Encyclopedia of Gerontology and Population Aging

Living Edition
| Editors: Danan Gu, Matthew E. Dupre

Computerized Provider Order Entry

  • Siyu QianEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-69892-2_444-1

Synonyms

Definition

To replace paper, computerized provider order entry (CPOE) systems allow providers such as physicians, nurse practitioners, or other ordering authorities to electronically order medications, tests, or other medical procedures for an individual patient (Taieb-Maimon et al. 2018). In hospital setting, this term tends to be used instead of e-prescribing which usually refers to electronic prescription of medications, because the scope of CPOE is broader than e-prescribing. It may also include other types of medical orders (Ahmed et al. 2016).

Overview

Globally, CPOE has been taken up by many countries such as the USA, Australia, Canada, and England (Samadbeik et al. 2017; Scott et al. 2018), with majority of research evidence from the USA (Scott et al. 2018). In the USA, 77% of all prescriptions were completed electronically in 2017 (Surescripts 2017).

Due to comorbidities and physiological changes, older people commonly use a large number of medications (Qian et al. 2016), have greater healthcare demands, and need long-term treatments (Tommelein et al. 2016). Thus, they are more vulnerable to potentially inappropriate prescriptions (Dalton et al. 2018). It is estimated that at least one potentially inappropriate prescription occurs in about 25–56% of hospitalized older people (Hamilton et al. 2011), nearly 30% of older people living in community (Morgan et al. 2016), and almost 50% in residential aged care homes (Stafford et al. 2011). Computerized provider order entry systems have been shown to reduce inappropriate prescriptions and aid in the prevention of adverse drug events for older people (Dalton et al. 2018). These beneficial effects on care quality have become one of the key driving forces for the introduction of CPOE, in addition to efficiency and cost saving.

Key Research Findings

Development History

In 1971, the first CPOE system was developed in the USA with rudimentary functions which allowed physicians to quickly order medications with a few clicks (OHSU 2015). In the 1980s, additional features were added to CPOE systems, including simple decision support, e.g., alerts for known adverse interactions and automatic reordering (OHSU 2015). The 1990s saw the rapid development of commercial CPOE systems; however, the uptake was slow with less than 10% of the US hospitals had fully operational CPOE systems in 2002 (Ash et al. 2004). Since CPOE became a core requirement in the Meaningful Use of Electronic Health Records, the usage rate of CPOE in the US hospitals has increased significantly from 14% in 2010 to 64% in 2016 (Castlight and Leapfrog 2016).

E-prescribing is a unique type of CPOE. While CPOE systems allow orders to be transmitted to a pharmacy information system and nursing services within a hospital, e-prescribing allows an outpatient department (e.g., emergency department), clinic, or other health services outside of a hospital to transmit prescriptions to retail pharmacies (StratisHealth 2009).

The development of e-prescribing includes four overlapping phases: adoption, utilization, quality, and optimization (HealthTechZone 2018). Beginning in 2005, the US health industry was focused on the adoption of e-prescribing (HealthTechZone 2018). To encourage the use of a qualified e-prescribing system, in 2009 the US government established an Electronic Prescribing Incentive Program (Centers for Medicare and Medicaid Services 2018). Although the program has ended in 2013, e-prescribing continues with the meaningful use (Centers for Medicare and Medicaid Services 2018). The development of CPOE has entered the third phase, focusing on improving the quality of prescription (HealthTechZone 2018).

Research on CPOE

There are an increasing number of research on CPOE. Much of the research was conducted in the USA, and a small number of studies were conducted in residential aged care homes. Scott et al. (2018) conducted a systematic review of 20 studies involving the use of CPOE to reduce potentially inappropriate medication prescriptions. Their review showed that 14 (70%) of these studies were conducted in the USA, 4 (20%) in Canada, 1 (5%) in Italy, and 1 (5%) in the Netherlands. Eight (40%) of these studies were conducted in hospitals, nine (45%) in primary care, and only three (15%) in residential aged care homes.

