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Patient Safety: Achieving a New Standard for Care 4 Health Care Data Standards CHAPTER SUMMARY Data standards are the principal informatics component necessary for information flow through the national health information infrastructure. With common standards, clinical and patient safety systems can share an integrated information infrastructure whereby data are collected and reused for multiple purposes to meet more efficiently the broad scope of data collection and reporting requirements. Common data standards also support effective assimilation of new knowledge into decision support tools, such as an alert of a new drug contraindication, and refinements to the care process. This chapter provides both a short overview introducing data standards to the lay reader and a more technical review of the specific data standards required for the informatics-oriented professional. Please note that in the technical portion of the paper, once a standard is introduced it will be referred to in its acronym form due to the number of data standards involved. Readers may refer to the list of acronyms in Appendix B for assistance as needed. OVERVIEW OF HEALTH CARE DATA STANDARDS Although much of the data needed for clinical care, patient safety, and quality improvement resides on computers, there is as yet no means to trans-
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Patient Safety: Achieving a New Standard for Care fer these data easily and economically from one computer to another, despite the availability of the communications technologies to support such data exchange. The chief obstacle to achieving this capability has been the haphazard adoption of data standards for organizing, representing, and encoding clinical information so that the data can be understood and accepted by the receiving systems (Hammond, 2002). At the level of the health care organization, the lack of common data standards has prevented information sharing between commercial clinical laboratories and health care facilities, between pharmacies and health care providers regarding prescriptions, and between health care organizations and payers for reimbursement (Hammond, 2002). The lack of standards has also prevented the reuse of clinical data to meet the broad range of patient safety and quality reporting requirements, shown in Table 4-1. The first column of this table lists the data sources often associated with an electronic health record (EHR); the second, those associated with clinical information systems, decision support tools, and external data sources; the third, state, regulatory, and private-sector patient safety reporting systems; and the fourth, federal reporting systems. The fact that there is no standard means of representing the data for any of these datasets or requirements is astonishing and highlights the amount of unnecessary work performed by health care and regulatory organizations to prepare, transmit, and use what amount to custom reports. The federal government has recognized this problem and is moving forward with the integration of its safety-related systems. This study goes further by recommending common standards for the clinical and patient safety data that span the full range of data sources listed in Table 4-1. Many of the data standards required are already available; others need further development. What Are Data Standards? In the context of health care, the term data standards encompasses methods, protocols, terminologies, and specifications for the collection, exchange, storage, and retrieval of information associated with health care applications, including medical records, medications, radiological images, payment and reimbursement, medical devices and monitoring systems, and administrative processes (Washington Publishing Company, 1998). Standardizing health care data involves the following: Definition of data elements—determination of the data content to be collected and exchanged. Data interchange formats—standard formats for electronically encod-
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Patient Safety: Achieving a New Standard for Care ing the data elements (including sequencing and error handling) (Hammond, 2002). Interchange standards can also include document architectures for structuring data elements as they are exchanged and information models that define the relationships among data elements in a message. Terminologies—the medical terms and concepts used to describe, classify, and code the data elements and data expression languages and syntax that describe the relationships among the terms/concepts. Knowledge Representation—standard methods for electronically representing medical literature, clinical guidelines, and the like for decision support. At the most basic level, data standards are about the standardization of data elements: (1) defining what to collect, (2) deciding how to represent what is collected (by designating data types or terminologies), and (3) determining how to encode the data for transmission. The first two points apply to both paper-based and computer-based systems; for example, a laboratory test report will have