Logical Observation Identifiers Names And Codes Codes Research Articles

Optimizing Integration with the Italian EHR: The Use of LOINC Codes.

According to the regulation "Decreto del Presidente del Consiglio dei Ministri" (DPCM) of September 29, 2015, n.178, the Logical Observation Identifiers Names and Codes (LOINC) system is included among the coding systems adopted in the Italian Electronic Health Record (EHR). As part of the Digital Health Solutions in Community Medicine (DHEAL-COM) project, one key goal is to categorize parameters using international classification systems. This enables the identification of appropriate Information and Communication Technology (ICT) solutions tailored to support people's health needs. Our objective is to incorporate LOINC codes for parameter categorization, thus anticipating the future use of EHR.

Monitoring Variability of Laboratory Results in a Clinical Data Warehouse Using Automatic Dashboard.

Hospital laboratory results are a significant data source in Clinical Data Ware-houses (CDW). To ensure comparability across healthcare organizations and for use in research studies, the results need to be interoperable. The LOINC (Logical Observation Identifiers, Names, and Codes) terminology provides a unique identifier for local codes for lab tests, enabling interoperability. However, in real-world, events occur over time and can disrupt the distribution of lab result values. For example, new equipment may be added to the analysis pipeline, a machine may be replaced, formulas may evolve due to new scientific knowledge, and legacy terminologies may be adopted. This article proposes a pipeline for creating an automated dashboard to monitor these events and data quality. We used automatic change point detection methods such as PELT for event detection in lab results. For a given LOINC code, we create a dashboard that summarizes the number of local codes mapped, and the number of patients (by sex, age, and hospital service) associated with the code. Finally, the dashboard enables the visualization of time events that disrupt the signal distribution. The biologists were able to explain to us the changes for several biological assays.

Establishing the Reportable Interval for Routine Clinical Laboratory Tests: A Data-Driven Strategy Leveraging Retrospective Electronic Medical Record Data.

This paper presents a data-driven strategy for establishing the reportable interval in clinical laboratory testing. The reportable interval defines the range of laboratory result values beyond which reporting should be withheld. The lack of clear guidelines and methodology for determining the reportable interval has led to potential errors in reporting and patient risk. To address this gap, the study developed an integrated strategy that combines statistical analysis, expert review, and hypothetical outlier calculations. A large data set from an accredited clinical laboratory was utilized, analyzing over 124 million laboratory test records from 916 distinct tests. The Dixon test was applied to identify outliers and establish the highest and lowest non-outlier result values for each test, which were validated by clinical pathology experts. The methodology also included matching the reportable intervals with relevant Logical Observation Identifiers Names and Codes (LOINC) and Unified Code for Units of Measure (UCUM)-valid units for broader applicability. Upon establishing the reportable interval for 135 routine laboratory tests (493 LOINC codes), we applied these to a primary care laboratory data set of 23 million records, demonstrating their efficacy with over 1% of result records identified as implausible. We developed and tested a data-driven strategy for establishing reportable intervals utilizing large electronic medical record (EMR) data sets. Implementing the established interval in clinical laboratory settings can improve autoverification systems, enhance data reliability, and reduce errors in patient care. Ongoing refinement and reporting of cases exceeding the reportable limits will contribute to continuous improvement in laboratory result management and patient safety.

The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices.

Personalized health care can be optimized by including patient-reported outcomes. Standardized and disease-specific questionnaires have been developed and are routinely used. These patient-reported outcome questionnaires can be simple paper forms given to the patient to fill out with a pen or embedded in digital devices. Regardless of the format used, they provide a snapshot of the patient's feelings and indicate when therapies need to be adjusted. The advantage of digitizing these questionnaires is that they can be automatically analyzed, and patients can be monitored independently of doctor visits. Although the questions of most clinical patient-reported outcome questionnaires follow defined standards and are evaluated by clinical trials, these standards do not exist for data processing. Interoperable data formats and structures would benefit multilingual and cross-study data exchange. Linking questionnaires to standardized terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Logical Observation Identifiers, Names, and Codes (LOINC) would improve this interoperability. However, linking clinically validated patient-reported outcome questionnaires to clinical terms available in SNOMED CT or LOINC is not as straightforward as it sounds. Here, we report our approach to link patient-reported outcomes from health applications to SNOMED CT or LOINC codes. We highlight current difficulties in this process and outline ways to minimize them.