Dalton et al. (2018) also conducted systematic review on the effectiveness of CPOE to reduce potentially inappropriate prescription. Their review included eight controlled trial studies. Except for one study that was conducted in Italy, all the other studies were conducted in the USA (87.5%) in hospital settings.

In an earlier review conducted by Clyne et al. (2012), 14 studies on using CPOE to reduce inappropriate medication use and polypharmacy in older people were included. Of these studies, 12 (85.7%) were conducted in the USA, and 2 (14.3%) were in Canada. Six studies (42.8%) were conducted in ambulatory care, four (28.6%) in hospitals, and four (28.6%) in residential aged care homes.

Barriers to Implementation

Although the world is embracing CPOE, the implementation of CPOE is still a daunting task. Research has identified barriers to implementation including disruptive changes in the workflow during the implementation (Adams et al. 2008), low training support (Elliott et al. 2016), lack of integration with other systems (Elliott et al. 2016), poor user interface (Graber et al. 2015; Taieb-Maimon et al. 2018), and physician resistance due to perception of loss of autonomy (Kruse and Goetz 2015). Among these, disruptive changes and low training support are the top two barriers (Kruse and Goetz 2015).

Benefits

The benefits of CPOE include reduced number of errors, reduced number of inappropriate orders of medications, decreased adverse drug events (ADEs), improved patient safety, improved efficiency, enhanced physician-patient communication, and cost saving.

Reduced Number of Errors Related to Poor Handwriting

As pharmacists do not have to interpret illegible handwritten orders (Elliott et al. 2016), the number of errors related to poor handwriting has been reduced by CPOE (Stürzlinger et al. 2009).

Reduced Number of Inappropriate Orders of Medications

With specific alerts embedded into a CPOE system, the number of orders of potentially inappropriate medications for hospitalized older people decreased quickly (Mattison et al. 2010).

Decreased Adverse Drug Events

Adverse drug events are harms caused by inappropriate medications (Bhavsar et al. 2017). Due to high chance of multiple chronic conditions, older people are at high risk of experiencing ADEs, nearly seven times more often than the other population groups (Lucado et al. 2006). It is estimated that over half of the ADEs are preventable (Von Laue et al. 2003) and up to 60% of ADEs identified during hospitalization actually occur at the time of ordering (Nebeker et al. 2005); therefore, the CPOE systems play a critical role to prevent ADEs.

A systematic literature review of 50 studies found that the CPOE systems were capable of preventing ADEs (Charles et al. 2014). A more recent study has also found that higher e-prescribing usage rate was linked to lower ADE rate (Bhavsar et al. 2017), because it can help overcome common risk factors of ADEs, such as the lack of communication between concurrent prescribers (Green et al. 2007) and inappropriate medication orders (Mattison et al. 2010).

Improved Patient Safety

Safety benefits of CPOE have been perceived by physicians, nurses, and pharmacists. A study conducted in an Australian residential aged care home found that the physicians perceived e-prescribing as beneficial to the safety of older people because the changes they made to prescriptions could be synchronized to the system used in the residential aged care home, ensuring that nurses had access to correct medication list (Elliott et al. 2016). Nurses who worked in the residential aged care home also felt that e-prescribing had safety benefits because it helped to avoid transcription errors and delays associated with a hybrid paper-electronic medication management system (Elliott et al. 2016). As the pharmacists did not have to interpret handwritten orders, they also saw the safety benefits that e-prescribing had to offer (Elliott et al. 2016).

Improved Efficiency

Computerized provider order entry has shown efficiency gain in residential aged care homes (Elliott et al. 2016; Subramanian et al. 2007), because no paper-based prescription forms need to be faxed among nurses, physicians, and pharmacists who work in different locations (Elliott et al. 2016).

Enhanced Physician-Patient Communication

In a survey study investigating older people’s perception of e-prescribing, more than 80% of them preferred e-prescribing, and more than 90% were satisfied with their physicians, because the system had led them to enjoy an increased communication with their physicians regarding medication-related topics (Schleiden et al. 2015).

