The results of our study revealed that e-prescribing did not greatly disrupt prescriber or staff workflow. Prescribers and staff spent more time on the computer at the e-prescribing sites, but, for prescribers, this additional time was compensated for by less time making written notes and orders. It is likely that the introduction of e-prescribing coincided with and reinforced a general shift in physician work patterns away from paper-based methods and toward computerization. The relatively high utilization of e-prescribing at the two sites where it was optional suggests general acceptance of this method among prescribers.
National data indicate that, in 2004, 64% of ambulatory care visits included an order for one or more new or continued medication; 26
this proportion has remained relatively stable over time. 27
In 2004, an average of 1.7 medications were ordered per ambulatory care visit, 26
an increase from 1.2 medications per visit in 1991. 27
Given the increasing volume of prescriptions in ambulatory care, it is essential that e-prescribing does not introduce any additional burden on physician or staff time. We did not find evidence to support the statement of Schade et al. 14
that fully implemented e-prescribing will offer substantial savings in physician and office staff time. We found that the mean time spent per prescription-related event was approximately 12 seconds longer for e-prescribing events. Although this result was not statistically significant, such an increase per prescription-related event may be clinically important. The 27 prescribers performed an average of 9 prescription-related events during the 3.5 hour observation period, suggesting that, over the course of the day, e-prescribing might contribute an additional 3 to 5 minutes to clinicians’ time. In our view, this small increment can be justified if e-prescribing improves the safety and quality of patient care.
Both nurses and medical assistants at the e-prescribing sites spent more time on computer tasks. E-prescribing, that allows computer-faxing directly to the retail pharmacy, may minimize transcription errors and improve the transmission process. This, in turn may benefit clinic support staff by reducing the amount of time spent phoning or faxing prescriptions to the pharmacy. However in our observations of staff, we did not find any substantial differences in the amount of time they spent using the fax machine or conducting pharmacy related telephone conversations. At all three sites, these tasks comprised a very small proportion (<2%) of the day. This finding is contrary to anecdotal evidence suggesting that prescriptions, particularly requests for renewals, are a time consuming task for clinic staff. 28
In part, this discrepancy might be due to the difficulty in adequately categorizing multi-layered and overlapping activities using time motion methods. For example, a ten minute telephone call from a patient primarily discussing symptoms might, in fact, be a pretext for a prescription renewal request. Therefore some activities, indirectly related to prescriptions, might be categorized under “phone patient” in our analysis. However, time spent on this task category did not differ significantly between staff at the paper-based and e-prescribing clinics.
The complete impact of e-prescribing on the clinic workforce is difficult to fully quantify. The increasing reliance on computers, particularly evident among the nurses in our study, may influence job satisfaction and outlook even if it does not introduce any workflow inefficiencies. Therefore, in concurrent work we are conducting focus groups with staff and prescribers to evaluate their expectations of e-prescribing prior to implementation and, subsequently, their views after implementation.
We tracked prescriber, nurse and medical assistant activities because they are an essential part of the ambulatory clinic team and deal with the bulk of prescription activities. Other groups will also be affected by e-prescribing. For example, receptionists, pharmacists and patients are involved in the process of safely and efficiently filling a prescription. Evaluation of all of these groups was beyond the scope of this cross-sectional study. However, we intend to conduct a before and after evaluation of receptionists at the clinic transitioning from paper-based to e-prescriptions in order to measure the impact of e-prescribing on tasks such as routing incoming prescription renewal requests to prescribers and faxing responses back to the pharmacy.
E-prescribing has the potential to be an important step toward improving the quality of patient care. Work in the inpatient setting has demonstrated a 55 percent reduction in the rate of serious medication errors following the introduction of computerized prescribing. 7
Evidence of the benefit of e-prescribing in the ambulatory setting is scarce. In one of the few studies to address this issue, Gandhi et al. found a non-significant trend toward lower error rates at clinics with basic e-prescribing systems compared to clinics with paper-based prescribing. 29
She and her colleagues have suggested that more sophisticated e-prescribing products that feature safety alerts, formulary alerts, dose calculators, and medication selection aids may be necessary to significantly reduce error rates in the ambulatory setting. However, each additional feature requires extra attention by prescribers. Non-specific alerts that raise frequent, petty or even false alarms will waste time and be ignored. 30,31
Given that prescriber buy-in is vital, it will be important to measure the impact of more sophisticated e-prescribing systems on clinicians’ time.