the same data elements whether paper or electronic. A data element is considered the basic unit of information, having a unique meaning and subcategories of distinct units or values (van Bemmel and Musen, 1997). In computer terms, data elements are objects that can be collected, used, and/or stored in clinical information systems and application programs, such as patient name, gender, and ethnicity; diagnosis; primary care provider; laboratory results; date of each encounter; and each medication. Data elements of specific clinical information, such as blood glucose level or cholesterol level, can be grouped together to form datasets for measuring outcomes, evaluating quality of care, and reporting on patient safety events. Associated with data elements are data types that define their form. Simple data types include date, time, numeric, currency, or coded elements that rely on terminologies (Hammond, 2002). Examples of complex data types are names (a structure for names) and addresses. For comparability and interchange, data types must be universal and must be carried through all uses of the data. The designation of common scientific units is also necessary. Units (e.g., kilograms, pounds) must be specified as another measure to prevent adverse events such as those related to dosing errors. Until recently, each institution or organization defined independently the data it wished to collect and the units employed, did not use data types, and created local vocabularies, resulting in fragmentation that prevented reuse. For data elements that rely on terminologies and their codes for definition, merely referencing a terminology alone does not provide enough speci-
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Patient Safety: Achieving a New Standard for Care TABLE 4-1 Comprehensive List of Health Care Data Sources and Reporting Requirements Clinical Datasets Other Data Sources for Patient Safety Information Histories Allergies Immunizations Social histories Vital signs Physical examination Physicians’ notes Nurses’ notes Laboratory tests Diagnostic tests Radiology tests Diagnoses Medications Procedures Clinical documentation Clinical measures for specific clinical conditions Patient instructions Dispositions Health maintenance schedules Policies and procedures Human resources records Materials management systems Time and attendance records Census records Decision support alert logs Coroners’ datasets Claims attachments Admissions data Disease registries Discharge data Malpractice data Patient complaints and reports of adverse events Reports to professional boards Trigger datasets (e.g., antidote drugs for adverse drug events) Computerized physician order entry systems Bar-code medication administration systems Clinical trial data
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Patient Safety: Achieving a New Standard for Care Patient Safety Datasets and Taxonomies Federal Reporting Systems Datasets Eindhoven classification taxonomy Near misses (development needed) Adverse events (development needed) Accreditation reporting dataset (Joint Commission on Accreditation of Healthcare Organizations [JCAHO]) Medical Specialty Society—such as Trauma/emergency Surgery Anesthesia Radiology Family practice Pediatrics Private sector—subsets Medical Event Reporting System for Transfusion Medicine (MERS TM) United States Pharmacopea (USP) National Coordinating Council for Medication Error Reporting and Prevention (NCC MERPS) MedMarx (by USP for medication events) Emergency Care Research Institute (ECRI) States with mandatory reporting systems Colorado California Connecticut Florida Georgia Kansas Massachusetts Maine Minnesota New Jersey New York Nevada Ohio Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Washington Oregon (voluntary system) Agency for Healthcare Research and Quality Prevention Quality Indicators (PQI) Quality Indicators for Patient Safety (QIPS) Centers for Disease Control and Prevention National Electronic Disease Surveillance System (NEDSS) Dialysis Surveillance Network (DSN) Vaccine Adverse Event Reporting System (VAERS) Vaccine Safety Datalink (VSD) National Nosocomial Infection Surveillance System (NNIS) National Center for Health Statistics (NCHS) Centers for Medicare and Medicaid Services Medicare Patient Safety Monitoring System (MPSMS) Minimum Data Set (MDS) for Nursing Home Care End-stage renal disease (ESRD) Outcome and Assessment Information Set (OASIS) for Home Care Food and Drug Administration Adverse Event Reporting System (AERS) Manufacturer and User Data Experience (MAUDE) Special Nutritionals Adverse Event Monitoring System (SNAEMS) Biological Product Deviation Reporting System (BPDR/BIODEV) Medical Product Surveillance Network (MedSun) MedWatch (postmarket surveillance) Nuclear Regulatory Commission Radiation events Noncommunicable Diseases Cancer Registry
