Transcription Errors of Blood Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Analysis Toward Electronic Medical Record-Glucometer Interoperability

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.

Timing errors and temporal uncertainty in clinical databases-A narrative review

A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.

Impact of Interfacing Near Point of Care Clinical Chemistry and Hematology Analyzers at Urgent Care Clinics at an Academic Health System

Background

Point-of-care (POC) testing equipment is commonly utilized in outpatient clinics. Our institution recently interfaced POC chemistry and hematology devices at two outpatient clinics via middleware software to the central electronic health record (EHR), facilitating a comparison of manual transcription versus automatic reporting via interface. This allowed for estimation of serious/obvious error rates and manual time savings. Additional goals were to develop autoverification rules and analyze broad trends of results in response to common clinician complaints on the POC testing.

Material and Methods

Data were obtained from two satellite clinic sites providing both primary and urgent care within an academic health system. Interface of devices was accomplished via Instrument Manager middleware software and occurred approximately halfway through the 38 month retrospective timeframe. Laboratory results for three testing POC chemistry and hematology panels were extracted with EHR tools.

Results

Nearly 100,000 lab values were analyzed and revealed that the rate of laboratory values outside reference range was essentially unchanged before and after interface of POC testing devices (2.0–2.1%). Serious/obvious errors, while rare overall, declined significantly, with none recorded after the interface with autoverified results and only three related to manual edits of results that failed autoverification. Fewer duplicated test results were identified after the interface, most notably with the hematology testing. Anion gap values of less than zero were observed more frequently in POC device tests when compared to central laboratory tests and are attributed to a higher proportion of Cl values greater than 110 mEq/L and CO₂ values greater than 30 mEq/L with POC results. Time savings of eliminating manual data entry were calculated to be 21.6 employee hours per month.

Conclusions

In a switch from manual entry to automatic interface for POC chemistry and hematology, the most notable changes were reduction of serious/obvious errors and duplicate results. Significant time employee time savings highlight an additional benefit of instrument interfacing. Lastly, a difference between POC and central laboratory instruments is a higher rate of high Cl and CO₂ values relative to the central laboratory.

A mobile application to support bedside nurse documentation and care: a time and motion study

 

Documentation at the bedside is still often initiated on paper before being entered in electronic charts, even after implementing electronic health records (EHRs). This 2-step process is time-consuming, a potential source of error, and hinders the use of real-time information. We developed the “Bedside mobility” smartphone application to facilitate bedside documentation in the EHR.

Objective

This study aims to evaluate the impact of our app in 2 wards of a teaching hospital with a pre-post design.

Materials and methods

The duration and location of all documentation activities were recorded using a time motion study.

Results

Using the app significantly decreased the duration of EHR documentation per hour of observation by 4.10 min (P = 0.003), while the time spent interacting with patient increased by 1.45 min although not significantly. Also, in the intervention period, the average duration of uninterrupted documentation episodes increased by 0.27 min (P = 0.16) and the uninterrupted interaction with patient increased by 8.50 min (P = 0.027).

Discussion

By reducing the fragmentation of documentation workflow, decreasing the overall EHR documentation time and allowing nurses to spend more time with their patients, app use led to potential higher quality of care and higher patient satisfaction and may help maintain a smoother workflow.

Conclusion

Our mobile app has the potential to positively impact bedside nurses’ clinical workflow and documentation, as well as patient–provider communication and relationship.

Smart agent system for insulin infusion protocol management: a simulation-based human factors evaluation study

OBJECTIVE

To compare the insulin infusion management of critically ill patients by nurses using either a common standard (ie, human completion of insulin infusion protocol steps) or smart agent (SA) system that integrates the electronic health record and infusion pump and automates insulin dose selection.

DESIGN

A within subjects design where participants completed 12 simulation scenarios, in 4 blocks of 3 scenarios each. Each block was performed with either the manual standard or the SA system. The initial starting condition was randomised to manual standard or SA and alternated thereafter.

SETTING

A simulation-based human factors evaluation conducted at a large academic medical centre.

SUBJECTS

Twenty critical care nurses.

INTERVENTIONS

A systems engineering intervention, the SA, for insulin infusion management.

MEASUREMENTS

The primary study outcomes were error rates and task completion times. Secondary study outcomes were perceived workload, trust in automation and system usability, all measured with previously validated scales.

MAIN RESULTS

The SA system produced significantly fewer dose errors compared with manual calculation (17% (n=20) vs 0, p<0.001). Participants were significantly faster, completing the protocol using the SA system (p<0.001). Overall ratings of workload for the SA system were significantly lower than with the manual system (p<0.001). For trust ratings, there was a significant interaction between time (first or second exposure) and the system used, such that after their second exposure to the two systems, participants had significantly more trust in the SA system. Participants rated the usability of the SA system significantly higher than the manual system (p<0.001).

CONCLUSIONS

A systems engineering approach jointly optimised safety, efficiency and workload considerations.

Outcomes of a feasibility trial using an innovative mobile health programme to assist in insulin dose adjustment

Objective

Intensification of diabetes therapy with insulin is often delayed for people with suboptimal glycaemic control. This paper reports on the feasibility of using an innovative mobile health (mHealth) programme to assist a diabetes insulin dose adjustment (IDA) service.

Methods

Twenty adults with diabetes referred to a tertiary hospital IDA service were recruited. They were provided with a cloud-based mobile remote monitoring system—the mobile diabetes management system (MDMS). The credentialled diabetes educator (CDE) recorded the time taken to perform IDA utilising the MDMS versus the conventional method—which is a weekly adjustment of insulin doses by a CDE through telephone contact based on three or more daily blood glucose readings. Participants and staff completed a feedback questionnaire.

Results

The CDE spent 55% less time performing IDA using MDMS than using the conventional method. The participants were satisfied with MDMS use and the CDEs reported improved efficiency.

Conclusion

Incorporating a mHealth programme for an IDA service has the potential to improve service delivery efficiencies while simultaneously improving the patient experience.