Electronic prescribing at the point of care: a time-motion study in the primary care setting.

OBJECTIVE: To evaluate the impact of an ambulatory computerized provider order entry (CPOE ) system on the time efficiency of prescribers. Two primary aims were to compare prescribing time between (1) handwritten and electronic (e-) prescriptions and (2) e-prescriptions using differing hardware configurations. DATA SOURCES/STUDY SETTING: Primary data on prescribers/staff were collected (2005-2007) at three primary care clinics in a community based, multispecialty health system. STUDY DESIGN: This was a quasi-experimental, direct observation, time-motion study conducted in two phases. In phase 1 (n=69 subjects), each site used a unique combination of CPOE software/hardware (paper-based, desktops in prescriber offices or hallway workstations, or laptops). In phase 2 (n=77), all sites used CPOE software on desktops in examination rooms (at point of care). DATA COLLECTION METHODS: Data were collected using TimerPro software on a Palm device. PRINCIPAL FINDINGS: Average time to e-prescribe using CPOE in the examination room was 69 seconds/prescription-event (new/renewed combined)-25 seconds longer than to handwrite (99.5 percent confidence interval [CI] 12.38), and 24 seconds longer than to e-prescribe at offices/workstations (99.5 percent CI 8.39). Each calculates to 20 seconds longer per patient. CONCLUSIONS: E-prescribing takes longer than handwriting. E-prescribing at the point of care takes longer than e-prescribing in offices/workstations. Improvements in safety and quality may be worth the investment of time.

Strategies to optimize medication use in the physician group practice: the role of the clinical pharmacist.

OBJECTIVES: To (1) describe the role of clinical pharmacists in providing population-based pharmaceutical care as employees of a physician group practice, (2) describe the strategies used by pharmacists to optimize medication use, (3) quantify improvements in care, and (4) illustrate the calculations used to quantify cost savings. SETTING: Community-based, multispecialty, physician group practice located in the north Puget Sound area between 2003 and 2007. PRACTICE DESCRIPTION: Using four cornerstones (evidence-based medicine, therapeutic interchange, academic detailing, and a local pharmacy and therapeutics committee), the pharmacists provided population-based pharmaceutical care, leading generic switches, target drug programs, and prescription to over-the-counter medication switches. They also led disease management programs, managed drug recalls, implemented electronic health records, negotiated budgets with health plans, and led patient assistance programs and prior authorization programs to improve patient satisfaction. PRACTICE INNOVATION: Implementing these strategies from the vantage point of a physician group presents a seldom-realized employment opportunity for pharmacists. MAIN OUTCOME MEASURES: The impact of these strategies is measured by process, use, and clinical outcomes metrics. These, in turn, are linked to incentive payments in the pay-for-performance environment or to a lowered per member, per month cost in the capitated environment. RESULTS: In 2006-2007, 71% of our hypertensive patients received generic agents compared with a network average for receiving generic agents of 43%, while the proportion of patients with controlled blood pressure increased from 45% to 60%. We saved $450,000 in inpatient costs for deep venous thrombosis. CONCLUSION: Clinical pharmacists employed in a physician group practice can optimize medication use, improve care, and reduce costs.

The impact of e-prescribing on prescriber and staff time in ambulatory care clinics: a time motion study.

Electronic prescribing has improved the quality and safety of care. One barrier preventing widespread adoption is the potential detrimental impact on workflow. We used time-motion techniques to compare prescribing times at three ambulatory care sites that used paper-based prescribing, desktop, or laptop e-prescribing. An observer timed all prescriber (n = 27) and staff (n = 42) tasks performed during a 4-hour period. At the sites with optional e-prescribing >75% of prescription-related events were performed electronically. Prescribers at e-prescribing sites spent less time writing, but time-savings were offset by increased computer tasks. After adjusting for site, prescriber and prescription type, e-prescribing tasks took marginally longer than hand written prescriptions (12.0 seconds; -1.6, 25.6 CI). Nursing staff at the e-prescribing sites spent longer on computer tasks (5.4 minutes/hour; 0.0, 10.7 CI). E-prescribing was not associated with an increase in combined computer and writing time for prescribers. If carefully implemented, e-prescribing will not greatly disrupt workflow.

Characterization of prescribing errors in an internal medicine clinic.

PURPOSE: A pilot study was conducted to characterize the epidemiology of prescribing errors, comparing those that occurred pre- to postimplementation of an electronic prescribing system; this article describes the results of the study during the preimplementation phase, when a handwritten prescription process was still in place. SUMMARY: A retrospective review of 1411 prescriptions that were handwritten during a five-month time frame was used to identify and characterize medication errors and potential medication errors. The review was conducted in an internal medicine clinic in a large health system that was preparing to implement an electronic prescribing system. The first phase was the implementation of a basic system-one that facilitated the writing of a more complete and legible prescription. The second phase consisted of adding more sophisticated clinical decision support (CDS) capabilities. Three data sources were reviewed: the handwritten prescription, the electronic health record and the prescription as it had been entered into the pharmacy computer system. Almost 28% of the prescriptions evaluated contained one or more errors or potential errors. Over 90% of the errors were potential errors. Only 0.2% of the errors caused patient harm. Non-clinical errors (illegibility, missing information, wrong dose) may be affected by a basic electronic prescribing system, and clinical errors (drug-disease interaction, contraindication of a drug) may be affected only when more sophisticated levels of CDS programming are added. CONCLUSION: Potential prescribing errors occurred frequently but few reached the patient or caused harm. The most severe errors were those that may be reduced by the implementation of an electronic prescribing system with CDS capabilities.