Heart Health: A Cardiovascular Prevention Choice Tool

Notes on its development

Submitted by Sandra Hartasanchez

Introduction

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

  1. 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 optionRR activity/med startedRR activity/med stopped
Not smoking0.61[5]1.64 (1/0.61)
Heart-Healthy Diet0.7[6]1.42 (1/0.7)
Exercise0.75[7]1.33 (1/0.75)
Statins medium0.75[8]1.33 (1/0.75)
Statins high0.6[8]1.66 (1/0.6)
Ezetimibe0.94[9]1.06 (1/0.94)
Aspirin0.91[10]1.09 (1/0.91)
Blood Pressure Lowering Medications0.88[11]1.13 (1/0.88)
PCSK-9 inhibitors0.86[12]1.16 (1/0.86)
GLP-1 Agonists0.88[13]1.13 (1/0.88)
SGLT-2 Inhibitors0.86[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 benefitsSide effects
Statins medium dosePrevents strokes by 29%[15]Muscle aches (0-5 in 100)[8]
Statins high dosePrevents strokes up to 48%[16]Muscle aches (0-10 in 100)[8]
EzetimibePrevents strokes up to 14%  alone or in combination with statins.[17]Muscle/joint aches, flu-like symptoms.[18]
AspirinPrevents colorectal cancer by 20%.[19]Easy bruising, bleeding (3 in 1000)[10]
Blood pressure medicationsPrevents strokes up to 40%; other benefits depend on medicine used.[11]Depends on medicine used
PCSK9-inhibitorsPrevents strokes up to 20%. [20]Flu-like symptoms.[12]
GLP1-agonistsPrevents 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-inhibitorsPrevents 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.

For more examples of SDM tools designed by researchers at the KER Unit, please refer to http://www.carethatfits.org/tools

References

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.

My life as a type I diabetic

“So what happened here,” and my doctor would point to a single blood sugar from three Thursdays ago, a 243 mg/dL at 3 am.

And I wracked my brain trying to remember what happened that night. Had I changed my pump site before bed? Sometimes that causes a high afterwards. Did I eat a snack and miscalculate the insulin dose to cover it? Did I over-treat a low?  I couldn’t remember specifically what happened.  Do I make something up?  Do I lie?

I shrugged with frustration. “I think diabetes happened there.”

Type 1 diabetes doesn’t exist outside of the context of life.  I wish it did; I wish diabetes was something that existed independent of everything else in my life, making it absent the influence of variables like exercise, eating, and emotions.  But diabetes is a pervasive, persistent thread that weaves its way around every aspect of my life, from breakfast to the last thought before falling asleep at night.  It’s the preposition that dangles off of every thought – “with diabetes” – and makes my disease a constant and necessary priority.

Minimally Disruptive Medicine makes sense as an approach for chronic illness because it flies in the face of what chronic illness attempts to do, which is to disrupt.  Diabetes is very disruptive and intrusive, so making my care approach towards the disease more streamlined and integrated creates a culture of hope, motivation, and effort.

When it comes to building a care plan with my medical team, my personalized variables need to take center stage.  Ask me what my goals are, instead of building treatment recommendations around what you think my goals should be.  Do I want an A1C that’s within ADA guidelines?  Of course.  But am I willing to achieve that goal by way of several low blood sugar events per week?  No way.  My doctor’s goal may be to improve my fasting blood sugars, while my goal might be to overcome my fear of overnight hypoglycemia.  How do we take medical guidelines and best practices and balance those within the context of my real life?

Diabetes maps differently in every single life, so personal preferences take precedence.  You recommend that I wear an insulin pump to help best control my blood sugars?  Prescribing the device is one thing, but I also need training on how to integrate this technology into my real life.  Connect me with peers who wear their insulin pumps safely and confidently at the beach, or while running, or while tending to the needs of their small child.

Show me “how” instead of telling me “why.”

Talk to me about my preferences, my goals, and my life, because that’s where my diabetes exists.  Diabetes exists around my life, not the other way around.  I don’t build my life around diabetes.  It’s not a hole in me or the whole of me.  There’s life to be found after diagnosis, and my focus remains on making the most of that life.

