Evidence-Based Project,

The collection of evidence is an activity that occurs with an endgame in mind. For example, law enforcement professionals collect evidence to support a decision to charge those accused of criminal activity. Similarly, evidence-based healthcare practitioners collect evidence to support decisions in pursuit of specific healthcare outcomes.

In this Assignment, you will identify an issue or opportunity for change within your healthcare organization and propose an idea for a change in practice supported by an EBP approach.

To Prepare:

· Reflect on the four peer-reviewed articles you critically appraised in Module 4.

· Reflect on your current healthcare organization and think about potential opportunities for evidence-based change.

The Assignment: (Evidence-Based Project)

Part 5: Recommending an Evidence-Based Practice Change

Create an 8- to 9-slide PowerPoint presentation in which you do the following:

· Briefly describe your healthcare organization, including its culture and readiness for change. (You may opt to keep various elements of this anonymous, such as your company name.)

· Describe the current problem or opportunity for change. Include in this description the circumstances surrounding the need for change, the scope of the issue, the stakeholders involved, and the risks associated with change implementation in general.

· Propose an evidence-based idea for a change in practice using an EBP approach to decision making. Note that you may find further research needs to be conducted if sufficient evidence is not discovered.

· Describe your plan for knowledge transfer of this change, including knowledge creation, dissemination, and organizational adoption and implementation.

· Describe the measurable outcomes you hope to achieve with the implementation of this evidence-based change.

· Be sure to provide APA citations of the supporting evidence-based peer reviewed articles you selected to support your thinking.

· Add a lessons learned section that includes the following:

· A summary of the critical appraisal of the peer-reviewed articles you previously submitted

· An explanation about what you learned from completing the evaluation table (1 slide)

· An explanation about what you learned from completing the levels of evidence table (1 slide)

· An explanation about what you learned from completing the outcomes synthesis table (1 slide)

Assignment Resources (attached):

Hoffman, T. C., Montori, V. M., & Del Mar, C. (2014). The connection between evidence-based medicine and shared decision making. Journal of the American Medical Association, 312(13), 1295–1296. doi:10.1001/jama.2014.10186

Kon, A. A., Davidson, J. E., Morrison, W., Danis, M., & White, D. B. (2016). Shared decision making in intensive care units: An American College of Critical Care Medicine and American Thoracic Society policy statement. Critical Care Medicine, 44(1), 188–201. doi:10.1097/CCM.0000000000001396

Opperman, C., Liebig, D., Bowling, J., & Johnson, C. S., & Harper, M. (2016). Measuring return on investment for professional development activities: Implications for practice. Journal for Nurses in Professional Development, 32(4), 176–184. doi:10.1097/NND.0000000000000483

Schroy, P. C., Mylvaganam, S., & Davidson, P. (2014). Provider perspectives on the utility of a colorectal cancer screening decision aid for facilitating shared decision making. Health Expectations, 17(1), 27–35. doi:10.1111/j.1369-7625.2011.00730.x

Last weeks’ articles : (not attached)

Ólafsdóttir, J., & Orjasniemi, T. (2018). Depression, anxiety, and stress from substance-use disorder among family members in Iceland. Nordic Studies on Alcoholic and Drugs, 35(8), 165-178.

Tracy, K., & Wallace, S. P. (2016). Benefits of peer support groups in the treatment of addiction. Substance Abuse Rehabilitation, 7, 143–154. doi: 10.2147/SAR.S81535

McQuaid, R. J., Jesseman, R., & Rush, B. (2018). Examining Barriers as Risk Factors for Relapse: A focus on the Canadian Treatment and Recovery System of Care. Canadian Journal of Addiction: 9(3), 5–12. doi:10.1097/CXA.0000000000000022

Staiger, P. K., Kyrios, M., Williams, J. S., Kambouropoulos, N., Howard, A., & Gruenert, S. (2014). Improving the retention rate for residential treatment of substance abuse by sequential intervention for social anxiety. BMC Psychiatry, 14(43), 1-10. Retrieved from https://doi.org/10.1186/1471-244X-14-43

VIEWPOINT

Tammy C. Hoffmann, PhD Centre for Research in Evidence-Based Practice, Faculty of Health Sciences and Medicine, Bond University, Queensland, Australia; and University of Queensland, Brisbane, Australia.

Victor M. Montori, MD, MSc Knowledge and Evaluation Research (KER) Unit, Mayo Clinic, Rochester, Minnesota.

Chris Del Mar, MD, FRACGP Centre for Research in Evidence-Based Practice, Faculty of Health Sciences and Medicine, Bond University, Queensland, Australia.

Viewpoint page 1293

The Connection Between Evidence-Based Medicine and Shared Decision Making

Evidence-based medicine (EBM) and shared decision making (SDM) are both essential to quality health care, yet the interdependence between these 2 approaches is not generally appreciated. Evidence-based medicine should begin and end with the patient: after finding and appraising the evidence and integrating its infer- ences with their expertise, clinicians attempt a deci- sion that reflects their patient’s values and circum- stances. Incorporating patient values, preferences, and circumstances is probably the most difficult and poorly mapped step—yet it receives the least attention.1 This has led to a common criticism that EBM ignores patients’ values and preferences—explicitly not its intention.2

Shared decision making is the process of clinician and patient jointly participating in a health decision af- ter discussing the options, the benefits and harms, and considering the patient’s values, preferences, and cir- cumstances. It is the intersection of patient-centered communication skills and EBM, in the pinnacle of good patient care (Figure).

One Without the Other?

These approaches, for the most part, have evolved in parallel, yet neither can achieve its aim without the other. Without SDM, authentic EBM cannot occur.3 It is a mechanism by which evidence can be explicitly brought into the consultation and discussed with the patient. Even if clinicians attempt to incorporate patient prefer- ences into decisions, they sometimes erroneously guess them. However, it is through evidence-informed

the best available research evidence. If SDM does not in- corporate this body of evidence, the preferences that pa- tients express may not be based on reliable estimates of the risks and benefits of the options, and the result- ing decisions not truly informed.

Why Is There a Disconnect?

A contributor to the existing disconnect between EBM and SDM may be that leaders, researchers, and teach- ers of EBM, and those of SDM, originated from, and his- torically tended to practice, research, publish, and col- laborate, in different clusters. Some forms of SDM have emerged from patient communication, with much of its research presented in conferences and journals in this field. A seminal paper in 19974 conceptualized SDM as a model of treatment decision making and as a patient- clinician communication skill. However, it did so with- out any connection to EBM—perhaps not surprisingly, be- cause EBM was in its infancy.2

Conversely, with its origins in clinical epidemiology, much of the focus of EBM has been on methods and resources to facilitate locating, appraising, and synthe- sizing evidence. There has been much less focus on dis- cussing this evidence with patients and engaging with them in its use (sometimes even disparagingly referred to as “soft” skills). Most of the EBM attention has involved scandals (eg, unpublished data, results “spin,” conflicts of interest) and the high technology mile- stones (eg, systems to make EBM better and easier). Information about using evidence in decision-making with patients has been scant.

Opinion

Corresponding Author: Victor M. Montori, MD, MSc, Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First St SW, Plummer 3-35, Rochester, MN 55905 (montori.victor @mayo.edu).

jama.com

deliberations that patients construct informed prefer- ences. For patients who have to implement the deci- sion and live with the consequences, it may be more per- tinent to realize that it is through this process that patients incorporate the evidence and expertise of the clinician, along with their values and preferences, into their decision-making. Without SDM, EBM can turn into evidence tyranny. Without SDM, evidence may poorly translate into practice and improved outcomes.

Likewise, without attention to the principles of EBM, SDM becomes limited because a number of its steps are inextricably linked to the evidence. For example, discus- sions with patients about the natural history of the con- dition, the possible options, the benefits and harms of each, and a quantification of these must be informed by

focused on forming questions and finding and criti- cally appraising evidence.5 Learning how to apply and integrate the evidence is usually absent, or mentioned in passing without skill training.

Realizing the Connection Between EBM and SDM

A logical place to start is by incorporating SDM skill train- ing into EBM training. This will help to address not only the aforementioned deficits in EBM training but also the lack of SDM training opportunities presently available. Additionally, it may facilitate the uptake of SDM and, more broadly, evidence translation. Recent calls for SDM to be routinely incorporated into medical education pre- sent an immediate opportunity to capitalize on closely aligning the approaches.

Without shared decision making, EBM can turn into evidence tyranny.

Disconnect between the 2 ap- proaches is also evident in, and main- tained by, the teaching provided to clinicians and students, again often reflecting the backgrounds of their teachers. Opportunities to attend EBM teaching abound with content largely

Downloaded From: https://jamanetwork.com/ by a Walden University User on 07/13/2019

Opinion Viewpoint

Figure. The Interdependence of Evidence-Based Medicine and Shared Decision Making and the Need for Both as Part of Optimal Care

recommended in guidelines when the options are closely matched in their advantages and disadvantages, when uncer- tainty in the evidence impairs determination of a clearly superior approach, or when the balance of benefits and risks depends on patient action, such as adherence to medication, monitoring, and diet in patients using warfarin.

Conclusions

Links between EBM and SDM have until recently been largely ab- sent or at best implied. However, encouraging signs of interaction are emerging. For example, there has been some integration of the teaching of both,7 exploration about how guidelines can be adapted to facilitate SDM,8,9 and research and resource tools that recog- nize both approaches. Examples of the latter include research agenda and priority setting occurring in partnership with patients and cli- nicians to help provide relevant evidence for decision making; and a new evidence criterion for the International Patient Decision Aids Standards requiring citation of systematically assembled and up- to-date bodies of evidence, with their trustworthiness appraised,10 thus aligning the development of SDM tools with contemporary re- quirements for the formulation of evidence-based guidelines. Also, independent flagship conferences focused on the practice of evi- dence-based health care and on the science of shared decision mak- ing are now convening joint meetings.

Medicine cannot, and should not, be practiced without up-to- date evidence. Nor can medicine be practiced without knowing and respecting the informed preferences of patients. Clinicians, researchers, teachers, and patients need to be aware of and actively facilitate the interdependent relationship of these approaches. Evidence-based medicine needs SDM, and SDM needs EBM. Patients need both.

Evidence-based medicine

Patient-centered communication skills

Optimal patient care

Shared decision making

Another place to start to bring EBM and SDM together is the development and implementation of clinical practice guidelines. Whereas most guidelines fail to consider patients’ preferences in formulating their recommendations,6 some advise clinicians to talk with patients about the options but provide no guidance about how to do this and communicate the evidence in a way patients will understand. Shared decision making may be strongly

ARTICLE INFORMATION

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Montori reported serving on the board of the International Society for Evidence-based Healthcare; serving as Chair of the Seventh International Shared Decision Making Conference in 2013; that he is a member of the Steering Committee of the International Patient Decision Aids Standards; and that he is a member of the GRADE Working Group. The KER Unit (Dr Montori’s research group) produces and tests evidence-based shared decision making tools that are freely available at http://shareddecisions.mayoclinic.org. Dr Hoffmann reported that she is supported by a National Health and Medical Research Council of Australia (NHMRC)/Primary Health Care Research Evaluation and Development Career Development Fellowship (1033038), with funding provided by the Australian Department of Health and Ageing. Drs Hoffmann and Del Mar reported that they are coeditors of a book on evidence-based practice, for which they receive royalties.