A recent systematic review identified twelve studies that compared the time-efficiency of paper-based records and EHR systems for physicians. 32
Few studies were based in the ambulatory setting 20,23
and none was focused solely on e-prescribing. These studies employed a variety of methods (i.e., work sampling, time-motion, surveys) to capture data. The EHR systems evaluated in each study were different in scope. Given this heterogeneity, it is not surprising that the results varied widely from a 22 percent reduction to a 328 percent increase in physician time, associated with the use of an EHR. Only three of the twelve studies reported that EHR resulted in physician time-savings. This demonstrates that, in many cases, physician concerns about the detrimental impact of EHRs on workflow are justified. On the other hand, it also indicates that an EHR does not inevitably introduce inefficiencies for physicians. Our primary result was that the average e-prescription took 12 seconds (27%) longer than a handwritten prescription, although this difference was not statistically significant. This finding falls well within the range reported by the systematic review. 32
Our results suggest that well-designed EHRs and e-prescribing systems might result in important improvements in the quality of care without greatly disrupting prescriber workflow.
One limitation of our study is that we were unable to track prescribing-related work that was conducted from home or beyond the four hour observation periods. Data from the e-prescribing system indicate that, during the period of our study, 84% of prescriptions at the two e-prescribing sites were performed between 8 AM and 5 PM. Our prescribers report that one of the primary efficiencies of e-prescribing is the ability to authorize renewals and send e-faxes from home.
Timing of clinical activities can be problematic when the individual observed is multi-tasking, for example writing a prescription and talking to a patient, or when the individual switches rapidly back and forth between two overlapping tasks. In these situations we standardized data collection by instructing the observer to prioritize prescription-related activities. Additionally, it was difficult for observers to identify all prescription-related events. Some non-specific tasks, for example locating medical charts, may, in fact, be caused by a need to look up past medications, but it is impossible for passive observers to definitively categorize them as prescription-related.
A limitation of our cross-sectional study design is that it is difficult to control for differences between clinics, other than e-prescribing, which might influence work patterns. To minimize this problem we selected three similar sized sites from the same integrated health system that had a similar mix of medical specialties. The fact that e-prescriptions took marginally longer both between sites and within sites where e-prescribing was optional suggests that differences are truly related to the e-prescribing system. We intend to conduct a follow up time-motion study after all three sites have switched to using the e-prescribing system at the point of care, to assess whether our initial findings are confirmed.
Time motion data rely on observers being able to reliably and unobtrusively categorize tasks. We provided training for all observers, but did not formally test inter-observer agreement. Overhage et al and Pizziferri et al have also studied ambulatory care physicians using almost identical data collection methods. 20,23
Comparison of our results to the range observed in the previous two studies provides support for the validity of time motion methods used. The proportions of physician time spent on direct patient care (52% in our study; 46%–49% range in previous studies), indirect patient care (34%; 33%–37% range), administration (3%; 2%–2% range) and miscellaneous tasks (11%; 12%–20% range) were similar between the three studies.
It is unclear why both hand written and electronic prescriptions took longer at the site with optional laptop prescribing. This may be due to differences in the types of medications prescribed, number of medications per script, or inter-physician variance in prescribing manner. Follow up data, once all sites have adopted desktop e-prescribing in the examination room, will allow us to explore this issue. Potentially, the availability of desktop computers at the point of care will improve the efficiency of e-prescribing by reducing time spent accessing the nearest terminal and minimizing the connectivity problems that can occur with wireless laptop prescribing.
It is difficult to assess the generalizability of our findings. Our study was set in community clinics that are not affiliated with large teaching hospitals. In this respect, we believe our findings will be relevant to many other community clinics that are currently considering investing in e-prescribing. Our data include many young patients attending pediatric and family medicine clinics. The number of medications per visit increases markedly after age 45, 26
therefore differences in the efficiency of hand written and e-prescriptions would be magnified at clinics with more elderly patients. Finally, our results are limited to an internally-developed e-prescribing system that had not implemented several clinical decision support functions (e.g. safety alerts, diagnosis-based reminders). We anticipate that many of these features would lead to longer e-prescribing times, which in some cases may not be justified by reductions in medication errors.