Kerri Sparling has been living with type 1 diabetes for over 29 years, diagnosed in 1986.  She manages her diabetes and lives her life by the mantra “Diabetes doesn’t define me, but it helps explain me.”

 Kerri is a passionate advocate for all-things diabetes. She is the creator and author of Six Until Me, one of the first and most widely-read diabetes patient blogs, reaching a global audience of patients, caregivers, and industry.  Well-versed in social media and its influence on patients, Kerri presents regularly at conferences and works full-time as a writer and consultant.  Her first book, Balancing Diabetes (Spry Publishing), was released in the Spring of 2014.

 Kerri and her husband, Chris, live in Rhode Island, USA with their daughter. 

2017 Workshop – Rochester

2017 Workshop – Rochester

 

Monday, October 23rd
7:30- 8:00 AM

8:00 – 9:00 AM

 

9:00 -9:30 AM

Registration

Plenary: Where have we been this year? Setting the stage for our two days together Kasey Boehmer, MPH — slides   video

Coffee Break – Move to Breakout Rooms

9:30 – 10:45 AM Large Workshop: My Life, My Healthcare Kasey Boehmer, MPH
11:00-12:30 PM Large Workshop: Capacity Coaching-Experience from the Front lines

Kathryn Havens, MD/Jason Soyring/Nicole Burow

2:30 – 3:45 PM Small Breakout 1: Chronic Disease Self-Management Program (CDSMP)

Lori Christiansen, MD, MSc

Small Breakout 2: Design Tips and Tricks from the Front Lines – Part 2

Ian Hargraves, PHD/Maggie Breslin

Small Breakout 3: Kidney Disease

MDM & SDM Group (Bjorg Thorsteinsdottir, MD/Bob Albright, MD/Elizabeth Lorenz, MD

 

 

 

 

3:45 – 4:15 PM

4:15-5:00 PM

Small Breakout 4: MDM in the wild

Kasey Boehmer, MPH

(Overview of CCM VS MDM)

Beth Rogers, MD CCC/Moain Abu Dabrh, MB, BCh, HIV Clinic work

Coffee Break – Move to Large Hall

Plenary: Workload and Bo T Experience in France

Viet-Thi Tran, MD — slides  video

 

 

Tuesday, October 24th

8:00 – 9:15 AM

9:15 –  9:45 AM

Plenary: Shared Decision Making Trials Erik Hess, MD — slides  video

Coffee Break – Move to Breakout Rooms

9:45 – 11:00 AM Large Workshop: Doing + Teaching SDM

Summer Allen, MD = clinical perspective; Marleen Kunneman, PHD = teaching perspective

11:15 – 12:30 PM Large Workshop: MPH: System-level SDM Implementation + Challenges

Summer Allen, MD/Kasey Boehmer, MPH

2:30 – 3:45 PM Small Breakout 1: Minimally Disruptive SDM Trials Annie LeBlanc, PhD/Sara Dick
Small Breakout 2: New Approaches and new contexts for SDM Rongchong (Lucy) Huang, MD/Victor Montori, MD, MSc
Small Breakout 3: SDM for step down/stopping decisions

Michael Gionfriddo, PhD, PharmD

 

 

 

3:45-4:15 PM

4:15-5:00 PM

 

5:00 PM

 

Small Breakout 4: Design Tips and Tricks from the Front Lines – Part 1

Ian Hargraves, PhD/Maggie Breslin

Coffee Break – Move to Large Hall

Plenary: Patient Revolution

Victor Montori, MD, MSc

 

Adjorn

 

4:15  5:00 PM  

 

 

 

 

Tuesday, October 24th

8:00 – 9:15 AM

9:15-9:45

AM

Plenary: Shared Decision Making Trials Erik Hess, MD — slides  video

Coffee Break – Move to Breakout Rooms

9:45 – 11:00 AM Large Workshop: Doing + Teaching SDM

Summer Allen, MD = clinical perspective; Marleen Kunneman, PHD = teaching perspective

11:15 – 12:30 PM Large Workshop: MPH: System-level SDM Implementation + Challenges

