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Dive into the research topics where Nathan M. Lawless is active.

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Journal of the American Medical Informatics Association | 2010

The impact of computerized provider order entry on medication errors in a multispecialty group practice

Emily Beth Devine; Ryan N. Hansen; Jennifer L. Wilson-Norton; Nathan M. Lawless; Albert W. Fisk; David K. Blough; Diane P. Martin; Sean D. Sullivan

OBJECTIVE Computerized provider order entry (CPOE) has been shown to improve patient safety by reducing medication errors and subsequent adverse drug events (ADEs). Studies demonstrating these benefits have been conducted primarily in the inpatient setting, with fewer in the ambulatory setting. The objective was to evaluate the effect of a basic, ambulatory CPOE system on medication errors and associated ADEs. DESIGN This quasiexperimental, pretest-post-test study was conducted in a community-based, multispecialty health system not affiliated with an academic medical center. The intervention was a basic CPOE system with limited clinical decision support capabilities. MEASUREMENT Comparison of prescriptions written before (n=5016 handwritten) to after (n=5153 electronically prescribed) implementation of the CPOE system. The primary outcome was the occurrence of error(s); secondary outcomes were types and severity of errors. RESULTS Frequency of errors declined from 18.2% to 8.2%-a reduction in adjusted odds of 70% (OR: 0.30; 95% CI 0.23 to 0.40). The largest reductions were seen in adjusted odds of errors of illegibility (97%), use of inappropriate abbreviations (94%) and missing information (85%). There was a 57% reduction in adjusted odds of errors that did not cause harm (potential ADEs) (OR 0.43; 95% CI 0.38 to 0.49). The reduction in the number of errors that caused harm (preventable ADEs) was not statistically significant, perhaps due to few errors in this category. CONCLUSIONS A basic CPOE system in a community setting was associated with a significant reduction in medication errors of most types and severity levels.


Journal of the American Medical Informatics Association | 2007

The Impact of e-Prescribing on Prescriber and Staff Time in Ambulatory Care Clinics: A Time–Motion Study

William Hollingworth; Emily Beth Devine; Ryan N. Hansen; Nathan M. Lawless; Bryan A. Comstock; Jennifer L. Wilson-Norton; Kathleen L. Tharp; Sean D. Sullivan

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.


Health Services Research | 2010

Electronic Prescribing at the Point of Care: A Time–Motion Study in the Primary Care Setting

Emily Beth Devine; William Hollingworth; Ryan N. Hansen; Nathan M. Lawless; Jennifer L. Wilson-Norton; Diane P. Martin; David K. Blough; Sean D. Sullivan

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.


Journal of The American Pharmacists Association | 2009

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

Emily Beth Devine; Susan Hoang; Albert W. Fisk; Jennifer L. Wilson-Norton; Nathan M. Lawless; Clifton Louie

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


American Journal of Health-system Pharmacy | 2007

Characterization of prescribing errors in an internal medicine clinic

Emily Beth Devine; Jennifer L. Wilson-Norton; Nathan M. Lawless; Ryan N. Hansen; Thomas K. Hazlet; Kerry Kelly; William Hollingworth; David K. Blough; Sean D. Sullivan

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.


Archive | 2008

Implementing an Ambulatory e-Prescribing System: Strategies Employed and Lessons Learned to Minimize Unintended Consequences

Emily Beth Devine; Jennifer L. Wilson-Norton; Nathan M. Lawless; Ryan N. Hansen; William Hollingworth; Albert W. Fisk; Sean Sullivan


Archive | 2009

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

Susan Hoang; Albert W. Fisk; Jennifer L. Wilson-Norton; Nathan M. Lawless; Clifton Louie; Emily Beth Devine; Jennifer Wilson-Norton; Nathan Lawless


Archive | 2008

Table 2, Summary of lessons learned

Emily Beth Devine; Jennifer L. Wilson-Norton; Nathan M. Lawless; Ryan N. Hansen; William Hollingworth; Albert W. Fisk; Sean Sullivan


Archive | 2008

Table 1, Timeline for the development and implementation of the electronic health record

Emily Beth Devine; Jennifer L. Wilson-Norton; Nathan M. Lawless; Ryan N. Hansen; William Hollingworth; Albert W. Fisk; Sean Sullivan


Archive | 2005

Table 2, NCC MERP Definition of a medication error and Risk Assessment Index

Emily Beth Devine; Jennifer L. Wilson-Norton; Nathan M. Lawless; Thomas K. Hazlet; Ryan N. Hansen; Kerry Kelly; Stephanie Te; Carolyn Wong

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Ryan N. Hansen

University of Washington

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Clifton Louie

University of California

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Susan Hoang

University of Washington Medical Center

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