Impact of computerized physician order entry on clinical practice in a newborn intensive care unit.

OBJECTIVE: To study the impact of computerized physician order entry (CPOE) on selected neonatal intensive care unit (NICU) practices. DESIGN: Retrospective review. SETTING: Nursing units in an academic health system where CPOE has been implemented in adult services since 2000 and in the NICU since 2002. STUDY POPULATION: Data from 111 very-low-birth-weight (VLBW) infants born consecutively within 6 months before and 100 VLBW infants born within 6 months after the implementation of CPOE were evaluated. The study is based on pre- and post-CPOE comparisons in medication error rates and on the initiation to completion time intervals for pharmacy orders and radiology procedures. The specific data subsets that were compared included caffeine and gentamicin. Radiology turn-around time (order to image display) for the first chest and abdominal X-ray taken following endotracheal intubation and/or umbilical catheter placement was studied. RESULTS: Statistically significant (p<0.01) reductions were seen in medication turn-around times for the loading dose of caffeine in pre-CPOE (n=41, mean 10.5+/-9.8 SD hours) and post-CPOE (n=48, mean 2.8+/-3.3 SD hours). After CPOE implementation, the percentage of cases during each period where caffeine was administered before 2 and 3 hours increased from 10 to 35% and 12 to 63%, respectively. Accuracy of gentamicin dose at the time of admission for 105 (pre-CPOE) and 92 (post-CPOE) VLBW infants was determined. In the pre-CPOE period, 5% overdosages, 8% underdosages, and 87% correct dosages were identified. In the post-CPOE, no medication errors occurred. Accuracy of gentamicin dosages during hospitalization at the time of suspected late-onset sepsis for 31 pre- and 28 post-CPOE VLBW infants was studied. Gentamicin dose was calculated incorrectly in two of 31 (6%) pre-CPOE infants. No such errors were noted in the post-CPOE period. Radiology response time decreased significantly from the pre-CPOE (n=107, mean 42+/-12 SD minutes) to post-CPOE (n=95, mean 32+/-16 SD minutes). CONCLUSION: The implementation of CPOE in our NICU resulted in a significant reduction in medication turn-around times and medication errors for selected drugs, and a decrease in ancillary service (radiology) response time. In spite of the complexities of medication orders in pediatric populations, commercially available software programs for CPOE can successfully be adjusted to accommodate NICU needs and to beneficially impact clinical practice.

Immediate benefits realized following implementation of physician order entry at an academic medical center.

OBJECTIVE: To evaluate the benefits of computerized physician order entry (POE) and electronic medication administration record (eMAR) on the delivery of health care. DESIGN: Inpatient nursing units in an academic health system were the setting for the study. The study comprised before-and-after comparisons between phase 1, pre-implementation of POE (pre-POE) and phase 2, post-implementation of POE (post-POE) and, within phase 2, a comparison of POE and the combination of POE plus eMAR. Length of stay and cost were compared pre- and post-POE for a period of 10 to 12 months across all services in the respective hospitals. MEASUREMENTS: Comparisons were made pre- and post-POE for the time intervals between initiation and completion of pharmacy (pre-POE, n=46; post-POE, n=70), radiology (pre-POE, n=11; post-POE, n=54), and laboratory orders (without POE, n=683; with POE, n=1,142); timeliness of countersignature of verbal order (University Hospitals [OSUH]: pre-POE, n=605; post-POE, n=19,225; James Cancer Hospital (James): pre-POE, n=478; post-POE, n=10,771); volume of nursing transcription errors (POE with manual MAR, n=888; POE with eMAR, n=396); length of stay and total cost (OSUH: pre-POE, n=8,228; post-POE, n=8,154; James: (pre-POE, n=6,471; post-POE, n=6,045). RESULTS: Statistically significant reductions were seen following the implementation of POE for medication turn-around times (64 percent, from 5:28 hr to 1:51 hr; p<0.001), radiology procedure completion times (43 percent, from 7:37 hr to 4:21 hr; p<0.05), and laboratory result reporting times (25 percent, from 31:3 min to 23:4 min; p=0.001). In addition, POE combined with eMAR eliminated all physician and nursing transcription errors. There were 43 and 26 percent improvements in order countersignature by physicians in OSUH and James, respectively. Severity-adjusted length of stay decreased in OSUH (pre-POE, 3.91 days; post-POE, 3.71 days; p=0.002), but not significantly in James (pre-POE, 3.68 days; post-POE, 3.61 days; p=0.356). Although total cost per admission decreased significantly in selected services, it did not change significantly across either institution (OSUH: pre-POE, 5,697 dollars; post-POE, 5,661 dollars; p=0.687; James: pre-POE, 6,427 dollars; post-POE, 6,518 dollars; p=0.502). CONCLUSION: Physician order entry and eMAR provided the framework for improvements in patient safety and in the timeliness of care. The significant cultural and workflow changes that accompany the implementation of POE did not adversely affect acuity-adjusted length of stay or total cost. The reductions in transcription errors, medication turn-around times, and timely reporting of results supports the view that POE and eMAR provide a good return on investment.

Key attributes of a successful physician order entry system implementation in a multi-hospital environment.

The benefits of computerized physician order entry have been widely recognized, although few institutions have successfully installed these systems. Obstacles to successful implementation are organizational as well as technical. In the spring of 2000, following a 4-year period of planning and customization, a 9-month pilot project, and a 14-month hiatus for year 2000, the Ohio State University Health System extensively implemented physician order entry across inpatient units. Implementation for specialty and community services is targeted for completion in 2002. On implemented units, all orders are processed through the system, with 80 percent being entered by physicians and the rest by nursing or other licensed care providers. The system is deployable across diverse clinical environments, focused on physicians as the primary users, and accepted by clinicians. These are the three criteria by which the authors measured the success of their implementation. They believe that the availability of specialty-specific order sets, the engagement of physician leadership, and a large-scale system implementation were key strategic factors that enabled physician-users to accept a physician order entry system despite significant changes in workflow.