Jon Keevil from Health Decision will be speaking about working with EMRs to incorporate icon arrays for physician-patient communication.
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Funded by the Department of Veterans Affairs
Funding Years: 2007-2012
Prostate cancer is the second leading cause of cancer related death among men in the United States, and accounts for 29% of all cancers diagnosed in men. Furthermore, approximately one in six men will be diagnosed with prostate cancer in their lifetime. Thus, 17% of male Veterans will be asked to make a decision about the treatment of their prostate cancer. The burden of this disease is further magnified when one considers that most patients will live for years following their diagnosis and with any adverse effects of therapy. Given that there have been no clinical trials showing that any prostate cancer treatment produces an increased likelihood of survival; men are asked to actively participate in treatment decisions. Previous research has revealed that men are often uninformed about their prostate cancer, particularly African American men and men with lower educational attainment. Thus, it is critical to develop and test decision aids that can help all men (especially men with low literacy skills) make an informed decision. The goal of the study was to compare the impact of a plain language decision aid (DA) to a conventional DA on prostate cancer patients’ decision making experience and communication with their physician.
PI(s): Angela Fagerlin, PhD and Peter A. Ubel, MD
Co-I(s): Khaled Hafez, MD; Bruce Ling, MD; Jeffrey Gingrich, MD; Sara Knight, PhD; Phillip Walther, MD; Margaret Holmes-Rovner, PhD; James Tulsky, MD; Stewart Alexander, PhD
Funded by NIH: National Cancer Institute
Funding Years: 2008-2014
Prostate cancer is a leading cause of cancer death among men, and thousands of men must make treatment decisions every year. Decision making for localized prostate cancer is especially challenging as clinical trials have shown that the standard treatment options of active surveillance, surgery and radiation are comparable in terms of survival. Thus, treatment for prostate cancer is a preference-sensitive decision, with the best choice depending in part on patient attitudes towards the risks and benefits of treatment alternatives. Therefore, ideally the treatment decision will be made with full consideration of patient preferences. As such, it is recommended that patients and their physicians discuss any preferences patients have that might be relevant to the treatment decision. This dialogue is complicated by patients’ lack of experience with sharing in these types of decisions. Additionally, physicians often use medical jargon, making it more difficult for patients to understand their diagnosis and treatment options. Research is needed to determine the best methods for helping patients communicate their preferences to their physicians so that patient values hold considerable weight in treatment decisions.
The goals of this study are two-fold:
- To demonstrate to patients some of the issues that might arise during their diagnosis visit that may prevent them from communicating preferences to physicians.
- To provide solutions that would enable greater patient participation in medical decision making.
PI(s): Angela Fagerlin, PhD and Peter A. Ubel, MD
Co-I(s): John T. Wei, MD; Brian Zikmund-Fisher, PhD; Margaret Holmes-Rovner, PhD; James Tulsky, MD; Stewart Alexander, PhD
Parent grant: Michigan Center for Health Communication Research II
Tell us how you'd respond to the results of a blood test for fetal chromosomal problems. And find out how your response compares with that of participants in a national survey.
Consider the following
Imagine that you are four months pregnant. You and your partner have talked with your doctor about prenatal screening tests for your fetus. Based on your family history and personal medical history, your doctor has told you that you're at low risk (2 in 1000) of having a fetus with chromosomal problems. Chromosomal problems include such conditions as Down Syndrome. In talking further with your doctor, you decide to have a routine blood test for chromosomal problems in your fetus. This test will help to give you a better estimate of the chance that your fetus would have a chromosomal problem.
Your doctor tells you that the results of this blood test have come back "abnormal." She clarifies that the blood test showed that your risk of fetal chromosomal problems is about 5 in 1000, which is higher than the number she had told you before the test. She next asks if you are interested in amniocentesis, a medical procedure in which a small amount of amniotic fluid is extracted from the amniotic sac surrounding the fetus. This procedure can tell you for sure whether or not the fetus has chromosomal problems. However, amniocentesis has its own risks. Your doctor explains that the risk of miscarriage as a result of amniocentesis may be as high as 5 in 1000.
- Definitely No
- Probably No
- Probably Yes
- Definitely Yes
How do your answers compare?
Many women decide to go ahead and have amniocentesis. There are two things in this scenario that could influence women's decisions about amniocentesis. First, the doctor described the test as "abnormal", a label that may increase worry about the possibility that the fetus would have a chromosomal problem. Second, the risk estimate of 5 in 1000 was higher than the original estimate of 2 in 1000, which also may increase concern.
CBDSM researchers, led by Brian Zikmund-Fisher, wanted to know how much influence labels such as "abnormal", "normal", "positive", or "negative" might have on people's decisions in situations like the one described above. To test this, they gave one group of women a scenario just like the one you read. In this scenario, the test results were described as either "abnormal" or "positive" before the risk estimate of 5 in 1000 was given. A second group of women read the same scenario, but in their scenario, the doctor presented only the numeric risk estimate, without any label.
Women whose test results were introduced using a qualitative label ("positive/abnormal") were significantly more worried - and significantly more likely to choose to have amniocentesis - than women who were told only the numeric risk estimate, without any label. Note that all of the women in this survey were told that they had the same final risk: 5 in 1000. The decision of the women in each group should have been the same, but adding that one qualitative label significantly changed what the women in the study decided to do.
Interestingly, the CBDSM researchers also found a reverse effect when test results were introduced with the labels "negative" or "normal." These labels tended to make women less worried and less likely to have amniocentesis than women in a comparison group. Again, these results show that adding a one-sentence introduction with a qualitative label could significantly change people's decisions.
Read the article:
Does labeling prenatal screening test results as negative or positive affect a woman's responses?
Zikmund-Fisher BJ, Fagerlin A, Keeton K, Ubel PA. American Journal of Obstetrics and Gynecology 2007;197(5):528.e1-528.e6.
What's the difference between opting in and opting out of an activity? Who decides if people will be put automatically into one category or another? Click this interactive decision to learn how default options work.
Imagine that you're a US Senator and that you serve on the Senate's Committee on Health, Education, Labor, and Pensions. The Infectious Diseases Society of America has come before your committee because they believe that too many health care workers are getting sick with influenza ("flu") each year and infecting others. As a result, your Senate committee is now considering a new bill that would require that all health care workers get annual influenza vaccinations ("flu shots") unless the worker specifically refuses this vaccination in writing.
Do you think you would support this bill for mandatory flu shots for health care workers?
Imagine that you're the human resources director at a mid-sized company that's initiating an employee retirement plan. Management is concerned that many employees are not saving enough for retirement. They're considering a policy that would automatically deduct retirement contributions from all employees' wages unless the employee fills out and submits a form requesting exemption from the automatic deductions.
Do you think a policy of automatic retirement deductions is reasonable for your company to follow?
Organ transplants save many lives each year, but there are always too many deserving patients and too few organs available. To try to improve the number of organs available for donation, the state legislature in your state is considering a new policy that all people who die under certain well-defined circumstances will have their organs donated to others. The system would start in three years, after an information campaign. People who do not want to have their organs donated would be given the opportunity to sign a refusal of organ donation when they renewed their drivers' licenses or state ID cards, which expire every three years. Citizens without either of these cards could also sign the refusal at any drivers' license office in the state. This is a policy similar to ones already in place in some European countries.
Does this seem like an appropriate policy to you?
How do your answers compare?
For many decisions in life, people encounter default options-that is, events or conditions that will be set in place if they don't actively choose an alternative. Some default options have clear benefits and are relatively straightforward to implement, such as having drug prescriptions default to "generic" unless the physician checks the "brand necessary" box. Others are more controversial, such as the automatic organ donation issue that you made a decision about.
Default options can strongly influence human behavior. For example, employees are much more likely to participate in a retirement plan if they're automatically enrolled (and must ask to be removed, or opt out) than if they must actively opt in to the plan. Researchers have found a number of reasons for this influence of default options, including people's aversion to change.
But default options can seem coercive also. So, an Institute of Medicine committee recently recommended against making organ donation automatic in the US. One reason was the committee's concern that Americans might not fully understand that they could opt out of donation or exactly how they could do so.
The policy scenarios presented to you here have been excerpted from a 2007 article in the New England Journal of Medicine titled "Harnessing the Power of Default Options to Improve Health Care," by Scott D. Halpern, MD, PhD, Peter A. Ubel, MD, and David A. Asch, MD, MBA. Dr. Ubel is the Director of the Center for Behavioral and Decision Sciences in Medicine.
This article provides guidance for policy-makers in setting default options, specifically in health care. Generally, default options in health care are intended to promote the use of interventions that improve care, reduce the use of interventions that put patients at risk, or serve broader societal agendas, such as cost containment.
In this NEJM article, the researchers argue that default options are often unavoidable-otherwise, how would an emergency-room physician decide on care for an uninsured patient? Many default options already exist but are hidden. Without either returning to an era of paternalism in medicine or adopting a laissez-faire approach, the authors present ways to use default options wisely but actively, based on clear findings in the medical literature.
Some examples of default policies that may improve health care quality:
- routine HIV testing of all patients unless they opt out.
- removal of urinary catheters in hospital patients after 72 hours unless a nurse or doctor documents why the catheter should be retained.
- routine ventilation of all newly intubated patients with lung-protective settings unless or until other settings are ordered.
Drs. Halpern, Ubel, and Asch conclude, "Enacting policy changes by manipulating default options carries no more risk than ignoring such options that were previously set passively, and it offers far greater opportunities for benefit."
Read the article:
Harnessing the power of default options to improve health care.
Halpern SD, Ubel PA, Asch DA. New England Journal of Medicine 2007;357:1340-1344.
Imagine that you are a patient with end-stage liver disease and you are currently on the liver transplant waiting list.
Available donor livers are limited and vary in quality. Donor characteristics such as age and cause of death can make a difference between a 20% and a 40% rate of liver transplant (graft) failure by 3-years post-transplant.
Now imagine that you and your doctor are discussing the risks and benefits of a liver transplant and whether you might consider a “less than perfect” liver (with a higher risk for graft failure). To help you in your decision making, you are provided with a decision aid to help you to consider the level of risk you would be willing to accept from a donated liver.
On the following page, consider an image representing your (pretend!) risk of dying or becoming too sick for a liver transplant within the next 3-months if you don’t get a transplant.
Brian Zikmund-Fisher, PhD, gave a talk at the Small Group Meeting on Risk 2.0: Risk Perception and Communication Regarding Vaccination Decisions in the Age of Web 2.0 at Universitat Erfurt, Erfurt, Germany, on May 13, 2011.
Funded by Patient Centered Outcomes Research Institute.
Funding Years: 2012-2014.
While substantial progress has occurred recognizing community expertise in research, and involving communities in decisions about research aims and methods, community influence on research priorities remains limited. Building on experience with developing, testing and using the award-winning CHAT (Choosing Healthplans All Together) tool, and propelled by a current project that is developing and evaluating a tool to engage minority and underserved communities in setting priorities for clinical and translational research, we plan to develop and test a method to engage the public and patients in deliberations about patient-centered outcomes research (PCOR) priorities. The proposed study expands public input on research priorities beyond the limited settings of advisory boards and disease advocates in which much public engagement currently functions and contribute to a more just and equitable system of PCOR. Importantly, by evaluating the tool this project will also add to the body of knowledge about methods, processes and outcomes of community engagement. For more information, visit PCORI.
PI(s): Susan Goold
Co-I(s): Lawrence An, Ray De Vries, Jennifer Griggs, Myra Kim