Use of medication technicians in US nursing homes: part of the problem or part of the solution?
OBJECTIVE
To determine the relation between organizational characteristics and medication technician (MT) use and quantify the impact of MTs on increasing the likelihood of using medications, employing the example of antiosteoporosis medications.


METHODS
Cross-sectional study.


METHODS
The setting included 6344 Medicare/Medicaid certified nursing homes in 23 states.


METHODS
Residents older than 65 years of age.


METHODS
On-line Survey and Certification of Automated Records (OSCAR) provided facility characteristics information including structural, resource, and staffing levels. The Minimum Data Set (MDS) provided information regarding use of antiosteoporosis medications and resident factors. Adjusted estimates of MT use on antiosteoporosis medication use were derived using logistic regression with generalized estimating equations.


RESULTS
MT use varied by state (6.7% in Alaska vs 85% in Kansas). Homes with greater nursing staffing levels per 100 beds (CNA, RN, LPN) were less likely to use MTs, while larger homes, homes using physician extenders, and contracting pharmacy services were more likely to use MTs. Homes with MTs were more likely to have medication error rates of at least 5% (10.1% vs 7.3%) than homes without MTs. After adjustment for resident and facility factors, residents in MT facilities were not more likely to receive antiosteoporosis treatment relative to those in homes without MTs.


CONCLUSIONS
These data call into question the use of MTs in nursing homes. Use of MTs may lead to more errors, yet not increase use of medications that are labor intensive to administer.
Hughes
Carmel M
CM
School of Pharmacy, Queen's University of Belfast, Belfast, Northern Ireland. c.hughes@qub.ac.uk
Wright
Rollin M
RM
Lapane
Kate L
KL
D000368:Aged; D001509:Bed Occupancy / Q000706:statistics & numerical data; D002568:Certification; D003430:Cross-Sectional Studies; D004358:Drug Therapy / Q000451:nursing*; D004363:Drug Utilization / Q000706:statistics & numerical data; D006276:Health Facility Size / Q000706:statistics & numerical data; D006302:Health Services Research; D006801:Humans; D016015:Logistic Models; D008484:Medicaid; D006278:Medicare; D008508:Medication Errors / Q000706:statistics & numerical data; D024802:Nurse's Role; D015401:Nursing Administration Research; D009728:Nursing Assistants / Q000193:education / Q000458:organization & administration*; D009735:Nursing Homes / Q000458:organization & administration*; D010024:Osteoporosis / Q000188:drug therapy; D010067:Ownership / Q000706:statistics & numerical data; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration*; D017009:Professional Autonomy; D012189:Retrospective Studies; D014481:United States