Additional Information: Additional information abut evidence-based medicine and shared decision making is available online in Evidence-Based Medicine: An Oral History at http://ebm .jamanetwork.com.

REFERENCES

1. StrausSE,JonesG.Whathasevidencebased medicine done for us? BMJ. 2004;329(7473):987- 988.

2. Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn’t. BMJ. 1996;312(7023):71-72.

3. Greenhalgh T, Howick J, Maskrey N; Evidence Based Medicine Renaissance Group. Evidence based medicine: a movement in crisis? BMJ. 2014; 348:g3725.

4. Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med. 1997;44(5):681-692.

5. Meats E, Heneghan C, Crilly M, Glasziou P. Evidence-based medicine teaching in UK medical schools. Med Teach. 2009;31(4):332-337.

6. Montori VM, Brito JP, Murad MH. The optimal practice of evidence-based medicine: incorporating patient preferences in practice guidelines. JAMA. 2013;310(23):2503-2504.

7. Hoffmann TC, Bennett S, Tomsett C, Del Mar C. Brief training of student clinicians in shared decision making: a single-blind randomized controlled trial. J Gen Intern Med. 2014;29(6):844-849.

8. DecisionAids.MAGICwebsite.http://www .magicproject.org/decision-aids/. Accessed July 24, 2014.

9. vanderWeijdenT,PieterseAH, Koelewijn-van Loon MS, et al. How can clinical practice guidelines be adapted to facilitate shared decision making? a qualitative key-informant study. BMJ Qual Saf. 2013;22(10):855-863.

10. Montori VM, LeBlanc A, Buchholz A, Stilwell DL, Tsapas A. Basing information on comprehensive, critically appraised, and up-to-date syntheses of the scientific evidence: a quality dimension of the International Patient Decision Aid Standards. BMC Med Inform Decis Mak. 2013;13 (suppl 2):S5.

jama.com

Downloaded From: https://jamanetwork.com/ by a Walden University User on 07/13/2019

Shared Decision-Making in Intensive Care Units

Executive Summary of the American College of Critical Care Medicine and

American Thoracic Society Policy Statement

Shared decision-making is a central component of patient-centered

care in the intensive care unit (ICU) (1

–

4); however, there remains

confusion about what shared decision-making is and when

shared decision-making ought to be used. Further, failure to

employ appropriate decision-making techniques can lead to

signi

cant problems. For example, if clinicians leave decisions

largely to the discretion of surrogates without providing adequate

support, surrogates may struggle to make patient-centered

decisions and may experience psychological distress (5).

Conversely, if clinicians make treatment decisions without

attempting to understand the patient

’

s values, goals, and

preferences, decisions will likely be predominantly based on the

clinicians

’

values, rather than the patient

’

s, and patients or

surrogates may feel they have been unfairly excluded from

decision-making (1, 2). Finding the right balance is therefore

essential. To clarify these issues and provide guidance, the

American College of Critical Care Medicine (ACCM) and

American Thoracic Society (ATS) recently released a policy

statement that provides a de

nition of shared decision-making

in the ICU environment, clari

cation regarding the range of

appropriate models for decision-making in the ICU, a set of skills

to help clinicians create genuine partnerships in decision-making

with patients/surrogates, and ethical analysis supporting the

ndings (6).

To develop a uni

ed policy statement, the Ethics Committee of

the ACCM and the Ethics and Con

ict of Interest Committee of the

ATS convened a writing group composed of members of these

committees. The writing group reviewed pertinent literature

published in a broad array of journals, including those with a focus

in medicine, surgery, critical care, pediatrics, and bioethics, and

discussed

ndings with the full ACCM and ATS ethics committees

throughout the writing process. Recommendations were generated

after review of empirical research and normative analyses published

in peer-reviewed journals. The policy statement was reviewed,

edited, and approved by consensus of the full Ethics Committee

of the ACCM and the full Ethics and Con

ict of Interest Committee

of the ATS. The statement was subsequently reviewed and approved

by the ATS, ACCM, and Society of Critical Care Medicine leadership,

through the organizations

’

standard review and approval processes.

ACCM and ATS endorse the following de

nition: Shared

decision-making is a collaborative process that allows patients, or

their surrogates, and clinicians to make health care decisions

together, taking into account the best scienti

c evidence

available, as well as the patient

’

s values, goals, and preferences.

Clinicians and patients/surrogates should use a shared

decision-making process to de

ne overall goals of care (including

decisions regarding limiting or withdrawing life-prolonging

interventions) and when making major treatment decisions that

may be affected by personal values, goals, and preferences (7, 8).

Once clinicians and the patient/surrogate agree on general goals of

care, clinicians confront many routine decisions (e.g., choice of

vasoactive drips and rates, laboratory testing,

uid rate). It is

logistically impractical to involve patients/surrogates in each of

these decisions. Partnerships in decision-making require that the

overall goals of care and major preference-sensitive decisions be

made using a shared decision-making approach. The clinician then

has a

duciary responsibility to use experience and evidence-based

practice when making day-to-day treatment decisions that are

consistent with the patient

’

s values, goals, and preferences.

Throughout the ICU stay, important, preference-sensitive choices

often arise. When they do, clinicians should employ shared

decision-making.

Clinicians should generally start with a default shared decision-

making approach that includes the following three main elements:

information exchange, deliberation, and making a treatment

decision. This model should be considered the default approach to

shared decision-making, and should be modi

ed according to the

needs and preferences of the patient/surrogate. Using such a model,

the patient or surrogate shares information about the patient

’

values, goals, and preferences that are relevant to the decision at

hand. Clinicians share information about the relevant treatment

options and their risks and bene

ts, including the option of

palliative care without life-prolonging interventions. Clinicians and

the patient/surrogate then deliberate together to determine which

option is most appropriate for the patient, and together they agree

on a care plan. In such a model, the authority and burden of

decision-making is shared relatively equally (9). Although data

suggest that a preponderance of patients/surrogates prefer to share

responsibility for decision-making relatively equally with clinicians,

many patients/surrogates prefer to exercise greater authority in

decision-making, and many other patients/surrogates prefer to

defer even highly value-laden choices to clinicians (10

–

13).

Ethically justi

able models of decision-making include a broad

range to accommodate such differences in needs and preferences.

In some cases, the patient/surrogate may wish to exercise

signi

cant authority in decision-making. In such cases, the clinician

should understand the patient

’

s values, goals, and preferences to a

suf

cient degree to ensure the medical decisions are congruent

with these values. The clinician then determines and presents the

range of medically appropriate options, and the patient/surrogate

chooses from among these options. In such a model, the

patient/surrogate bears the majority of the responsibility and

burden of decision-making. In cases in which the patient/surrogate

demands interventions the clinician believes are potentially

inappropriate, clinicians should follow the recommendations

presented in the recently published multiorganization policy

statement on this topic (14).

In other cases, the patient/surrogate may prefer that clinicians

bear the primary burden in making even dif

cult, value-laden

choices. Research suggests that nearly half of surrogates of critically

ill patients prefer that physicians independently make some

types of treatment decisions (10

–

13). Further, data suggest that

approximately 5

–

20% of surrogates of ICU patients want clinicians

to make highly value-laden choices, including decisions to limit or

1334

American Journal of Respiratory and Critical Care Medicine Volume 193 Number 12

June 15 2016

EDITORIALS

withdraw life-prolonging interventions (12, 13). In such cases,

using a clinician-directed decision-making model is ethically

justi

able (15

–

24).

Employing a clinician-directed decision-making model

requires great care. The clinician should ensure that the surrogate

’

preference for such a model is not based on inadequate

information, insuf

cient support from clinicians, or other

remediable causes. Further, when the surrogate prefers to defer a

speci

c decision to the clinician, the clinician should not assume

that all subsequent decisions are also deferred. The surrogate

should therefore understand what speci

c choice is at hand and

should be given as much (or as little) information as the surrogate

wishes. Under such a model, the surrogate cedes decision-making

authority to the clinician and does not need to explicitly agree

to (and thereby take responsibility for) the decision that is made.

The clinician should explain not only what decision the clinician is

making but also the rationale for the decision, and must then

explicitly give the surrogate the opportunity to disagree. If the

surrogate does not disagree, it is reasonable to implement the care

decision (19

–

24). Readers may review references 19

–

24 for detailed

descriptions and ethical analyses of clinician-directed decision-making.

The statement was intended for use in all ICU environments.

Patients and surrogate decision-makers have similar rights both

to participate in decision-making when appropriate and to rely more

heavily on providers when they wish to do so, regardless of the type

of ICU. Similarly, the statement is equally applicable in pediatric and

neonatal settings, where decision-making partnerships between

parents and the ICU team are equally important. As noted in the

statement, including children in some decisions can often be

appropriate as well. The statement is also intended to be applicable

internationally. Although patient and surrogate decision-making

preferences may differ globally, the default approach presented

and the recommendation to adjust the decision-making model to

the preferences of the patient or surrogate are universal. Both

ACCM and ATS are international organizations, and the literature

review included publications from many countries. The statement

focuses on the ICU environment because critically ill patients are

often, but not always, unable to participate in decision-making

themselves, and because many decisions in the ICU are value-

sensitive. The recommendations in the statement, however, could be

equally applicable in all patient care settings.

To optimize shared decision-making, clinicians should be

trained in speci

c communication skills. Core categories of

skills include establishing a trusting relationship with the

patient/surrogate; providing emotional support; assessing

patients

’

/surrogates

’

understanding of the situation; explaining

the patient

’

s condition and prognosis; highlighting that there

are options to choose from; explaining principles of surrogate

decision-making; explaining treatment options; eliciting patient

’

values, goals, and preferences; deliberating together; and making

a decision. The full policy statement provides signi

cant guidance

and examples in these areas (6).

Finally, ACCM and ATS recommend further research to

assess the use of various approaches to decision-making in the

ICU. The use of decision aids, communication skills training,

implementation of patient navigator or decision support counselor

programs, and other interventions should be subjected to

randomized controlled trials to assess ef

cacy. Considerations

regarding the cost and time burdens should be weighed against

anticipated bene

ts from such interventions when determining

which efforts to implement.

Author disclosures

are available with the text of this article at

www.atsjournals.org.

Acknowledgment:

The views expressed in this article represent the official

position of the American College of Critical Care Medicine, the Society

of Critical Care Medicine, and the American Thoracic Society. These views

do not necessarily reflect the official policy or position of the U.S. Department

of the Navy, U.S. Department of Defense, U.S. National Institutes of Health,

U.S. Department of Veterans Affairs, U.S. Food and Drug Administration,

or U.S. Government.

Alexander A. Kon, M.D.

Naval Medical Center San Diego

San Diego, California

and

University of California San Diego

San Diego, California

Judy E. Davidson, D.N.P., R.N.