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Patient Safety: Achieving a New Standard for Care ficity. To ensure data comparability, specific codes must be identified within each terminology set to represent the data elements. This becomes a major issue for some of the larger clinical terminologies, which may have hundreds or thousands of terms. It is also a major issue given the amount of data that must be collected for the data sources and requirements listed in Table 4-1 and that will be encompassed by the national health information infrastructure (NHII). Common data standards are essential to simplify and streamline data requirements and allow the information systems that carry the data to function as an integrated enterprise. TECHNICAL REVIEW OF HEALTH CARE DATA STANDARDS1 This section provides a detailed technical review of the three primary areas in which standards for health care data need to be developed: data interchange, terminologies, and knowledge representation. The final subsection addresses the implementation of these data standards. Data Interchange Standards In the area of data interchange, standards are needed for message format, document architecture, clinical templates, user interface, and patient data linkage. Message Format Standards Message format standards facilitate interoperability through the use of common encoding specifications, information models for defining relationships between data elements, document architectures, and clinical templates for structuring data as they are exchanged. In March 2003, the Consolidated Health Informatics (CHI) initiative announced its requirement that all federal health care services agencies adopt the primary clinical messaging format standards (i.e., the Health Level Seven [HL7] Version 2.x [V2.x] series for clinical data messaging, Digital Imaging and Communications in Medicine [DICOM] for medical images, National Council for Prescription Drug Programs [NCPDP] Script for retail pharmacy messaging, Institute of Elec- 1 Numerous acronyms appear in the discussion in this section. We follow the convention of defining each upon its first appearance and using the acronym thereafter. All acronyms used here are defined in Appendix B.
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Patient Safety: Achieving a New Standard for Care trical and Electronics Engineers [IEEE] standards for medical devices, and Logical Observation Identifiers, Names and Codes [LOINC] for reporting of laboratory results) (Office of Management and Budget, 2003). It is worth noting that HL7, through its Laboratory Automated Point-of-Care Special Interest Group, has also developed messaging standards for the devices used in laboratory automation (e.g., robots and laboratory instruments) and point-of-care test devices (e.g., blood glucose monitors). These standards are in the process of being incorporated into IEEE standards and eventually will become standards at the International Organization for Standardization (ISO). The HL7 V2.x series is the primary data interchange standard for clinical messaging and is presently adopted in 90 percent of large hospitals (American National Standards Institute, 2002). However, there have been a number of technical problems with the standard that have been difficult to resolve. For one, “conditional optionality” was built into the framework such that it permits a number of terminologies to represent a data element (e.g., Systemized Nomenclature of Human and Veterinary Medicine [SNOMED], LOINC) without being precise about the specific codes (i.e., allowable values) within the terminology (Hammond, 2002). The “openness” of the optionality has led to discrepancies in the application of the standard and misunderstanding of the specifications due to different vendor information models. Also, although V2.x does not support Web-based protocols (e.g., Simple Object Access Protocol [SOAP]), it can be sent over the Internet and expressed as an extensible markup language (XML) syntax standard (Hammond, 2002). However, V2.x does not incorporate an information model that is needed for more advanced messaging of clinical information. Resolving these issues would be time consuming and labor intensive and could easily be accomplished by completion and implementation of HL7 Version 3.0 (V3), in which few of the data fields are open to interpretation. Currently, the scope of the V3 standard remains the same as that of V2.x; however, the initial release of V3 did not include the domains for patient referrals, patient care, or laboratory automation, all of which are important to patient safety (Health Level Seven, 2001). To move forward, the first step is to accelerate the completion of V3 and develop implementation guides for the use of both V2.x and V3 and interoperability between the two standards, with clear definition of the standard specifications. Both V2.x and V3 require a controlled terminology specified at the data element level to support interoperability. Additionally, since V2.x will probably continue to be widely used for some time, it is important that any difficulties with interoperability between this standard and others be resolved. For example,
