Original Article

From the Virginia Commonwealth

University, Richmond, Virginia.

Address correspondence to Patrick

Coyne, MSN, APRN, ACHPN, ACNS-BC,

FAAN, FPCN, Palliative Care, Virginia

Commonwealth University, 1300

East Marshall St., PO Box 985934,

Richmond, VA 23298. E-mail:

user479069@aol.com

Received March 21, 2011;

Revised October 12, 2011;

Accepted October 17, 2011.

1524-9042/$36.00

� 2013 by the American Society for

Pain Management Nursing

doi:10.1016/j.pmn.2011.10.005

Managing Pain withAlgorithms: AnOpportunity forImprovement? Or: TheDevelopment andUtilization of Algorithmsto Manage Acute Pain

--- Patrick Coyne, MSN, APRN, ACHPN,

ACNS-BC, FAAN, FPCN, Laurie Lyckholm, MD,

Barton Bobb, APRN, ACHPN,

Donna Blaney-Brouse, MSN, RN-BC, CEN,

Sarah Harrington, MD, and Leanne Yanni, MD

- ABSTRACT:Pain management in a hospital setting remains a challenge today.

Many health care providers remain anxious and uninformed regard-

ing analgesic titration within a hospital setting. Overcoming the po-

tential risks to obtain the benefits of opiate titration is a challenge

within any health care setting. Virginia Commonwealth University,

a tertiary medical center which houses schools of medicine, nursing,

and pharmacy, evaluated the use of algorithms for managing acute

pain. This article describes the Pain Committee’s efforts and offers one

potential intervention for safe analgesic opioid titration, an algorithm

for acute pain management.

� 2013 by the American Society for Pain Management Nursing

Inadequate pain management within acute care settings continues to be a prob-lem. It is estimated that 73 million patients undergo surgical procedures each

year in the United States. Of these, 80% experience acute postoperative pain,

and �20% experience severe pain (Hutchison, 2007). Inadequate pain manage-

ment causes suffering and may increase length of stay, development of chronic

pain, and complications such as infection and venous thrombosis. Adverse out-

comes related to pain management may be due to errors in choice of medication

and/or calculation, lack of education and/or mentoring, insufficient monitoring,

and poor communication (Winterstein, Johns, Rosenberg, Hatton, Gonzalez-Rothi, & Kanjanarat, 2004; Cordts, Grant, Brandt, & Mears, 2011; Elcigil,

Maltepe, Esrefgil, & Mutafoglu, 2011; Murnion, Gnjidic, & Hilmer, 2010). The

Pain Management Nursing, Vol 14, No 4 (December), 2013: pp e185-e188

e186 Coyne et al.

trends of care may vary in different health care systems

(Anderson, Ramanujam, Hensel, & Sirio, 2010).

METHODS

In 2006, the Institutional Pain Committee at Virginia

Commonwealth University Medical Center, a 750-bed

tertiary teaching hospital, was charged with improving

pain management while ensuring safety. This interpro-fessional group reviewed all pain-related hospital poli-

cies and standards before implementing educational

programs for all health care disciplines, as well as pa-

tients and families. The impact of these interventions

was demonstrated by a significant increase in patient

pain satisfaction scores. The committee also reviewed

‘‘misadventures’’ in pain management, including use of

naloxone, patient/family complaints, medication er-rors of all types, and other patient safety issues.

A subgroup of the committee then explored op-

tions for standardizing the use of opioid analgesics to

help reduce potential errors side effect burden and im-

prove pain management. The use of algorithms, some-

times referred to as ‘‘decision trees,’’ was perceived as

an opportunity to reduce variations in prescribing

practice. When successful, algorithms have beenshown to improve safety, efficacy, and save money

(Bigham, Bull, Morrison, Burgess, Maher, Brooks, &

Morrison, 2011; Carey & Stefos, 2010; Fitzgerald,

Farrow, Scicluna, Murray, Xiao, & Mackenzie, 2008;

FIGURE 1. - Algorithm fo

Newton, Smiley, Bode, Kitabchi, Davidson, Jacobs, &

Umpierrez, 2010; Pushkin, Frassetto, Tsourounis, Segal,

& Kim, 2010; Schmeltz, 2009; Tsai, Clark, & Camargo,

2010; Undeland, Kowalski, Berth, & Gundrum, 2010;

Zaratkiewicz, Whitney, Lowe, Taylor, O’Donnell, &

Minton-Foltz, 2010). Although algorithms of all types

are commonly used in the health care setting, wewere unable to identify any to meet our specific

acute pain needs. The group ultimately decided to

develop algorithms for acute pain management.

We were originally told by our administration to

put the concept on hold, because concerns existed

that The Joint Commission (TJC) would not be sup-

portive of such an intervention, and TJC pain standards

were still relatively new. However, after months of on-going discussion and increasingly critical needs in pain

management, the hospital administration agreed to pi-

lot this tool and later agreed to promote it as a means to

improve safe use of opioids.

The 2002 National Comprehensive Cancer

Network cancer pain algorithm was selected as a foun-

dation for our project. Over the course of several years,

the acute pain algorithm (which later became two, onefor opioid-tolerant and one for opioid-na€ıve patients)

was reviewed numerous times by the entire Pain Com-

mittee, the Pharmacy and Therapeutics Committee,

Risk Management, and leaders in hospital administra-

tion. The American Pain Society’s Principles of

Analgesia (2003) and Fast Facts (published by the

End of Life/Palliative Education Resource Center,

r the opioid naive.

FIGURE 2. - Algorithm for the opioid tolerant.

e187Development and Utilization of Algorithms to Manage Acute Pain

Medical College of Wisconsin) were utilized to develop

this tool http://www.eperc.mcw.edu.

RESULTS

After 2 years of extensive reviews and revisions, the

Virginia Commonwealth University acute pain algo-

rithms were sent to outside expert reviewers for

further input. Following this process, with final sup-

port from the pain committee, the algorithms wereintroduced as a pilot program on our orthopedics,

neurosurgery/neurology, and oncology units. All of

these units’ staff, including physicians, nurses, ad-

vanced practice nurses, and pharmacists, received

case-based training demonstrating how the algo-

rithms could be used. After a 5-month piloting

process, no problematic issues were identified re-

lated to the algorithms. They were then graduallyintroduced to all medical, pharmacy, and nursing

staff through inservices and electronically as a tool

to help improve acute pain management via a safe,

effective, and standardized approach that allows con-

sideration of the individual patient’s opioid tolerance

and requirements.

DISCUSSION

The acute pain algorithms have now been in place in

our system for more than a year, and their impact

continues to be monitored. No untoward effects

from the algorithms have been reported or identified,and inservices with providers continue on a regular ba-

sis. Since their introduction, our Risk Management de-

partment reports fewer opioid errors and higher

patient satisfaction scores throughout the institution.

However, we are unable to demonstrate that these im-

provements are the direct impact of our algorithms be-

cause too many variables exist.

CONCLUSION

In conclusion, using algorithms for managing acute

pain in both opioid-na€ıve (Fig. 1) and opioid-tolerant

(Fig. 2) patients may offer a safe and effective way to

manage acute pain, decreasing variations in practice,and improving safety and satisfaction. The algorithms

have also proven to be an effective educational tool.

We plan ongoing monitoring of safety and satisfaction,

continued education, formal clinical trials, and quality

improvement assessment to determine the actual ben-

efits and any potential risks.

Acknowledgments

The authors acknowledge the efforts of the entire Pain Com-

mittee at Virginia Commonwealth University.

e188 Coyne et al.

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