IN ANESTHESIA (IOTA)
USA: 1. Dr. Terri G. Monk, Professor, Department of Anesthesiology, Duke University Hospi-
tal, Durham, NC: International Organization for Terminology in Anesthesia (IOTA) 2. Dr. Iain Sanderson, Associate Clinical Professor, Department of Anesthesiology, Duke University Medical Center, Durham, NC 3. Dr. Ronald A. Gabel, Committee on Performance and Outcomes Measurements, American Society of Anesthesiologists, Professor Emeritus of Anesthesiology, University of Rochester, Rochester, New York 4. Dr. David L. Reich, Professor, Department of Anesthesiology, Mount Sinai School of Medicine, New York, New York UK: 1. Dr. Andrew Norton, Consultant Anaesthetist, Pilgrim Hospital, Lincolnshire, Chairman, Clinical Standards Group, Society for Computing and Technology in Anaesthesia (SCATA) 2. Dr. Roger Tackley, Consultant Anesthesiologist, Member of SNOMED International Editorial Board, Chairman, Society for Computing and Technology in Anaesthesia (SCATA), South Devon Healthcare, National Health Service Trust, Torbay Hospital, Devon, UK 3. Dr. Martin Hurrell, Member, Clinical Standards Group, Society for Computing and Technology in Anaesthesia (SCATA), Chief Executive Officer, Clinical Informatics, Ltd., Glasglow, Scotland, UK CANADA: 1. Dr. Homer Yang, Canadian Anesthesiologists Society, Professor and Chair, Department of Anesthesiology, University of Ottawa, Ottawa, Canada 2. Dr John Muir, Residency Program Director, Dalhousie University, Department of Anesthesia, Halifax, Nova Scotia, Canada REFERENCES 1. Committee on Quality of Health Care in America, Institute of Medicine: To err is human building a safer health system. Washington DC, National Academy Press, 1999 2. Centers for Disease Control and Prevention (National Center for Health Statistics): Births and deaths: preliminary data for 1998. National Vital Statistics Reports 47:6, 1999 3. Committee on Quality of Health Care in America, Institute of Medicine: Crossing the quality chasm. Washington DC, National Academy Press, 2001 4. Donham RT, et al: Am J Anesthesiol Suppl. September, 1996