Frequency of laboratory test utilization in the intensive care unit and its implications for large-scale data collection efforts.
(9/54)
OBJECTIVE: Mapping local use names to standardized nomenclatures such as LOINC (Logical Observation Identifiers Names and Codes) is a time-consuming task when done retrospectively or during the configuration of new information systems. The author sought to identify a subset of intensive care unit (ICU) laboratory tests, which, because of their frequency of use, should be the focus of efforts to standardize test names in ICU information systems. DESIGN: The author reviewed the ordering practices in medical, surgical, and pediatric ICUs within a large university teaching hospital to identify the subset of laboratory tests that represented the majority of tests performed in these settings. The author compared the results of his findings with the laboratory tests required to complete several of the most frequently used ICU acuity scoring systems. RESULTS: It was found that between 104 and 202 tests and profiles represented 99% of all testing in the three ICUs. All the laboratory studies needed for six commonly used ICU scoring systems fell into the top 21 laboratory studies and profiles performed in each ICU. CONCLUSION: The author identified a small subset of the LOINC database that should be the focus of efforts to standardize test names in ICU information systems. Mapping this subset of laboratory tests and profiles to LOINC vocabulary will simplify the process of collecting data for large-scale databases such as ICU scoring systems and the configuration of new ICU information systems. (+info)
Development of an information model for storing organ donor data within an electronic medical record.
(10/54)
OBJECTIVE: To develop a model to store information in an electronic medical record (EMR) for the management of transplant patients. The model for storing donor information must be designed to allow clinicians to access donor information from the transplant recipient's record and to allow donor data to be stored without needlessly proliferating new Logical Observation Identifier Names and Codes (LOINC) codes for already-coded laboratory tests. DESIGN: Information required to manage transplant patients requires the use of a donor's medical information while caring for the transplant patient. Three strategies were considered: (1) link the transplant patient's EMR to the donor's EMR; (2) use pre-coordinated observation identifiers (i.e., LOINC codes with *(wedge)DONOR specified in the system axes) to identify donor data stored in the transplant patient's EMR; and (3) use an information model that allows donor information to be stored in the transplant patient's record by allowing the "source" of the data (donor) and the "name" of the result (e.g., blood type) to be post-coordinated in the transplant patient's EMR. RESULTS: We selected the third strategy and implemented a flexible post-coordinated information model. There was no need to create new LOINC codes for already-coded laboratory tests. The model required that the data structure in the EMR allow for the storage of the "subject" of the test. CONCLUSION: The selected strategy met our design requirements and provided an extendable information model to store donor data. This model can be used whenever it is necessary to refer to one patient's data from another patient's EMR. (+info)
Toward semantic interoperability in home health care: formally representing OASIS items for integration into a concept-oriented terminology.
(11/54)
OBJECTIVE: The authors aimed to (1) formally represent OASIS-B1 concepts using the Logical Observation Identifiers, Names, and Codes (LOINC) semantic structure; (2) demonstrate integration of OASIS-B1 concepts into a concept-oriented terminology, the Medical Entities Dictionary (MED); (3) examine potential hierarchical structures within LOINC among OASIS-B1 and other nursing terms; and (4) illustrate a Web-based implementation for OASIS-B1 data entry using Dialogix, a software tool with a set of functions that supports complex data entry. DESIGN AND MEASUREMENTS: Two hundred nine OASIS-B1 items were dissected into the six elements of the LOINC semantic structure and then integrated into the MED hierarchy. Each OASIS-B1 term was matched to LOINC-coded nursing terms, Home Health Care Classification, the Omaha System, and the Sign and Symptom Check-List for Persons with HIV, and the extent of the match was judged based on a scale of 0 (no match) to 4 (exact match). OASIS-B1 terms were implemented as a Web-based survey using Dialogix. RESULTS: Of 209 terms, 204 were successfully dissected into the elements of the LOINC semantics structure and integrated into the MED with minor revisions of MED semantics. One hundred fifty-one OASIS-B1 terms were mapped to one or more of the LOINC-coded nursing terms. CONCLUSION: The LOINC semantic structure offers a standard way to add home health care data to a comprehensive patient record to facilitate data sharing for monitoring outcomes across sites and to further terminology management, decision support, and accurate information retrieval for evidence-based practice. The cross-mapping results support the possibility of a hierarchical structure of the OASIS-B1 concepts within nursing terminologies in the LOINC database. (+info)
Standardizing laboratory data by mapping to LOINC.
