Even if care seems right from a medical perspective, if care doesn’t fit for each individual, we may ‘deliver care’ without actually ‘caring’. The recently published Making Care Fit Manifesto (1) states that for care to fit, care should be maximally responsive to patients’ unique situation and supportive to their priorities. Care should also be minimally disruptive to patients’ lives, loved ones, and social network. Making care fit requires patients (and their caregivers) and clinicians to collaborate, both in content and manner, and it is an ongoing and iterative process where care plans should continuously be evaluated and modified.
This is especially pertinent for young adults living with type 1 diabetes. Previous research showed that young adults with type 1 diabetes have relatively poor biomedical and psychosocial outcomes (2,3). For example, HbA1c levels are higher in younger adults compared to other age groups, and strikingly, they are also higher now than they were a decade ago (3).
At the same time, very little is known about what young adults do to implement diabetes care in their lives, and what price they have to pay in terms of negative effects on themselves and their surroundings. Also, how do we bridge what happens in their personal environment (‘point of life’) and what happens during clinical encounters (‘point of care’)? Because whatever is left undiscussed with their clinicians, is also left unconsidered when designing care plans.
We explored experiences of young adults with type 1 diabetes trying to make care fit into their lives. First, we asked 62 young adults with type 1 diabetes (Median age: 27, IQR 24 to 27, 80% women) from the French ‘Community of Patients for Research’ (ComPaRe) for their experiences with the burden of treatment. They reported a high burden4 of diabetes treatment (76.5 out of 150, IIQR 59 to 94). Importantly, 3 of every 4 young adults (74%) reported that their investment of time, energy, and efforts in healthcare is unsustainable over time. This is about twice as high as for other people with chronic conditions.
Second, the Dutch ééndiabetes foundation (for and by young adults with type 1 diabetes) asked its members for their experiences in making diabetes care fit into their lives. Nine of 25 young adults (36%) indicated that their diabetes care regularly or very often hinders their education, work, hobbies, leisure or social lives. When asked to describe their biggest efforts to fit diabetes care into their daily life, they responded:
“You want to live a fun and spontaneous life, but you have to nonstop keep an eye on your sugars.”
“Food. Everything you have to do then. That’s why I sometimes skip my meals.”
“Planning. Not just the hospital appointments and changing needles, but also planning with the energy I will or will not have.”
“Regulating hypo’s and then compensating for the time I couldn’t function well due to a hypo.”
“On time and constant planning ahead. What do I need? Do I have all my stuff before I leave? When do I have to place new orders to make sure I don’t run out?”
Additionally, we asked young adults what they do to fit diabetes care into their lives, but what they don’t discuss with their clinician. Some indicated they discuss “everything” or “nothing” with their clinician. Others said:
“I use a DIY loop. (I did tell my clinician but he wasn’t interested)”
“I delay my hospital appointments as long as I can.”
“I discuss everything about my diabetes with my clinician. All the better they can help me.”
“I bolus less to prevent hypos.”
Communication is key to bridge efforts of making care fit at the point of life and at the point of care. Our future work will focus on uncovering better ways to improve the quality of diabetes care through improving conversations. And to help young adults with their lifelong, daily and ongoing endeavor of making diabetes care fit.
References
Kunneman M, Griffioen IPM, Labrie NHM, Kristiansen M, Montori VM, van Beusekom MM, Making Care Fit Working G. Making care fit manifesto. BMJ Evid Based Med. 2021.
Johnson B, Elliott J, Scott A, Heller S, Eiser C. Medical and psychological outcomes for young adults with Type 1 diabetes: no improvement despite recent advances in diabetes care. Diabet Med. 2014;31(2):227-231.
Redondo MJ, Libman I, Maahs DM, Lyons SK, SaracoM, Reusch J, Rodriguez H, DiMeglio LA. The Evolution of Hemoglobin A1c Targets for Youth With Type 1 Diabetes: Rationale and Supporting Evidence. Diabetes Care. 2021;44(2):301-312.
Tran VT, Harrington M, Montori VM, Barnes C, Wicks P, Ravaud P. Adaptation and validation of the Treatment Burden Questionnaire (TBQ) in English using an internet platform. BMC Med. 2014;12:109.
Cardiovascular disease (CVD) continues to be a leading cause of mortality and disease burden worldwide. There are several approaches to prevent CVD and new ones continue to emerge.[1] The American College of Cardiology/American Heart Association guidelines for primary cardiovascular (CV) prevention recommend shared decision making (SDM) to co-create individualized plans for preventive care.[2]
As part of an NIH-funded project which aims to identify implementation approaches that promote high-quality SDM about CV prevention for patients in primary care settings, designers and researchers at the KER Unit created Heart Health: A CV Prevention Choice Tool, an SDM tool for use by patients and clinicians during the clinical encounter to co-create a plan of care. The tool:
Offers an individualized 10-year estimate of the patient’s atherosclerotic cardiovascular disease (ASCVD) risk.
Offers lifestyle interventions – Mediterranean diet, physical activity, smoking cessation – and medication – e.g., statins, aspirin, other lipid, blood pressure, glucose lowering drugs – options and demonstrates how starting/continuing/stopping them can affect jointly and cumulatively the patient’s ASCVD risk.
Supports a conversation by which patient and clinician create an evidence-based plan that is desirable, useful, and feasible for both.
This document describes the development process we followed to produce Heart Health.
Steps for the design, evaluation, and prototyping of the tool
A. Literature review
Clinicians and researchers thoroughly searched the literature for relative risk reduction estimates of major adverse cardiovascular events for the interventions of interest. The relative risk (RR) is the probability of an outcome in the exposed group compared to the probability of an outcome in the unexposed group.[3] The RR estimates for smoking cessation and exercise come from observational cohort studies. All other estimates of the effect of these interventions on CV risk come from randomized trials. When possible, we sought high quality systematic reviews of these primary studies. For each intervention we also sought evidence on other (nonCV) benefits, medication administration routine, adverse effects, and patient out-of-pocket costs.
