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Unadjusted hazard ratios of the organizational characteristics revealed no associations with time to implementation of automated eGFR reporting.
We did not expect to find that none of the facility level characteristics from the CPOS-COS survey were associated with time to implementation of automated eGFR reporting.
As those involved in the SRIP were already contributing a considerable amount of time to implementation activities, which in turn were taking longer than predicted, the timescale for these planned improvements had to move from pre-implementation to post-implementation.
To test for association with time to implementation, survival analysis was performed using proportional hazards models.
To relate timing of implementation to organizational characteristics, facilities were categorized into stages of implementation, early, mid, or late stage with median time to implementation of 0.6, 1.8, and 3.2 years, respectively.
Integrating information technology into geriatric services (gero-informatics) might allow reduction of the time to implementation of geriatric recommendations and prevent the initiation of potentially harmful medications and procedures during the critical first 48 hours of hospitalization.
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It is also why these organizations called for a national audit of uptake of NICE guidance to be published annually, in order to put an end to wildly inequitable and all too variable spending on cancer drugs and response times to implementation of appraisal findings by Primary Care Trusts.
At the same time, many challenges to implementation in BC were identified by those on the ground responsible for implementing core programs.
By the time it came to implementation, enough exceptions and loopholes could also have been introduced to reduce the measure's real bite.
We developed three eGFR implementation outcomes for each facility: 1) a dichotomous indicator of implementation status by September 2009; 2) time to initial implementation of automated eGFR reporting (between July 2004 and September 2009); and 3) time to full implementation of automated eGFR reporting (between July 2004 and September 2009).
To detect variation in time to initial implementation and time to full implementation of eGFR reporting, we examined univariate distributions.
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