Impact of computerized provider order entry (CPOE) on length of stay and mortality

Understanding how physician perceptions of job demand and process benefits evolve during CPOE implementation

We examine how physicians' perceptions of two computerized provider order entry (CPOE) capabilities, standardisation of care protocols and documentation quality, are associated with their perceptions of turnaround time, medical error, and job demand at three phases of CPOE implementation: pre-go-live, initial use, and continued use. Through a longitudinal study at a large urban hospital, we find standardisation of care protocols is positively associated with turnaround time reduction in all phases but positively associated with job demand increase only in the initial use phase. Standardisation also has a positive association with medical error reduction in the initial use phase, but later this effect becomes fully mediated through turnaround time reduction in the continued use phase. Documentation quality has a positive association with medical error reduction in the initial use phase and this association strengthens in the continued use phase. Our findings provide insights to effectively manage physicians' response to CPOE implementation.

Assessing EHR Data for Use in Clinical Improvement and Research

ABSTRACT

Data from electronic health records (EHRs) are becoming accessible for use in clinical improvement projects and nursing research. But the data quality may not meet clinicians' and researchers' needs. EHR data, which are primarily collected to document clinical care, invariably contain errors and omissions. This article introduces nurses to the secondary analysis of EHR data, first outlining the steps in data acquisition and then describing a theory-based process for evaluating data quality and cleaning the data. This process involves methodically examining the data using six data quality dimensions-completeness, correctness, concordance, plausibility, currency, and relevance-and helps the clinician or researcher to determine whether data for each variable are fit for use. Two case studies offer examples of problems that can arise and their solutions.

Impact of an Intensive Care Information System on the Length of Stay of Surgical Intensive Care Unit Patients: Observational Study

Background

The implementation of computerized monitoring and prescription systems in intensive care has proven to be reliable in reducing the rate of medical error and increasing patient care time. They also showed a benefit in reducing the length of stay in the intensive care unit (ICU). However, this benefit has been poorly studied, with conflicting results.

Objective

This study aimed to show the impact of computerization on the length of stay in ICUs.

Methods

This was a before-after retrospective observational study. All patients admitted in the surgical ICU at the Rouen University Hospital were included, from June 1, 2015, to June 1, 2016, for the before period and from August 1, 2016, to August 1, 2017, for the after period. The data were extracted from the hospitalization report and included the following: epidemiological data (age, sex, weight, height, and body mass index), reason for ICU admission, severity score at admission, length of stay and mortality in ICU, mortality in hospital, use of life support during the stay, and ICU readmission during the same hospital stay. The consumption of antibiotics, biological analyses, and the number of chest x-rays during the stay were also analyzed.

Results

A total of 1600 patients were included: 839 in the before period and 761 in the after period. Only the severity score Simplified Acute Physiology Score II was significantly higher in the postcomputerization period (38 [SD 20] vs 40 [SD 21]; P<.05). There was no significant difference in terms of length of stay in ICU, mortality, or readmission during the stay. There was a significant increase in the volume of prescribed biological analyses (5416 [5192-5956] biological exams prescribed in the period before Intellispace Critical Care and Anesthesia [ICCA] vs 6374 [6013-6986] biological exams prescribed in the period after ICCA; P=.002), with an increase in the total cost of biological analyses, to the detriment of hematological and biochemical blood tests. There was also a trend toward reduction in the average number of chest x-rays, but this was not significant (0.55 [SD 0.39] chest x-rays per day per patient before computerization vs 0.51 [SD 0.37] chest x-rays per day per patient after computerization; P=.05). On the other hand, there was a decrease in antibiotic prescribing in terms of cost per patient after the implementation of computerization (€149.50 [$164 USD] per patient before computerization vs €105.40 [$155 USD] per patient after computerization).

Conclusions

Implementation of an intensive care information system at the Rouen University Hospital in June 2016 did not have an impact on reducing the length of stay.

Impact of IPMOE on nursing tasks in the medical ward: A time-motion study

Introduction

The In-patient Medication Order Entry System (IPMOE) was first implemented in the medical ward of Princess Margaret Hospital, Hong Kong. It was a local developed close-loop system including prescription, dispensing and administration modules. Evaluation on its impact on nursing tasks would be important for practice improvement and subsequent system enhancement.

Objective

The study was conducted to quantify the nursing times across medication-associated tasks for paper-based MAR and computer-based IPMOE, including change in the tasks and time patterns before and after IPMOE implementation.