Among the interventions of interest were nutrition and diet, antiplatelet medication, glucose-lowering medications, lipid-lowering therapy, blood pressure lowering medications, smoking cessation, and weight loss.
B. Summarizing the evidence
What to include?
After completing this extensive review, and discussing our findings with preventive cardiologists, we decided to include in the final tool:
Activities:
Mediterranean diet
Exercise
Smoking cessation
Medications
Statins- medium and high dose
Ezetimibe
PCSK-9 inhibitors
Aspirin
Blood pressure lowering medications
GLP-1 agonists
SGLT2- inhibitors
Decisions made on content
In addition to the factors needed to compute a 10-year ASCVD risk, we also included 2 additional parameters: lipoprotein (a) and coronary calcium score. We assumed these could be helpful in patients who were unsure as to how intensely to pursue preventive care (e.g., patients at so-called intermediate CV risk). When evaluating the use of the tool in clinic, it was evident that these parameters were rarely available in primary care settings and rarely used in discussions. Thus, we decided to exclude them from the current version.
Another parameter that was added after discussing with experts was a question on family history of premature (males <55 years, females <65 years) myocardial infarction, stroke, or sudden death in a first degree relative. This question did not affect the risk calculations per se but, if selected, a disclaimer would be displayed when calculating risk: “Your family history of heart disease means that your risk may be higher than shown. Consider further discussion with a preventive cardiologist.”
For the activities included (smoking cessation, Mediterranean diet, and exercise), it was decided to include links to patient education websites created by the Mayo Clinic for each activity, where patients and clinicians could obtain more detailed information and suggestions on how to make these changes to their lifestyle.
For Diabetes medications, we included GLP-1 agonists and SGLT-2 inhibitors. If the patient has diabetes, these two medications are part of the medications table. If the patient does not have diabetes, they are not initially included. However, the option of adding them to the table is available.
Risk calculators
This tool uses the ASCVD risk calculator to estimate the patient’s 10-year ASCVD risk and a 100-person pictograph to display this risk. Then we use best estimates of risk reduction against this risk estimation to propose a revised ASCVD risk given the interventions chosen, assuming independence. This is based on the approach used in the highly popular and effective Statin Choice tool.
To calculate the current risk of having a coronary event (described as “heart attack” in the tool) in the next 10 years, the tool uses the ASCVD risk calculator equation and data such as: age, sex at birth (M/F), African American (Y/N), smoker (Y/N), Diabetes (Y/N), treated blood pressure (Y/N), total cholesterol (100-350 mg/dL), HDL cholesterol (10-120 mg/dL), and systolic blood pressure (90-250 mmHg) that has to be completed by the clinician or auto-populated from the electronic health record. For further detail on how to calculate the current risk using the ASCVD risk calculator, please refer to pages 32-34 on the 2013 Report on the Assessment of Cardiovascular Risk: Full Work Group Report Supplement. [4]
For each intervention, we used their RR estimates to calculate the future risk of having a coronary event in the next 10 years if the patient started, or stopped using, that intervention. The future risk is calculated by multiplying the current risk by the RR overall. The RR overall is calculated using the RR estimates shown in the following table, which are different depending on the use status of the intervention (i.e. if a medicine and/or an activity is started or stopped). A patient’s RR overall is calculated by multiplying together the RRs of each intervention that the patient chooses to start using, and then further multiplying by the RRs of each intervention that the patient is currently using that they and their clinician elect to stop using. The RR of an intervention that is stopped is the inverse of the RR of an intervention that is started
RR overall = RR interventions started * RR interventions stopped
This means for example, that if a patient switches from medium to high dose statins, then: RR overall = RRstatins high * 1/RRstatins medium
Any interventions that the patient is currently using and will continue to use are not included in the calculation of RR overall and therefore do not contribute to the estimate of future risk. This reflects the difficulty of determining any further risk reduction that may be achieved by continuing current interventions beyond that which is reflected in the patient’s current risk estimate. (For example, for a patient currently taking a blood pressure lowering medicine, the benefits of the intervention are reflected in the estimate of the patient’s current risk and it is unknown to what extent continued use of the medication will lower that risk further.)
Future risk= current risk * RR overall.
Activity or medicine option
RR activity/med started
RR activity/med stopped
Not smoking
0.61[5]
1.64 (1/0.61)
Heart-Healthy Diet
0.7[6]
1.42 (1/0.7)
Exercise
0.75[7]
1.33 (1/0.75)
Statins medium
0.75[8]
1.33 (1/0.75)
Statins high
0.6[8]
1.66 (1/0.6)
Ezetimibe
0.94[9]
1.06 (1/0.94)
Aspirin
0.91[10]
1.09 (1/0.91)
Blood Pressure Lowering Medications
0.88[11]
1.13 (1/0.88)
PCSK-9 inhibitors
0.86[12]
1.16 (1/0.86)
GLP-1 Agonists
0.88[13]
1.13 (1/0.88)
SGLT-2 Inhibitors
0.86[14]
1.16 (1/0.86)
As described above, in order to overcome evidence limitations, a number of methodological compromises were made in calculating future risk. This reflects principles that are used in developing all our shared decision-making tools:
Patients and clinicians need support in making decisions even when optimal evidence does not exist.
Risk is only a device that in some circumstances may be helpful in decision making.
It is more important that any risk presented is a useful approximation that can help people make reasonable decisions than that it is precise, particularly when imprecision is unlikely to affect the final decision.
The most appropriate method for calculating risk should be based on the quality of the reasonably applicable evidence and its ability to contribute usefully and feasibly to patient and clinician decision making.