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Patient Safety: Achieving a New Standard for Care V2.x is used for medication order messages in the inpatient and outpatient settings, while the NCPDP Script standard is used by retail pharmacies. Ensuring interoperability between these two standards is necessary for high-functioning systems. HL7 V3 differs from V2.x in that it incorporates a Reference Information Model (RIM) for setting up the messaging format. The RIM is based on object-oriented modeling, expressing the classes of information required and the properties of those classes, including attributes, relationships, and states (Health Level Seven, 2001). The structured specifications of the information requirements minimize optionality by clearly defining each aspect of the RIM (Van Hentenryck, 2001). Data fields are populated with explicit controlled vocabulary, increasing semantic interoperability among various code sets (Van Hentenryck, 2001). Additionally, HL7 V3 messages are encoded using XML (as are Versions 2.4 and 2.5), which is easy to use, extensible, capable of representing complex data, and Internet compatible (Van Hentenryck, 2001). XML is used to exchange the data quickly and simply, but the RIM is needed to provide the necessary semantic interoperability. At the core of the RIM are four high-level classes from which all other classes are derived—entity, role, participation, and act. Figure 4-1 is a simplified depiction of the structural relationships encompassed by the RIM that should aid in understanding the basis of the model. Information modeling facilitates recognition of high-risk procedures having a direct impact on patient safety (Russler, 2002). Both safe active patient care and retrospective analysis for a patient safety event depend on proper information relationships (Russler, 2002). To this end, the information model must facilitate the process of care such that the link from entities to their intentional actions can support the information relationships used in analyzing patient safety issues, as well as larger issues of cost and quality improvement (Russler, 2002). Using an analogy from aviation, examination of the link between a precipitating event and an adverse event is as important as comparing the data from a flight data recorder with the data from the voice recording in the cockpit in the case of an airline accident (Russler, 2002). HL7 V3 and the RIM are particularly important to the advancement of integrated clinical systems because they provide the backbone for the next set of standards needed for the EHR including those required for the use of concept-oriented terminologies, document architectures, clinical templates, alerts and reminders, and automated clinical guidelines, all of which would result in improved interoperability and structuring of clinical and patient data.
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Patient Safety: Achieving a New Standard for Care FIGURE 4-1 HL7 reference information model. SOURCE: Hammond, 2002.
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Patient Safety: Achieving a New Standard for Care Document Architecture A method for representing electronically clinical data such as discharge summaries or progress notes and patient safety reports requires a standardized document architecture. This need stems from the desire to access the considerable content currently stored in free-text clinical notes and to enable comparison of content from documents created on information systems of widely varying characteristics (Dolin et al., 2001). The architecture should be designed as a markup standard (Dolin et al., 2001) so that clinical documents can be revised as needed or appended to existing documents. It should also be able to accommodate the desire for rich narrative text that makes up a significant portion of patient safety information from voluntary and mandatory reports. One example is the HL7 Clinical Document Architecture (CDA), a defined, complete information object that can include text, images, sounds, and other multimedia content (Dolin et al., 2001). The CDA provides a hierarchical set of specifications for the structure of clinical documents and derives its semantic content from the RIM (Dolin et al., 2001). Initial specifications define the document header in detail (i.e., identifying document name, type, source, author, date–time, and the like, including an area for narrative text), while the document body is structured to represent narrative clinical notes. This structure minimizes technical barriers to the adoption of the standard in that it intentionally lacks some of the complex semantics used in HL7 V3 messages. The initial specifications lay the foundation for future specifications that will incorporate clinical templates and additional RIM-derived markup, enabling the clinical content to be expressed more formally to the extent that it can be encompassed fully in the RIM or V3 message (Dolin et al., 2001). Again, because both HL7 V2.x and V3 will be in use for the short term and midterm, implementation protocols should include the ability of systems to translate CDA documents to and from V2.x and V3. Clinical Templates HL7 V3 provides the mechanism to specify further constraints on the optionality of the data elements through the use of templates that can be applied against a V3 message or document. The HL7 V3 messages maintain moderate optionality, although the RIM provides some constraints. For greater precision in standardization of clinical data, more targeted specifications of the allowable values for the data elements must be applied. A tem-
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Patient Safety: Achieving a New Standard for Care plate in the broadest sense is simply a constraint on a more generic model that permits, among other things, the definition of a complex object, such as a blood chemistry measurement or a heart murmur (Hammond, 2002). For example, an HL7 message format for laboratory observations may specify that the data elements for a complete blood count test must include measurements for hemoglobin, hematocrit, and platelets. The design of constraints will be left to the discretion of health care organizations and providers, as HL7 provides the mechanisms and technical specifications for their use. Clinical templates will be important in the development of electronic structure for the collection and analysis of clinical and patient safety data, particularly those related to the 20 priority areas identified by the Institute of Medicine (2003). User Interface The medical device industry is well versed in developing user interfaces that make devices safer, more effective, and easier to use by employing a voluntary standard for human factors design established by the Association for the Advancement of Medical Instrumentation (AAMI) and approved by the American National Standards Institute (ANSI) (Association for the Advancement of Medical Instrumentation, 2001). This standard—the ANSI/ AAMI HE74 Human Factors Design Process for Medical Devices—establishes tools and techniques to support the analysis, design, testing, and evaluation of both simple and complex systems; these tools and techniques have been applied for many years in the engineering of consumer products, military applications, aviation equipment, and nuclear power systems. Consideration of the HE74 standard may provide insight into the processes employed for designing and developing user-friendly clinical information systems, including electronic patient safety reporting systems. An overview of the human factors engineering process that governs HE74 is provided in Figure 4-2. The specific activities at each step in the cycle vary with the particular development effort (Association for the Advancement of Medical Instrumentation, 2001). The cycle in Figure 4-2 emphasizes the iterative nature of the development process, whereby the outcomes (i.e., outputs) of one step provide input to the next step, but also, as needed, the output of some steps feeds back to previous steps. Although entry into the cycle can begin at any step, involving users at the early stages of development is critical. Once user needs and the consequent concept for the device (system) have been well defined, it becomes possible to address the design criteria/requirements that define the operating conditions, user
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Patient Safety: Achieving a New Standard for Care NCVHS on clinical semantic interoperability by Dr. James R. Campbell of the University of Nebraska Medical Center (Campbell, 2002). Knowledge Representation Biomedical literature knowledge bases are powerful tools for clinical reference. These knowledge bases hold the vast body of medical research findings from both a historical perspective and the perspective of current best evidence-based practice. At present, most digital sources of evidence are operating as stand alone systems without the ability to link to clinical information systems. With the development and use of common data standards, this linkage for enhanced access to medical knowledge bases can occur. Clinical Guideline Representation Model An earlier IOM report defines practice guidelines as systematically developed statements to assist practitioners and patients in making decisions about health care for specific circumstances (Institute of Medicine, 1992). The National Guideline Clearinghouse alone contains nearly 1,000 publicly accessible guidelines (Maviglia et al., 2003). There are gaps and inconsistencies in the medical literature supporting one practice versus another, as well as biases based on the perspective of the authors, who may be specialists, general practitioners, payers, marketers, or public health officials (Maviglia et al., 2003). Few national guidelines can be implemented in clinical information systems because of the lack of a way to represent the knowledge in machine-executable formats. Automating guidelines requires a computer-readable format that is unambiguous and makes use of stored patient data. A number of computational models and tools for extracting, organizing, presenting, and sharing clinical guidelines are currently in developmental use. Box 4-2 lists the most common of these. Few guidelines have been successfully translated and incorporated into real clinical settings (Advandi et al., 1999) because the language of which most text-based guidelines are composed is ambiguous. Eligibility criteria and severity of disease or symptoms often are not explicitly defined, and when they are defined, they may not map to computable data within an EHR (Maviglia et al., 2003) or other decision support systems. Simpler decision support that has worked successfully has been in the form of “if–then” rules triggered by EHR data that result in alert/reminder messages (Maviglia
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Patient Safety: Achieving a New Standard for Care BOX 4-2 Guideline Representation Models and Tools Arden Syntax, Columbia University DILEMMA/PRESTIGE model, Europe EON/DHARMA model, Stanford University PROforma model, Imperial Cancer Research Center, United Kingdom Siegfried system, Duke University Guideline Interchange Format (GLIF) model, InterMed Collaboratory Asbru model, Vienna University of Technology and Ben-Gurion University GUIDE/PatMan model, University of Pavia PRODIGY model, University of Newcastle, United Kingdom GASTON framework, University of Maastricht Torino model, University of Torino SOURCE: Wang et al., 2002b. et al., 2003). The multitude of guideline models are dissimilar—they capture different features of a guideline and were created for different purposes. For example, guidelines can be used to support clinical work flow, to foster background utilization review and monitoring, to drive consultations, or to capture the process flow in a clinical protocol. As a consequence, no single model enables all of the features of the various models to be fully encoded. One potential approach to data sharing is a model known as Guideline Interchange Format (GLIF), developed by the InterMed Collaboratory (comprising Harvard, Stanford, and Columbia universities), that encodes the essential features of guidelines common to all models (i.e., a maximal subset of features, not a superset) (Greenes et al., 2001). The goal of GLIF is to be able to (1) encode different requirements of clinicians during decision making, (2) support automatic verification and validation of guidelines, (3) facilitate standard approaches to guideline dissemination and local adaptation, and (4) be used as a template for the integration of automated clinical guidelines with clinical information systems (Wang et al., 2002b). Following a workshop hosted by InterMed in March 2000, the collaboratory decided to develop a standard model for representing guidelines with HL7. Rather than pursue agreement on one model, the group decided to focus on the building-block components that all guideline mod-
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Patient Safety: Achieving a New Standard for Care els must accommodate, such as a way to formulate queries and to express decision logic, a way to express the models’ logical rules, a way to reference data (the data model), an approach to resolving terminology issues, and a way to represent the process flow/work flow of a guideline (Greenes, 2003). The common language for representing the components is GELLO (Guideline Expression Language, Object-Oriented), which was developed for GLIF. GELLO includes an object-oriented query language—that is, syntax for querying the EHR—thus specifying how one retrieves elements to be used in the logic expressions (Sordo et al., 2003). GELLO depends on the existence of an object-oriented data model (i.e., HL7 RIM). Additional work on GELLO is being undertaken by HL7 with the intent of making it an official standard. Sufficient resources should be made available for revisions to resolve specifications for GLIF and to complete GELLO. Another aspect to consider with GLIF is recognition that guidelines most often are not executed in their entirety (Greenes, 2003). Instead, certain steps of a guideline may be implemented within different parts of a clinical information system (Greenes, 2003). For example, some steps may bear upon evaluation of clinical findings and may offer suggestions for diagnostic assessment or workup strategy; some may bear on the choice of particular medications or other procedures and may be implemented as order entry suggestions or templates; some may relate to the interpretation to be made and the action to be taken when an abnormal laboratory result is obtained and might be implemented as alerts; and some may trigger reminders or scheduling events (Sordo et al., 2003). Thus a guideline should be considered in terms of the application services or functions required by its various steps to be most effective (Sordo et al., 2003). These requirements will differ from one clinical information system to another based on functionality supported by the system (e.g., whether computerized physician order entry is present, or whether automatic alerts or time-driven scheduling reminders are supported), as well as institutional preferences about how to interface recommendations with actions (e.g., whether to offer them as suggestions or to trigger them as default actions that need to be overridden to be ignored, and what user interactions with the clinical information system will be affected by them) (Greenes, 2003). Thus work is proceeding on developing a taxonomy for application services that might be invoked by guidelines, as well as ways of marking up particular steps with the details of how the action is to be carried out in a specific clinical information system implementation (Greenes, 2003).