University of California Health System

San Diego, California

Wynne Morrison, M.D.

Children

’

s Hospital of Philadelphia

Philadelphia, Pennsylvania

Marion Danis, M.D.

National Institutes of Health

Bethesda, Maryland

Douglas B. White, M.D., M.A.S.

University of Pittsburgh School of Medicine

Pittsburgh, Pennsylvania

References

1. Carlet J, Thijs LG, Antonelli M, Cassell J, Cox P, Hill N, Hinds C, Pimentel

JM, Reinhart K, Thompson BT. Challenges in end-of-life care in the ICU:

statement of the 5th International Consensus Conference in Critical Care:

Brussels, Belgium, April 2003.

IntensiveCareMed

2004;30:770

–

784.

2. Thompson BT, Cox PN, Antonelli M, Carlet JM, Cassell J, Hill NS, Hinds

CJ, Pimentel JM, Reinhart K, Thijs LG; American Thoracic Society;

European Respiratory Society; European Society of Intensive Care

Medicine; Society of Critical Care Medicine; Soci`

et`

ede R`

eanimation

de Langue Française. Challenges in end-of-life care in the ICU:

statement of the 5th International Consensus Conference in Critical

Care: Brussels, Belgium, April 2003: executive summary.

Crit Care

Med

2004;32:1781

–

1784.

3. Davidson JE, Powers K, Hedayat KM, Tieszen M, Kon AA, Shepard E,

Spuhler V, Todres ID, Levy M, Barr J,

et al

.; American College of

Critical Care Medicine Task Force 2004-2005, Society of Critical Care

Medicine. Clinical practice guidelines for support of the family in the

patient-centered intensive care unit: American College of Critical Care

Medicine Task Force 2004-2005.

Crit Care Med

2007;35:605

–

622.

4. Lanken PN, Terry PB, Delisser HM, Fahy BF, Hansen-Flaschen J,

Heffner JE, Levy M, Mularski RA, Osborne ML, Prendergast TJ,

et al

ATS End-of-Life Care Task Force. An of

cial American Thoracic

Society clinical policy statement: palliative care for patients with

respiratory diseases and critical illnesses.

Am J Respir Crit Care Med

2008;177:912

–

927.

5. Gries CJ, Engelberg RA, Kross EK, Zatzick D, Nielsen EL, Downey L,

Curtis JR. Predictors of symptoms of posttraumatic stress and

depression in family members after patient death in the ICU.

Chest

2010;137:280

–

287.

Editorials

1335

EDITORIALS

Measuring Return on Investment for Professional Development Activities Implications for Practice

Cathleen Opperman, DNP, RN, NEA-BC, CPN ƒ Debra Liebig, MLA, BSN, RN-BC ƒ

Judith Bowling, MSN, MHA, RN-BC ƒ Mary Harper, PhD, RN-BC

Carol Susan Johnson, PhD, RN, NE-BC ƒ

The synthesis of the studies on educational interven- tions providing a calculation of financial aspects shows no consistent method to describe financial and clinical im- pact of professional development activities (Opperman, Liebig, Bowling, Johnson, & Harper, 2016). The trend of reporting outcomes associated with learning activities has given rise to the next level of expectation: demonstra- tion of financial impact of educational interventions.

This article defines the concepts of an economic assess- ment including simple cost analysis, benefitYcost ratios, cost-effectiveness analysis (CEA), and ROI, as well as pro- viding formulas to calculate each. Three fictional examples of various-sized educational programs are used to demon- strate how to make these calculations and use them for decision-making.

COST ANALYSIS, BENEFIT–COST RATIOS, AND COST-EFFECTIVENESS ANALYSIS Prior to discussing ROI, an understanding of the concepts of cost analysis, benefitYcost ratios, and CEA is essential when calculating the actual financial impact of professional development activities.

From a financial perspective, cost analysis is the initial consideration when developing an educational program. Cost analysis simply determines the least expensive option. The formula for cost analysis is to add all the costs for the program and divide it by the number of participants to ob- tain the cost per participant. See Figure 1 for the formulas for cost analysis. When considering multiple learning mo- dalities, this simple cost per participant can be compared.

Although cost analysis provides information on the effi- ciency or least expensive modality, it does not consider program outcomes. The benefitYcost analysis compares program benefits to program costs as a ratio using dollars. The first step is clearly identifying desired program out- comes that can be observed and measured.

The next step is calculating all program costs. The benefitYcost ratio formula uses all benefits (i.e., increased productivity, quality, safety improvements, reduced turn- over, increased patient volumes) and all costs (i.e., program development time, faculty costs, training supplies,

July/August 2016

What is the return on investment (ROI) for the time and resources spent for professional development activities? This is Part 2 of a two-part series to report findings and demonstrate how financial analysis of educational activities can drive decision-making. The resources consumed for professional development activities need to be identified and quantified to be able to determine the worth of such activities. This

article defines terms and formulas for financial analysis for nursing professional development practitioners to use in analysis of their own programs. Three fictitious examples of common nursing professional development learning activities are provided with financial analysis. This article presents the ‘‘how to’’ for the busy practitioner.

As nursing professional development (NPD) prac- titioners, we are challenged by the question ‘‘What is the return on investment (ROI) for professional development activities?’’ As described in Part 1 of this series, NPD practitioners are often the first to be called when a problem exists and the first to have funding restricted when budgets are tight. In Part 1, we discussed the Kirkpatrick, Phillips, and Paramoure program evalua- tion models, followed by a summary of the literature reporting on ROI for professional development activities.

Cathleen Opperman, DNP, RN, NEA-BC, CPN, is Nurse Specialist, Profes- sional Development, Nationwide Children’s Hospital, Columbus, Ohio.

Debra Liebig, MLA, BSN, RN-BC, is Director, Nursing Retention, Truman Medical Center, Kansas City, Missouri.

Judith Bowling, MSN, MHA, RN-BC, is Clinical Learning Educator, Baptist Health South Florida, Miami, Florida.

Carol Susan Johnson, PhD, RN, NE-BC, is NCC MagnetA Appraiser and member, Commission on Accreditation, Fort Wayne, Indiana.

Mary Harper, PhD, RN-BC, is Director, Nursing Professional Develop- ment, Association for Nursing Professional Development.

The authors have disclosed that they have no significant relationship with, or financial interest in, any commercial companies pertaining to this article.

ADDRESS FOR CORRESPONDENCE: Cathleen Opperman, Nation- wide Children’s Hospital, 255 East Main St., Columbus, OH 43205 (oppermancs@gmail.com).

DOI: 10.1097/NND.0000000000000274 176 www.jnpdonline.com

FIGURE 1 Formulas for cost analysis, benefitYcost ratio, and return on investment.

equipment costs, facility fees, salary cost for employee attendance/replacement cost) to determine the financial re- turn from the program (Warren, 2013). See Figure 1 for the formulas for cost analysis, benefit-cost ratio and re- turn on investment.

CEA goes one step further in economic assessment, be- cause it compares two or more different educational interventions and their outcomes. The NPD practitioner may have an option of a self-study that takes the learner an average of 2 hours to complete or a 90-minute live work- shop with the same content. Both modalities are intended to accomplish the same outcome. The costs must be mon- etary values and calculated as cost analysis for each possible intervention. The outcomes, however, do not need to be monetary values; consider them the benefits gained from the educational intervention. For example, nonmonetary outcomes might be ‘‘increased patient en- gagement’’ or ‘‘fewer staff reporting incivility.’’ The combination of the cost per participant (cost analysis) and the benefits, whether monetary (calculated as a benefitY cost ratio) or nonmonetary, are used to determine the CEA.

BenefitYcost ratio and CEA collectively impact decisions about program changes and resources. Although cost anal- ysis alone may demonstrate efficiency through lower costs, the benefitYcost analysis may demonstrate that the same program is not as effective in achieving desired outcomes. Clearly, comprehensive program evaluation requires consideration of both componentsVefficiency and effec- tiveness (Kettner, Moroney, & Martin, 2013).

ROI CALCULATION

In the economic assessment of a program, calculation of ROI provides further data for administrative decision- making. Calculating ROI (a) provides information for

Journal for Nurses in Professional Development

justification of programs for budgetary planning, (b) contributes to clinical decision-making and resource al- location, and (c) demonstrates the value of education.

Because of the complexity of determining ROI for pro- grams, pragmatically, it is used in only about 5%Y10% of program planning processes for priority decision-making like regulatory, higher-risk, or more expensive programs (DeSilets, 2010.)

Steps in calculating ROI:

1. Identify program desired outcomes.

2. Describeeducationalinterventionsproposedtomeet

these outcomes.

3. Plan the logistics of the educational intervention with

sufficient detail to identify expenses.

4. Calculate program costs (planning time, supplies, setup

time, faculty and staff time, etc.).

5. Calculatepotentialsavings(costofturnover,pressure

ulcer, litigation, inefficiency of program changes).

6. Compare costs to savings (efficiency).

7. Determine specific outcomes using observable and

measurable terms (effectiveness).

In order to calculate the benefit of educational inter-

ventions, an outcome must be quantified. For example, changes in orientation should lead to greater new em- ployee competence, confidence, and satisfaction, therefore reducing turnover. Another example is that an education activity on the catheter-associated urinary tract infections (CAUTI) bundle should lead to reduction of CAUTIs. When calculating the benefit of the educational interven- tion for either of these examples, the cost (of a new RN leaving or the average cost of a CAUTI) should be used to counter the cost of the program. For examples of published average costs per case of poor outcomes, see Table 1. The formula for calculating the ROI is found in Figure 1.

EXAMPLES OF FINANCIAL IMPACT ANALYSIS

Consider these examples of fictitious educational activities and how financial impact can be calculated through cost analysis, benefitYcost ratios, CEA, and ROI analysis.

Example 1: One-hour self-study compared to live class.

Situation. The organization considers requiring a 1-hour Web-based self-study module for 650 learners on changes in the procedure for pressure ulcer preven- tion bundle.

Background. The hospital incidence of hospital- acquired pressure ulcers (HAPU) is 25%, which is above the national average of 17% (Roe & Williams, 2014). The Centers for Medicare and Medicaid Services no longer re- imburses facilities for patients with newly acquired Stage 3 and Stage 4 pressure ulcers.

According to the Agency for Healthcare Research and Quality (2014b), a full-thickness pressure ulcer costs an average of $17,286 per incident to treat. This does not

www.jnpdonline.com 177

include the additional emotional and physical burden for the patient.

According to the American Faculty Association (2012), the time needed to develop educational programs is 4 hours of preparation for each hour of class presented. This varies widely from Kapp and Defelice (2009) that estimates 40 (self-instructional print), 43 (stand-up class- room training), and 49 (instructor-led, Web-based training) hours per hour of training are needed for devel- opment. For purposes of these fictitious scenarios, it is assumed that the NPD practitioner is well informed of changes in pressure ulcer care and is an experienced NPD practitioner; consequently, the number of develop- ment hours is less.

Assessment. The program costs of a Web-based self- study module for supplies, salaries, and equipment are calculated as follows. The computers and software are in place; thus, no further initial expense for equipment is needed.

