Reduction of errors in hospitals due to adoption of computerized provider order entry systems

From Clinfowiki
Jump to: navigation, search

Radley et al. conducted a study to assess if there are reduction of medication errors in hospitals due to adoption of the computerized provider order entry (CPOE) systems. This article is a review of their study.[1]

Background and significance

The Institute of Medicine (IOM) [2] estimates that,on average,hospitalized patients are subject to at least one medication error per day[3]. Medication errors are expensive and sometimes harmful to the patients.[4] [5] The IOM estimates that at least a quarter of all medication-related injuries are preventable, and recommends electronic prescribing (e-prescribing) through a CPOE system to reduce medication errors and patient harm.[6] This decreases errors from illegible handwriting or incorrect transcription. CPOE also includes functionalities, such as drug dosage support, alerts about helpful interactions, and clinical decision support(CDS), which may further reduce errors. CPOE on the other hand is also known to cause errors,more secondary to human error than a fault with the technology.[7] ARRA and HITECH([1]) acts of 2009 incentivised payments to individual physicians and hospitals to support health IT implementation,including CPOE as a core requirement.[8]


Radley DC et al. did a study to provide a baseline national estimate of medication errors averted in hospitals due to use of CPOE using data on CPOE use in 2008,before implementation of HITECH Act. This gives us a baseline upon which we can track national progress on CPOE adoption,use and outcomes, and to inform the evolving federal strategy to build an effective health IT infrastructure.

Materials and methods

Radley et al.'s study conducted two phases:

  1. They developed supporting statistics describing CPOE adoption and implementation:
  • The volume of medication errors processed in the target population of hospitals
  • The number and proportion of medication errors processed through CPOE
  • The expected error rate without CPOE, and
  • The percentage of reduction in medication error rates from CPOE adoption.
  1. Use of supporting statistics to derive estimates about percentage and absolute reduction in medication errors in acute care hospitals over a one year period due to CPOE use

Data Sources

The Study data utilized for the study included the 2007 American Hospital Association(AHA) survey;the 2008 AHA hospital EHR adoption survey;the 2006 American Society of Health-System Pharmacists(ASHP) national survey of hospital pharmacies;[9] Systematic review of peer reviewed literature.

Target Population

Hospitals represented in the AHA survey were included if they provided general or pediatric acute medical and surgical care and self-identified as private for-profit,private not-for-profit, or public. This came upto 4701 hospitals.

Supporting statistics

CPOE adoption and implementation

EHR survey was used to estimate this. The response rate from the eligible hospitals is 60.9% and the response rate to CPOE questions is 60.3%. A hospital was counted as a CPOE adopter if it had at least one unitof operational CPOE system capable of processing prescription drug orders. Responders' mean value was imputed to facilities with CPOE not responding to this questionnaire.

Medication order volume

ASHP survey data was used to estimate this. Published results include the average number of prescription drug orders per patient day for hospitals stratified by bed size. This was multiplied by the AHA survey estimates of total bed days to estimate the total number of prescription orders processed during a year.

Number and proportion of medication orders processed through CPOE

The authors then combined hospital-specific estimate of CPOE adoption and implementation from the 2008 survey with estimates of medication order volume from the 2006 AHSP to obtain a nationally representative estimate of the total number and proportion of medication orders processed using CPOE.

Expected medication error rate without CPOE and expected reduction in medication error rates resulting from CPOE

At the time of publishing of this article, no nationally representative data set exists linking CPOE use to medication errors. Thus, Radley et al. extracted data from a systematic review of literature and used meta-analytic random effects techniques to estimate three parameters: medication error rates when CPOE is not used, medication error rates when CPOE is used, and the percentage of difference between them.

Inclusion criteria

An e-prescribing system was compared with handwritten ordering in a in-patient section of a acute care hospital. Quasi-experimental design was followed. Relevant data elements were extracted for pre- and post-intervention periods from each study.

Outcome statistics

The percentage reduction in medication error frequency due to CPOE is the product of the expected percentage in medication error rates resulting from CPOE, and the proportion of medication errors processed through CPOE in hospitals that have used CPOE.

Estimate bounds

Radley et al.'s approach of pooling data from the systematic review produced a probability-based Confidence Interval(CI).There is no credible approach for deriving probability-based estimates of variance for adoption of CPOE adoption and implementation.


Approximately 34% of US acute care hospitals adopted CPOE in 2008. Larger hospitals(>=400 beds) were more likely to have adopted CPOE(56%) compared to medium-sized or small hospitals(35% and 30% respectively). CPOE adoption was more common among urban hospitals(41% vs 28% among rural hospitals)and major teaching hospitals(53% vs 32% in non-teaching hospitals). CPOE adoption was high among private not-for-profit(37%) compared with public hospitals(31%) and private for-profit hospitals(32%). Many CPOE adopters indicated a very high degree of implementation(>90% of orders processed by CPOE). Still, 42% of responding hospitals had <50% implementation. Approximately 26% of medication errors in acute care hospitals were processed using CPOE.