(12/54)
The authors describe a pilot project to standardize local laboratory data at five Indian Health Service (IHS) medical facilities by mapping laboratory test names to Logical Observation Identifier Names and Codes (LOINC). An automated mapping tool was developed to assign LOINC codes. At these sites, they were able to map from 63% to 76% of the local active laboratory tests to LOINC using the mapping tool. Eleven percent to 27% of the tests were mapped manually. They could not assign LOINC codes to 6% to 19% of the laboratory tests due to incomplete or incorrect information about these tests. The results achieved approximate other similar efforts. Mapping of laboratory test names to LOINC codes will allow IHS to aggregate laboratory data more easily for disease surveillance and clinical and administrative reporting efforts. This project may provide a model for standardization efforts in other health systems. (+info)
A system for automated lexical mapping.
(13/54)
OBJECTIVE: To automate the mapping of disparate databases to standardized medical vocabularies. BACKGROUND: Merging of clinical systems and medical databases, or aggregation of information from disparate databases, frequently requires a process whereby vocabularies are compared and similar concepts are mapped. DESIGN: Using a normalization phase followed by a novel alignment stage inspired by DNA sequence alignment methods, automated lexical mapping can map terms from various databases to standard vocabularies such as the UMLS (Unified Medical Language System) and LOINC (Logical Observation Identifier Names and Codes). MEASUREMENTS: This automated lexical mapping was evaluated using three real-world laboratory databases from different health care institutions. The authors report the sensitivity, specificity, percentage correct (true positives plus true negatives divided by total number of terms), and true positive and true negative rates as measures of system performance. RESULTS: The alignment algorithm was able to map 57% to 78% (average of 63% over all runs and databases) of equivalent concepts through lexical mapping alone. True positive rates ranged from 18% to 70%; true negative rates ranged from 5% to 52%. CONCLUSION: Lexical mapping can facilitate the integration of data from diverse sources and decrease the time and cost required for manual mapping and integration of clinical systems and medical databases. (+info)
Validity of International Classification of Diseases, Ninth Revision, Clinical Modification Codes for Acute Renal Failure.
(14/54)
Administrative and claims databases may be useful for the study of acute renal failure (ARF) and ARF that requires dialysis (ARF-D), but the validity of the corresponding diagnosis and procedure codes is unknown. The performance characteristics of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for ARF were assessed against serum creatinine-based definitions of ARF in 97,705 adult discharges from three Boston hospitals in 2004. For ARF-D, ICD-9-CM codes were compared with review of medical records in 150 patients with ARF-D and 150 control patients. As compared with a diagnostic standard of a 100% change in serum creatinine, ICD-9-CM codes for ARF had a sensitivity of 35.4%, specificity of 97.7%, positive predictive value of 47.9%, and negative predictive value of 96.1%. As compared with review of medical records, ICD-9-CM codes for ARF-D had positive predictive value of 94.0% and negative predictive value of 90.0%. It is concluded that administrative databases may be a powerful tool for the study of ARF, although the low sensitivity of ARF codes is an important caveat. The excellent performance characteristics of ICD-9-CM codes for ARF-D suggest that administrative data sets may be particularly well suited for research endeavors that involve patients with ARF-D. (+info)
Mapping Department of Defense laboratory results to Logical Observation Identifiers Names and Codes (LOINC).
(15/54)
The Department of Defense (DoD) has used a common application, Composite Health Care System (CHCS), throughout all DoD facilities. However, the master files used to encode patient data in CHCS are not identical across DoD facilities. The encoded data is thus not interoperable from one DoD facility to another. To enable data interoperability in the next-generation system, CHCS II, and for the DoD to exchange laboratory results with external organizations such as the Veterans Administration (VA), the disparate master file codes for laboratory results are mapped to Logical Observation Identifier Names and Codes (LOINC) wherever possible. This paper presents some findings from our experience mapping DoD laboratory results to LOINC. (+info)
SPIN query tools for de-identified research on a humongous database.
(16/54)
The Shared Pathology Informatics Network (SPIN), a research initiative of the National Cancer Institute, will allow for the retrieval of more than 4 million pathology reports and specimens. In this paper, we describe the special query tool as developed for the Indianapolis/Regenstrief SPIN node, integrated into the ever-expanding Indiana Network for Patient care (INPC). This query tool allows for the retrieval of de-identified data sets using complex logic, auto-coded final diagnoses, and intrinsically supports multiple types of statistical analyses. The new SPIN/INPC database represents a new generation of the Regenstrief Medical Record system - a centralized, but federated system of repositories. (+info)