scholarly journals A Clustering Approach for Detecting Implausible Observation Values in Electronic Health Records Data

2019 ◽  
Author(s):  
Hossein Estiri ◽  
Shawn N. Murphy
Keyword(s):  
Anomaly Detection ◽  
Laboratory Test ◽  
Outlier Detection ◽  
Laboratory Tests ◽  
Unsupervised Clustering ◽  
Silver Standard ◽  
Algorithmic Solution ◽  
Laboratory Test Results ◽  
Clustering Approach ◽  
Electronic Health

AbstractBackgroundIdentifying implausible clinical observations (e.g., laboratory test and vital sign values) in Electronic Health Record (EHR) data using rule-based procedures is challenging. Anomaly/outlier detection methods can be applied as an alternative algorithmic approach to flagging such implausible values in EHRs.ObjectiveThe primary objectives of this research were to develop and test an unsupervised clustering-based anomaly/outlier detection approach for detecting implausible observations in EHR data as an alternative algorithmic solution to the existing procedures.MethodsOur approach is built upon two underlying hypotheses that, (i) when there are large number of observations, implausible records should be sparse, and therefore (ii) if these data are clustered properly, clusters with sparse populations should represent implausible observations. To test these hypotheses, we applied an unsupervised clustering algorithm to EHR observation data on 50 laboratory tests. We tested different specifications of the clustering approach and computed confusion matrix indices against a set of silver-standard plausibility thresholds. We compared the results from the proposed approach with conventional anomaly detection (CAD) approach’s, including standard deviation and Mahalanobis distance.ResultsWe found that the clustering approach produced results with exceptional specificity and high sensitivity. Compared with the conventional anomaly detection approaches, our proposed clustering approach resulted in significantly smaller number of false positive cases.ConclusionOur contributions include (i) a clustering approach for identifying implausible EHR observations, (ii) evidence that implausible observations are sparse in EHR laboratory test results, (iii) a parallel implementation of the clustering approach on i2b2 star schema, and (3) a set of silver-standard plausibility thresholds for 50 laboratory tests that can be used in other studies for validation. The proposed algorithmic solution can augment human decisions to improve data quality. Therefore, a workflow is needed to complement the algorithm’s job and initiate necessary actions that need to be taken in order to improve the quality of data.

scholarly journals Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery

2019 ◽  
Author(s):  
Xingmin Aaron Zhang ◽  
Amy Yates ◽  
Nicole Vasilevsky ◽  
JP Gourdine ◽  
Leigh C. Carmody ◽  
...  
Keyword(s):  
Laboratory Test ◽  
Electronic Health Record ◽  
Laboratory Tests ◽  
Health Record ◽  
Phenotype Ontology ◽  
Test Results ◽  
Human Phenotype ◽  
Laboratory Test Results ◽  
Electronic Health ◽  
Deep Phenotyping

AbstractElectronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to the Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2421 commonly used laboratory tests with HPO terms. Using these annotations, a software assesses laboratory test results and converts each into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows reusing readily available laboratory tests in EHR for deep phenotyping and using the hierarchical structure of HPO for association studies with medical outcomes and genomics.One Sentence SummaryWe present an approach to semantically integrating LOINC-encoded laboratory data with the Human Phenotype Ontology and show that the integrated LOINC data can be used to identify biomarkers for asthma from electronic health record data.

