Electronic medical record, error detection, and error reduction: a pediatric critical care perspective

The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis

Background

As a result of the overwhelming proportion of medication errors occurring each year, there has been an increased focus on developing medication error prevention strategies. Recent advances in electronic health record (EHR) technologies allow institutions the opportunity to identify medication administration error events in real time through computerized algorithms. MED.Safe, a software package comprising medication discrepancy detection algorithms, was developed to meet this need by performing an automated comparison of medication orders to medication administration records (MARs). In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site.

Objective

The purpose of this study is to determine the portability and generalizability of the MED.Safe software at a second site by assessing the performance and fit of the algorithms through comparison of discrepancy rates and other metrics across institutions.

Methods

The MED.Safe software package was executed on medication use data from the implementation site to generate prescribing ratios and discrepancy rates. A retrospective analysis of medication prescribing and documentation patterns was then performed on the results and compared to those from the development site to determine the algorithmic performance and fit. Variance in performance from the development site was further explored and characterized.

Results

Compared to the development site, the implementation site had lower audit/order ratios and higher MAR/(order + audit) ratios. The discrepancy rates on the implementation site were consistently higher than those from the development site. Three drivers for the higher discrepancy rates were alternative clinical workflow using orders with dosing ranges; a data extract, transfer, and load issue causing modified order data to overwrite original order values in the EHRs; and delayed EHR documentation of verbal orders. Opportunities for improvement were identified and applied using a software update, which decreased false-positive discrepancies and improved overall fit.

Conclusions

The execution of MED.Safe at a second site was feasible and effective in the detection of medication administration discrepancies. A comparison of medication ordering, administration, and discrepancy rates identified areas where MED.Safe could be improved through customization. One modification of MED.Safe through deployment of a software update improved the overall algorithmic fit at the implementation site. More flexible customizations to accommodate different clinical practice patterns could improve MED.Safe’s fit at new sites.

A Predictive Model for Height Tracking in an Adult Male Population in Bangladesh to Reduce Input Errors

The advancement of ICT and affordability of medical sensors enable healthcare data to be obtained remotely. Remote healthcare data is erroneous in nature. Detection of errors for remote healthcare data has not been significantly studied. This research aims to design and develop a software system to detect and reduce such healthcare data errors. Enormous research efforts produced error detection algorithms, however, the detection is done at the server side after a substantial amount of data is archived. Errors can be efficiently reduced if the suspicious data can be detected at the source. We took the approach to predict acceptable range of anthropometric data of each patient. We analyzed 40,391 records to monitor the growth patterns. We plotted the anthropometric items e.g., Height, Weight, BMI, Waist and Hip size for males and females. The plots show some patterns based on different age groups. This paper reports one parameter, height of males. We found three groups that can be classified with similar growth patterns: Age group 20–49, no significant change; Age group 50–64, slightly decremented pattern; and Age group 65–100, a drastic height loss. The acceptable range can change over time. The system estimates the updated trend from new health records.

Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit

Background

Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows.

Methods

Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children's Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools).

Results

Physicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P < .001).

Conclusions

The automated system demonstrated improved capacity for identifying MAEs while guarding against alert fatigue. It also showed promise for reducing patient exposure to potential harm following MAE events.

Automated detection of medication administration errors in neonatal intensive care

OBJECTIVE

To improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.

METHODS

We developed novel computerized algorithms to identify MAEs within the EHR of all neonatal patients treated in a level four neonatal intensive care unit (NICU) in 2011 and 2012. We evaluated the rates and types of MAEs identified by the automated algorithms and compared their performance to incident reporting. Performance was evaluated by physician chart review.

RESULTS

In the combined 2011 and 2012 NICU data sets, the automated algorithms identified MAEs at the following rates: fentanyl, 0.4% (4 errors/1005 fentanyl administration records); morphine, 0.3% (11/4009); dobutamine, 0 (0/10); and milrinone, 0.3% (5/1925). We found higher MAE rates for other vasoactive medications including: dopamine, 11.6% (5/43); epinephrine, 10.0% (289/2890); and vasopressin, 12.8% (54/421). Fluid administration error rates were similar: intravenous fluids, 3.2% (273/8567); parenteral nutrition, 3.2% (649/20124); and lipid administration, 1.3% (203/15227). We also found 13 insulin administration errors with a resulting rate of 2.9% (13/456). MAE rates were higher for medications that were adjusted frequently and fluids administered concurrently. The algorithms identified many previously unidentified errors, demonstrating significantly better sensitivity (82% vs. 5%) and precision (70% vs. 50%) than incident reporting for error recognition.

CONCLUSIONS

Automated detection of medication administration errors through the EHR is feasible and performs better than currently used incident reporting systems. Automated algorithms may be useful for real-time error identification and mitigation.

Identification of Hypertension Management-related Errors in a Personal Digital Assistant-based Clinical Log for Nurses in Advanced Practice Nurse Training

PURPOSE

The purposes of this study were to develop a taxonomy for detection of errors related to hypertension management and to apply the taxonomy to retrospectively analyze the documentation of nurses in Advanced Practice Nurse (APN) training.

METHOD

We developed the Hypertension Diagnosis and Management Error Taxonomy and applied it in a sample of adult patient encounters (N = 15,862) that were documented in a personal digital assistant-based clinical log by registered nurses in APN training. We used Standard Query Language queries to retrieve hypertension-related data from the central database. The data were summarized using descriptive statistics.

RESULT

Blood pressure was documented in 77.5% (n = 12,297) of encounters; 21% had high blood pressure values. Missed diagnosis, incomplete diagnosis and misdiagnosis rates were 63.7%, 6.8% and 7.5% respectively. In terms of treatment, the omission rates were 17.9% for essential medications and 69.9% for essential patient teaching. Contraindicated anti-hypertensive medications were documented in 12% of encounters with co-occurring diagnoses of hypertension and asthma.

CONCLUSION

The Hypertension Diagnosis and Management Error Taxonomy was useful for identifying errors based on documentation in a clinical log. The results provide an initial understanding of the nature of errors associated with hypertension diagnosis and management of nurses in APN training. The information gained from this study can contribute to educational interventions that promote APN competencies in identification and management of hypertension as well as overall patient safety and informatics competencies.

The effect of seniority and education on departmental dictation utilization

BACKGROUND

Electronic medical records (EMR) are considered the best solution to improved dissemination of health information for patients. The associated transcription caused a significant cost increase in an academic pediatric center. An educational campaign was implemented to achieve cost-effective transcriptions without compromising the number of EMR transcriptions.

METHODS

We analyzed the effect of seniority on transcription times over a 4-month period. We also compared the dictation volume before and 4 months after educational interventions. This study was performed in a pediatric academic center with both inpatient and outpatient transcription utilization analyzed. All clinicians providing pediatric care and utilizing the hospital-based transcription over the study time period were analyzed. Interventions included targeted education about efficiencies in transcription, time-based dictation costs, avoidance of lengthy pauses and unnecessary detail, shortening of total transcriptions, superfluous phrases as well as structured templates. Level of training by postgraduate year of training and seniority within faculty were measured for impact on dictation time and effect of education to improve times.

RESULTS

Learners in year one had an average dictation time of 7.5 ± 2.2 minutes, which decreased with seniority to an average of 4.1 ± 2.2 minutes for senior faculty (0.0007, ANOVA). After educational initiatives were implemented, there was progressive decline in dictation utilization. The total dictation time decreased from 8,750 minutes per month in August 2009 to 4,296 minutes in December of 2009 (p = 0.0045, unpaired t-test).

CONCLUSION

We identified a substantial need for education in dictation utilization and demonstrated that relatively simple interventions can result in substantial costs savings.

Identification of patient information corruption in the intensive care unit: using a scoring tool to direct quality improvements in handover

OBJECTIVE

To use a handover assessment tool for identifying patient information corruption and objectively evaluating interventions designed to reduce handover errors and improve medical decision making. The continuous monitoring, intervention, and evaluation of the patient in modern intensive care unit practice generates large quantities of information, the platform on which medical decisions are made. Information corruption, defined as errors of distortion/omission compared with the medical record, may result in medical judgment errors. Identifying these errors may lead to quality improvements in intensive care unit care delivery and safety.

DESIGN

Handover assessment instrument development study divided into two phases by the introduction of a handover intervention.

SETTING

Closed, 17-bed, university-affiliated mixed surgical/medical intensive care unit.

SUBJECTS

Senior and junior medical members of the intensive care unit team.

INTERVENTIONS

Electronic handover page.

MEASUREMENTS AND MAIN RESULTS

Study subjects were asked to recall clinical information commonly discussed at handover on individual patients. The handover score measured the percentage of information correctly retained for each individual doctor-patient interaction. The clinical intention score, a subjective measure of medical judgment, was graded (1-5) by three blinded intensive care unit experts. A total of 137 interactions were scored. Median (interquartile range) handover scores for phases 1 and 2 were 79.07% (67.44-84.50) and 83.72% (76.16-88.37), respectively. Score variance was reduced by the handover intervention (p < .05). Increasing median handover scores, 68.60 to 83.72, were associated with increases in clinical intention scores from 1 to 5 (chi-square = 23.59, df = 4, p < .0001).

CONCLUSIONS

When asked to recall clinical information discussed at handover, medical members of the intensive care unit team provide data that are significantly corrupted compared with the medical record. Low subjective clinical judgment scores are significant associated with low handover scores. The handover/clinical intention scores may, therefore, be useful screening tools for intensive care unit system vulnerability to medical error. Additionally, handover instruments can identify interventions that reduce system vulnerability to error and may be used to guide quality improvements in handover practice.