BACKGROUND: Hospital care on weekends has been associated with reduced quality and poor clinical outcomes, suggesting that decreases in overall intensity of care may have important clinical effects. We describe a new measure of hospital intensity of care based on utilization of the electronic health record (EHR). METHODS: We measured global intensity of care at our academic medical center by monitoring the use of the EHR in 2011. Our primary measure, termed EHR interactions, was the number of accessions of a patient's electronic record by a clinician, adjusted for hospital census, per unit of time. Our secondary measure was percent of total available central processing unit (CPU) power used to access EHR servers at a given time. RESULTS: EHR interactions were lower on weekend days as compared to weekdays at every hour (P < 0.0001), and the daytime peak in intensity noted each weekday was blunted on weekends. The relative rate and 95% confidence interval (CI) of census-adjusted record accessions per patient on weekdays compared with weekends were: 1.76 (95% CI: 1.74-1.77), 1.52 (95% CI: 1.50-1.55), and 1.14 (95% CI: 1.12-1.17) for day, morning/evening, and night hours, respectively. Percent CPU usage correlated closely with EHR interactions (r = 0.90). CONCLUSIONS: EHR usage is a valid and easily reproducible measure of intensity of care in the hospital. Using this measure we identified large, hour-specific differences between weekend and weekday intensity. EHR interactions may serve as a useful measure for tracking and improving temporal variations in care that are common, and potentially deleterious, in hospital systems.
BACKGROUND: Hospital care on weekends has been associated with reduced quality and poor clinical outcomes, suggesting that decreases in overall intensity of care may have important clinical effects. We describe a new measure of hospital intensity of care based on utilization of the electronic health record (EHR). METHODS: We measured global intensity of care at our academic medical center by monitoring the use of the EHR in 2011. Our primary measure, termed EHR interactions, was the number of accessions of a patient's electronic record by a clinician, adjusted for hospital census, per unit of time. Our secondary measure was percent of total available central processing unit (CPU) power used to access EHR servers at a given time. RESULTS: EHR interactions were lower on weekend days as compared to weekdays at every hour (P < 0.0001), and the daytime peak in intensity noted each weekday was blunted on weekends. The relative rate and 95% confidence interval (CI) of census-adjusted record accessions per patient on weekdays compared with weekends were: 1.76 (95% CI: 1.74-1.77), 1.52 (95% CI: 1.50-1.55), and 1.14 (95% CI: 1.12-1.17) for day, morning/evening, and night hours, respectively. Percent CPU usage correlated closely with EHR interactions (r = 0.90). CONCLUSIONS: EHR usage is a valid and easily reproducible measure of intensity of care in the hospital. Using this measure we identified large, hour-specific differences between weekend and weekday intensity. EHR interactions may serve as a useful measure for tracking and improving temporal variations in care that are common, and potentially deleterious, in hospital systems.
Authors: Saul Blecker; Keith Goldfeld; Naeun Park; Daniel Shine; Jonathan S Austrian; R Scott Braithwaite; Martha J Radford; Marc N Gourevitch Journal: Am J Med Date: 2013-12-11 Impact factor: 4.965
Authors: Saul Blecker; Keith Goldfeld; Hannah Park; Martha J Radford; Sarah Munson; Fritz Francois; Jonathan S Austrian; R Scott Braithwaite; Katherine Hochman; Richard Donoghue; Bernard A Birnbaum; Marc N Gourevitch Journal: J Gen Intern Med Date: 2015-05-07 Impact factor: 5.128
Authors: Luc Dubois; Kelly Vogt; Chris Vinden; Jennifer Winick-Ng; J Andrew McClure; Pavel S Roshanov; Chaim M Bell; Amit X Garg Journal: CMAJ Date: 2016-10-17 Impact factor: 8.262
Authors: Saul Blecker; Daniel Shine; Naeun Park; Keith Goldfeld; R Scott Braithwaite; Martha J Radford; Marc N Gourevitch Journal: Int J Qual Health Care Date: 2014-07-03 Impact factor: 2.038