Cost Saving

A study conducted in six hospitals in Massachusetts in the USA found that a CPOE system could save a hospital up to US $2.7 million per year, despite the onetime implementation cost of US $2.1 million and the annual maintenance cost of US $ 435,000 (Adams et al. 2008). An average hospital could expect full payback in about 26 months (Adams et al. 2008).

Unintended Adverse Consequences

It is now well recognized that health information technology including CPOE may introduce unintended adverse consequences (Vélez-Díaz-Pallarés et al. 2017), with more than 60% of the technology-based medication incidents related to CPOE (Samaranayake et al. 2012). A study investigated prescription errors associated with CPOE among hospitalized older people in Spain (Vélez-Díaz-Pallarés et al. 2017). It found that during the 6 months of using the CPOE system, 107 errors were introduced by the system (Vélez-Díaz-Pallarés et al. 2017). Almost all these errors were related to human-machine interactions due to wrong or partial entries (Vélez-Díaz-Pallarés et al. 2017).

Use Errors

Due to a lack of attention in human-computer interaction, use errors such as selection of a wrong patient by slipping off a dropdown list may occur (Graber et al. 2015; Taieb-Maimon et al. 2018). Research has found that improvements in human-computer interface design such as highlighting selected patient and adding patient photo can draw the attention of users and significantly improve the recognition of wrong patient error (Graber et al. 2015).

Alert Fatigue

Out of the intention to improve patient safety, a growing number of alerts have been built in CPOE systems to alert providers for drug-drug interactions, dose check, allergy, laboratory test value, etc. (Vanderman et al. 2017). However, these alerts caused fatigue to providers and were overridden 49–96% of the time (Van Der Sijs et al. 2006). Alert fatigue has become the most common complaints about CPOE (Ash et al. 2007).

To reduce alert fatigue, mitigation strategies have been developed. Riedmann et al. (2011) developed a context model for prioritizing drug safety alerts. This model contains 20 factors grouped into 3 categories: characteristics of the patient or case (e.g., diagnosis), characteristics of the organizational unit or user (e.g., experienced users may receive less intrusive alerts regarding certain drugs), and characteristics of the alert (e.g., less serious ADEs may be presented in a less intrusive way) (Riedmann et al. 2011). Vanderman et al. (2017) implemented a simple, non-interruptive, age-specific alert function to identify inappropriate prescription at the point of CPOE in an ambulatory care setting. They found a significant decrease in the incidence of the most frequently prescribed inappropriate medications for older patients receiving care (Vanderman et al. 2017).

Inefficiency

Due to lack of system interoperability, use of hybrid paper-electronic systems, and legal requirements, end users have experienced inefficiency with CPOE systems. As physicians usually look after older people living in different residential aged care homes, they may have to learn to use different information systems for medication prescription when visiting each residential aged care home (Elliott et al. 2016). This impedes work efficiency.

If e-prescribing is in place but medication administration records are still on paper, nurses would have to print out the electronic prescriptions for transcribing into paper-based medication administration records (Rochon et al. 2005). Even though the medication administration records are in electronic systems, manual transcription may still be required because of a lack of interoperability between the e-prescribing system used in a physician’s office and the electronic system used in a residential aged care home (Bollen et al. 2005).

The legal requirement may also impede work efficiency. One study conducted in an Australian residential aged care home identified that physicians had to wait for nurses to print out paper copy and sign on it, because electronic signature was not acceptable by law (Elliott et al. 2016).

Future Directions of Research

Despite the increasing adoption and implementation of CPOE systems in healthcare, the relevant research of CPOE use in residential aged care homes is still limited. More research needs to be conducted in this setting where more vulnerable older people were concentrated. Although improvements have been made in CPOE systems to mitigate the unintended adverse consequences, ongoing monitoring is required as newer systems may introduce new types of unintended adverse consequences. Prospective studies need to be conducted to analyze in detail how these unintended adverse consequences occur and to establish strategies to prevent them (Vélez-Díaz-Pallarés et al. 2017). As the true impact of CPOE on care outcomes is still unclear, large-scale multicenter randomized controlled trials are needed (Dalton et al. 2018).