Reporting of other outcomes
For each medication stated above, we created a table with key information, the most common adverse effects, and other benefits of the medication. We selected the most discussed in practice and the most relevant to the clinical context of primary prevention. Also, we gathered information on average cost of these medications per month according to the GoodRx service, recognizing that these estimates vary greatly depending on the patient’s insurance.
Other benefits
Side effects
Statins medium dose
Prevents strokes by 29%[15]
Muscle aches (0-5 in 100)[8]
Statins high dose
Prevents strokes up to 48%[16]
Muscle aches (0-10 in 100)[8]
Ezetimibe
Prevents strokes up to 14% alone or in combination with statins.[17]
Muscle/joint aches, flu-like symptoms.[18]
Aspirin
Prevents colorectal cancer by 20%.[19]
Easy bruising, bleeding (3 in 1000)[10]
Blood pressure medications
Prevents strokes up to 40%; other benefits depend on medicine used.[11]
Depends on medicine used
PCSK9-inhibitors
Prevents strokes up to 20%. [20]
Flu-like symptoms.[12]
GLP1-agonists
Prevents death by 11%, loss of >5% of body weight, prevents kidney failure by 20%. [21, 22]
Nausea-vomiting (2-3 in 10), diarrhea (1 in 10).[23]
SGLT2-inhibitors
Prevents death by 20%, loss of 2% of body weight, prevents kidney failure by 30%.[14]
Urinary and genital infections (200 in 1000 over 5 years), DKA (4 in 1000 over 5 years).[24, 25]
Design process: Prototyping and refinement of the tool
Designers at the KER Unit start the process of designing the tool by reviewing how relevant conversations take place in medical encounters in usual practice. After obtaining patient and clinician informed consent, we video recorded 5 preventive cardiology visits which were then reviewed by members of the team. These observations were fundamental to creating the first version of the Heart Health tool.
We invited 4 clinicians working in preventive cardiology, consultative internal medicine, and primary care at Mayo Clinic to test this tool prototype with their patients. We tested two versions of the tool with a total of 8 patients. All encounters were video recorded and reviewed. Each version of the tool was improved considering user experience, observed misuses, and clinician recommendations for adding, removing, or modifying the tool’s content.
One example of the kind of refinements done to the tool (that came from the observation of its use in clinical practice and from expert feedback) is the change to the order of display of the intervention screens. Initially, our tool showed the medications tab first and the activities tab second. The order of these seemed to imply that the best approach to discussing CV risk prevention was by focusing on medications first.
However, when observing the videos, we saw that the conversation always started with changes in lifestyle given their large impact on CV health. We also recognized that the issues presented in the tool that pertained to medicines weren’t particularly informative when considering lifestyle changes (e.g. side effects). We wanted our tool to support as much as possible the usual conversation that patients have with their clinicians. For this reason, we made the very simple but relevant decision to order the tool so that activities were available for discussion before medicines.
The third version of the prototype tool was considered the final one and was sent to a software development company that later released the tool to be implemented within the electronic workflow and online as a standalone webapp. The team at KER Unit was in constant communication with the software development team, and together we worked on improving the tool’s visual display while making sure the purpose and logic of each screen was maintained.
1. Roth Gregory, A., et al., Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019. Journal of the American College of Cardiology, 2020. 76(25): p. 2982-3021.
2. Arnett, D.K., et al., 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol, 2019. 74(10): p. e177-e232.
3. Tenny S, H.M., Relative Risk. [Updated 2021 Mar 30]. 2021: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing.
4. Goff David, C., et al., 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk. Journal of the American College of Cardiology, 2014. 63(25_Part_B): p. 2935-2959.
5. Duncan, M.S., et al., Association of Smoking Cessation With Subsequent Risk of Cardiovascular Disease. Jama, 2019. 322(7): p. 642-650.
6. Martínez-González, M.A., A. Gea, and M. Ruiz-Canela, The Mediterranean Diet and Cardiovascular Health. Circulation Research, 2019. 124(5): p. 779-798.
7. Wahid, A., et al., Quantifying the Association Between Physical Activity and Cardiovascular Disease and Diabetes: A Systematic Review and Meta-Analysis. Journal of the American Heart Association, 2016. 5(9): p. e002495.
8. Taylor, F., et al., Statins for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev, 2013. 2013(1): p. Cd004816.
9. Zhan, S., et al., Ezetimibe for the prevention of cardiovascular disease and all‐cause mortality events. Cochrane Database of Systematic Reviews, 2018(11).
10. Gelbenegger, G., et al., Aspirin for primary prevention of cardiovascular disease: a meta-analysis with a particular focus on subgroups. BMC Medicine, 2019. 17(1): p. 198.
11. Sakima, A., et al., Optimal blood pressure targets for patients with hypertension: a systematic review and meta-analysis. Hypertens Res, 2019. 42(4): p. 483-495.
12. Schmidt, A.F., et al., PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews, 2017(4).
13. Jia, X., et al., GLP-1 Receptor Agonists and Cardiovascular Disease: a Meta-Analysis of Recent Cardiac Outcome Trials. Cardiovasc Drugs Ther, 2018. 32(1): p. 65-72.
14. Zou, C.-Y., et al., Effects of SGLT2 inhibitors on cardiovascular outcomes and mortality in type 2 diabetes: A meta-analysis. Medicine, 2019. 98(49): p. e18245.
15. Chou, R., et al., Statins for Prevention of Cardiovascular Disease in Adults: Evidence Report and Systematic Review for the US Preventive Services Task Force. Jama, 2016. 316(19): p. 2008-2024.
16. Watson, K.E., The JUPITER trial: How will it change clinical practice? Rev Cardiovasc Med, 2009. 10(2): p. 91-6.
17. Ouchi, Y., et al., Ezetimibe Lipid-Lowering Trial on Prevention of Atherosclerotic Cardiovascular Disease in 75 or Older (EWTOPIA 75). Circulation, 2019. 140(12): p. 992-1003.