Reduction of Laboratory Utilization in the Intensive Care Unit

2016 ◽  
Vol 32 (8) ◽  
pp. 500-507 ◽  
Author(s):  
Samih Raad ◽  
Rachel Elliott ◽  
Evan Dickerson ◽  
Babar Khan ◽  
Khalil Diab
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Laboratory Test ◽  
Laboratory Tests ◽  
Routine Laboratory ◽  
Test Results ◽  
Staff Education ◽  
Arterial Blood ◽  
Laboratory Test Results ◽  
Order Sets

Objective: In our academic intensive care unit (ICU), there is excess ordering of routine laboratory tests. This is partially due to a lack of transparency of laboratory-processing costs and to the admission order plans that favor daily laboratory test orders. We hypothesized that a program that involves physician and staff education and alters the current ICU order sets will lead to a sustained decrease in routine laboratory test ordering. Design: Prospective cohort study. Setting: Academic closed medical ICU (MICU). Patients: All patients admitted to the MICU. Methods: We consistently educated residents, faculty, and staff about laboratory test costs. We removed the daily laboratory test option from the admission order sets and asked residents to order needed laboratory test results every day. We only allowed the G3+I-STAT (arterial blood gas only) cartridges in the MICU in hopes of decreasing duplicative laboratory test results. We added laboratory review to the daily rounding checklist. Measurement and Main Results: Total number of laboratory tests per patient-day decreased from 39.43 to an average of 26.74 ( P <.001) over a 9-month period. The number of iSTAT laboratory tests per patient-day decreased from 7.37 to an average of 1.16 ( P < .001) over the same time period. The number of iSTAT/central laboratory processing duplicative laboratory tests per patient-day decreased from 0.17 to an average of 0.01 ( P < .001). The percentage of patients who have daily laboratory test orders decreased from 100% to an average of 11.94% ( P <. 001). US$123 436 in direct savings and US$258 035 dollars in indirect savings could be achieved with these trends. Intensive care unit morbidity and mortality were not impacted. Conclusion: A simple technique of resident, nursing, and ancillary staff education, combined with alterations in order sets using electronic medical records, can lead to a sustained reduction in laboratory test utilization over time and to significant cost savings without affecting patient safety.

scholarly journals Graphical display of diagnostic test results in electronic health Records: a comparison of 8 systems

2015 ◽  
Vol 22 (4) ◽  
pp. 900-904 ◽  
Author(s):  
Dean F Sittig ◽  
Daniel R Murphy ◽  
Michael W Smith ◽  
Elise Russo ◽  
Adam Wright ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Laboratory Test ◽  
Clinical Laboratory ◽  
Negative Impact ◽  
Laboratory Data ◽  
Test Results ◽  
Health Records ◽  
Laboratory Test Results ◽  
Data Points ◽  
Electronic Health

Abstract Accurate display and interpretation of clinical laboratory test results is essential for safe and effective diagnosis and treatment. In an attempt to ascertain how well current electronic health records (EHRs) facilitated these processes, we evaluated the graphical displays of laboratory test results in eight EHRs using objective criteria for optimal graphs based on literature and expert opinion. None of the EHRs met all 11 criteria; the magnitude of deficiency ranged from one EHR meeting 10 of 11 criteria to three EHRs meeting only 5 of 11 criteria. One criterion (i.e., the EHR has a graph with y-axis labels that display both the name of the measured variable and the units of measure) was absent from all EHRs. One EHR system graphed results in reverse chronological order. One EHR system plotted data collected at unequally-spaced points in time using equally-spaced data points, which had the effect of erroneously depicting the visual slope perception between data points. This deficiency could have a significant, negative impact on patient safety. Only two EHR systems allowed users to see, hover-over, or click on a data point to see the precise values of the x–y coordinates. Our study suggests that many current EHR-generated graphs do not meet evidence-based criteria aimed at improving laboratory data comprehension.

scholarly journals Association of Test Results for 33 Frequently Used Laboratory Tests with Body Mass Index (BMI)

2020 ◽  
pp. 1-4
Author(s):  
Anders Larsson ◽  
Anders Larsson ◽  
Johan Ärnlöv ◽  
Johanna Helmersson-Karlqvist ◽  
Lars Lind ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Laboratory Test ◽  
Body Mass ◽  
Cystatin C ◽  
Apolipoprotein B ◽  
Laboratory Tests ◽  
Hdl Cholesterol ◽  
Apolipoprotein A1 ◽  
Test Results ◽  
Laboratory Test Results

Once considered a problem only for high-income countries, obesity rates are now rising worldwide. When evaluating test results from obese patients it is important to be aware of the effect of obesity on individual laboratory test results. The aim of the present study was to study the association between body mass index (BMI) and a group of frequently requested laboratory tests to evaluate which of these analytes that are affected by BMI. We analyzed the association between body mass index (BMI) and Alanine aminotransaminase (ALT), Albumin, Alkaline phosphatase, Pancreatic amylase, Apolipoprotein A1, Apolipoprotein B, Apolipoprotein B/Apolipoprotein A1 ratio, Aspartate aminotransferase (AST), AST/ALT ratio, Bilirubin, Calcium, Calprotectin, Cholesterol, HDL-cholesterol, Creatinine kinase (CK), Creatinine, C-reactive protein, Cystatin C, Gamma-glutamyl transferase (GGT), Iron, Iron saturation, Lactate dehydrogenase (LDH), Magnesium, Phosphate, Transferrin, Triglycerides, Urate, Urea, Zink, Hemoglobin, Platelet count and White blood cell count in an 80-year old population (n=531, 266 females and 265 males). There were significant Spearman rank associations between BMI and laboratory test results for several of the studied markers in both females and males. The strongest associations with BMI were noted for ALT, Apolipoprotein A1, HDL-cholesterol, Hemoglobin, CRP, Cystatin C, Triglycerides and Urate. In conclusion, several of the most frequently used laboratory markers are significantly associated with BMI. To be able to correctly interpret a test result it is important to be aware of the effects of BMI on the test results.

scholarly journals Reminders of Drug Effects on Laboratory Test Results

2000 ◽  
Vol 46 (9) ◽  
pp. 1395-1400 ◽  
Author(s):  
Marita Kailajärvi ◽  
Timo Takala ◽  
Paula Grönroos ◽  
Nils Tryding ◽  
Jorma Viikari ◽  
...  
Keyword(s):  
Laboratory Test ◽  
Laboratory Tests ◽  
Drug Effects ◽  
Study Drug ◽  
Test Results ◽  
Potential Drug ◽  
Reminder System ◽  
Patient Medication ◽  
Laboratory Test Results ◽  
Laboratory Results

Abstract Drug effects on laboratory test results are difficult to take into account without an online decision support system. In this study, drug effects on hormone test results were coded using a drug-laboratory effect (DLE) code. The criteria that trigger the reminders were defined. To issue reminders, it was necessary to write a computer program linking the DLE knowledge base with databases containing individual patient medication and laboratory test results. During the first 10 months, 11% of the results from hormone samples were accompanied by one or more DLE reminders. The most common drugs to trigger reminders were glucocorticoids, furosemide, and metoclopramide. Physicians facing the reminders completed a questionnaire on the usefulness of the reminders. All respondents considered them useful. In addition, DLE reminders had caused 74% of respondents to refrain from additional, usually performed examinations. In conclusion, drug effects on laboratory tests should always be considered when interpreting laboratory results. An online reminder system is useful in displaying potential drug effects alongside test results.

The Usefulness of Laboratory Tests in Health-Screening Programs

1973 ◽  
Vol 19 (4) ◽  
pp. 366-372 ◽  
Author(s):  
David L Sackett
Keyword(s):  
Laboratory Test ◽  
Laboratory Tests ◽  
Health Screening ◽  
Test Results ◽  
Screening Programs ◽  
Laboratory Screening ◽  
Rare Exception ◽  
Laboratory Test Results ◽  
The Mean ◽  
Regression Toward The Mean

Abstract I discuss pitfalls in laboratory-screening programs: regression toward the mean on repeated biochemical determinations; the problem of defining normalcy in the interpretation of laboratory test results; and a remarkable professional myopia in which clinical chemists have, with rare exception, failed to accept responsibility for evaluating whether the programs in which they are engaged are of benefit to patients.

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