Lack of Standardized Coding Limits Accuracy of Electronic Clinical Quality Measure for Pulmonary Embolism Diagnosis

Introduction: ASH Guidelines for diagnosis of pulmonary embolism (PE) start with risk assessment using a pretest probability tool (PTP) followed by D-dimer testing or CT pulmonary angiography (CTPA) depending on risk. We aimed to develop an electronic clinical quality measure (eCQM) to encourage broader use of a validated PTP scoring tool in emergency departments (EDs). The Centers for Medicare & Medicaid Services require reporting of eCQMs which consist of value sets or lists of standardized codes linked to patient data within the electronic health record. We sought to identify a standardized way to identify CTPA and abnormal D-dimer tests using administrative codes (i.e. CPT, LOINC, SNOMEDCT codes) across three institutions. Methods: Value sets identifying CTPA and D-dimers were reviewed from existing eCQMs. Three institutions representing 38 EDs from across the country submitted data from all ED encounters between September 12, 2022 and January 11, 2023. Imaging types were reviewed from each of the CPT codes and LOINC (Logical Observation Identifiers Names and Codes). We determined the number of ED encounters, pre-test probability tool use, and diagnosis of PE using different codes. We calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV, respectively). Results: Over the 4-month study period, 270,214 encounters were identified across 38 EDs. Each institution identified CTPA with site-specific codes (Table 1). The Full CTPA Value Set identified 55% more encounters than the site-specific codes (Full set 15,054/9679 site-specific codes). LOINC code 88322-3 was only used at one site. CPT code 71275 had the best sensitivity and specificity, but still had false positives (PPV 82%) (Table 2). D-dimer values were identified as LOINC code 48065-7 and 91556-1 at Site 1 and 48067-3 at Site 2. SNOMEDCT codes were not used at any site despite being the way other eCQMs identify elevated D-dimer results. Different D-dimer tests with different normal ranges were used at each site. All sites did not have an electronically extractable positive indicator for D-dimer. Conclusion: Due to persistent false positives using CPT Code 71275 for identification of CTPA, using this code would lead to inclusion of patients for whom PTP was not required or recommended. Additionally, coding for an abnormal D-dimer test result are not standardized across institutions. Therefore, administrative codes cannot be used to develop eCQMs whose aim is to evaluate whether CTPA is ordered appropriately based on the PTP risk level and laboratory testing.

Mis-mappings between a producer's quantitative test codes and LOINC codes and an algorithm for correcting them.

To access the accuracy of the Logical Observation Identifiers Names and Codes (LOINC) mapping to local laboratory test codes that is crucial to data integration across time and healthcare systems. We used software tools and manual reviews to estimate the rate of LOINC mapping errors among 179 million mapped test results from 2 DataMarts in PCORnet. We separately reported unweighted and weighted mapping error rates, overall and by parts of the LOINC term. Of included 179537986 mapped results for 3029 quantitative tests, 95.4% were mapped correctly implying an 4.6% mapping error rate. Error rates were less than 5% for the more common tests with at least 100000 mapped test results. Mapping errors varied across different LOINC classes. Error rates in chemistry and hematology classes, which together accounted for 92.0% of the mapped test results, were 0.4% and 7.5%, respectively. About 50% of mapping errors were due to errors in the property part of the LOINC name. Mapping errors could be detected automatically through inconsistencies in (1) qualifiers of the analyte, (2) specimen type, (3) property, and (4) method. Among quantitative test results, which are the large majority of reported tests, application of automatic error detection and correction algorithm could reduce the mapping errors further. Overall, the mapping error rate within the PCORnet data was 4.6%. This is nontrivial but less than other published error rates of 20%-40%. Such error rate decreased substantially to 0.1% after the application of automatic detection and correction algorithm.

BGLM: big data-guided LOINC mapping with multi-language support

Mapping internal, locally used lab test codes to standardized logical observation identifiers names and codes (LOINC) terminology has become an essential step in harmonizing electronic health record (EHR) data across different institutions. However, most existing LOINC code mappers are based on text-mining technology and do not provide robust multi-language support. We introduce a simple, yet effective tool called big data-guided LOINC code mapper (BGLM), which leverages the large amount of patient data stored in EHR systems to perform LOINC coding mapping. Distinguishing from existing methods, BGLM conducts mapping based on distributional similarity. We validated the performance of BGLM with real-world datasets and showed that high mapping precision could be achieved under proper false discovery rate control. In addition, we showed that the mapping results of BGLM could be used to boost the performance of Regenstrief LOINC Mapping Assistant (RELMA), one of the most widely used LOINC code mappers. BGLM paves a new way for LOINC code mapping and therefore could be applied to EHR systems without the restriction of languages. BGLM is freely available at https://github.com/Bin-Chen-Lab/BGLM.