18. Brar, K.S., Ezetimibe (Zetia). Medical journal, Armed Forces India, 2004. 60(4): p. 388-389.
19. Cao, Y., et al., Population-wide Impact of Long-term Use of Aspirin and the Risk for Cancer. JAMA oncology, 2016. 2(6): p. 762-769.
20. Sabatine, M.S., et al., Evolocumab and Clinical Outcomes in Patients with Cardiovascular Disease. N Engl J Med, 2017. 376(18): p. 1713-1722.
21. Kristensen, S.L., et al., Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol, 2019. 7(10): p. 776-785.
22. Khera, R., et al., Association of Pharmacological Treatments for Obesity With Weight Loss and Adverse Events: A Systematic Review and Meta-analysis. Jama, 2016. 315(22): p. 2424-34.
23. Filippatos, T.D., T.V. Panagiotopoulou, and M.S. Elisaf, Adverse Effects of GLP-1 Receptor Agonists. The review of diabetic studies : RDS, 2014. 11(3-4): p. 202-230.
24. Halimi, S. and B. Vergès, Adverse effects and safety of SGLT-2 inhibitors. Diabetes & Metabolism, 2014. 40(6, Supplement 1): p. S28-S34.
25. Musso, G., et al., Diabetic ketoacidosis with SGLT2 inhibitors. BMJ, 2020. 371: p. m4147.
By Ian Hargraves, Maggie Breslin, Nassim Jafarinaimi
Healthcare, like any care, is the product of what people can do and who they can be for each other in the midst of suffering. The relationship of people attending to suffering finds its most direct expression in contemporary healthcare in the relationship of patient and clinician. The ways in which these two come together lies at the heart of how we conceive of and organize the healthcare enterprise. If we conceive of the meeting of patient and clinician as rooted in the knowledge and expertise of the medical expert then we may establish paternalistic and patriarchal structures and relationships by which to deploy that knowledge. Beyond this, we may seek to improve and innovate healthcare by heightening the knowledge, technology, and efficiency of the medical expert. Alternatively if, in the coming together of patient and clinician, we focus attention on the demands of the patient who is commissioning and paying for care we may set the suffering person in the role of consumer. Let the buyer beware then becomes the organizing principle, a principle that calls for an empowered patient equipped with authority, information, choice, and control in the face of illness. This is a situation in which we think that if the suffering person would and could only be more—more knowledgeable, more assertive, more discriminating as a purchaser—then illness would be less. There is a third possibility in the coming together of patient and clinician. In this way, the joining of people is called for by the situation of suffering. The reason for healthcare is not the deployment of technical expertise, or the exercise of choice. The reason for healthcare is to attend to the challenges of suffering. This is the reason that in clinic rooms throughout the country and world patients and clinicians sit together, talk, and together take action in attending to suffering or the threat of suffering. In the KER unit, we explore the hypothesis that the medium in which this relationship is made productive and caring is conversation
Shared decision making (SDM) is a patient-centered approach in which clinicians and patients work together to find and choose the best course of action for each patient’s particular situation [1]. This approach is pertinent to the care of patients with chronic conditions [2]. Six key elements of shared decision making can be identified [1-4]:
situation diagnosis (understanding the patient’s situation and establishing the aspects require action)
choice awareness (indicating that multiple options are available and highlighting the
importance of the patient’s preferences in deciding on the course of action)
option clarification (explaining the available options)
discussion of harms and benefits (explaining the harms and benefits of each option)
deliberation of patient preferences (discussing the preferences of the patient)
making the decision (clinician and patient making together the decision)
Decision aids SDM can be facilitated by decision aids that have been developed for use by clinicians and patients, either during or in preparation of the clinical encounter [5-7]. Decision aids can help patients choose an option that is congruent with their values, reduce the proportion of patients remaining undecided and/or who play a passive role in the decision-making process, and improve patient knowledge, decisional conflict, and patient-clinician communication [7-11].
The International Patient Decision Aid Standards (IPDAS) Collaboration developed a minimal set of standards for qualifying a tool as a decision aid, which require that a decision aid support all key elements but making the decision [12].
Systematic review We conducted a systematic review to assess the extent to which decision aids support the six key SDM elements and how this relates to their impact.
We found 24 articles reporting on 23 RCTs of 20 DAs (10 DAs for cardiovascular disease, two DAs for respiratory diseases, and eight DAs for diabetes). With the exception of one, all studies have an unclear or high risk of bias for all outcomes assessed in this review. The option clarification element (included in 20 of 20 DAs; 100%) and the harms and benefits discussion (included in 18 of 20 DAs; 90%; unclear in two DAs) are the elements most commonly clearly included in the DAs. The other elements are less common and more uncertainty is present whether these elements are included, especially with regard to choice awareness (uncertain in 14 out of 20 DAs; 70%). All elements were clearly supported in four DAs (20%). We found no association between the presence of these elements and SDM outcomes.
Conclusion Thus, despite the IPDAS minimal set of qualifying criteria, our systematic review showed that decision aids for cardiovascular diseases, chronic respiratory diseases, and diabetes mostly support the option clarification and the discussion of harms and benefits elements of SDM, while the other SDM elements are less often incorporated.
Future research Possibly, some SDM elements may be left out of decision aids by design. This choice may depend on what features were thought most important by the developers (e.g., patient education, risk communication, preference elicitation, or patient empowerment). The importance of incorporation of SDM elements in decision aids may be situation-dependent, but the way this works is unclear. Therefore, future research should clarify this situation-dependence and eventually inform possible reconsideration of the IPDAS minimum standards for decision aid qualification. The relationship between the extent to which decision aids support SDM elements and outcomes is yet unknown and should be studied in future research as well.