Expected medication error rate without CPOE and expected change in error rate associated with CPOE use

Medication error rates were approximately 48% lower after the CPOE implementation.

Percentage and absolute reduction in medication errors

Medication error rates decreased by approximately 12.5% which equals approximately 17.4 million fewer medication errors over a one year period than would be expected without CPOE. If all hospitals adopted CPOE at current rates of implementation, approximately 51 million medication errors per year could be averted. Finally, if all hospitals were to use CPOE to process all medication orders, approximately 104 million medication errors per year could be averted.


Radley et al. contended that this is the first attempt to generate a nationally representative estimate of the effect of CPOE on medication error frequency. The study findings suggest that CPOE can substantially reduce medication errors in hospitals. This indicates potential gains as health IT systems are expanded and more deeply integrated in care delivery systems nationwide. However, it is unclear whether reduced medication errors translate to reduced patient harm from medications. Prescribing errors,where clinicians chose the wrong drug or dose from a pull down menu,attributed a prescription to the wrong patient, or entered duplicate orders and transcribing errors, where pharmacy staff incorrectly recorded medication order information, reduces the margin of benefit from CPOE usage. Highest error rates are found through direct observation,[10] followed by chart review, then automated surveillance, and voluntary reporting.[11] While there is a risk that errors will be double-counted in studies using multiple modes,[11] studies that use single and/or less rigorous detection modes may seriously under count medication errors.

Limitations of the study

  • Detection mode and medication error definitions varied across the included studies
  • Hospitals included in this study might not be representative of all US hospitals
  • Authors might have falsely assumed that the effect of CPOE adoption on medication error rates is constant even if the number of hospitals adopting CPOE increase.

Key areas for future research

  • Further research is needed to characterize the effect of CPOE implementation on order volume and patterns, and heterogeneity in the likelihood of medication errors for different types of medications,orders,and settings. In addition, consistent medication errors and serious medication errors should be defined.
  • Further work is needed to explain variation in findings across studies
  • Future model refinements might weight analyses according to detection-mode sensitivity
  • Finally, additional evidence is needed to establish more concrete links between medication errors,adverse drug events(ADEs), and patient harm.


Radley et al.'s estimation approach may be useful for estimating other health IT-related impacts on healthcare system and patient outcomes. Future research in this area will be critically important to inform policy and funding decisions regarding the development and implementation of CPOE in care delivery.


Radley DC et al. did a study to check the baseline yearly medication errors with and without the use of CPOE by individual physicians and hospitals in the United States in 2008,prior to the implementation of HITECH Act in 2009. HITECH Act of 2009 gave impetus to the health IT adoption by the healthcare facilities by providing incentives and at the same time imposing penalties on providers who do not meet the laid Meaningful Use(MU)standards. I agree with the authors’ statement that medication errors are reduced with the use of CPOE systems. Medications errors happened with the use of CPOE systems but it is more human error(prescribing and transcribing errors) than the fault of technology which led to these. A year and 8 months has passed since this article was published in JAMIA. A lot of research has been happening to the tune of 2000 new papers being published in Medline (Pubmed) per day! However, Radley et al. gave some future research parameters upon which some later research probably was based on. The adoption of CPOE/CDS in EHRs has increased leaps and bounds since 2009 and is portends good for the future, including reduction of medication errors with the use of CPOE systems.


  1. Radley DC et al. June 2013. Reduction of errors in hospitals due to adoption of computerized provider order entry systems.J Am Med Inform Assoc. 2013 May-Jun; 20(3): 470–476
  2. Wikipedia: Institute of Medicine (IOM)
  3. Aspden P, Wolcott J, Bootman J, et al. Preventing medication errors. Washington, DC: National Academic Press, 2007
  4. Bates DW, Spell N, Cullen DJ, et al. The costs of adverse drug events in hospitalized patients. Adverse Drug Events Prevention Study Group. JAMA 1997;277:307–11
  5. Lesar TS, Briceland LL, Delcoure K, et al. Medication prescribing errors in a teaching hospital. JAMA 1990;263:2329–34
  6. Committee on Quality of Health Care in America Crossing the quality chasm: a new health system for the 21st century. Washington, DC: Institute of Medicine, National Academy Press, 2001
  7. Berger RC, Kichak BA. Computerized physician order entry: helpful or harmful? J Am Med Inform Assoc 2004;11:100–3
  8. Department of Health and Human Services, Centers for Medicare and Medicaid Services 42 CFR parts 412, 413, 422 et al. Medicare and Medicaid Programs: Electronic Health Record Incentive Program; Final Rule 2010
  9. Pedersen CA, Schneider PJ, Scheckelhoff DJ. ASHP national survey of pharmacy practice in hospital settings: monitoring and patient education–2006. Am J Health Syst Pharm 2007;64:507–20
  10. Flynn EA, Barker KN, Pepper GA, et al. Comparison of methods for detecting medication errors in 36 hospitals and skilled-nursing facilities. Am J Health Syst Pharm 2002;59:436–46
  11. 11.0 11.1 Jha AK, Kuperman GJ, Teich JM, et al. Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc 1998;5:305–14