Methods

This was a prospective observation study in medical wards before (Jan 2014–Jun 2014) and after (Mar 2015–Jun 2015) the implementation of IPMOE. We conducted 8-hr observation studies of individual nurses with a customized application to time various pre-categorized nursing tasks. Statistical inferences and interrupted time series analysis was performed to identify the change in the intercept and trends over time after implementation.

Result

The average number of medication-related tasks was significantly reduced from 61.07 to 29.81, a reduction of 31.26 episodes per duty (P < 0.001, 95% CI 22.9–39.63). The time for the medication-related tasks was reduced from 32 min (SD = 21.57) to 26.57 min (SD = 11.35) and the medication administration time increased from 37.93 min (SD = 14.78) to 44.37 min (SD = 19.45), but there was no overall significant difference in the time spent on each duty (P = 0.315) between the two groups. An improving trend in the delayed effect was observed (P = 0.03), which indicated a run-in period for new application was needed in clinical setting.

Conclusion

Our study had shown the time motion observation could be applied to measure the impact of the IPMOE in a busy clinical setting. Through classification of activities, validation, objective measurement and longitudinal evaluation, the method could be applied in various systems as well as different clinical settings in measure efficiency.

Learning from errors: analysis of medication order voiding in CPOE systems

OBJECTIVE

Medication order voiding allows clinicians to indicate that an existing order was placed in error. We explored whether the order voiding function could be used to record and study medication ordering errors.

MATERIALS AND METHODS

We examined medication orders from an academic medical center for a 6-year period (2006-2011; n  = 5 804 150). We categorized orders based on status (void, not void) and clinician-provided reasons for voiding. We used multivariable logistic regression to investigate the association between order voiding and clinician, patient, and order characteristics. We conducted chart reviews on a random sample of voided orders ( n  = 198) to investigate the rate of medication ordering errors among voided orders, and the accuracy of clinician-provided reasons for voiding.

RESULTS

We found that 0.49% of all orders were voided. Order voiding was associated with clinician type (physician, pharmacist, nurse, student, other) and order type (inpatient, prescription, home medications by history). An estimated 70 ± 10% of voided orders were due to medication ordering errors. Clinician-provided reasons for voiding were reasonably predictive of the actual cause of error for duplicate orders (72%), but not for other reasons.

DISCUSSION AND CONCLUSION

Medication safety initiatives require availability of error data to create repositories for learning and training. The voiding function is available in several electronic health record systems, so order voiding could provide a low-effort mechanism for self-reporting of medication ordering errors. Additional clinician training could help increase the quality of such reporting.

The Impact of Order Source Misattribution on Computerized Provider Order Entry (CPOE) Performance Metrics

BACKGROUND

One strategy to foster adoption of computerized provider order entry (CPOE) by physicians is the monthly distribution of a list identifying the number and use rate percentage of orders entered electronically versus on paper by each physician in the facility. Physicians care about CPOE use rate reports because they support the patient safety and quality improvement objectives of CPOE implementation. Certain physician groups are also motivated because they participate in contracted financial and performance arrangements that include incentive payments or financial penalties for meeting (or failing to meet) a specified CPOE use rate target. Misattribution of order sources can hinder accurate measurement of individual physician CPOE use and can thereby undermine providers' confidence in their reported performance, as well as their motivation to utilize CPOE. Misattribution of order sources also has significant patient safety, quality, and medicolegal implications.

OBJECTIVE

This analysis sought to evaluate the magnitude and sources of misattribution among hospitalists with high CPOE use and, if misattribution was found, to formulate strategies to prevent and reduce its recurrence, thereby ensuring the integrity and credibility of individual and facility CPOE use rate reporting.

METHODS

A detailed manual order source review and validation of all orders issued by one hospitalist group at a midsize community hospital was conducted for a one-month study period.

RESULTS

We found that a small but not dismissible percentage of orders issued by hospitalists-up to 4.18 percent (95 percent confidence interval, 3.84-4.56 percent) per month-were attributed inaccurately. Sources of misattribution by department or function were as follows: nursing, 42 percent; pharmacy, 38 percent; laboratory, 15 percent; unit clerk, 3 percent; and radiology, 2 percent. Order management and protocol were the most common correct order sources that were incorrectly attributed.

CONCLUSION

Order source misattribution can negatively affect reported provider CPOE use rates and should be investigated if providers perceive discrepancies between reported rates and their actual performance. Preventive education and communication efforts across departments can help prevent and reduce misattribution.