Summary

Computerized provider order entry systems allow orders of medications, tests, or other medical procedures to be made electronically. Due to comorbidities and physiological changes, older people are particularly at risk of inappropriate prescriptions; therefore, they are the major beneficiaries of the CPOE systems. Despite the ample benefits of CPOE, unintended adverse consequences including use errors, alert fatigue, and inefficiency may also occur, hindering the benefits it promised. Therefore, ongoing monitoring and responding mechanisms need to be in place to prevent the unintended adverse consequences and maximize the benefits of CPOE.

Cross-References

References

  1. Adams M, Bates D, Coffman G et al (2008) Saving lives, saving money: The imperative for computerised physician order entry in Massachusetts hospitals. Massachusetts Technology Collaborative and New England Healthcare Institute, MassachusettsGoogle Scholar
  2. Ahmed Z, Garfield S, Jani Y et al (2016) Impact of electronic prescribing on patient safety in hospitals: Implications for the UK. Clin Pharm 8(5):144–151.  https://doi.org/10.1211/CP.2016.20201013CrossRefGoogle Scholar
  3. Ash JS, Gorman PN, Seshadri V et al (2004) Computerised physician order entry in US hospitals: results of a 2002 survey. J Am Med Inform Assoc 11(2):95–99.  https://doi.org/10.1197/jamia.M1427CrossRefGoogle Scholar
  4. Ash JS, Sittig DF, Campbell EM et al (2007) Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc:26–30Google Scholar
  5. Bhavsar GP, Probst JC, Bennett KJ et al (2017) Community-level electronic prescribing and adverse drug event hospitalisations among older adults. Health Inform J:1–15.  https://doi.org/10.1177/1460458217720396
  6. Bollen C, Warren JR, Whenan G (2005) Introduction of electronic prescribing in an aged care facility. Aust Fam Physician 34(4):283–287Google Scholar
  7. Castlight and Leapfrog (2016) Preventing medication errors in hospitals. https://www.leapfroggroup.org/sites/default/files/Files/Leapfrog-Castlight%20Medication%20Safety%20Report.pdf. Accessed on 14 Feb 2019
  8. Centers for Medicare & Medicaid Services (2018) Electronic prescribing (eRx) incentive program. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/ERxIncentive/index.html. Accessed 11 Feb 2019
  9. Charles K, Cannon M, Hall R et al (2014) Can utilising a computerised provider order entry (CPOE) system prevent hospital medical errors and adverse drug events? Perspect Health Inf Manag 11(Fall):1bGoogle Scholar
  10. Clyne B, Bradley MC, Hughes C et al (2012) Electronic prescribing and other forms of technology to reduce inappropriate medication use and polypharmacy in older people: a review of current evidence. Clin Geriatr Med 28(2):301–322.  https://doi.org/10.1016/j.cger.2012.01.009CrossRefGoogle Scholar
  11. Dalton K, O’Brien G, O’Mahony D et al (2018) Computerised interventions designed to reduce potentially inappropriate prescribing in hospitalised older adults: a systematic review and meta-analysis. Age Ageing 47(5):670–678.  https://doi.org/10.1093/ageing/afy086CrossRefGoogle Scholar
  12. Elliott RA, Lee CY, Hussainy SY (2016) Electronic prescribing and medication management at a residential aged care facility. Appl Clin Inform 7(01):116–127.  https://doi.org/10.4338/ACI-2015-08-RA-0098CrossRefGoogle Scholar
  13. Graber ML, Siegal D, Riah H et al (2015) Electronic health record-related events in medical malpractice claims. J Patient Saf.  https://doi.org/10.1097/PTS.0000000000000240