Thomas Wieringa is a post-doc researcher at the department of Epidemiology at the University Medical Center Groningen (UMCG), the Netherlands. He did his PhD, focused on shared decision making and patient-reported outcomes in type 2 diabetes, at the VU University Medical Center. He visited and collaborated with the Knowledge and Evaluation Research (KER) Unit of the Mayo Clinic in the context of his PhD.
References
Hargraves I, LeBlanc A, Shah ND, Montori VM. Shared decision making: The need for patient-clinician conversation, not just information. Health Affairs. 2016;35(4):627-9.
Montori VM, Gafni A, Charles C. A shared treatment decision-making approach between patients with chronic conditions and their clinicians: The case of diabetes. Health Expectations. 2006;9(1):25-36.
Kunneman M, Engelhardt EG, Ten Hove FL, Marijnen CA, Portielje JE, Smets EM, et al. Deciding about (neo-) adjuvant rectal and breast cancer treatment: Missed opportunities for shared decision making. Acta Oncologica. 2016;55(2):134-9.
Stiggelbout AM, Pieterse AH, De Haes JCJM. Shared decision making: Concepts, evidence, and practice. Patient Education and Counseling. 2015;98(10):1172-9.
Montori VM, Kunneman M, Brito JP. Shared decision making and improving health care: The answer is not in. JAMA: Journal of the American Medical Association. 2017;318(7):617-8.
Stacey D, Légaré F, Lewis K, Barry MJ, Bennett CL, Eden KB, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database of Systematic Reviews. 2017;(4):CD001431.
Durand MA, Carpenter L, Dolan H, Bravo P, Mann M, Bunn F, et al. Do interventions designed to support shared decision-making reduce health inequalities? A systematic review and meta-analysis. PloS One. 2014;9(4):e94670.
Légaré F, Turcotte S, Stacey D, Ratté S, Kryworuchko J, Graham ID. Patients’ perceptions of sharing in decisions. The Patient – Patient-Centered Outcomes Research. 2012;5(1):1-19.
Dwamena F, Holmes-Rovner M, Gaulden CM, Jorgenson S, Sadigh G, Sikorskii A, et al. Interventions for providers to promote a patient-centred approach in clinical consultations. The Cochrane Library. 2012;(12):CD003267.
Joosten EA, DeFuentes-Merillas L, De Weert GH, Sensky T, Van Der Staak CPF, de Jong CA. Systematic review of the effects of shared decision-making on patient satisfaction, treatment adherence and health status. Psychotherapy and Psychosomatics. 2008;77(4):219-26.
12. Joseph-Williams N, Newcombe R, Politi M, Durand M-A, Sivell S, Stacey D, et al. Toward minimum standards for certifying patient decision aids: A modified Delphi consensus process. Medical Decision Making. 2014;34(6):699-710.
Measuring shared decision making (SDM) is challenging. Previous research showed discrepancies between observer-based and self-reported scores. Patient-reported SDM scores are usually higher and tend to have ceiling effects (high scores without much variance), possibly due to halo effects (difficulty to disentangle SDM from overall experience of care).
We wanted to test whether introducing a pause (“stop-and-think”) before filling in SDM scores would slow patients down and encourage them to reflect above and beyond their assessment of general satisfaction with the clinician or the visit. Also, we wanted to assess how much intellectual, emotional, or practical sense the care plan made to patients.
In two studies, we asked a diverse group of patients to reflect on their care before completing the 3-item CollaboRATE SDM measure. In the first study, adding the reflection questions lowered the CollaboRATE score (“less” SDM) and reduced the proportion of patients giving the maximum scores. The differences, while tantalizing in magnitude and direction, were not significant. In the second study, the reflection questions did not change the distribution of CollaboRATE scores or top scores.
In general, patients indicated high scores on the sense of their care plan. However, this ‘sense’ was only weakly correlated with the total CollaboRATE scores. One of every two patients indicated their care plan made less than ideal sense, yet they still gave maximum scores on the CollaboRATE.
Our studies showed limited and somewhat inconsistent evidence that reflection-before-quantification interventions may improve the performance of patient-reported SDM measures. Also, we showed that it is conceivable that scoring high on the “technical steps of SDM” as assessed by SDM measures, may not necessarily lead to a decision that makes sense and vice versa.
The full paper was published in Health Expectations and can be found here.
This study was part of the Fostering Fit by Recognizing Opportunity STudy (FROST) program, and has been made possible by a Mapping the Landscape, Journeying Together grant from the Arnold P. Gold Foundation Research Institute.
Submitted by: Marleen Kunneman, Christina LaVecchia, Naykky Singh Ospina, Abd Moain Abu Dabrh, Emma Behnken, Patrick Wilson, Megan Branda, Ian Hargraves, Kathleen Yost, Richard Frankel, Victor M. Montori
The importance of shared decision making (SDM) is widely recognized and its practice is encouraged. However, some people face major challenges as they are invited to participate in SDM, and may not recognize or understand the concept of SDM within healthcare. There is a limited amount of literature about SDM in patients who have limited English proficiency (LEP) – defined as “not speaking English as a primary language and potentially having a limited ability to read, speak, write, or understand English.” We do know, however, that older age, poor health literacy, and language barriers are obstacles to using SDM. LEP often occurs together with low health literacy and cultural obstacles; this triad is aptly referred to as the “triple threat.”
The basic tenets of SDM – sharing of information and preferences, consensus building and reaching agreement – may be foreign to many. Here, we examine the steps involved in SDM, and clarify the potential issues that may arise in the context of people with LEP.
Process of information sharing
Those with LEP may have a different worldview with cultural norms that diverge substantially from Western norms. Some people with LEP believe, either for faith-based or cultural reasons, in non-disclosure and deliberately hide a poor diagnosis, poor prognosis, and alternative treatment options from their loved ones who are patients. This is not done with ill-intent but to protect their loved ones from experiencing potential hopelessness and depression from learning of impending death or a non-curative condition. LEP patients may also be more likely to use alternative therapies and be reluctant to share this information as they sense that clinicians may not approve. Those with LEP are more likely to experience bias or perceive they have experienced discrimination, and therefore have less trust in their clinicians inhibiting information sharing. Clinicians in turn may share less information with those who have LEP due to a variety of factors including lack of time, interpreter availability, and concerns about comprehension.
Deliberation and Decision making
The importance of family in decision making among those with LEP is also key, with large groups of relatives often involved in decisions that for most US families would involve a patient acting alone or with a surrogate or with very close family members only. The collectivist approach to making decisions is at risk of impeding deliberation and shared decision making as the needs, preferences, and understanding of what is best for the patient as voiced by the patient may get crowded out by the many voices wanting to be heard.
Decision making models
The US promotes patient autonomy (with designated surrogates as needed) as vital in all decision making and a driver of shared decision making, while other cultures support a paternalistic model with the clinicians considered expert and driving the decision making process.
Decision aids developed for specific populations may help bridge the gap between inadequate communication and improved decision making. Decision aids adapted from English to other languages require more than translation to ensure their usability and effectiveness; an enormous challenge. Interpreters will need to be involved in the process of developing and implementing tools as they will be central to their uptake and effectiveness in practice. There remain huge challenges to supporting and measuring SDM even with isolated language barriers unrelated to other health literacy and cultural differences.
The purpose of this commentary is not to stereotype groups into those “capable” of SDM and those that are not. The purpose of this commentary is to draw attention to a wider range of cultural approaches to decision making in healthcare. The healthcare team should assess each patient’s interest in being part of a SDM process. For some with LEP, SDM will appeal and help them make informed and meaningful decisions about their healthcare. For others it will be a baffling and potentially distressing encounter. We must not coerce patients into “complying” with Western decision-making approaches when seeking care. In respecting patients, we need to consider flexible and culturally adept decision-making processes that acknowledge the fundamental role family and other factors play in clinical decision making.
Clinicians should be mindful of the other more pressing barriers to decision making that exist for those with LEP and accept other potentially unfamiliar approaches to providing compassionate and culturally sensitive care. It may help to exercise some cultural humility, accepting decisions that clash with usual expectations and being skeptical of SDM as the preferred way to reach decisions with patients. For some with LEP there are limits to the practical use of SDM and requiring them to conform to SDM is unrealistic and may be counter-productive and uncaring.
Amelia Barwise is an assistant Professor of Medicine within the Division of Pulmonary and Critical Care Medicine at Mayo Clinic. She is currently working on her PhD focused on end of life care among patients with limited English proficiency.
Decisions on whether to initiate statin therapy for cardiovascular risk reduction should be based on individual patient risk and occur in the context of a shared decision making (SDM) conversation. The Statin Choice Conversation Aid is a web-based tool that incorporates patient variables to calculate and present an individual-level risk. It has been shown in multiple randomized trials to facilitate SDM when used in the clinical encounter.
Despite being freely available and well accepted by patients and clinicians, the Statin Choice tool had not been institutionally adopted and integrated into the clinical work flow at any site prior to 2014. This lack of implementation was and is representative of many SDM interventions which, in routine settings, are often not prioritized. The reasons for this are complex but, at least at some level, result from the competing priorities healthcare systems must address and the often-fixed resources they have to do this work. In this context, it stands to reason that health systems and other settings will be more likely to undertake the work of implementing SDM when it is understood clearly to be low. Unfortunately, in most cases, the work of implementing any individual SDM intervention is poorly understood at the outset. The most effective and efficient strategies for facilitating implementation are often even more ambiguous.
In this study, we sought to address these foundational problems by both characterizing the work of implementing the Statin Choice tool and identifying the most useful strategies for doing this work. Specifically, we recruited 3 health systems in the Mayo Clinic Care Network and carefully observed and tracked their efforts to integrate the tool into their EHR and into routine use across all of primary care over an 18-month period.
We used Normalization Process Theory, an implementation theory that organizes the types of work required to embed new practices, to describe the implementation process at each site. We collected multiple types of data from many sources to track the success (or outcomes) of implementation as well. By carefully examining the things teams did (e.g. the strategies they used) to do the work of implementation and the results of this effort (e.g. the outcomes the work achieved), we were able to identify the most useful strategies for making SDM implementation happen. We were also able to gain a clear understanding of the types and amount of work that would be required.
With this knowledge, we were able to develop a multi-component toolkit that could be provided to other settings to support implementation of the tool. As part of this toolkit, we were also able to provide a brief organizational readiness and context assessment. More clearly, because we had observed the implementation process, we were able to provide an assessment that would guide clinical stakeholders in thinking about the specific things they would need to be able to do (e.g. integrate into the record, train clinicians), the ways in which these things can be done (e.g. workflow examples, training methods), and whether the provided toolkit resources (e.g. EHR code language, implementation team manuals, educational templates) was sufficient support to justify going forward.
Importantly, our study identified several strategies that were judged to be of low value in facilitating implementation. This knowledge was critical to the development of the toolkit and to stakeholders as it allowed us to avoid inclusion of things that will only cause more work for clinical teams with little to no benefit.
The conceptual advancements of our research to the field of implementation science include (1) a theoretical connection between the work that stakeholders do to implement SDM and the outcomes this achieves and (2) an appreciation of the need to develop useful toolkits that can support clinical settings in understanding and doing the work of implementation.
It is not our impression, however, that the toolkit we developed will be necessarily appropriate for other SDM interventions. Rather, we believe our research should be used as a template that can be replicated by other teams in other settings and for other interventions.