35.03. The Web-based class costs $2.25 less per partici- pant. Additional considerations for cost analysis include the cost of educating new hires. If the organization hires an additional 50 nurses over the course of a year, the only expense for the Web-based course is the hourly salary of the new nurses. No additional program costs are incurred. If presented in a live format, in addition to the newly hired nurses’ salary, additional costs would include the NPD practitioner salary for each class and the additional admin- istrative support salary.

BenefitYcost ratio. To calculate benefitYcost ratio, bene- fits are divided by total costs. Using $17,286 as the per case cost to treat a full thickness pressure ulcer, prevention of two pressure ulcers results in cost savings of $34,572. This results in a positive benefitYcost ratio for both Web-based and live class formats.

BenefitYcost ratio: Using Web-based self-study module:

$34,572 = 1.62 BCR

$21,310 Using live class format:

$34,572 = 1.52 BCR

$22,775 (G1 = negative impact, 9 = positive impact) Cost-effectiveness analysis. The difference in cost be-

tween the Web-based modules at $21,310 and the live classes at $22,775 is $1,465. The savings for the Web- based format is only positive if the Web-based course and the live classes are comparable in outcomes. CEA requires an evaluation of effectiveness of each modality in achiev- ing the same outcomes. In this scenario, the Web-based course was determined to be equal in effectiveness re- sulting in similar outcomes to the live presentation. As a result, the Web-based course is more cost-effective.

ROI. To evaluate the ROI in this example, the cost of the pressure ulcer treatment must be compared to the cost of the education. As previously stated, if this educa- tional intervention prevents two pressure ulcers, $34,572 is saved. The ROI for the Web-based self-study is 62.2%, and the live class format is 51.7%.

Using Web-based self-study module:

$34,572j $21,310 100 = 62.2% ROI $21,3107

Using live class format: $34,572j $22,775 100 = 51.7% ROI

$22,775 Recommendation. In both the live and Web-based

courses, the ROI is positive and easily justifies the educa- tion. The Web-based course, however, shows a higher ROI. The cost analysis, benefitYcost ratio, CEA, and ROI all demonstrate a more positive financial impact with the Web-based course. As a result, the NPD practitioner rec- ommends development of a Web-based educational program on prevention of pressure ulcers.

One-Hour Self-Study Expenses
Item	Hours X hourly pay	Total
NPD practitioner salary (development)	40 hours $35/hour	$1400
NPD practitioner salary (coordination)	6 hours $35/hour	$210
Admin support salary	4 hours $20/hour	$80
IT support salary	4 hours $30/hour	$120
Participants salaries	1 hour 650 participants $30/hour	$19,500
	Total cost	$21,310
	Cost per participant ($21,310 / 650)	$32.78
Additional Costs for a Live Class
Item	Hours X hourly pay	Total
NPD practitioner salary (classroom time)	35 classes $35/hour	$1,225
Admin support salary (record-keeping)	12 hours $20/hour	$240
	Total additional costs	$1,465
	Live program total cost (from above + additional)	$22,775
	Cost per participant ($22,775 / 650)	$35.03

Cost analysis. The cost for a Web-based module is $32.78 per participant, and the cost of the live classes is

178 www.jnpdonline.com

July/August 2016

TABLE 1	Known Costs of Outcomes
Outcome	Reported average cost	Sources
Active surveillance screening for MRSA	Universal surveillance screening cost-effectiveness ratio of $14,955 per MRSA	Kang, Mandsager, Biddle, and Weber (2012)
Adverse drug events	Estimated extra cost per case $5,000	Agency for Healthcare Research and Quality (2014a)
Asthma/COPD treatment	$1,681Y$8,533 annual mean expenditure per person	Agency for Healthcare Research and Quality (2014b)
Breast milk	Biological mother’s milk ranged $0.051 to $7.93, depending on the volume pumped daily Donor human milk cost was $14.84 Commercial formula was $3.18	Jegler et al. (2013)
Cancer treatment	$5,631Y$21,573 annual mean expenditure per person	Agency for Healthcare Research and Quality (2014b)
Care for child with autism spectrum disorder	Intensive behavior intervention $4,000/month Gluten-free diet, $150 every 2 weeks	Fletcher, Markoulakis, and Bryden (2012)
Catheter-associated urinary tract infections (CAUTI)	Additional $1,000 per admission	Agency for Healthcare Research and Quality (2014a)
Catheter-related bloodstream Infections (CRBSI)	CRBSI, $11,971Y$56,167 Adult ICU, $33,000Y$44,000 Surgical ICU, $54Y$75,000 Pediatric ICU, $48,379 Multicenter study, $20,647 General wards, $20,647	Hollenbeak (2011)
Central line-associated bloodstream infections (CLABSI)	Additional $17,000 per admission	Agency for Healthcare Research and Quality (2014a)
Employee musculoskeletal injuries	$28,866 per strain $33.528 per sprain	OSHA Safety Pays Program Estimator (2016) ANA’s Handle with Care Program (2016)
Employee needle sticks	$22,716 per incident	OSHA Safety Pays Program Estimator (2016)
Falls	Estimated extra cost $7,234 per case Mean cost of hospitalization related to a fall is $17,483 per event	Agency for Healthcare Research and Quality (2014a) Trepanier and Hilsenbeck (2014)
Family support network for child with cancer	$2,776 Canadian dollars for 3 months	Tsimicalis et al. (2013)
Healthcare-acquired infection data dates 1999Y2007	CAUTI, $758/weight adjusted mean cost estimate MRSA, $6,400/MRSA infection C-difficile, $5,042/infection CLABSI, $12,000/infectionSurgical never events, $62,000/event Falls prevention, $4,233/event VTE prevention,$10,804/event-DVT$16,644/event-PE Pressure ulcer prevention, $1,878/event	Schifalacqua, Mamula, and Mason (2011)
Heart disease	$4,349Y$14,492 annual mean expenditure per person	Agency for Healthcare Research and Quality (2014b)

Continued

TABLE 1	Known Costs of Outcomes, Continued
Outcome	Reported average cost	Sources
Hospital-acquired pressure ulcer	Estimated extra cost $17,286 per case	Agency for Healthcare Research and Quality (2014a)
Hospital-based violence intervention program (VIP)	Savings of $4,100 for 100 individuals Average hospital costs post recidivism (base case) $6,513 (range, $1,996Y$100,000)	Juillard et al. (2015)
Hospital-centered violence intervention programs	Cost of VIP, $2,810 Average hospital costs post recidivism with standard referrals, $18,722	Chong et al. (2015)
Hospitalizations for pediatric mental health disorders	Total resource utilization charges/mean charges per visit (Pediatrics): Depression, 1.33 billion/$13,200 Bipolar, 702 million/$17,058Psychosis, 540 million/$19,676 Externalizing disorder, 264 million/$18,784 Anxiety disorder, 149 million/$19,118 ADHD, 133 million/$19,118 Eating disorder, 108 million/$46,130 Substance abuse, 102 million/$12,098 Reaction disorder, 100 million/$8,444	Bardach et al. (2014)
Infection with clostridium difficile	Outpatient and inpatient setting Total of $11,314.70	Kuntz et al. (2012)
New RN orientation cost	$49,000Y$92,000 (includes replacement costs)	Trepanier, Early, Ulrich, and Cherry (2012)
Nonmedical out-of-pocket expenses venous thromboembolism (VTE)	VTE annual cost, $1.5 billion Estimated total cost (in Australian dollars): Baseline, $5,078,522 12 months after implementation, $4,833,083 Prophylaxis implementation: Baseline, $104,311; 12 months $142,846 LMWH regimen: Baseline, $71,313; 12 months, $92,295 LDUH regimen: Baseline, $32,998; 12 months, $50,569 DVT treatment: Baseline, $2,375,532; 12 month, $2,143,767 PE treatment: Baseline, $470,284; 12 months, $420,180 Major bleeds: Baseline, $762,057; 12 months, $828,977 HIT: Baseline, $118,605; 12 months, $180,298 Postthrombotic syndrome: Baseline, $1,247,732; 12 month, $1,116,997	Duff, Walker, Omari, and Stratton (2013) Data from January 2010 to January 2011
OB adverse events	Estimated extra cost per case $3,000	Agency for Healthcare Research and Quality (2014a)
Postop venous thromboembolism	Estimated extra hospitalization cost $8,000	Agency for Healthcare Research and Quality (2014a)
RN turnover	RN replacement cost $22,000Y$64,000 with average cost per RN leaving $36,567	Robert Wood Johnson Foundation (2010)

180 www.jnpdonline.com

July/August 2016

Continued

TABLE 1	Known Costs of Outcomes, Continued
Outcome	Reported average cost	Sources
Subcutaneous drug delivery	Administration: Subcutaneous, $30.19 Intravenous, $113.13	Dychter, Gold, and Haller (2012)
Surgical site infections	Estimated extra cost per case $21,000	Agency for Healthcare Research and Quality (2014a)
Treatment of mental disorders (adult)	$1,849Y$6,003 annual mean expenditure per person	Agency for Healthcare Research and Quality (2014b)
Treatment of trauma-related disorders	$2,609Y$12,,975 annual mean expenditure per person	Agency for Healthcare Research and Quality (2014b)
Ventilator-associated pneumonia	$21,000 per incident	Agency for Healthcare Research and Quality (2014a)
Copyrighted ANPD. All resources were accessed between June 22, 2015 and December 30, 2015.

Example 2: Eight-hour live class on workplace violence.

Situation. On the basis of an identified professional practice gap, a continuing education program on work- place violence is planned for 25 hourly employees from the Emergency Department.

Background: At a large, central city acute care facility, gun violence is a concern. The United States has the greatest number of gun-related injuries per capita com- pared to all other industrialized nations at 10.3 per 100,000 (fatal and nonfatal) occurring in 2011 (Jena, Sun, & Prasad, 2014). The average cost per incident is $18,722

(Chong et al., 2015). Escalation of violent behaviors resulted in 11 reported incidents in the Emergency De- partment last year. Because domestic violence frequently involves gun injuries, the community is at high risk for gun-related injuries, and escalation of violent behaviors has occurred at increasing frequency in the Emergency Department, an educational program is proposed to help increase employee safety.

Assessment. In calculating the costs for this program, the NPD department purchased predeveloped content for this course, so development time was reduced. A content expert was used to review potential programs for pur- chase, select one, and prepare to facilitate the course with the identified development time. Instead of 43 hours per hour of content (43 7 = 301 development hours) required to develop this course, 70 hours were needed (7 content hours 10 hours = 70 hours to review, select, and prepare to facilitate this program).

Cost analysis. Simple cost analysis is the total cost of the educational intervention divided by the number of staff mem- bers participating in the education. Cost of the class per person: $15,350/25 participants = $ 614.00 per participant.

BenefitYcost ratio. When calculating the benefitYcost ratio, the total benefit is divided by the total cost. In this sce- nario, if one incident of violence is prevented in 1 year at $18,722 average cost per incident (Chong et al., 2015), the net benefit is $18,722. See Table 1 for published costs of outcomes. A positive benefit to the organization is noted with the calculation:

$18,722 = 1.22 BCR

$15,350 Cost-effectiveness analysis. In this example, the cost-

effectiveness compares the cost of providing the 8-hour program with the current practice of no educational

Eight-Hour Live Class Expenses
Item	Hours X hourly pay	Total
Predeveloped content purchase		$6,000
Content expert salary (development)	70 hours $35	$2,450
Content expert salary (event)	8 hours $35	$280
NPD practitioner salary (event)	8 hours $35	$280
Admin support salary	4 hours $20	$80
Participant salary	8 hours 25 participants $30	$6,000
Supplies	$8/person 25 participants	$200
Marketing (internal)	3 hours $20	$60
Total		$15,350

Journal for Nurses in Professional Development

www.jnpdonline.com 181

Multi-day Orientation Expenses
Item	Hours X hourly pay	Total
SALARIES for Development:
NPD practitioner coordination	6 hours $35	$210
Clerical Support	3.5 hours $20	$70
NPD practitioner post program	3 hours $35	$105
SALARIES for Presenters:
NPD practitioner classroom	34 hours $35	$1,190
CNO/Administration	1 hour $60	$60
Shared governance rep.	1 hour $30	$30
Social worker	1 hour $35	$35
Risk Management	1 hour $45	$45
QI	2 hours $30	$60
Pharmacy	1 hour $40	$40
Informatics Nurse	16 hours $30	$480
	Subtotal	$2,325
SALARIES for Skills Stations Faculty:
Lab tech	4 hour $30	$120
Respiratory therapist	4 hour $35	$140
Lactation specialist	4 hour $35	$140
Epidemiology RN	4 hour $35	$140
Code team RN	4 hour $35	$140
	Subtotal	$680
Consumable supplies	$8 45 participants	$360
	Total cost	$3,365

During the year following education, two fewer inci- dents are reported:

$18,722 j $15,350 100 = 22% ROI $15,350

Recommendation. With the increased incidents of workplace violence, employers must demonstrate due diligence to protect employees, patients, and visitors by preventing these incidents. From the combination of CEA, benefitYcost ratios, and ROI calculations, this pro- gram is clearly recommended.

Example 3: Frequency of a multi-day orientation.

Situation. An organization conducts a 7-day interpro- fessional orientation 10 times per year. It is considering increasing to 12 times per year to accommodate more timely incorporation of newly hired employees.

Background. The number of participants per cohort has ranged from 30 to 70. The significant range of cohort sizes makes it difficult to plan for room size, number of stations for skills lab, computer training rooms, faculty schedules, and handout preparation. In addition, par- ticipant satisfaction drops with decreased learner engagement in large classes. If decreased engagement leads to poor socialization and increased turnover, Robert Wood Johnson Foundation (2010) places the average cost for replacing an RN at $36,567. An increase to offering orientation 12 times a year eliminates cohorts with more than 45 participants and saves last minute planning time related to human resources communica- tion, room scheduling, class coordinating, and faculty availability. Because the classes are already developed and current, additional development time is not needed.

Additional Expenses for Large Cohorts
Item	Hours X hourly pay	Total
SALARIES for additional coordination:
NPD practitioner coordination	15 hours $35	$525
Clerical staff support (OT)	10 hours $30	$300
Human resources	2 hours $30	$60
Room scheduling	2 hours $20	$40
Informatics nurse	(2 additional days)	$480
Addition equipment rental	$175
Transport of supplies and equipment to university	$280
Room rental from university	$1,000
Added faculty for skills (twice the number faculty)	$680
Total additional cost	$3,540

program. The cost comparison is $614.00 per participant versus no educational expense. The outcome is the num- ber of incidents reported. If the educational intervention demonstrates a reduced incidence of workplace injury from violence, that outcome is better that the current data of 11 incidents last year.

Return on investment. If the proposed program pro- duces a modest result of one less reported incident of workplace violence, the ROI is 22%.

182 www.jnpdonline.com

July/August 2016

The comparison is between the current state of 10 offerings a year and the proposed change to 12 offerings per year.

Assessment. Program costs are more extensive for a 7-day program. Salaries, supplies, equipment, and even rental of space are considered.

Additional costs incurred when a cohort is over 45 par- ticipants includes two additional Informatics Nurse instructor-days for the electronic medical record class due to lack of computers and doubling the skills stations on the skills day, requiring more faculty, equipment, and space. For illustration, when a cohort is 65 instead of 45, the following costs are added:

The cost of orienting a large cohort (65 participants) is calculated by starting with the costs of the 45 participant cohort and adding expenses incurred for the larger group.

Large class per orientation cost (65 in cohort): $3,365 (first 45 participants) $160 (supplies for 20 more participants)

(added salaries for coordination and faculty, $3,540 room, and equipment rental) $7,065 (for 65 participants)

Cost analysis. The cost per participant is calculated by adding the expenses from a year’s worth of orienta- tion classes and dividing it by the total number of people oriented.

10 scheduled courses for 510 new employees/year 7 months $3,365 (45 participants) = $23,555 3 months $7,065 (65 participants) = $21,195 10 months (510 participants) = $44,750

Total cost $44,750/510 = $87.74/participant 12 scheduled courses for 510 new employees/year

12 months $3,365 (G45 participants) = $40,380

Total cost $40,380 / 510 = $79.17/participant

BenefitYcost ratio. If offering orientation 12 times per year improves the socialization, confidence, and compe- tence of new nurses resulting in two fewer nurses leaving before their first anniversary, a savings of $73,134 ($36,567 2) is realized. On the basis of 12 offerings, the benefitYcost ratio is positive and reflects positive organiza- tional impact.

Calculation of the benefitYcost ratio: 12 offerings per year

$73,134 = 1.81 BCR

$40,380 Cost-effectiveness analysis. This proposal indicates a cost

of $79.17/participant for the planned 12 offerings, which is less than $87.74 /participant for 10 offerings with three large groups. If the pattern seen from large orientation classes is a higher turnover rate by first year anniversary of employ- ment, improving socialization to the institution through more personal contact in the first weeks of orientation

Journal for Nurses in Professional Development

should improve retention. With CEA, the outcomes of both interventions (10 offerings and 12 offerings) must be com- pared. A conservative cost of turnover is $36,567 per employee (Robert Wood Johnson Foundation, 2010). See Table 1 for further published costs of outcomes including new RN orientation cost.

Return on investment. The ROI for offering 12 orienta- tions per year is calculated by using the average cost for replacing two RNs of $73,134 and the cost of 12 months of offering the orientation at $40,380 in the ROI formula. The result is an ROI of 81.11%.

Return on investment: 12 offerings

$73,134 j $40,380 100 = 81.11% ROI 40,380

Recommendation. By combining the calculations for cost analysis, benefitYcost ratio, CEA, and ROI, strong sup- port for increasing the frequency of the offerings of orientation is noted. The decrease in cost per participant from $87.74 to $79.17 is a financial argument, yet when the ROI of reducing turnover is considered, it becomes a strong recommendation. Smaller cohorts allow more small group exercises to be incorporated and require fewer skills stations. Smoother centralized orientation, offered at closer intervals, should improve the new employee experience and contribute to satisfaction and retention.

This example was conservative on the benefit calcula- tion both in averaging the cost of replacement and in estimating the number of retained staff after 1 year as a re- sult of this educational intervention. The recommendation to reduce the cohort size and increase the frequency of of- ferings from 10 to 12 times per year is based on the financial and clinical impact as manifested in the better outcome of higher retention and less cost per participant.

IMPLICATIONS FOR FUTURE RESEARCH

No consistent method is routinely reported in the literature to describe the financial and clinical impact of professional development activities. Researchers and NPD practitioners reporting on educational program evaluations must regu- larly calculate financial impact when disseminating and publishing results. This evidence can be used to guide de- cisions for limited resources and to better position NPD as integral in the decision-making process in healthcare organizations.

CONCLUSIONS

NPD practitioners must measure the impact of education interventions to demonstrate the success of professional development activities. One seldom addressed aspect is the financial impact measurement. The two articles in this series show how routine approaches have been used (e.g., Kirkpatrick’s Levels of Evaluation, Phillips’ Five-Level ROI Framework, and Paramoure’s Measurable Instructional

www.jnpdonline.com 183

Design) to measure ROI in professional development. Critical appraisal of the literature, both quantitative and qualitative, revealed the importance of reporting more than participant satisfaction.

Four methods for evaluating the financial impact of educational activities were reviewed, including cost analy- sis, benefitYcost ratio, CEA, and ROI; plus examples were given using these methods. More consistent measuring and reporting of the financial and clinical impact of NPD activ- ities is warranted.

The NPD practitioners must proactively demonstrate the value of educational programs. During lean economic times, participant attendance and satisfaction are not adequate metrics to convince leaders of the organizational value of educational activities.

References

Agency for Healthcare Research and Quality. (2014a). Interim update on 2013 annual hospital-acquired condition rate and estimates of cost savings and deaths averted from 2010 to 2013. Retrieved from http://www.ahrq.gov/sites/default/files/wysiwyg/professionals/ quality-patient-safety/pfp/interimhacrate2013.pdf

Agency for Healthcare Research and Quality. (2014b). The concen- tration of health care expenditures and related expenses for costly medical conditions, 2012 (Agency for Healthcare Research & Quality Medical Expenditure Panel Survey Statistical Brief #455). Retrieved from http://meps.ahrq.gov/mepsweb/data_files/ publications/st455/stat455.pdf

American Faculty Association. (2012, February 8). Hours for teaching and preparation rule of thumb: 2Y4 hours of prep for 1 hour of class. Retrieved from http://americanfacultyassociation.blogspot. com/2012/02/hours-for-teaching-and-preparation-rule.html

ANA’s Handle With Care Program (2016). Safe patient handling and mobility. Retrieved from http://www.nursingworld.org/handle withcare

Bardach, N. S., Coker, T. R., Zima, B. T., Murphy, J. M., Knapp, P., Richardson, L. P., I Mangione-Smith, R., (2014). Common and costly hospitalizations for pediatric mental health disorders. Pediatrics, 133(4), 602Y609.

Chong, V. E., Smith, R., Garcia, A., Lee, W. S., Ashley, L., Marks, A., I Victorino, G. P. (2015). Hospital-centered hospital violence intervention programs: A cost-effectiveness analysis. American Journal of Surgery, 209, 597Y603.

DeSilets, L. D. (2010). Calculating the financial return on educational programs. Journal of Continuing Education in Nursing, 41(4), 149Y150.

Duff, J., Walker, K., Omari, A., & Stratton, C. (2013). Prevention of venous thromboembolism in hospitalized patients: Analysis of reduced cost and improved clinical outcomes. Journal of Vascular Nursing, 31(1), 9Y14.

Dychter, S., Gold, D., & Haller, M. (2012). Subcutaneous drug delivery. The Art and Science of Infusion Nursing, 35(3), 154Y160.

Fletcher, P. C., Markoulakis, R., & Bryden, P. J. (2012). The cost of caring for a child with autism spectrum disorder. Issues in Comprehensive Pediatric Nursing, 35, 45Y69.

Hollenbeak, C. (2011). The cost of catheter-related bloodstream infections. The Art and Science of Infusion Nursing, 1(5), 309Y313.

Jegler, B. J., Johnson, T. J., Engstrom, J. L., Patel, A. L., Loera, F., & Meier, P. (2013). The institutional cost of acquiring 100 mL of human milk for very low birth weight infants in the neonatal intensive care unit. Journal of Human Lactation, 29(3), 390Y399.

Jena, A. B., Sun, E. C., & Prasad, V. (2014). Does the declining lethality of gunshot injuries mask a rising epidemic of gun violence in the United States? Journal of General Internal Medicine, 29(7), 1065Y1069.

Juillard, C., Smith, R., Anaya, N., Garcia, A., Kahn, J. G., & Dicker, R. A. (2015). Saving lives and saving money: Hospital-based violence intervention is cost-effective. The Journal Trauma Acute Care Surgery, 78(2), 252Y257.

Kang, J., Mandsager, P., Biddle, A. K., & Weber, D. J. (2012). Cost- effectiveness analysis of active surveillance screening for methicillin-resistant staphylococcus aureus in an academic hospital setting. Infection Control and Hospital Epidemiology, 33(5), 477Y486.

Kapp, K., & Defelice, R. (2009). Time to develop one hour of training. Retrieved from https://www.td.org/Publications/Newsletters/ Learning-Circuits/Learning-Circuits-Archives/2009/08/Time-to- Develop-One-Hour-of-Training

Kettner, P., Moroney, R., & Martin, L. (2013). Designing and managing programs: An effectiveness-based approach. Washington, DC: Sage Publications, Inc.

Kuntz, J. L., Johnson, E. S., Raebel, M. A., Petrik, A. F., Yang, X., Thorp, M. L., I Smith, D. H. (2012). Epidemiology and health- care costs of incident clostridium difficile infections identified in the outpatient healthcare setting. Infection Control and Hos- pital Epidemiology, 33(10), 1031Y1038.

Opperman, C., Liebig, D., Bowling, J., Johnson, C., & Harper, M. (2016). Measuring return on investment for professional development activities: A review of the literature. Journal for Nurses in Professional Development, 32(3), 122Y129.

OSHA Safety Pays Program Estimator. (2016). Estimated costs of occupational injuries and illnesses and estimated impact on a company’s profitability worksheet. Retrieved from https://www.osha.gov/dcsp/smallbusiness/safetypays/ estimator.html

Robert Wood Johnson Foundation. (2010). Wisdom at work: Retaining experienced nurses. Retrieved from http://www.rwjf. org/en/library/research/2010/07/wisdom-at-work–retaining- experienced-nurses.html

Roe, E., & Williams, D. L. (2014). Using evidence-based practice to prevent hospital acquired pressure ulcers and promote wound healing. The American Journal of Nursing, 114(8), 61Y65.

Schifalacqua, M. M., Mamula, J., & Mason, A. R. (2011). Return on invest- ment imperative. Nursing Administration Quarterly, 35(1), 15Y20. Trepanier, S., Early, S., Ulrich, B., & Cherry, B. (2012). New graduate nurse residency program: A costYbenefit analysis based on turnover and contract labor usage. Nursing Economic$, 30(4), 207Y214. Trepanier, S., & Hilsenbeck, J. (2014). A hospital system approach at decreasing falls with injuries and cost. Nursing Economic$, 32(3),

135Y141. Tsimicalis, A., Stevens, B., Ungar, W. J., Greenberg, M., McKeever, P.,

Agha, M., I Moineddin, R. (2013). Determining the costs of families’ support networks following a child’s cancer diagnosis. Cancer Nursing, 36(2), E8YE19.

Warren, J. I. (2013). Program evaluation and return on investment. In Bruce, S. L. (Ed.), Core curriculum for nursing professional development (4th ed. pp. 547Y68). Chicago, IL: Association for Nursing Professional Development.

Provider perspectives on the utility of a colorectal cancer screening decision aid for facilitating shared decision making

Paul C. Schroy III MD MPH,* Shamini Mylvaganam MPH and Peter Davidson MDà

*Director of Clinical Research, Section of Gastroenterology, Boston Medical Center, Boston, MA, Study Coordinator, Section of Gastroenterology, Boston Medical Center, Boston, MA and àClinical Director, Section of General Internal Medicine, Boston Medical Center, Boston, MA, USA

Correspondence

Paul C. Schroy III, MD MPH Boston Medical Center 85 E. Concord Street Suite 7715

Boston MA 02118 USA E-mail: paul.schroy@bmc.org

Accepted for publication

8 August 2011

Keywords: decision aids, informed decision making, shared decision making

Abstract Background Decision aids for colorectal cancer (CRC) screening

have been shown to enable patients to identify a preferred screening option, but the extent to which such tools facilitate shared decision making (SDM) from the perspective of the provider is less well established.

Objective Our goal was to elicit provider feedback regarding the impact of a CRC screening decision aid on SDM in the primary care setting.

Methods Cross-sectional survey.

Participants Primary care providers participating in a clinical trial evaluating the impact of a novel CRC screening decision aid on SDM and adherence.

Main outcomes Perceptions of the impact of the tool on decision- making and implementation issues.

Results Twenty-nine of 42 (71%) eligible providers responded, including 27 internists and two nurse practitioners. The majority (>60%) felt that use of the tool complimented their usual approach, increased patient knowledge, helped patients identify a preferred screening option, improved the quality of decision making, saved time and increased patientsÕ desire to get screened. Respondents were more neutral is their assessment of whether the tool improved the overall quality of the patient visit or patient satisfaction. Fewer than 50% felt that the tool would be easy to implement into their practices or that it would be widely used by their colleagues.

Conclusion Decision aids for CRC screening can improve the quality and efficiency of SDM from the provider perspective but future use is likely to depend on the extent to which barriers to implementation can be addressed.

Ó 2011 John Wiley & Sons Ltd Health Expectations, 17, pp.27–35

doi: 10.1111/j.1369-7625.2011.00730.x

28 Colorectal cancer screening decision aid, P C Schroy, S Mylvaganam and P Davidson

Introduction

Engaging patients to participate in the decision- making process when confronted with prefer- ence-sensitive choices related to cancer screening or treatment is fundamental to the concept of patient-centred care endorsed by the Institute of Medicine, US Preventive Services Task Force and the Centers for Disease Control and Pre- vention.1–3 Ideally, this process should occur within the context of shared decision making (SDM), whereby patients and their health-care providers form a partnership to exchange information, clarify values and negotiate a mutually agreeable medical decision.4,5 SDM, however, has been difficult to implement into routine clinical practice in part owing to lack of time, resources, clinician expertise and suitabil- ity for certain patients or clinical situations.6,7 The use of patient-oriented decision aids outside of the context of the provider–patient interac- tion has been proposed as a potentially effective strategy for circumventing several of these bar- riers.3,8 Decision aids are distinct from patient education programmes in that they serve as tools to enable patients to make an informed, value-concordant choice about a particular course of action based on an understanding of potential benefits, risks, probabilities and sci- entific uncertainty.9–11 Besides facilitating informed decision making (IDM), decision aids also have the potential to facilitate SDM by improving the quality and efficiency of the patient–provider encounter and by empowering users to participate in the decision-making process.11 Studies to date have demonstrated that while decision aids enhance knowledge, reduce decisional conflict, increase involvement in the decision-making process and lead to informed value-based decisions, their impact on the quality of the decision, satisfaction with the decision making process and health outcomes remains unclear.11

Besides enabling patients to make informed choices, decision aids also have the potential to facilitate SDM by improving the quality and efficiency of the patient–provider encounter. Relatively few studies have examined the utility

of decision aids for promoting effective SDM from the perspective of the provider. Studies to date have largely focused on provider perspec- tives on the quality of the decision tools themselves or issues related to implementation into clinical practice.11–15 The overall objective of this study was to elicit provider feedback regarding the extent to which the use of a novel colorectal cancer (CRC) screening decision aid facilitated SDM in the primary care setting within the context of a randomized clinical trial.

Methods

Brief overview of decision aid and randomized clinical trial

Details of the decision aid, recruitment process, study design and secondary outcome results have been previously published.16 The overall objective of the trial was to evaluate the impact of a novel computer-based decision aid on SDM and patient adherence to CRC screening rec- ommendations. The decision aid uses video- taped narratives and state-of-the-art graphics in digital video disc (DVD) format to convey key information about CRC and the importance of screening, compare each of five recommended screening options using both attribute- and option-based approaches, and elicit patient preferences. A modified version of the tool also incorporated the web-based ÔYour Disease Risk (YDR)Õ CRC risk assessment tool (http:// www.yourdiseaserisk.wustl.edu). To assess its impact on SDM and screening adherence, average-risk, English-speaking patients 50– 75 years of age due for CRC screening were randomized to one of the two intervention arms (decision aid plus the YDR personalized risk assessment tool with feedback or decision aid alone) or a control arm, each of which involved an interactive computer session just prior to a scheduled visit with their primary care provider at either the Boston Medical Center or the South Boston Community Health Center. After completing the computer session, patients met with their providers to discuss screening and

Ó 2011 John Wiley & Sons Ltd Health Expectations, 17, pp.27–35

identify a preferred screening strategy. Although providers were blinded to their patientsÕ ran- domization status, they received written notifi- cation in the form of a hand-delivered flyer from all study patients acknowledging that they were participating in the ÔCRC decision aid studyÕ to ensure that screening was discussed. Outcomes of interest were assessed using pre ⁄ post-tests, electronic medical record and administrative databases. The study to date has found that the tool enables users to identify a preferred screening option based on the relative values they place on individual test features, increases knowledge about CRC screening, increases sat- isfaction with the decision-making process and increases screening intentions compared to non- users. The study also finds that screening intentions and test ordering are negatively influenced in situations where patient and pro- vider preferences differ. The toolÕs impact on patient adherence awaits more complete follow- up data, which should be available in early 2011.

Study design

We conducted a cross-sectional survey of primary care providers participating in the ran- domized clinical trial in January and February of 2009. At the time of the survey, 725 eligible patients had been randomized to one of the three study arms. The surveys were distributed just prior to monthly business meetings conducted by the Sections of General Internal Medicine and WomenÕs Health at Boston Medical Center and Adult Medicine at the South Boston Com- munity Health Center. Respondents were asked to sign an attestation sheet if they completed the survey to identify providers not in attendance. For those who were not in attendance, the sur- vey was distributed electronically as an email attachment; respondents were asked to return the survey via facsimile to preserve anonymity. Two email reminders with attached surveys were sent 2 weeks apart after the initial email to optimize response. The study was deemed exempt by the Institutional Review Boards at both participating institutions.

Ó 2011 John Wiley & Sons Ltd Health Expectations, 17, pp.27–35

Subjects

The survey sample included board-certified primary care providers (general internists and nurse practitioners) at Boston Medical Center and the South Boston Community Health Center who had referred patients to the randomized clinical trial. Of the 50 providers who had referred patients to the study since its commencement in 2005, 42 were still practicing at the participating sites at the time of the survey. All had exposure to at least one patient in an intervention arm and at least one patient in the control arm; all but two of the targeted providers had multiple patients in each arm. None of the participants had formally reviewed the content of the decision aid nor received special training in SDM.

Practice settings

The Boston Medical Center is a private, non-profit academic medical centre affiliated with the Boston University School of Medicine, which serves a mostly minority patient population (only 28% White, non-Hispanic). The South Boston Com- munity Health Center is a community health centre affiliated with BMC, which serves a mostly White, non-Hispanic, low-income patient population.

Survey instrument

The survey instrument included a cover letter, 23 closed-ended questions and two open-ended questions. Much of the content was derived from instruments used in previously published studies by Holmes-Rovner et al. and Graham et al.6,15 The cover letter briefly described the purpose of the study, a statement that participation was completely voluntary, the approximate amount of time required to complete the survey, and a statement that all responses are anonymous and confidential. The closed-ended questions include one item related to eligibility [confirmation of participation in the clinical trial (yes ⁄ no)], two items related to demographics (provider degree and year of graduation), 12 items related to perspectives on the impact of the tool on various patient and provider components of SDM for

Colorectal cancer screening decision aid, P C Schroy, S Mylvaganam and P Davidson 29

30 Colorectal cancer screening decision aid, P C Schroy, S Mylvaganam and P Davidson

CRC screening (see Table 1), and eight items related to perspectives on implementation or content modification (see Tables 2 and 3). The framing of the questions inferred a comparison between patients exposed to the decision aid and those not exposed, i.e., standard care patients, regardless of their involvement in the study. All of the items related to SDM used a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Six of the items related to implementation or content modification also used the same 5-point Likert scale, and two used a single best answer format. The two open-ended questions inquired about suggestions for improving the decision aid and complaints. The questionnaire took 10 min to complete.

Statistical analyses

Descriptive statistics were used to characterize the study population and response data for all closed-ended questions. Frequency data for the 5-point Likert scale items were collapsed into three categories: Ôagreed ⁄ strongly agreedÕ, Ôneu-

tralÕ and Ôdisagreed ⁄ strongly disagreedÕ. Mean response scores ± standard deviations were also calculated for the same data using Micro- soft Excel functions. Responses to open-ended questions were summarized according to themes.

Results

Study population

In total, 29 of the 42 (71%) possible providers, including 27 physicians and two nurse practitio- ners, responded to the survey and acknowledged that they had referred patients to the randomized clinical trial. Of the 29 respondents, 4 (14%) had received their degrees between 2000 and 2009, 15 (52%) between 1990 and 1999, and 6 (28%) before 1990; two declined to answer the question.

Perspectives on SDM

As shown in Table 2, the majority of providers (>60%) agreed or strongly agreed that the decision aid complemented their usual approach

Table 1 Provider perspectives on the utility of the decision aid for facilitating SDM Response category, n (%)

From my clinical perspective, the decision aid

4. Complemented my usual approach to CRC screening

5. Improved my usual approach to CRC screening

6. Helped me tailor my counselling about CRC

screening to my patientÕs needs

7. Saved me time

8. Improved the quality of patient visits

9. Increased my patientsÕ satisfaction with my care

10. Is an appropriate use of my patientÕs clinic time

11. Increase patient knowledge about the different

CRC screening options

12. Helped patients understand the benefits ⁄ risks

of the recommended screening options

13. Helped patients in identifying preferred

screening option

14. Improved the quality of the decision making

15. Increased patientsÕ desire to get screened

Strongly agree ⁄ agree

24 (86) 16 (59) 12 (44)

18 (64) 14 (52) 10 (40) 27 (93) 26 (90)

24 (83) 21 (72)

22 (79) 21 (75)

Neutral

4 (14)

8 (30) 11 (41)

6 (21)

9 (33) 13 (52)

1 (3) 3 (10)

5 (17) 7 (24)

6 (21) 5 (18)

Strongly disagree ⁄ disagree

0 3 (11) 4 (15)

4 (14) 4 (15) 2 (8) 1 (3) 0

1 (3) 0

2 (7)

Mean item score (SD)*

4.3 ± 0.7 3.7 ± 1.0 3.5 ± 1.0

3.8 ± 1.0 3.6 ± 1.0 3.4 ± 0.8 4.1 ± 0.6 4.3 ± 0.6

4.1 ± 0.7 4.0 ± 0.8

4.0 ± 0.7 3.9 ± 0.9

CRC, colorectal cancer; SD, standard deviation; SDM, shared decision making. *1 = strongly disagree; 5 = strongly agree.

Ó 2011 John Wiley & Sons Ltd Health Expectations, 17, pp.27–35

Colorectal cancer screening decision aid, P C Schroy, S Mylvaganam and P Davidson 31 Table 2 Provider perspectives on decision aid implementation

Response category, n (%) Strongly

Strongly disagree ⁄ disagree

4 (16) 4 (17)

4 (15) 8 (31) 2 (7) 4 (15)

Mean item score (SD)*

3.4 ± 1.0 3.6 ± 1.1

3.4 ± 0.9 3.5 ± 1.2 3.7 ± 0.9 3.6 ± 0.9

The decision aid

16. Would be easy to use in my practice outside of a research stetting

17. Use would require reorganization of my practice for routine clinical use

18. Is likely to be used by most of my colleagues

19. Should include a discussion of costs

20. Should be disseminated as an Internet-based tool

21. Should be disseminated as a DVD-based tool

DVD, digital video disc; SD, standard deviation. *1 = strongly disagree; 5 = strongly agree.

Table 3 Preferences for clinical use and content modification

agree ⁄ agree

12 (48) 14 (58)

11 (41) 13 (50) 17 (63) 15 (56)

Neutral

9 (36)

6 (25) 12 (44)

5 (19) 8 (30) 8 (30)

Item

22. When would you want your patient to view the decision aid:

Before initiating CRC screening discussion (pre-visit)

After initiating CRC discussion (post-visit)

Both 23. Would you prefer the decision aid to

contain information about: All of the recommended screening options A more restricted list of options No opinion

CRC, colorectal cancer.

N (%)

21 (72) 6 (21)

2 (7)

15 (52) 12 (41) 2 (7)

Perspectives on clinical use and content modification

There was less consensus when asked about implementation of the tool into routine clinical practice. As shown in Table2, <50% of respondents agreed or strongly agreed that the decision aid would be easy to use in their prac- tice outside of a research setting or that it would be used by most of their colleagues. A slim majority (58%) also believed that implementa- tion would require reorganization of their practice. Respondents mostly agreed or were neutral in their assessment of whether the deci- sion aid should be disseminated as an Internet- or DVD-based tool. When asked to identify a preferred time for having their patients review the tool (Table 3), 72% chose prior to initiating the CRC screening discussion, 21% chose after initiating the screening discussion, and 7% chose both. Among the 21 providers who chose the pre-visit approach, 13 preferred that the tool be used in the office just prior to the pre-arranged visit, five preferred at home use and three pre- ferred both; among the six providers who chose the post-visit approach, five preferred in-office use and one preferred at home use.

There was also a lack of consensus when asked about content modification. Whereas 50% of respondents agreed or strongly agreed that the decision aid should include a discussion of costs, 31% disagreed or strongly disagreed

to CRC screening, was an appropriate use of their patientÕs clinic time, saved them time, increased patient knowledge about the various CRC screening options and their risks and benefits, helped the patients identify a preferred screening option, improved the quality of deci- sion making, and increased their patientsÕ desire to get screened. Providers were more neutral in their assessment of the decision aidÕs utility for improving their usual approach to CRC screening, helping them tailor their counselling style to their patientsÕ needs, improving the quality of patient visits, and increasing patient satisfaction with their care. Relatively few pro- viders disagreed or strongly disagreed with any of these measures.

Ó 2011 John Wiley & Sons Ltd Health Expectations, 17, pp.27–35

32 Colorectal cancer screening decision aid, P C Schroy, S Mylvaganam and P Davidson

(Table 2). Similarly, whereas 52% of providers preferred that the decision aid include a discus- sion of all of the recommended screening options, 41% preferred a more restricted list of options and 7% had no opinion on the issue (Table 3).

Only seven providers made suggestions for improving the current decision aid. These included creating non-English versions of the tool (n = 2), clearly distinguishing colonoscopy as the best screening option (n = 2), enabling patients to print out their preferred screening option (n = 2), and taking into consideration that patients may not have access to the Internet at home if the decision aid was to be dissemi- nated as a web-based tool (n = 1). There were no complaints.

Discussion

Decision aids are evidence-based tools that enable patients to make informed, value-con- cordant choices, but the extent to which such tools facilitate SDM from the perspective of the provider is less well established. In an effort to gain new insight into the issue, we conducted a survey of primary care providers participating in a clinical trial evaluating the impact of a novel, DVD-formatted decision aid on SDM and adherence to CRC screening. Our study finds that a majority of providers perceived that the tool was a useful, time-saving adjunct to their usual approach to counselling about CRC screening and increased the overall quality of decision making. Moreover, providers also felt that review of the tool just prior to a scheduled office visit was an appropriate use of patientÕs time as it enabled the patient to make an informed choice among the different screening options. Together, these findings suggest that much of the toolÕs perceived utility was related to its ability to better prepare patients for the screening discussion outside of the clinical encounter and, in so doing, increased both the efficiency and quality of the interaction.

Few studies have explored provider perspec- tives on the utility of decision aids for improving SDM. A trial by Green et al. evaluating the

effectiveness of genetic counselling vs. counsel- ling preceded by use of a computer-based deci- sion aid for breast cancer susceptibility found that although there were no significant differ- ences in perceived effectiveness, use of the tool saved time and shifted the focus away from basic education towards a discussion of personal risk and decision making.17 A second study by Sim- inoff et al. found that a decision aid for breast cancer adjuvant therapy facilitated a more interactive, informed discussion and helped physicians understand patient preferences.13 Similarly, Brackett et al. also found that pre- visit use of decision aids for prostate and CRC screening was associated with greater physician satisfaction, as it saved time during the visit and changed the conversation from one of the informational exchanges to one of the values and preferences.18 A fourth study by Graham et al. explored provider perceptions of three decision aids prior to their actual use.15 Although responses were based on perceptions alone and not on clinical experience, their find- ings were similar to our own. A majority agreed or strongly agreed that the decision aids could meet patientsÕ informational needs about risks and benefits and enable patients to make informed decisions. Similarly, although many felt that the decision aids were likely to com- plement their usual approach, responses were more neutral when asked about the overall impact of the tools on the quality of the patient encounter, patient satisfaction and issues related to implementation. The most striking difference, however, was that relatively few of the respon- dents in the study by Graham et al. felt that use of the tool saved time, which could be a reflec- tion of either the complexity of the decisions under consideration and ⁄ or the lack of explicit instructions regarding how the tools were to be used with respect to the timing of the interven- tion and ⁄ or need for provider involvement.

Our findings also corroborate a more exten- sive body of literature on barriers to the imple- mentation of decision aids into clinical practice.14 Even though our study design cir- cumvented many of the barriers related to workflow, accessibility and costs, only 48% of

Ó 2011 John Wiley & Sons Ltd Health Expectations, 17, pp.27–35

providers felt that actual implementation of the decision aids into their practices outside of the context of a clinical trial would be easy. Based on their feedback, however, most preferred that the tool be used prior to initiating the screening discussion rather than after initiation of the discussion. Moreover, regardless of the timing, a majority preferred that the tool be used in the office rather than at home. Although it is quite possible that their preferences reflected their personal experiences with our study protocol, Brackett et al. also found that pre-visit use was preferred over post-visit use.18

One of the most commonly cited barriers to implementation of SDM is the time requirement. Although studies to date have provided con- flicting data regarding the impact of decision aids on consultation time for other condi- tions,17–22 we postulated that by educating patients about the risks and benefits of the dif- ferent screening options and facilitating IDM prior to the provider–patient encounter, our decision aid would have the potential of improving the efficiency of SDM and thus save time, as noted by Green et al. and Brackett et al.17,18 We found that although a majority of providers agreed or strongly agreed that pre-visit use of the tool saved time, 21% were neutral on the issue and 14% disagreed or strongly dis- agreed. It is conceivable that this diversity of opinion might be a reflection of the extent to which provider and patient preferences agreed or disagreed. In instances where there was concor- dance between preferences, as was often the case that since colonoscopy was preferred by major- ity of both patients and providers,16 one would expect that the time required for deliberation and negotiation would be substantially shorter than in situations where there was discordance. Alternatively, these differences might reflect differences in case mix with respect to patient factors, such as literacy level or desired level of participation in the decision-making process.

A secondary objective of our study was to elicit provider feedback regarding content and format preferences to gain insight into potential modifications that might enhance future uptake. Because of an ongoing debate in the CRC

Ó 2011 John Wiley & Sons Ltd Health Expectations, 17, pp.27–35

screening literature,23–27 we focused on content issues related to cost information and number of screening options to include in the decision aid. Both questions elicited a divergence of opinions. Whereas nearly 50% of respondents felt that cost information should be included, the remainder was either neutral or opposed to its inclusion. Similarly, when asked about the number of screening options to include, 50% preferred the full menu of options and 40% preferred a more limited menu. This diversity of opinion highlights some of the key challenges in designing tools with broad dissemination potential. In the light of recent evidence sug- gesting that the number of screening options may influence test choice but not interest in screening and that the importance of out-of- pocket costs declines as the number of screening options discussed increases,26 one approach would be to develop one tool that presents the full menu of screening options without cost information and a second that includes a more limited set of options with cost information. A more appealing approach would be to develop a more comprehensive tool that includes both the full menu of options and cost information in a format that permits navigation so that patients could tailor their use to fit their own informa- tional needs and ⁄ or recommendations of their provider. Internet-based tools are ideally suited for this purpose but, as noted by several par- ticipants in our study, access remains a potential barrier for a sizeable, albeit declining, propor- tion of the target population. Providers in our study felt that both Internet- and DVD-for- matted tools were viable options for dissemina- tion, even though the DVD-formatted tool offers less navigation potential.

Our study has several notable limitations. First, the survey was conducted among primary care providers at only two institutions, and hence, the findings may not be generalizable to providers in other health care settings. It is noteworthy, however, that the study was con- ducted among a diverse patient population with respect to both race ⁄ ethnicity and educational status.16 Second, as participating providers never formally reviewed the decision aid, we

Colorectal cancer screening decision aid, P C Schroy, S Mylvaganam and P Davidson 33

34 Colorectal cancer screening decision aid, P C Schroy, S Mylvaganam and P Davidson

were unable to assess their opinions with respect to actual content or format. Third, the content of our survey instrument did not allow us to tease out the extent to which use of the decision aid impacted on individual steps of the SDM process.4,5 Even though satisfaction with the decision-making process was universally high among patients participating in the clinical trial,16 especially those in the intervention groups, only a relative minority of providers felt that use of the tool helped them tailor their counselling about CRC screening to their patientsÕ needs or increased patient satisfaction with their care. Fourth, the anonymous nature of our survey precluded any attempt to correlate response data with exposure rates. It is con- ceivable that the perceptions of providers exposed to multiple patients in the intervention arms might differ from those exposed to only a few patients. Lastly, we cannot rule out the possibility of social response bias, whereby respondents may have felt compelled to offer more positive responses than they actually believed.

In conclusion, our study finds that a majority of providers perceived that pre-clinic use of our decision aid for CRC screening was a useful, time-saving adjunct to their usual approach to counselling about CRC screening and increased the overall quality of decision making. Never- theless, many of the providers felt that imple- mentation of the decision aid into their practices outside of the context of a clinical trial would be challenging, thus highlighting the need for cost- effective strategies for addressing provider, practice and organizational level barriers to routine use. We speculate that Internet-based tools with enhanced navigation functionality have the greatest dissemination potential, as they offer a feasible, low-cost solution to many of the structural barriers to implementation, as well as a way to reconcile the diversity of opin- ion related to content.

Acknowledgement

None.

Conflicts of interest

The authors have no conflict of interests.

Funding

This study was supported by grant RO1 HS013912 from the Agency for Healthcare Research and Quality.

References

1 Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press, 2001.

2 Sheridan SL, Harris RP, Woolf SH. Shared decision making about screening and chemoprevention. A suggested approach from the U.S. Preventive Services Task Force. American Journal of Preventive Medicine, 2004; 26: 56–66.

3 Briss P, Rimer B, Reilley B et al. Promoting informed decisions about cancer screening in communities and healthcare systems. American Journal of Preventive Medicine, 2004; 26: 67–80.

4 Charles C, Gafni A, Whelan T. Shared decision- making in the medical encounter: what does it mean? (or it takes at least two to tango). Social Science and Medicine, 1997; 44: 681–692.

5 Charles C, Gafni A, Whelan T. Decision-making in the physician-patient encounter: revisiting the shared treatment decision-making model. Social Science and Medicine, 1999; 49: 651–661.

6 Holmes-Rovner M, Valade D, Orlowski C, Draus C, Nabozny-Valerio B, Keiser S. Implementing shared decision-making in routine practice: barriers and opportunities. Health Expectations, 2000; 3: 182–191.

7 Legare F, Ratte S, Gravel K, Graham ID. Barriers and facilitators to implementing shared decision- making in clinical practice: update of a systematic review of health professionalsÕ perceptions. Patient Education and Counseling, 2008; 73: 526–535.

8 Rimer BK, Briss PA, Zeller PK, Chan EC, Woolf SH. Informed decision making: what is its role in cancer screening? Cancer, 2004; 101: 1214–1228.

9 International Patient Decision Aid Standards (IPDAS) Collaboration. IPDAS Collaboration Background Document, 2005. Available at: http:// ipdas.ohri.ca/IPDAS_Background.pdf, accessed 26 October 2010.

10 Elwyn G, OÕConnor A, Stacey D et al. Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. BMJ, 2006; 333: 417.

Ó 2011 John Wiley & Sons Ltd Health Expectations, 17, pp.27–35

11. 11 OÕConnor AM, Bennett CL, Stacey D et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database of Systematic Reviews, 2009; 3: CD001431.

12. 12 OÕConnor AM, Llewellyn-Thomas HA, Sawka C, Pinfold SP, To T, Harrison DE. PhysiciansÕ opinions about decision aids for patients considering systemic adjuvant therapy for axillary-node negative breast cancer. Patient Education and Counseling, 1997; 30: 143–153.

13. 13 Siminoff LA, Gordon NH, Silverman P, Budd T, Ravdin PM. A decision aid to assist in adjuvant therapy choices for breast cancer. Psycho-Oncology, 2006; 15: 1001–1013.

14. 14 OÕDonnell S, Cranney A, Jacobsen MJ, Graham ID, OÕConnor AM, Tugwell P. Understanding and over- coming the barriers of implementing patient decision aids in clinical practice. Journal of Evaluation in Clinical Practice, 2006; 12: 174–181.

15. 15 Graham ID, Logan J, Bennett CL et al. PhysiciansÕ intentions and use of three patient decision aids. BMC Medical Informatics and Decision Making, 2007; 7: 20.

16. 16 Schroy PC III, Emmons K, Peters E et al. The impact of a novel computer-based decision aid on shared decision making for colorectal cancer screening: a randomized trial. Medical Decision Making, 2011; 31: 93–107.

17. 17 Green MJ, Peterson SK, Baker MW et al. Use of an educational computer program before genetic coun- seling for breast cancer susceptibility: effects on duration and content of counseling sessions. Genetics in Medicine, 2005; 7: 221–229.

18. 18 Brackett C, Kearing S, Cochran N, Tosteson AN, Blair Brooks W. Strategies for distributing cancer screening decision aids in primary care. Patient Education and Counseling, 2010; 78: 166–168.

19. 19 Whelan T, Sawka C, Levine M et al. Helping patients make informed choices: a randomized trial of a

decision aid for adjuvant chemotherapy in lymph node-negative breast cancer. Journal of the National Cancer Institute, 2003; 95: 581–587.

20 Bekker HL, Hewison J, Thornton JG. Applying decision analysis to facilitate informed decision making about prenatal diagnosis for Down syn- drome: a randomised controlled trial. Prenatal Diag- nosis, 2004; 24: 265–275.

21 Butow P, Devine R, Boyer M, Pendlebury S, Jackson M, Tattersall MH. Cancer consultation preparation package: changing patients but not physicians is not enough. Journal of Clinical Oncology, 2004; 22: 4401– 4409.

22 Nannenga MR, Montori VM, Weymiller AJ et al. A treatment decision aid may increase patient trust in the diabetes specialist. The Statin Choice randomized trial. Health Expectations, 2009; 12: 38–44.

23 Leard LE, Savides TJ, Ganiats TG. Patient prefer- ences for colorectal cancer screening. Journal of Family Practice, 1997; 45: 211–218.

24 Pignone M, Bucholtz D, Harris R. Patient preferences for colon cancer screening. Journal of General Internal Medicine, 1999; 14: 432–437.

25 Lafata JE, Divine G, Moon C, Williams LK. Patient- physician colorectal cancer screening discussions and screening use. American Journal of Preventive Medicine, 2006; 31: 202–209.

26 Griffith JM, Lewis CL, Brenner AR, Pignone MP. The effect of offering different numbers of colorectal cancer screening test options in a decision aid: a pilot randomized trial. BMC Medical Informatics and Decision-Making, 2008; 8: 4.

27 Jones RM, Vernon SW, Woolf S. Is discussion of colorectal cancer screening options associated with heightened patient confusion? Cancer Epidemiology, Biomarkers and Prevention, 2010; 19: 2821–2825.

Ó 2011 John Wiley & Sons Ltd Health Expectations, 17, pp.27–35

"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"

Academic Writing Help

Quicl Links

Talk to Us