  14. Green JL, Hawley JN, Rask KJ (2007) Is the number of prescribing physicians an independent risk factor for adverse drug events in an elderly outpatient population? Am J Geriatr Pharmacother 5(1):31–39.  https://doi.org/10.1016/j.amjopharm.2007.03.004CrossRefGoogle Scholar
  15. Hamilton H, Gallagher P, Ryan C et al (2011) Potentially inappropriate medications defined by STOPP criteria and the risk of adverse drug events in older hospitalised patients. Arch Intern Med 171(11):1013–1019.  https://doi.org/10.1001/archinternmed.2011.215CrossRefGoogle Scholar
  16. HealthTechZone (2018) Electronic prescribing moving toward optimisation. http://www.healthtechzone.com/topics/healthcare/articles/2018/11/05/440147-electronic-prescribing-moving-toward-optimization.htm. Accessed 10 Feb 2019
  17. Kruse CS, Goetz K (2015) Summary and frequency of barriers to adoption of CPOE in the U.S. J Med Syst 39(2):15.  https://doi.org/10.1007/s10916-015-0198-2CrossRefGoogle Scholar
  18. Lucado J, Paez K, Elixhauser A (2006) Medication-related adverse outcomes in US hospitals and emergency departments, 2008. In: Healthcare cost and utilisation project (HCUP) statistical briefs [internet]. Agency for Healthcare Research and Quality (US), Rockville, pp 1–17Google Scholar
  19. Mattison ML, Afonso KA, Ngo LH et al (2010) Preventing potentially inappropriate medication use in hospitalised older patients with a computerised provider order entry warning system. Arch Intern Med 170(15):1331–1336.  https://doi.org/10.1001/archinternmed.2010.244CrossRefGoogle Scholar
  20. Morgan SG, Hunt J, Rioux J et al (2016) Frequency and cost of potentially inappropriate prescribing for older adults: a cross-sectional study. CMAJ open 4(2):E346–E351.  https://doi.org/10.9778/cmajo.20150131CrossRefGoogle Scholar
  21. Nebeker JR, Hoffman JM, Weir CR et al (2005) High rates of adverse drug events in a highly computerised hospital. Arch Intern Med 165(10):1111–1116.  https://doi.org/10.1001/archinte.165.10.1111CrossRefGoogle Scholar
  22. OHSU (2015) History of computerised physician order entry. http://clinfowiki.org/wiki/index.php/History_of_computerized_physician_order_entry. Accessed 10 Feb 2019
  23. Qian S, Yu P, Hailey DM et al (2016) Factors influencing nursing time spent on administration of medication in an Australian residential aged care home. J Nurs Manag 24(3):427–434.  https://doi.org/10.1111/jonm.12343CrossRefGoogle Scholar
  24. Riedmann D, Jung M, Hackl WO et al (2011) Development of a context model to prioritise drug safety alerts in CPOE systems. BMC Med Inform Decis Mak 11(1):35.  https://doi.org/10.1186/1472-6947-11-35CrossRefGoogle Scholar
  25. Rochon PA, Field TS, Bates DW et al (2005) Computerised physician order entry with clinical decision support in the long-term care setting: insights from the Baycrest Centre for Geriatric Care. J Am Geriatr Soc 53(10):1780–1789.  https://doi.org/10.1111/j.1532-5415.2005.53515.xCrossRefGoogle Scholar
  26. Samadbeik M, Ahmadi M, Sadoughi F et al (2017) A comparative review of electronic prescription systems: lessons learned from developed countries. J Res Pharm Pract 6(1):3–11.  https://doi.org/10.4103/2279-042X.200993CrossRefGoogle Scholar
  27. Samaranayake N, Cheung S, Chui W et al (2012) Technology-related medication errors in a tertiary hospital: a 5-year analysis of reported medication incidents. Int J Med Inform 81(12):828–833.  https://doi.org/10.1016/j.ijmedinf.2012.09.002CrossRefGoogle Scholar