The full paper was published in BMC Health Services Research and can be found here. This study was made possible by a CTSA Grant (UL1 TR000135) from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH).
In oncology, as in other healthcare settings, shared decision making (SDM) is increasingly advocated when more than one treatment strategy is available. However, we previously found that cancer patient treatment preferences are often left undiscussed, and that patients are hardly involved in treatment decision making. If patients are unclear about their preferences, or if these preferences are left unspoken, patients may not receive the treatment that fits them best.
Values clarification methods (VCMs) have been developed to support patients in weighing treatment benefits and harms and harms and to help them voice what matters most to them . We developed a stand-alone VCM that asks patients to make explicit trade-offs between treatment benefits and harms. This VCM is adaptive, in that it ensures that the trade-offs presented to patients are tailored to the preferences of the patient as revealed in the exercise so far.
We tested this VCM in patients newly-diagnosed with rectal cancer who were facing the decision whether or not to undergo short-course pre-operative radiotherapy. Radiotherapy increases the likelihood that the cancer will stay away at the initial site (i.e., local control), however, it also increases the likelihood of fecal incontinence and of sexual dysfunction. We hypothesized that the VCM would aid patients to become more confident on their preferences and to voice them more often during consultations, based on results among treated rectal cancer patients asked to consider the decision hypothetically. We expected that going through the VCM would lead to patients’ preferences to be more often integrated in treatment decisions, and that patients would experience less regret over the decision and would cope better with treatment harms.
Values clarification method
The online VCM was offered in advance of the first encounter of the patient with the radiation oncologist, a visit in which the treatment decision is usually made. The VCM started with lay explanations of the three outcomes (local control, fecal incontinence, and male or female sexual dysfunction), and stated that survival was the same across situations. It then asked patients to rate how important they considered differences between best and worst probabilities of outcomes, that varied within a clinically realistic range (see print screens). Next, the VCM asked patients to indicate their preference for pairs of outcomes, where outcome probabilities differed in each pair. The final page of the VCM showed the patient’s relative importance for the three outcomes in percentages. It did not show which treatment should suit the patient best, as it was meant to support patients in considering the options and they still were to meet with their radiation oncologist.
Patients were initially randomized to be offered the VCM or not. Later on in the study, we offered the VCM to all patients due to practical difficulties and low recruitment rates. We compared the outcomes in patients who agreed to receive the link to the VCM versus those who did not receive the link.
Findings
Of the 135 patients who had their consultation audiotaped and completed questionnaires, 35 received and accessed the VCM-link. Patients in the VCM-group slightly more often expressed their views on treatment and treatment outcomes than the patients who had not, although such utterances were still uncommon. This points to very limited discussion between patients and clinicians on how patients consider benefit-harm trade-offs. This may further explain why the questionnaire data showed that patients in the VCM-group did not differ in how clear their values were.
An important finding is that patients who completed the VCM felt less regret over the treatment decision at follow-up, and experienced less impact of faecal incontinence and sexual dysfunction six months after treatment. As hypothesized, explicitly considering trade-offs may have helped patients to better understand the pros and cons involved, and supported them to live with the consequences later on. Of note, the radiation oncologists in this study reported that almost all decisions had been made before the consultation, either by the referring physician or by the tumour board, without input from the patient. Patients clearly lacked room to contribute.
Conclusion
This is the first study to assess the effect of an adaptive conjoint analysis-based VCM on actual patient-clinician communication, and long-term decision regret and impact of treatment harms. Decisions to undergo short-course preoperative radiotherapy in rectal cancer had in almost all cases been made prior to the consultation, without patient input. The VCM hardly could affect final decisions in this setting. Even so, our results suggest a favourable effect of being explicitly invited to think about benefits and harms of treatment on the extent to which patients endorse treatment decisions and can live with treatment consequences.
The full paper was published in Acta Oncologica and can be found here (open access).
This study was made possible by a grant from the Dutch Cancer Society (UL2009-4431).
Arwen H. Pieterse is associate professor in medical decision making at the Leiden University Medical Center, the Netherlands. She studied Cognitive Psychology and graduated (cum laude) in 1998. She obtained her PhD in 2005. She was Research fellow of the Dutch Cancer Society (2008-2011). She published well over 50 international peer-reviewed articles on patient-physician communication, patient and physician treatment preferences, patient-physician (shared) decision making, and psychometric properties of measurement instruments. Based on her research, she co-developed e-learnings to teach shared decision making skills to medical students and clinicians. She received the 2018 Jozien Bensing award from the International Association on Communication in Healthcare (EACH), granted biennially to early-career researchers.
She is Associate editor of Patient Education and Counseling since 2017. She was the scientific co-chair of the 2018 European meeting of the Society of Medical Decision Making. She chairs the EACH standing committee on research since 2018 and is the co-chair of the upcoming EACH Forum, September 16-18 2019, Leiden, the Netherlands.
At the beginning of our research journey into shared decision making (SDM), we thought that fostering collaboration between patients and clinicians would promote their partnership and advance mutual trust. Yet, in this trial reported in 2008, we only measured patient trust in the clinician, and we found that disclosing uncertainty (as the intervention required) did not reduce trust in the clinician and may have even improved it despite the measure’s ceiling effect. To my knowledge, we have not measured this outcome in our trials of SDM intervention since then.
Four years earlier, when that trial was being planned, Entwistle reflected on studies that strongly suggested that trust, as a bridge between protective barriers, could favor shared decision making, and shared decision making could result in greater trust in treatment plans. This view was supported by clinicians interviewed by Charles and colleagues. That patients who trust their clinician may be comfortable taking a passive role in following treatment plans their clinician recommends, was substantiated in a report of a survey of Canadian patients that year.
Four years after our publications, in 2012, Peak and colleagues noted a bidirectional relationship between trust and SDM. In focus groups comprised of African American persons living with diabetes, participants reported how clinician efforts to engage them in shared decision making may promote trust, how their own trust in the physician may facilitate their participation in SDM, and how race (including aspects of implicit bias and cultural discordance) can affect both.
And this month, Academic Medicine publishes an important essay by Wheelock, a second year internal medicine resident in Boston, in which she poignantly asks how might we develop relationships of trust needed for shared decision making as industrial healthcare destroys any vestige of continuity of care.
As we review the videos that are produced in the course of the conduct of our clinical trials of SDM interventions, I have noticed another angle in the relationship between trust and SDM, which, as far as I know, remains largely unexplored. We have caught clinicians, using SDM tools in a manner that reveals they simply do not trust their patients to wisely consider the issues and contribute to form care that fits their life situation. Instead, they seem to use the tools as a speaker would use PowerPoint, to build the case for a particular action, to argue in an uninterrupted monologue that concludes in a strong recommendation. It is clear that these clinicians have met these diseases before, but not the people who have them. Nonetheless, the encounter will finish, and the clinicians will know little about these people or their situation, satisfied that they got consent to proceed as they thought would be appropriate, perhaps even before entering the consultation. It is as if their professional commitment to the welfare of their patients prevents them from running the risk of trusting the patient into the decision making process. They appear afraid that these patients may enter a conversation that may finish at an impasse, at a disagreement, or at a substandard plan. The issues discussed in the last two decades that applied mostly to patient trust in the clinician, may need to be explored in the opposite direction, with an eye on the harmful effect of industrial healthcare.
SDM researchers may therefore do well in considering clinician trust in the patient as a potential modifier – barrier or facilitator – of the collaborative work necessary to form programs of care that make sense and advance the situation of patients.
Submitted by Marleen Kunneman, Fania R Gärtner, Ian G Hargraves, Victor M Montori
In a recent commentary published in the Journal of Argumentation in Context, we aimed to draw a contrast between technically correct shared decision making (SDM), and a humanistic approach to SDM.1 We stated:
“To address a patient’s problematic situation, patients and clinicians must work together to figure out a way forward that maximally supports meeting the patient’s goals, such as cure or better quality of life, while minimally disrupting their lives and loves, such as family life, work, or leisure. This work takes place in a conversation in which patients and clinicians test, or ‘try on’, the available options as ‘hypotheses’ until they identify one that fits best. The option that ‘fits best’ is the one that makes the most intellectual, emotional, and practical sense. This means that not only do patients and clinicians know and understand that it is the best option at hand, it also feels right and can be implemented in the life of the patient. The conversational dance between the patient and clinician2 and the trying out of different options and making sense of these options is sometimes called shared decision making or SDM.2,3 SDM shifts the focus of healthcare from care for ‘patients like this’ to care for ‘this patient’.”
We commented on a study by Akkermans et al, who studied the stereotypicality of argumentation in SDM encounters.4 We highlighted that “focusing on learning and using the correct communication (or techniques or steps of SDM) only makes sense if using these techniques and structures advances the situation of the patient.” We noted that:
“Since the emergence of SDM, research and implementation has primarily focused on getting the structure of SDM right: to take the right steps at the right time. It suggests that there is a technically correct sequence of steps, one that is best able to lead to identifying the best option, the best care for this patient.”
We noted the value of this approach insofar as it has shown that ‘technically correct SDM’ is rare in practice.7,8 Yet, it is unclear to us whether having a technically correct structure of the SDM process improves the likelihood that the care decisions made will contribute to improve the patient situation. We worry that focus on technical steps may encourage clinicians to ‘go through the motions’ or ‘check the boxes’ to achieve efficient productivity. This may indicate that current SDM evaluations “may lack validity, overestimate the occurrence of SDM as a caring process, and, to the extent that the conversation is necessary for SDM to exert its salutary effects, may underestimate the impact SDM could have on patient outcomes when applied in its caring form.” A focus on technically correct SDM, and on policies that promote it, may not improve the patient situation.
We concluded:
“The way forward may need to focus on responding to each patient’s problematic situation, and then explore the structures necessary, of SDM and argumentation, to achieve this response. We believe that in shifting this focus, we will look beyond what is technically correct, to uncover humanistic SDM and caring conversations.”
Recently, our teams (KER Unit and dept Medical Decision Making, LUMC) have been exploring the differences and value of technical versus humanistic SDM and its assessment. Part of this work has been made possible by Mapping the Landscape, Journeying Together grants from the Arnold P. Gold Foundation Research Institute. Stay tuned for the findings of these projects!
References
Kunneman M, Gärtner FR, Hargraves IG, Montori VM. Commentary on “The stereotypicality of symptomatic and pragmatic argumentation in consultations about palliative systemic treatment for advanced cancer”. Journal of Argumentation in Context. 2018;7(2):205-209.
Kunneman M, Montori VM, Castaneda-Guarderas A, Hess E. What is shared decision making? (and what it is not). Acad Emerg Med. 2016;23(12):1320-1324.
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.
Akkermans A, Labrie N, Snoeck Henkemans F, Henselmans I, Van Laarhoven HW. The stereotypicality of symptomatic and pragmatic argumentation in consultations about palliative systemic treatment for advanced cancer. Journal of Argumentation in Context. 2018.
Stiggelbout AM, Pieterse AH, de Haes JCJM. Shared decision making: Concepts, evidence, and practice. Patient Educ Couns. 2015;98(10):1172-1179.
Elwyn G, Durand MA, Song J, et al. A three-talk model for shared decision making: multistage consultation process. BMJ. 2017;359:j4891.
Stacey D, Legare F, Lewis K, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4:CD001431.
Montori VM, Kunneman M, Brito JP. Shared Decision Making and Improving Health Care: The Answer Is Not In. JAMA. 2017;318(7):617-618.