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Patient Safety: Achieving a New Standard for Care Representation of Medical Literature The volume of medical information continues to grow exponentially, leaving some clinicians feeling that it has become unmanageable (Jerome et al., 2001). Yet the value of having the most recent medical literature for reference at the point of care is clear. Leveraging the efficiencies of information technology and expanding the services of medical libraries can facilitate this objective. Advances in communication technologies, application interface tools, and standardization in the representation of medical literature should allow such requests and retrievals to be completed through a fully automated system so that reliance on a librarian is not necessary, and information access is available to clinicians around the clock (Humphreys, 2003a). Since NLM holds the largest and most comprehensive database of medical literature, the development of application interface tools should initially be targeted to accessing the NLM databases. Automated data retrieval would require a direct connection to the various medical literature topics, rather than linkage through the NLM Web site as is now the case (Humphreys, 2003a). Such application interface tools would greatly enhance the usability of medical knowledge bases and capabilities for information seeking at the point of care (Humphreys, 2003a). The committee recommends further study into what characteristics of information and what design of the interface tools would be most useful to clinicians in this regard. In addition, resources should continue to be provided to NLM to maintain its services in making medical literature available to consumers through its MEDLINEplus program. MEDLINEplus identifies information that is easy to read for the consumer and makes more than 150 interactive tutorials available in English, which include voice corresponding to the information printed on the screen (Humphreys, 2003b). The interactive tutorials are a popular feature in part because they are also suitable for those with low literacy (Humphreys, 2003b). In fact, those who select material for inclusion in MEDLINEplus actively seek low-literacy materials. NLM also encourages the institutes of the National Institutes of Health and producers of patient and consumer health materials to both convert their existing materials to electronic form and produce more of these materials. The Cochrane Collaboration—an international effort for preparing, maintaining, and promoting the accessibility of systematic reviews of the effects of health care interventions—is another important source of medical knowledge. Its database was designed to produce up-to-date summaries of the results of reliable research and is now considered one of the world’s best sources of medical evidence on treatments, diagnostic techniques, and preventive interventions (Cochrane Collaboration, 2003).
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Patient Safety: Achieving a New Standard for Care Other types of knowledge bases are highly important to patient safety and decision support tools. Disease registries are special databases that contain information about people diagnosed with a specific disease. Most registries are either hospital or population based and are used for a number of purposes, such as patient outcome tracking, support for self-care, epidemiological research, and public health surveillance (New York State Department of Health, 1999). They are also used for direct patient care, such as providing reminders for follow-up visits. Knowledge bases, such as those for pharmacology and pharmacokinetics, hold a vast amount of medical knowledge critical to the accurate prescription drugs and surveillance of drug reactions. These knowledge bases provide information on drug–body interactions to support decisions about what drugs to prescribe; drug–drug comparisons; advice on administration (Duclos-Cartolano and Venot, 2003); information on contraindications, interactions, or therapeutic strategies related to the physiological conditions of a particular disease; and listings of drugs according to some of their properties (Duclos-Cartolano and Venot, 2003). Common data standards can facilitate interconnections with bar-code medication administration systems, computerized physician order entry systems, and other decision support tools for the clinician and can support the self-care of patients by providing access to drug interaction checking programs. Clinician and patient access to vital information about medications contained in labeling (i.e., package inserts) is also important to patient safety. NLM is playing a key role in the standardization of the information on medication package inserts so the information can be made available in electronic format over the Internet. The DailyMed database, as its name suggests, is intended to provide updates of medication information to the public on a daily basis. Labels are also being restructured so they will be easier to understand and useful to both nonprofessionals and information systems (Brown et al., 2003). A major innovation will be the inclusion of labeling highlights that include recent label changes, indications, usage, dosage, administration, how supplied, contraindications, warnings/precautions, drug interactions, and use in special populations (Brown et al., 2003). Finally, NLM is working on the UNII project intended to code the molecular structure and other features of each new medication. With the pending market entry of about 1,500 new drugs in the next few years, the NLM Molfile database of molecular and manufacturing information on new drugs will augment pharmacy and pharmacogenetic databases by supplying more detailed information about medication functions and the prevention of adverse reactions.