  28. Schleiden LJ, Odukoya OK, Chui MA (2015) Older adults’ perceptions of e-prescribing: impact on patient care. Perspect Health Inf Manag 12(Winter):1dGoogle Scholar
  29. Scott IA, Pillans PI, Barras M et al (2018) Using EMR-enabled computerised decision support systems to reduce prescribing of potentially inappropriate medications: A narrative review. Ther Adv Drug Saf 9(9):559–573.  https://doi.org/10.1177/2042098618784809CrossRefGoogle Scholar
  30. Stafford AC, Alswayan MS, Tenni PC (2011) Inappropriate prescribing in older residents of Australian care homes. J Clin Pharm Ther 36(1):33–44.  https://doi.org/10.1111/j.1365-2710.2009.01151.xCrossRefGoogle Scholar
  31. StratisHealth (2009) Optimisation strategies for CPOE and e-prescribing. https://www.stratishealth.orgdocuments/HITToolkitHospital/2.Utilize/2.2Effective%20Use/2.2Optimization_Strategies_for_CPOE.doc. Accessed on 14 May 2019.
  32. Stürzlinger H, Hiebinger C, Pertl D et al (2009) Computerised physician order entry-effectiveness and efficiency of electronic medication ordering with decision support systems. GMS Health Technol Assess 5:Doc07.  https://doi.org/10.3205/hta000069CrossRefGoogle Scholar
  33. Subramanian S, Hoover S, Gilman B et al (2007) Computerised physician order entry with clinical decision support in long-term care facilities: Costs and benefits to stakeholders. J Am Geriatr Soc 55(9):1451–1457.  https://doi.org/10.1111/j.1532-5415.2007.01304.xCrossRefGoogle Scholar
  34. Surescripts (2017) 2017 National progress report. Surescripts, ArlingtonGoogle Scholar
  35. Taieb-Maimon M, Plaisant C, Hettinger AZ et al (2018) Increasing recognition of wrong-patient errors through improved interface design of a computerised provider order entry system. Int J Hum Comput Interact 34(5):383–398.  https://doi.org/10.1080/10447318.2017.1349249CrossRefGoogle Scholar
  36. Tommelein E, Mehuys E, Petrovic M et al (2016) Potentially inappropriate prescribing in nursing home residents detected with the community pharmacist specific GheOP(3)S-tool. Int J Clin Pharm 38(5):1063–1068.  https://doi.org/10.1007/s11096-016-0366-6CrossRefGoogle Scholar
  37. Van Der Sijs H, Aarts J, Vulto A et al (2006) Overriding of drug safety alerts in computerised physician order entry. J Am Med Inform Assoc 13(2):138–147.  https://doi.org/10.1197/jamia.M1809CrossRefGoogle Scholar
  38. Vanderman AJ, Moss JM, Bryan WE III et al (2017) Evaluating the impact of medication safety alerts on prescribing of potentially inappropriate medications for older veterans in an ambulatory care setting. J Pharm Pract 30(1):82–88.  https://doi.org/10.1177/0897190015621803CrossRefGoogle Scholar
  39. Vélez-Díaz-Pallarés M, Díaz AMÁ, Caro TG et al (2017) Technology-induced errors associated with computerised provider order entry software for older patients. Int J Clin Pharm 39(4):729–742.  https://doi.org/10.1007/s11096-017-0474-yCrossRefGoogle Scholar
  40. Von Laue NC, Schwappach DL, Koeck CM (2003) The epidemiology of preventable adverse drug events: a review of the literature. Wien Klin Wochenschr 115(12):407–415.  https://doi.org/10.1007/BF03040432CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for IT-enabled Transformation, School of Computing and Information Technology, Faculty of Engineering and Information SciencesUniversity of WollongongWollongongAustralia

Section editors and affiliations

  • Ping Yu
    • 1
  1. 1.Faculty of Engineering & Information Sciences, School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia