SGEM#159: Computer Games – Computer Provider Order Entry (CPOE)

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Date: July 5th, 2016

Guest Skeptic: Dr. Chris Bond. Chris is an emergency physician and clinical lecturer at the University of Calgary. He is currently the host of CAEP Casts, which highlights educational innovations from emergency medicine residency programs across Canada. Chris also has his own #FOAMed blog called Standing on the Corner Minding My Own Business (SOCMOB).

Reference: Gray A et al. The impact of computerized provider order entry on emergency department flow. CJEM 2016.

Background: Emergency department crowding is a growing issue across Canada. As more tertiary care EDs implement computerized provider order entry (CPOE), it is important to analyze emergency department metrics to see how CPOE may impact throughput.

Previous studies have shown that CPOE has no impact on mortality, and may in fact improve pain treatment and adherence to certain common presenting complaint medication protocols (such as stroke and renal colic) [1-4].

Some studies have shown there may be an impact on throughput in a number of possible areas such as decreased physician productivity, increased LOS for admitted patients, or increased time to order labs and imaging [5-7].

Other studies have shown that CPOE fixes some errors, creates new ones and frustrates physicians [8]. There is no consistent or comprehensive evidence in favor of CPOE [9].

A study looking at productivity in a community hospital emergency department showed the mean percentage of time spent on data entry was more than 40% and less than 30% spent on direct patient care. They calculated in a busy 10hr shift the number of mouse clicks was almost 4,000 [10].

To our knowledge there have been no studies to directly evaluate the impact of CPOE on emergency department wait times, a key variable in throughput and crowding.

Question: What impact will CPOE have on emergency department patient throughput?

Reference: Gray A et al. The impact of computerized provider order entry on emergency department flow. CJEM 2016.

Population: Emergency department patients 18 years and older presenting to two quaternary hospitals in July and August of 2013 and 2014.
- Excluded: Patients with negative wait times or extreme outliers that exceeded 24 hours (presumed to represent an erroneously wrong day recorded). Also excluded any patients missing vital statistics (eg. Gender, age or CTAS score).
Intervention: Computerized provider order entry (CPOE)
Control: Non-computerized order entry
Outcome:
- Primary Outcome: Emergency department throughput
  - Wait Time (WT): Time to first physician assessment after triage (minutes)
  - Length of Stay (LOS): Time to disposition after triage (minutes)
  - Left Without Being Seen (LWBS): Proportion of patients that LWBS/total patients for a given time period (%)
- Secondary Outcome: Subgroup analysis
  - CTAS 1-5 (WT, LOS and LWBS) and admitted patients (WT and LOS)

Canadian Triage and Acuity Scale (CTAS): This was a national program started in (1999) to standardize emergency department triage in Canada.

Author’s Conclusions: ”CPOE implementation detrimentally impacted all patient flow throughput measures that we examined. The most striking clinically relevant result was the increase in LOS of 63 minutes for admitted patients. This raises the question as to whether the potential detrimental effects to patient safety of CPOE implementation outweigh its benefits.”

Quality Checklist for Observational Trials:

Did the study address a clearly focused issue? Yes. The question was what impact did implementation of a new CPOE system has on wait times, length of stay and left without being seen.
Did the authors use an appropriate method to answer their question? Yes. This was before and after CPOE implementation using administrative data.
Was the cohort recruited in an acceptable way? Yes
Was the exposure accurately measured to minimize bias? Yes
Was the outcome accurately measured to minimize bias? Yes. The outcomes were objective (eg. WT, LOS, LWBS)
Have the authors identified all-important confounding factors? No. This was only over a 2-month period before and after implementation of a new CPOE. A new CPOE is bound to have more difficulties in the initial start up phase as staff are learning to use it. Longer-term outcomes are needed.
How precise are the results? Unsure. We will talk more about this in the Nerdy section.
Do you believe the results? Yes
Can the results be applied to the local population? Unsure. This depends on if you are using the same/similar CPOE and how much the CPOE does for you (eg. meds, diagnostic imagine, all orders, etc.). It also depends how long the CPOE has been in use in your particular setting, as more time will likely address some of the weaknesses of the CPOE and users will become better with it.
Do the results of this study fit with other available evidence? Yes/No. There is conflicting evidence with respect to the effect of CPOE on LOS and other patient flow parameters.

Key Results:

Median WT increased by 5 minutes (78 vs. 83)
Median LOS increased by 10 minutes (254 vs. 264)
Proportion of LWBS increased by 0.9% (7.2% vs. 8.1%)
Median LOS for admitted patients increased by 63 minutes (713 vs. 776)
Proportion of LWBS increased significantly for CTAS 3, 4 and 5 patients (CTAS 5 patients 24% vs. 42%)

Dr. Andrew Gray

Listen to the podcast to hear Andy’s responses to our questions.