Addressing the Gap in Data Communication from Home Health Care to Primary Care during Care Transitions: Completeness of an Interoperability Data Standard.

In a future where home health care is no longer an information silo, patient information will be communicated along transitions in care to improve care. Evidence-based practice in the United States supports home health care patients to see their primary care team within the first two weeks of hospital discharge to reduce rehospitalization risk. We sought to identify a parsimonious set of home health care data to be communicated to primary care for the post-hospitalization visit. Anticipating electronic dataset communication, we investigated the completeness of the international reference terminology, Logical Observation Identifiers Names and Codes (LOINC), for coverage of the data to be communicated. We conducted deductive qualitative analysis in three steps: (1) identify home health care data available for the visit by mapping home health care to the information needed for the visit; (2) reduce the resulting home health care data set to a parsimonious set clinicians wanted for the post-hospitalization visit by eliciting primary care clinician input; and (3) map the parsimonious dataset to LOINC and assess LOINC completeness. Our study reduced the number of standardized home health care assessment questions by 40% to a parsimonious set of 33 concepts that primary care team physicians wanted for the post-hospitalization visit. Findings indicate all home health care concepts in the parsimonious dataset mapped to the information needed for the post-hospitalization visit, and 84% of the home health care concepts mapped to a LOINC term. The results indicate data flow of parsimonious home health care dataset to primary care for the post-hospitalization visit is possible using existing LOINC codes, and would require adding some codes to LOINC for communication of a complete parsimonious data set.

COVID-19 Vaccination Is Safe and Effective in Patients With Inflammatory Bowel Disease: Analysis of a Large Multi-institutional Research Network in the United States

COVID-19 Vaccination Is Safe and Effective in Patients With Inflammatory Bowel Disease: Analysis of a Large Multi-institutional Research Network in the United States

Postcoordination of LOINC Codes in SNOMED CT.

The objectives of this paper are to analyze the terminologies SNOMED CT and Logical Observation Identifiers Names and Codes (LOINC) and to provide a guideline for the translation of LOINC concepts to SNOMED CT. Verified research data sets were used for this study, so this experiment is replicable with other research data. 50 LOINC concepts of frequently performed laboratory services were translated to SNOMED CT. Information would be lost with pre-coordinated mapping but the compositional grammar of SNOMED CT allows for the linking of individual concepts into complicated postcoordinated expressions including all embedded information in LOINC concepts. All information can thus be transferred smoothly to SNOMED CT.

No lab is an island: universal coding of laboratory test names.

The local laboratory with a local client-base, that never needs to exchange information with any outside entity, is a dying breed. As marketing channels, animal movement, and reporting requirements become increasingly national and international, the need to communicate about laboratory tests and results grows. Local and proprietary names of laboratory tests often fail to communicate enough detail to distinguish between similar tests. To avoid a lengthy description of each test, laboratories need the ability to assign codes that, although not sufficiently user-friendly for day-to-day use, contain enough information to translate between laboratories and even languages. The Logical Observation Identifiers Names and Codes (LOINC) standard provides such a universal coding system. Each test-each atomic observation-is evaluated on 6 attributes that establish its uniqueness at the level of clinical-or epidemiologic-significance. The analyte detected, analyte property, specimen, and result scale combine with the method of analysis and timing (for challenge and metabolic type tests) to define a unique LOINC code. Equipping laboratory results with such universal identifiers creates a world of opportunity for cross-institutional data exchange, aggregation, and analysis, and presents possibilities for data mining and artificial intelligence on a national and international scale. A few challenges, relatively unique to regulatory veterinary test protocols, require special handling.

Impact of Obesity on Outcomes of Patients With Coronavirus Disease 2019 in the United States: A Multicenter Electronic Health Records Network Study

Impact of Obesity on Outcomes of Patients With Coronavirus Disease 2019 in the United States: A Multicenter Electronic Health Records Network Study

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes.

