Patient visit metrics appear to be negatively impacted in the ED during EHR implementation despite additional staffing and availability of the overflow clinic. Time to physician, left without being seen numbers, and overall LOS for both admitted and discharged patients were significantly higher during the initial EHR implementation phase. The effect appeared to be temporary; LOS was back at pre-implementation baselines within 3 months of implementation, when corrected for patient volumes.
Using the model of input/throughput/output to describe ED flow in our setting,11
the input (number of visits, time to room placement) and output (numbers of admitted and discharged patients) measures were consistent between each of the 2-week blocks. Given that no other interventions were employed either in the ED or within the hospital during this time (other than increasing staffing in the ED), we conclude that the impact for delays in care were directly related to the implementation of EHR.
The difference in LOS was seen in the time to provider, and was not reflected in time to room placement. Time to room placement is a proxy for the amount of time doing rapid nursing assessments and triage. Before implementation, there was a concern that this process, designed to take less than 10 min per patient, would be affected greatly by the new EHR, but that was not the case. All staff experienced the same change in system, but more ‘bottlenecks’ appeared to be attributable to provider slowdown. This is clearly in keeping with previous research, in which loss of physician efficiency was recognized to be a potential drawback to EHR systems.9
This effect might be more pronounced in our setting, where the frequent rotation of residents (involved in approximately 50% of visits) increased ‘new’ provider users to the system. Whether this is offset by improving revenues with increased charting or capturing of charges was not addressed by our study.
A critical question was whether the overflow clinic model could be quickly adapted to offload the ED for the implementation of EHR. The clinic was successful at decreasing LOS during our flu surge, but it was not effective in diverting patients during EHR implementation, perhaps partly due to fewer patients being diverted to the clinic. The clinic was designed to divert patient with ‘flu’ symptoms, and once that specific population declined, the process for triaging patients to the clinic did not work well, and fewer patients were diverted. It is unclear from our data whether increasing the specific number of patients diverted would have made a difference in decreasing LOS during the implementation.
We saw continued improvement in LOS from 3 to 6 months after implementation, leading to an overall improvement compared with baseline. It appears from productivity data that the total number of hours worked by all providers has remained constant since H1N1 and implementation, despite an overall reduction in patient visits, which probably explains this continued drop. The drop in total ED visits was unexpected in the following year, and probably impacts the perceived loss of provider ‘productivity’ seen 1 year after implementation.
Provider productivity was included to determine how much an effect was due to increasing provider and patient services staffing during the go live. Initial decreased productivity seen in December 2009 could be due to the product, but it is interesting that this effect was not seen in the month of implementation. Due to the structuring and reporting of these data, it was not possible to segment it further.
As this was an observational study, no causality can be formally attributed from our data. No effort was made to control between the groups, but as there were no other major operational changes during this time, it would appear that the EHR rollout was the largest contributing factor to the slowdown.