A dataset examining the impact of direct electronic medical record interfacing on the accuracy of point-of-care urinalysis results

Point-of-care testing is widely used in a variety of clinical settings. While this testing provides immediate and actionable clinical information, it is prone to error in both the interpretation and reporting of results. Point-of-care urinalysis presents unique opportunities for errors, ranging from variation in visual interpretation to input of results. The data included here represent the results from 63,279 urinalyses from 36,780 unique patients performed over a span of three years at an academic medical center and its associated clinics. The data include the patient age/legal sex, methodology (instrument and test strip used), and the available test results (color, clarity, glucose, bilirubin, ketones, specific gravity, blood, pH, protein, urobilinogen, nitrite, and leukocyte esterase). Additionally, we include the method of interface between the testing instrumentation and our electronic medical record (EMR). These fell into one of three broad categories: "Interfaced" (results directly transmitted from the urinalysis instrument to the EMR via specialized data interface), "Manual" (results input by selecting from a drop-down menu in the laboratory information system), and "Enter/Edit" (results typed freely into a text field in the EMR). Analysis of this data was primarily a direct comparison of detectable errors (typos, uninterpretable results, and results outside the reportable range) as a function of the method of entry into the EMR. Secondary analysis comparing the impact of restricting drop-down menu options for urine color and clarity was also performed. These data are of use to others as they are diverse in terms of the test performed and the method of interface. Others may wish to analyze these data when making decisions as to how to perform and report these tests and when estimating risks of error with various methods of data entry.

Development and Implementation of a Standard Format for Clinical Laboratory Test Results

OBJECTIVES

Surprisingly, laboratory results, the principal output of clinical laboratories, are not standardized. Thus, laboratories frequently report results with identical meaning in different formats. For example, laboratories report a positive pregnancy test as "+," "P," or "Positive." To assess the feasibility of a widespread implementation of a result standard, we (1) developed a standard result format for common laboratory tests and (2) implemented a feedback system for clinical laboratories to view their unstandardized results.

METHODS

In the largest integrated health care system in America, 130 facilities had the opportunity to collaboratively develop the standard. For 15 weeks, clinical laboratories received a weekly report of their unstandardized results. At the study's conclusion, laboratories were compared with themselves and their peers by metrics that reflected their unstandardized results.

RESULTS

We rereviewed 156 million test results and observed a 51% decline in the rate of unstandardized results. The number of facilities with fewer than 23 unstandardized results per 100,000 (Six Sigma σ > 5) increased by 58% (52 to 82 facilities; β = 1.79; P < .001).

CONCLUSIONS

This study demonstrated significant improvement in the standardization of clinical laboratory results in a relatively short time. The laboratory community should create and promulgate a standardized result format.

Order Indication Solicitation to Assess Clinical Laboratory Test Utilization: D-Dimer Order Patterns as an Illustrative Case

Background:

A common challenge in the development of laboratory clinical decision support (CDS) and laboratory utilization management (UM) initiatives stems from the fact that many laboratory tests have multiple potential indications, limiting the ability to develop context-specific alerts. As a potential solution, we designed a CDS alert that asks the ordering clinician to provide the indication for testing, using D-dimer as an exemplar. Using data collected over a nearly 3-year period, we sought to determine whether the indication capture was a useful feature within the CDS alert and whether it provided actionable intelligence to guide the development of an UM strategy.

Methods:

We extracted results and ordering data for D-dimer testing performed in our laboratory over a 35-month period. We analyzed order patterns by clinical indication, hospital service, and length of hospitalization.

Results:

Our final data set included 13,971 result-order combinations and indeed provided actionable intelligence regarding test utilization patterns. For example, pulmonary embolism was the most common emergency department indication (86%), while disseminated intravascular coagulation was the most common inpatient indication (56%). D-dimer positivity rates increased with the duration of hospitalization and our data suggested limited utility for ordering this test in the setting of suspected venous thromboembolic disease in admitted patients. In addition, we found that D-dimer was ordered for unexpected indications including the assessment of stroke, dissection, and extracorporeal membrane oxygenation.

Conclusions:

Indication capture within a CDS alert and correlation with result data can provide insight into order patterns which can be used to develop future CDS strategies to guide appropriate test use by clinical indication.

What can millions of laboratory test results tell us about the temporal aspect of data quality? Study of data spanning 17 years in a clinical data warehouse

OBJECTIVE

To identify common temporal evolution profiles in biological data and propose a semi-automated method to these patterns in a clinical data warehouse (CDW).

MATERIALS AND METHODS

We leveraged the CDW of the European Hospital Georges Pompidou and tracked the evolution of 192 biological parameters over a period of 17 years (for 445,000 + patients, and 131 million laboratory test results).

RESULTS

We identified three common profiles of evolution: discretization, breakpoints, and trends. We developed computational and statistical methods to identify these profiles in the CDW. Overall, of the 192 observed biological parameters (87,814,136 values), 135 presented at least one evolution. We identified breakpoints in 30 distinct parameters, discretizations in 32, and trends in 79.

DISCUSSION AND CONCLUSION

our method allowed the identification of several temporal events in the data. Considering the distribution over time of these events, we identified probable causes for the observed profiles: instruments or software upgrades and changes in computation formulas. We evaluated the potential impact for data reuse. Finally, we formulated recommendations to enable safe use and sharing of biological data collection to limit the impact of data evolution in retrospective and federated studies (e.g. the annotation of laboratory parameters presenting breakpoints or trends).

[Developing and application of an autoverification system for clinical chemistry and immunology test results]

Objective: To develop and validate an autoverification system for biochemistry and immunology test results for application in routine work. Methods: Algorithms was designed and translated into the laboratory information system. Parameters including verify limit, delta check, logic relation between tests was set up in the system. Verification rate of every test and the causes of fails were analyzed, according to which the system and parameters were modified. The autoverified reports were evaluated by chief technicians. Only when all of the autoverified results pass the evaluation, the system applied for routine work of releasing the results. Autoverification rate and turnaround time(TAT) were calculated for evaluation of the efficiency of the system. Results: A brand new autoverification system was developed and applied for routine work. The autoverification rate for each single test was 91.1%-96.6%. The autoverification rate for reports was 74%. With the autoverification system, the media of TAT reduced from 111.6(53.9-270.7) min to 87.2(45.4-202.4) min, whereas the time from instrument finishing analysis to releasing the reports reduced from 18.6(1.0-99.3) min to 0.1(0-58.3)min. The number of staff specified for results validation reduced from three to one. Conclusions: The newly developed system can be used for autoverification of biochemistry and immunology test results. The autoverification system can greatly reduce TAT and raise working efficiency. It's essential to employ carefully designed algorithm, appropriate parameters and comprehensive evaluation when developing a new autoverification system.

How Are Doctors Using Mobile Electronic Medical Records? An In-Depth Analysis of the Usage Pattern

In recent years, major hospitals have started using mobile electronic medical records (m-EMR). However, usage patterns based on actual log data have rarely been discussed. In this study, we investigated usage patterns of a m-EMR in several dimensions to understand the m-EMR in depth.

Learning from the crowd while mapping to LOINC

OBJECTIVE

To describe the perspectives of Regenstrief LOINC Mapping Assistant (RELMA) users before and after the deployment of Community Mapping features, characterize the usage of these new features, and analyze the quality of mappings submitted to the community mapping repository.

METHODS

We evaluated Logical Observation Identifiers Names and Codes (LOINC) community members' perceptions about new "wisdom of the crowd" information and how they used the new RELMA features. We conducted a pre-launch survey to capture users' perceptions of the proposed functionality of these new features; monitored how the new features and data available via those features were accessed; conducted a follow-up survey about the use of RELMA with the Community Mapping features; and analyzed community mappings using automated methods to detect potential errors.

RESULTS

Despite general satisfaction with RELMA, nearly 80% of 155 respondents to our pre-launch survey indicated that having information on how often other users had mapped to a particular LOINC term would be helpful. During the study period, 200 participants logged into the RELMA Community Mapping features an average of 610 times per month and viewed the mapping detail pages a total of 6686 times. Fifty respondents (25%) completed our post-launch survey, and those who accessed the Community Mapping features unanimously indicated that they were useful. Overall, 95.3% of the submitted mappings passed our automated validation checks.

CONCLUSION

When information about other institutions' mappings was made available, study participants who accessed it agreed that it was useful and informed their mapping choices. Our findings suggest that a crowd-sourced repository of mappings is valuable to users who are mapping local terms to LOINC terms.

Supporting interoperability of genetic data with LOINC

Electronic reporting of genetic testing results is increasing, but they are often represented in diverse formats and naming conventions. Logical Observation Identifiers Names and Codes (LOINC) is a vocabulary standard that provides universal identifiers for laboratory tests and clinical observations. In genetics, LOINC provides codes to improve interoperability in the midst of reporting style transition, including codes for cytogenetic or mutation analysis tests, specific chromosomal alteration or mutation testing, and fully structured discrete genetic test reporting. LOINC terms follow the recommendations and nomenclature of other standards such as the Human Genome Organization Gene Nomenclature Committee's terminology for gene names. In addition to the narrative text they report now, we recommend that laboratories always report as discrete variables chromosome analysis results, genetic variation(s) found, and genetic variation(s) tested for. By adopting and implementing data standards like LOINC, information systems can help care providers and researchers unlock the potential of genetic information for delivering more personalized care.