Summer Allen, MD/Kasey Boehmer, MPH

2:30 – 3:45 PM Small Breakout 1: Minimally Disruptive SDM Trials Annie LeBlanc, PhD/Sara Dick
Small Breakout 2: New Approaches and new contexts for SDM Rongchong (Lucy) Huang, MD/Victor Montori, MD, MSc
Small Breakout 3: SDM for step down/stopping decisions

Michael Gionfriddo, PhD, PharmD

 

 

3:45-4:15

PM

Small Breakout 4: Design Tips and Tricks from the Front Lines – Part 1

Ian Hargraves, PhD/Maggie Breslin

Coffee Break – Move to Large Hall

4:15 – 5:00 PM Plenary: Patient Revolution Victor Montori, MD, MSc— slides video

2017 Workshop – Rochester

 

Monday, October 23rd
7:30- 8:00

8:00 – 9:00 AM

 

9:00 -9:30

Registration

Plenary: Where have we been this year? Setting the stage for our two days together Kasey Boehmer, MPH — slides   video

Coffee Break – Move to Breakout Rooms

9:30 – 10:45 AM Large Workshop: My Life, My Healthcare Kasey Boehmer, MPH
11:00-12:30 PM Large Workshop: Capacity Coaching-Experience from the Front lines

Kathryn Havens, MD/Jason Soyring/Nicole Burow

2:30 – 3:45 PM Small Breakout 1: Chronic Disease Self-Management Program (CDSMP)

Lori Christiansen, MD, MSc

Small Breakout 2: Design Tips and Tricks from the Front Lines – Part 2

Ian Hargraves, PHD/Maggie Breslin

Small Breakout 3: Kidney Disease

MDM & SDM Group (Bjorg Thorsteinsdottir, MD/Bob Albright, MD/Elizabeth Lorenz, MD

 

 

 

 

 

 

3:45-4:15

PM

4:15-5:00

PM

Small Breakout 4: MDM in the wild

Kasey Boehmer, MPH

(Overview of CCM VS MDM)

Beth Rogers, MD CCC/Moain Abu Dabrh, MB, BCh, HIV Clinic work

Coffee Break – Move to Large Hall

Plenary: Workload and Bo T Experience in France

Viet-Thi Tran, MD — slides  video

 

 

Tuesday, October 24th

8:00 – 9:15 AM

9:15-9:45

AM

Plenary: Shared Decision Making Trials Erik Hess, MD — slides  video

Coffee Break – Move to Breakout Rooms

9:45 – 11:00 AM Large Workshop: Doing + Teaching SDM

Summer Allen, MD = clinical perspective; Marleen Kunneman, PHD = teaching perspective

11:15 – 12:30 PM Large Workshop: MPH: System-level SDM Implementation + Challenges

Summer Allen, MD/Kasey Boehmer, MPH

2:30 – 3:45 PM Small Breakout 1: Minimally Disruptive SDM Trials Annie LeBlanc, PhD/Sara Dick
Small Breakout 2: New Approaches and new contexts for SDM Rongchong (Lucy) Huang, MD/Victor Montori, MD, MSc
Small Breakout 3: SDM for step down/stopping decisions

Michael Gionfriddo, PhD, PharmD

 

 

3:45-4:15

PM

Small Breakout 4: Design Tips and Tricks from the Front Lines – Part 1

Ian Hargraves, PhD/Maggie Breslin

Coffee Break – Move to Large Hall

4:15 – 5:00 PM Plenary: Patient Revolution Victor Montori, MD, MSc— slides video

2017 Workshop – Rochester

 

Monday, October 23rd
7:30- 8:00

8:00 – 9:00 AM

 

9:00 -9:30

Registration

Plenary: Where have we been this year? Setting the stage for our two days together Kasey Boehmer, MPH — slides   video

Coffee Break – Move to Breakout Rooms

9:30 – 10:45 AM Large Workshop: My Life, My Healthcare Kasey Boehmer, MPH
11:00-12:30 PM Large Workshop: Capacity Coaching-Experience from the Front lines