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

A Survey of LOINC Code Selection Practices Among Participants of the College of American Pathologists Coagulation (CGL) and Cardiac Markers (CRT) Proficiency Testing Programs.

Biomedical terminologies such as Logical Observation Identifiers, Names, and Codes (LOINC) were developed to enable interoperability of health care data between disparate health information systems to improve patient outcomes, public health, and research activities. To ascertain the utilization rate and accuracy of LOINC terminology mapping to 10 commonly ordered tests by participants of the College of American Pathologists (CAP) Proficiency Testing program. Questionnaires were sent to 1916 US and Canadian laboratories participating in the 2018 CAP coagulation (CGL) and/or cardiac markers (CRT) surveys requesting information on practice setting, instrument(s) and test method(s), and LOINC code selection and usage in the laboratory and electronic health records. Ninety of 1916 CGL and/or CRT participants (4.7%) responded to the questionnaire. Of the 275 LOINC codes reported, 54 (19.6%) were incorrect: 2 codes (5934-2 and 12345-1) (0.7%) did not exist in the LOINC database and the highest error rates were observed in the property (27 of 275, 9.8%), system (27 of 275, 9.8%), and component (22 of 275, 8.0%) LOINC axes. Errors in LOINC code selection included selection of the incorrect component (eg, activated clotting time instead of activated partial thromboplastin time); selection of panels that can never be used to obtain an individual analyte (eg, prothrombin time panel instead of international normalized ratio); and selection of an incorrect specimen type. These findings of real-world LOINC code implementation across a spectrum of laboratory settings should raise concern about the reliability and utility of using LOINC for clinical research or to aggregate data.

Implementing LOINC - Current Status and Ongoing Work at a Medical University.

The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the consortium of the HiGHmed project, LOINC is one of the central terminologies used for health data sharing across all university sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing efforts in implementing LOINC to our laboratory information system and research infrastructure, as well as our challenges and the lessons learned. 407 local terms could be mapped to 376 LOINC codes of which 209 are already available to routine laboratory data. In our experience, mapping of local terms to LOINC is a widely manual and time consuming process for reasons of language and expert knowledge of local laboratory procedures.

Logical Observation Identifiers Names and Codes for Laboratorians.

The Logical Observation Identifiers Names and Codes (LOINC) system is supposed to facilitate interoperability, and it is the federally required code for exchanging laboratory data. To provide an overview of LOINC, emerging issues related to its use, and areas relevant to the pathology laboratory, including the subtleties of test code selection and importance of mapping the correct codes to local test menus. This review is based on peer-reviewed literature, federal regulations, working group reports, the LOINC database (version 2.65), experience using LOINC in the laboratory at several large health care systems, and insight from laboratory information system vendors. The current LOINC database contains more than 55 000 numeric codes specific for laboratory tests. Each record in the LOINC database includes 6 major axes/parts for the unique specification of each individual observation or measurement. Assigning LOINC codes to a laboratory's test menu should be a defined process. In some cases, LOINC can aid in distinguishing laboratory data among different information systems, whereby such benefits are not achievable by relying on the laboratory test name alone. Criticisms of LOINC include the complexity and resource-intensive process of selecting the most correct code for each laboratory test, the real-world experience that these codes are not uniformly assigned across laboratories, and that 2 tests that may have the same appropriately assigned LOINC code may not necessarily have equivalency to permit interoperability of their result data. The coding system's limitations, which subsequently reduce the potential utility of LOINC, are poorly understood outside of the laboratory.

Progress in the Development of the Standardized Public Health Emergency Preparedness Terminology