Excluded Patients: You excluded 466 patients before CPOE and 1,235 after CPOE. Why did you have three times as many patients excluded after CPOE and do you think that impacted the results?
Interquartile Range: You represented wait times and length of stay as medians with interquartile ranges. Why did you use these statistics to describe your data and do you think this gives you a precise estimate of the results?
Statistical vs. Clinical Significance: You demonstrated statistically significant changes (a few minutes for WT and LOS, ~1% increases LWBS and ~1hr increase LOS for admitted patients who were waiting a median of 12hrs already) but do you think these represent clinically significant changes?
Two Months of the Year: You only looked at two months (July and August) in 2013 before CPOE and 2014 after CPOE. These are summer months when you have new residents starting and lots of people taking holidays. Do you think these two months are representative of the whole year?
Start Up Phase: The CPOE was introduced to the entire hospital system as part of a program called HUGO (Healthcare Undergoing Optimization) in April 2014. There is a learning curve with new systems. Perhaps more training or better training was needed. In other words, could the impact on emergency department flow be related to CPOE difficulties in the start-up phase of the HUGO project?
External Validity: This study took place at the London Health Science Centre (LHSC) that included two quaternary care emergency departments in London, Ontario. Do you think your study has external validity to other emergency departments (Non-Teaching, Community, Rural, Non-Canadian)?
Before/After: One of the problems with before and after studies is other changes over time could have been responsible for the differences observed. Do you think any other factors could have played a role besides CPOE?
Patient Oriented Outcomes: You measured WT, LOS and LWBS but did you consider and measure other patient oriented outcomes like medication errors, adherence to evidence based medicine protocols, time to pain medications and overall patient satisfaction? These are other quality indicators that have been investigated in other CPOE studies.
Lack of In-Patient Beds: Many Canadian hospitals, including yours, have occupancy above 80% and sometimes as high as 125%. This can lead to overcrowding in the emergency departments. What impact if any do you think this had on your study?
Physician Satisfaction: A new study came out in the Mayo Clinic Proceedings showing that physicians’ satisfaction with electronic health records (EHRs) and CPOE was generally low and those using EHRs and CPOE were at higher risk for professional burnout (Shanafelt et al 2016). Did you see any issues with physician satisfaction due to the introduction of CPOE?

Comment on Authors’ Conclusion Compared to SGEM Conclusion: We agree that this implementation of CPOE at this quaternary hospital system had a detrimental impact on emergency department patient flow. It is unsure if these increased WT, LOS and LWBS rates are clinically important. We also question whether the potential benefits of CPOE outweigh the potential detrimental effects of CPOE on patient safety.

SGEM Bottom Line: Implementation of CPOE may initially be met with some difficulties, worsen emergency department patient flow and contribute to emergency department over-crowding. The long-term impact on patient oriented outcome and physician satisfaction remains to be seen.

FOAMed Resources: Check out the video by ZdoggMD called EHR State of Mind

Keener Kontest: Last weeks’ winner was Patricia van den Berg from the Netherlands. She knew India was a country suffering from a cholera outbreak in the 1960’s that prompted the development of rehydration electrolyte solutions.

Listen to the podcast for this weeks’ keener question. Send your answer to TheSGEM@gmail.com The first correct answer will receive a cool skeptical prize.

SGEMHOP Social Media: Now it is your turn to have a say. What do you think about this SGEMHOP episode? What questions do you have for Dr. Gray about CPOE? Join the conversation on Twitter (#SGEMHOP), Facebook or the SGEM blog. The best social media feedback will be published in CJEM.

References:

Netherton et al. Computerized physician order entry and decision support improves emergency department analgesic ordering for renal colic. Am J Emerg Med 2014
Yang et al. Implementation of a clinical pathway based on a computerized physician order entry system for ischemic stroke attenuates off-hour and weekend effects in the ED. Am J Emerg Med 2014
Brunette et al. Implementation of computerized physician order entry for critical patients in an academic emergency department is not associated with a change in mortality rate. West J Emerg Med 2013
Blankenship et al. Prospective evaluation of the treatment of pain in the ED using computerized physician order entry. Am J Emerg Med 2012
Bastani et al. Computerized prescriber order entry decreases patient satisfaction and emergency physician productivity. Ann of Emerg Med 2010
Spalding et al. Impact of computerized physician order entry on ED patient length of stay. Am J Emerg Med 2011
Syed et al. Computer order entry systems in the emergency department significantly reduce the time to medication delivery for high acuity patients. Int J Emerg Med 2013
Schiff GD et al. Computerized physician order entry-related medication errors: analysis of reported errors and vulnerability testing of current systems. BMJ Qual Saf April 2015
Georgiou A et al. The effect of CPOE systems on clinical care and work processes in emergency departments: a systematic review of the quantitative literature. Ann Emerg Med 2013
Hill RG Jr, Sears LM, Melanson SW. 4000 clicks: a productivity analysis of electronic medical records in a community hospital. Am J Emerg Med 2013