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Patient Safety: Achieving a New Standard for Care Implementation of Data Standards Implementing data standards is just as important as developing and selecting standards. In preparing to implement standards, several issues tend to arise that should be considered when establishing a mechanism for compliance; these issues include vendor readiness, organizational readiness, cost of compliance tools, unresolved issues related to terminologies and coding, identifiers for providers and patients, and interpretation of the implementation guides and standard specifications. Help in dealing with these issues is critical. Most recently, in the implementation of HIPAA, the Workgroup on Electronic Data Interchange (WEDI) stepped forward to lead the Strategic National Implementation Process and provided guidance, assistance, and advice on the implementation of and compliance with HIPAA standards. The workgroup has developed a number of white papers that provide specific guidance on the technical aspects of implementing the associated code sets, messaging formats, security features, and privacy policies. It has also provided guidance on the testing and certification of clinical systems for compliance (i.e., conformity assessment) with the standards—testing organizational systems internally as well as testing systems externally with trading partners. The committee recommends that a similar entity be identified to assist with the implementation of clinical and patient safety data standards for the NHII. Such an entity might best be established with AHRQ as coordinator. The entity might assist organizations in increasing staff awareness and education; undertaking a gap analysis of current and desired standards; formulating a strategic plan, budget, and timeline to meet the CHI requirements; implementing the plan and certifying conformance; and providing an audit process for ongoing monitoring and enforcement. In contrast with HIPAA, however, self-certification should not be an option for compliance with clinical data standards. In addition to the establishment of an oversight organization and a national implementation plan, a mechanism for assessing conformance with the data standards is needed. Conformity assessment, an integral part of the utilization of standards, is the comprehensive term for measures taken by manufacturers, their customers, regulatory authorities, and independent third parties to evaluate and determine whether products and processes conform to particular standards (National Research Council, 1995). The National Institute of Standards and Technology (NIST) could perhaps serve as the body supporting the implementation process as the developer of protocols for conformance tests, information assurance, and certification procedures to verify vendors’ compliance with the standards.
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Patient Safety: Achieving a New Standard for Care Because the core terminology group for the EHR and other health-related applications will be housed for public availability within the UMLS, NLM will play a vital role in the coordination, mapping, and dissemination of the terminologies for national adoption. NLM will share responsibility for the maintenance and regular updating of the terminologies with the terminology developers. As the chief standards development organization for the EHR, HL7, in collaboration with government organizations (e.g., Centers for Medicare and Medicaid Services), will develop the specifications for the actual implementation of the terminologies. As stated in Chapter 3, AHRQ can facilitate the standards adoption process by functioning as a coordinating body and provider of technical assistance for the efforts of CHI, NCVHS, NLM, FDA, and HL7 in the area of data standards and for the Quality Interagency Coordination Task Force, evidence-based practice centers, specialty societies, academic institutions, and professional organizations involved in the determination of best practices that become translated into electronic data systems. AHRQ should be fully funded to function in this capacity. Assessing the costs related to the development, implementation, and dissemination of data standards will involve a coordinated set of evaluations by AHRQ and NLM. AHRQ would most likely have the responsibility for estimating the costs related to the establishment and operation of a WEDI-like entity for standards implementation and conformity assessment. NLM would have responsibility for estimating the costs related to the development and maintenance of the core terminology group and mappings to supplemental terminologies. Together, these organizations should engage in a comprehensive evaluation of the costs to provide the data standards needed for the NHII and patient safety systems. REFERENCES Advandi, A., S. Tu, M. O’Connor, R. Coleman, M. K. Goldstein, and M. Musen. 1999. Integrating a Modern Knowledge-Based System Architecture with a Legacy VA Database: The ATHENA and EON Projects at Stanford. Proceedings of the Annual American Medical Informatics Association Symposium. Philadelphia, PA: Hanley and Belfus. American National Standards Institute. 2002. ANSI Procedures for the Development and Coordination of American National Standards . Online. Available: http://public.ansi.org/ansionline/Documents/Standards%20Activities/American%20National%20Standards/Procedures,%20Guides,%20and%20Forms/anspro2002r.doc [accessed June 1, 2003].
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Representative terms from entire chapter: