Featured Guest Co-Bloggers: Geri Lynn Baumblatt, M.A. and Nananda Col, M.D., M.P.H.
Excerpt from “The Value and Challenges of Shared Decision Making”, Dorland Review, February 2013
On the surface, Shared Decision Making (SDM) seems simple and straightforward. A clinician involves the patient in making a decision about their health. The clinician conveys the necessary medical information to the patient, explains their treatment options, and helps the patient understand how their values, goals and preferences can guide them to find the best treatment. And together they come to a preferred treatment plan. Though widely endorsed, SDM is uncommon in clinical practice.1
SDM is a process that goes beyond “informed decision making,” which is sometimes erroneously used interchangeably with SDM. But shared decision making is not simply about whether the patient has been informed and received enough information about their condition and its treatment, but a process where the provider helps the patient understand the decision, its consequences, and then helps them apply their values and preferences in order to make a decision.2
SDM is typically thought about in context of preference-sensitive decisions. That is, a decision where a patient faces two or more effective treatment options and the “best” treatment depends on how the patient values the different outcomes. However, SDM is relevant to many different types of decisions, and many argue that most health decisions are preference based. SDM has often been studied in the context of a one-time yes/no decision, for example, whether or not to get the Shingles (Zoster) vaccine. This likely reflects the simplicity of studying such decisions.
SDM is increasingly applied to more complex and higher-stakes decisions. Take for example, low-risk prostate cancer, where men have to choose between active surveillance, radiation, or radical prostatectomy. All treatment options have a similar impact on longevity, but the choice can have a drastic and often irreversible impact on many dimensions of a man’s quality of life. These can include incontinence, impotence, and rectal symptoms.3 Therefore, a man needs to understand the short- and long-term effects of these options, and be able to integrate this knowledge into his personal values and preferences. Is it important for him to know that the cancer is removed? Is he willing to risk permanent impotence in order to obtain this peace of mind?
SDM is also highly relevant to chronic disease management. Patients with conditions such as coronary artery disease, diabetes, or asthma are often faced with a plethora of treatment options that have similar effectiveness but different side effects and costs. Many treatments differ in their complexity or burden to the patients (e.g. taking multiple pills many times a day vs. once a day, or requiring injections). In these situations, SDM can help patients choose an initial treatment with a suitable risk/benefit profile for their particular circumstances.4 If the patient later experiences undesirable side effects, SDM can help with decisions about whether to continue the treatment or switch to a different one. It can also help with decisions about whether to intensify treatment (e.g., by adding additional medications or increasing the dosage of current medications).5
Though SDM is typically thought about in context of choosing a treatment, it may also be relevant for treatment adherence, especially when adherence to the chosen treatment plan requires daily reaffirmation of a patient’s decision about treatment. With adherence to medications for common chronic disease running as low as 40 to 50 percent.6 increasing attention is given to exploring whether SDM can be used to promote adherence.
Other times patients face complex clinical situations where they need to understand the future trajectory of decisions, where one decision can set them up for a series of future decisions. For example, decisions about screening for prostate cancer using the Prostate Specific Antigen (PSA) test are complex because a positive PSA test results in a series of predictable but difficult subsequent decisions. A “decision map”, which depicts the known cascade of subsequent decisions a patient will later face, can be helpful (Figure 1). Consider early-stage breast cancer. Before a woman chooses between lumpectomy and mastectomy, she not only needs to understand the risks of both procedures, but if she chooses a mastectomy would she then prefer a prosthetic breast or reconstruction surgery? And would she opt for delayed or immediate reconstruction? And if she chooses reconstruction, she will also need to choose between a breast implant versus tissue flaps. And all of those choices and consequences emerge from and are shaped by her initial decision between mastectomy and lumpectomy.
Figure 1: Example of a decision map from Treatment Options for Early-stage Invasive Breast Cancer EmmiDecide® Program. Courtesy Emmi Solutions, LLC.
In this example, to choose the ‘right’ treatment, the woman needs to consider how she might feel wearing a prosthesis, how it would impact activities like swimming, and her relationship with her partner. These preferences are uniquely personal and not typically elicited as part of a standard clinical assessment.
If the patient’s doctor is not aware of how a patient feels about these matters, a treatment might be selected based on criteria that are less important to the patient. If the doctor assumed the patient wanted to preserve her breast, whereas the patient just wanted to deal with the problem once and for all and avoid the inconvenience of radiation therapy, the doctor might recommend a lumpectomy when a mastectomy might be better for that patient. Clinicians cannot predict each patient’s unique preferences; and this is one reason SDM is crucial. An inability to predict a patient’s unique preferences is not a failing on the part of the clinician. Every patient is different and the possible reasons patients and their families may opt for counter-intuitive or unexpected treatment decisions are impossible to predict. And clinicians should not try to predict this.
Research shows that when clinicians assume they know what patients prefer, they are often wrong. In one study7, physicians thought most women (71 percent) with breast cancer would rank conserving their breast as a top treatment goal. But when women were surveyed, only seven percent reported it was a top goal for them. And while no physicians in this study (0 percent) believed women would be very concerned about avoiding a prosthetic breast, 33 percent of patients placed great importance on this. As you can see, without SDM, the huge disconnect between clinician assumptions and reality would send many women down a treatment pathway that does not reflect their treatment goals and would potentially leave them with a greatly diminished quality of life. This mismatch is sometimes called a “preference misdiagnosis.”
To download a free PDF of the full article click here.
To purchase the February 2013 Special Report: Shared Decision Making: A Tool to Ensure Patient-Centered Care, go here: The Dorland Review.
About the Authors:
Geri Lynn Baumblatt, M.A. is the Editorial Director at Emmi Solutions, where she oversees the creation of multimedia patient engagement, education and shared decision making programs, including focus group testing. She is the editor of an annual Health Literacy Month blog series on Engaging the Patient. She’s also serves on an AHRQ Technical Expert Panel to improve the quality of patient education. Additionally, you can catch Geri speaking at both the IHA Health Literacy Conference and the Wisconsin Health Literacy Summit this year.
Nananda Col, M.D., M.P.H., M.P.P., F.A.C.P. is Professor of Medicine and Geriatrics at University of New England’s School of Osteopathic Medicine and Center of Excellence in Neuroscience. A medical internist and decision scientist, her primary interest is advancing the field of shared decision making to help patients make decisions that reflect their personal circumstances, risks, preferences, and values. She serves on the International Patient Decision Aid Standards (IPDAS) Collaboration, the Cochrane Collaboration Review of Patient Decision Aids, and the FDA’s Risk Communication Advisory Committee.
- Braddock, C.H. Edwards, K.A., Hasenberg, N.M., Laidley, T.L., Levinson, W. (1999). Informed Decision Making in Outpatient Practice: Time to Get Back to Basics. JAMA, 282,2313-2320.
- Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: What does it mean? Soc Sci Med. 1997:44:681-692.
- Ganz PA, Barry J.M., Burke W, Col N.F., et al. National Institutes of Health State-of-the-Science Conference: Role of Active Surveillance in the Management of Men With Localized Prostate Cancer. Annals Internal Medicine 2012 Vol. 156 Issue 8, p591-595.
- Mullan, R.J., Montori, V.M., Shah, N.D., Christianson, T.J.H., Bryant, S.C., et al. (2009). The Diabetes Mellitus Medication Choice Decision Aid: A Randomized Trial. Archives Internal Medicine, 169, 1560-1568.
- Huang, E. (2010). Personalized decision support for older patients with diabetes. Retrieved April 23, 2011, from http://www.diabetes.org/news-research/research/research-database/ personalized-decision-support-for-older-patients-with-diabetes.html.
- Caro J.J., Speckman J.L., Salas M., Raggio G., Jackson J.D. Effect on initial drug choice on persistence with antihypertensive therapy: the importance of actual practice data. Canadian Medical Association Journal 1999;160:41–46.
- Lee, Clara N., Dominik, R., Carrie A. Levin, C.A., Barry, M.J., Cosenza, C., O’Connor, A.M., Mulley, A.G. and Sepucha, K.R. (2010). Development of instruments to measure the quality of breast cancer treatment decisions. Health Expect, 13(3), 258‐72. doi: 10.1111/j.1369-7625.2010.00600.x