ObjectiveThe purpose of this project is to demonstrate the progress in development of a standardized public health (PH) emergency preparedness and response data ontology (terminology) through collaboration between the Centers for Disease Control and Prevention (CDC), Division of Emergency Operations (DEO), and the Logical Observation Identifiers Names and Codes (LOINC) system.IntroductionThe U.S. Department of Homeland Security National Incident Management System (NIMS) establishes a common framework and common terminology that allows diverse incident management and support organizations to work together across a wide variety of functions and hazard scenarios1. Using common terminology helps avoid confusion and enhances interoperability, particularly in fast-moving public health (PH) emergency responses. In addition, common terminology allows diverse incident management and support organizations to work together across a wide variety of functions and scenarios1. LOINC is one of a suite of designated standards for the electronic exchange of public health and clinical information. Implementation of LOINC facilitates improvement of semantic interoperability, including unified terminology2. More than 68,100 registered users from 172 countries use LOINC to move interoperable data seamlessly between systems3. The CDC Division of Emergency Operations (DEO) leads development of standardized PH emergency preparedness and response terminology to improve effective and interoperable communications between national and international partners. Realizing the scale of LOINC support and implementation across the global public health arena, CDC DEO collaborates with LOINC to further enhance and harmonize the current PH emergency response terminology and to attain critical PH emergency management and preparedness and response requirements.MethodsDEO analyzed 87,863 LOINC terms that were included in LOINC version 2.64, released on 06/15/20183. Access to this LOINC version was obtained through the Regenstrief LOINC Mapping Assistant (RELMA). RELMA is a Windows-based LOINC utility developed by the Regenstrief Institute (Indiapolis, USA) for searching the LOINC database and mapping local codes to LOINC codes4. The relevance of LOINC terminology to PH emergency preparedness and response was assessed through evaluating existing LOINC terminology against terminology specified by the World Health Organization PH Emergency Operation Centers (EOC). The following functions were evaluated: 1) Managing and Commanding; 2) Operating; 3) Planning/Intelligence; 4) Logistics and 5) Finance/Administration5. LOINC terminology was also evaluated against the CDC PH EOC Minimum Data Set (MDS)6 that contains 315 standardized terms. Analysis of fully specified LOINC terms was conducted through assessment of such LOINC term parts (attributes) as the code, name (component), system, method and class. Recommendations of gaps and enhancements were coordinated with LOINC management for inclusion of the new terminology in the release of version 2.65 .ResultsA new LOINC method, “CDC.EOC”, is under development. Currently, the “emergency management incident” terminology presented by LOINC is limited by such characteristics as event type, event location and event name and requires amplification regarding to PH operations (i.e., communication, logistics etc.).As a result of this investigation, emergency management terms are now being classified according to the type of incident or event (i.e., hurricane, outbreak, etc.) under LOINC code 80394-0. Similarly laboratory and clinical terms are being classified under a provisional LOINC code (89724-9). Two panels were created: 1) The emergency medical systems from the National Emergency Medical Services Information System (NEMSIS) was added under the NEMSIS.Panel (n= 177 terms) and 2) the Data Elements for Emergency Departments Systems (DEEDS) panel (n = 152 terms) was added with two subpanels: Attach.ED and Panel.ED.Assessing existing LOINC taxonomy and codification, DEO is working with the LOINC management team on evaluating additional options for reconciliation the PH emergency preparedness and response common information exchange reference model and LOINC standard. This process aims to further improve semantic interoperability of PH emergency preparedness and response information.ConclusionsThe LOINC terminology standardization is essential for improving PH preparedness and response data exchange and semantic interoperability. Collaboration with the Regenstrief Institute (LOINC) allows CDC to meet the terminology needs of PH emergency management and defines new opportunities for reconciliation data exchange between NIMS partners. This collaborative effort incorporates critically needed PH emergency and preparedness terminology and hierarchical structure in the LOINC standard.

NPU, LOINC, and SNOMED CT: a comparison of terminologies for laboratory results reveals individual advantages and a lack of possibilities to encode interpretive comments

AbstractBackgroundTerminologies facilitate data exchange and enable laboratories to assist in patient care even if complex treatment pathways involve multiple stakeholders. This paper examines the three common terminologiesNomenclature for Properties and Units(NPU),Logical Observation Identifiers Names and Codes(LOINC), andSNOMED Clinical Terms(SNOMED CT).MethodsThe potential of each terminology to encode five exemplary laboratory results is assessed. The terminologies are evaluated according to scope, correctness, formal representations, and ease of use.ResultsNPU is based on metrological concepts with strict rules regarding the coding of the measurand and the result value. Clinically equivalent results are regularly mapped to the same code but there is little support to differentiate results from non-standardized measurements. LOINC encodes analyses as offered by the laboratory. Its large number of entries allows different mappings for the same analysis. SNOMED CT contains few analyses natively, but its formal composition mechanism allows representing measurements by post-coordinated expressions that are equivalent to LOINC codes. SNOMED CT’s strength lies in its support of many non-numerical result values. Implicit code hierarchies exist in NPU and LOINC. SNOMED CT has explicit, elaborate axioms that elucidate the meaning of its content. Its complexity and its license conditions, however, impede a more widespread use. Interpretive comments, a crucial part of laboratory results, are still difficult to encode with any of the terminologies.ConclusionsAll three terminologies have distinct potentials and limitations, but the approximation of SNOMED CT and LOINC suggests using them together. Terminologies need to be expanded to also cover interpretive comments.

NPU, LOINC, and SNOMED CT: a comparison of terminologies for laboratory results reveals individual advantages and a lack of possibilities to encode interpretive comments

Background Terminologies facilitate data exchange and enable laboratories to assist in patient care even if complex treatment pathways involve multiple stakeholders. This paper examines the three common terminologies Nomenclature for Properties and Units (NPU), Logical Observation Identifiers Names and Codes (LOINC), and SNOMED Clinical Terms (SNOMED CT). Methods The potential of each terminology to encode five exemplary laboratory results is assessed. The terminologies are evaluated according to scope, correctness, formal representations, and ease of use. Results NPU is based on metrological concepts with strict rules regarding the coding of the measurand and the result value. Clinically equivalent results are regularly mapped to the same code but there is little support to differentiate results from non-standardized measurements. LOINC encodes analyses as offered by the laboratory. Its large number of entries allows different mappings for the same analysis. SNOMED CT contains few analyses natively, but its formal composition mechanism allows representing measurements by post-coordinated expressions that are equivalent to LOINC codes. SNOMED CT’s strength lies in its support of many non-numerical result values. Implicit code hierarchies exist in NPU and LOINC. SNOMED CT has explicit, elaborate axioms that elucidate the meaning of its content. Its complexity and its license conditions, however, impede a more widespread use. Interpretive comments, a crucial part of laboratory results, are still difficult to encode with any of the terminologies. Conclusions All three terminologies have distinct potentials and limitations, but the approximation of SNOMED CT and LOINC suggests using them together. Terminologies need to be expanded to also cover interpretive comments.

The potential adoption benefits and challenges of LOINC codes in a laboratory department: a case study.

Logical Observation Identifiers Names and Codes (LOINC) are a standard for identifying and reporting laboratory investigations that were developed and are maintained by the Regenstrief Institute. LOINC codes have been adopted globally by hospitals, government agencies, laboratories, and research institutions. There are still many healthcare organizations, however, that have not adopted LOINC codes, including rural hospitals in low- and middle- income countries. Hence, organizations in these areas do not receive the benefits that accrue with the adoption of LOINC codes. We conducted a literature search by utilizing PubMed, CINAHL, Google Scholar, ACM Digital Library, and the Biomed Central database to look for existing publications on the benefits and challenges of adopting LOINC. We selected and reviewed 16 publications and then conducted a case study via the following steps: (1) we brainstormed, discussed, analyzed, created and revised iteratively the patient's clinical encounter (outpatient or ambulatory settings) process within a laboratory department via utilizing a hypothetical patient; (2) we incorporated the work experience of one of the authors (CU) in a rural hospital laboratory department in Nigeria to break down the clinical encounter process into simpler and discrete steps and created a series of use cases for the process; (3) we then analyzed and summarized the potential usage of LOINC codes (clinically, administratively, and operationally) and the benefits and challenges of adopting LOINC codes in such settings by examining the use cases one by one. Based on the literature review, we noted that LOINC codes' ability to improve laboratory results' interoperability has been recognized broadly. LOINC-coded laboratory results can improve patients' safety due to their consistent meaning as well as the related reduction of duplicate lab tests, easier assessment of workloads in the laboratory departments, and accurate auditing of laboratory accounts. Further, the adoption of LOINC codes may motivate government agencies to upgrade hospitals' infrastructures, which could increase the possibility of international recognition of laboratory test results from those hospitals over the long term. Meanwhile, a lack of LOINC codes in paper format and a lack of LOINC codes experts are major challenges that may limit LOINC adoption. In this paper, we intend to provide a snapshot of the possible usage of LOINC codes in rural hospitals in low- and middle-income countries via simpler and detailed use cases. Our analysis may aid policymakers to gain a deeper understanding of LOINC codes in regard to clinical, administrative, and operational aspect and to make better-informed decisions in regard to LOINC codes adoption. The use case analysis also can be used by information system designers and developers to reference workflow within a laboratory department. We recognize that this manuscript is only a case study and that the exact steps and workflows may vary in different laboratory departments; however, the core steps and main benefits should be consistent.