Kathryn Havens, MD/Jason Soyring/Nicole Burow

2:30 – 3:45 PM Small Breakout 1: Chronic Disease Self-Management Program (CDSMP)

Lori Christiansen, MD, MSc

Small Breakout 2: Design Tips and Tricks from the Front Lines – Part 2

Ian Hargraves, PHD/Maggie Breslin

Small Breakout 3: Kidney Disease

MDM & SDM Group (Bjorg Thorsteinsdottir, MD/Bob Albright, MD/Elizabeth Lorenz, MD

 

 

 

 

 

 

3:45-4:15

PM

4:15-5:00

PM

Small Breakout 4: MDM in the wild

Kasey Boehmer, MPH

(Overview of CCM VS MDM)

Beth Rogers, MD CCC/Moain Abu Dabrh, MB, BCh, HIV Clinic work

Coffee Break – Move to Large Hall

Plenary: Workload and Bo T Experience in France

Viet-Thi Tran, MD — slides  video

 

 

Tuesday, October 24th

8:00 – 9:15 AM

9:15-9:45

AM

Plenary: Shared Decision Making Trials Erik Hess, MD — slides  video

Coffee Break – Move to Breakout Rooms

9:45 – 11:00 AM Large Workshop: Doing + Teaching SDM

Summer Allen, MD = clinical perspective; Marleen Kunneman, PHD = teaching perspective

11:15 – 12:30 PM Large Workshop: MPH: System-level SDM Implementation + Challenges

Summer Allen, MD/Kasey Boehmer, MPH

2:30 – 3:45 PM Small Breakout 1: Minimally Disruptive SDM Trials Annie LeBlanc, PhD/Sara Dick
Small Breakout 2: New Approaches and new contexts for SDM Rongchong (Lucy) Huang, MD/Victor Montori, MD, MSc
Small Breakout 3: SDM for step down/stopping decisions

Michael Gionfriddo, PhD, PharmD

 

 

3:45-4:15

PM

Small Breakout 4: Design Tips and Tricks from the Front Lines – Part 1

Ian Hargraves, PhD/Maggie Breslin

Coffee Break – Move to Large Hall

4:15 – 5:00 PM Plenary: Patient Revolution Victor Montori, MD, MSc— slides video

 

Shared Decision Making Called for by the Situation of Suffering

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

Decision aids that facilitate elements of shared decision making in chronic illnesses

Submitted by Thomas Wieringa

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]:

  1. situation diagnosis (understanding the patient’s situation and establishing the aspects require action)
  2. choice awareness (indicating that multiple options are available and highlighting the
  3. importance of the patient’s preferences in deciding on the course of action)
  4. option clarification (explaining the available options)
  5. discussion of harms and benefits (explaining the harms and benefits of each option)
  6. deliberation of patient preferences (discussing the preferences of the patient)
  7. 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.

The full paper was published in Systematic Reviews and can be found here: https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-019-1034-4.

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

  1. 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.
  2. 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.
  3. 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.
  4. Stiggelbout AM, Pieterse AH, De Haes JCJM. Shared decision making: Concepts, evidence, and practice. Patient Education and Counseling. 2015;98(10):1172-9.
  5. IPDAS Collaboration. What are patient decision aids? http://ipdas.ohri.ca/what.html (2017). Accessed 30 Oct 2018.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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. 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.

Overcoming Depth of Field Limitations

Some methods and equipment allow altering the apparent DOF, and some even allow the DOF to be determined after the image is made. For example, Focus stacking combines multiple images focused on different planes, resulting in an image with a greater (or less, if so desired) apparent depth of field than any of the individual source images. Similarly, in order to reconstruct the 3-dimensional shape of an object, a depth map can be generated from multiple photographs with different depths of field. This method is called “shape from focus.”

Other technologies use a combination of lens design and post-processing: Wavefront coding is a method by which controlled aberrations are added to the optical system so that the focus and depth of field can be improved later in the process.

from Wikipedia article which is released under the Creative Commons Attribution-Share-Alike License 3.0.

Butterfly Lighting

Butterfly lighting uses only two lights. The key light is placed directly in front of the subject, often above the camera or slightly to one side, and a bit higher than is common for a three-point lighting plan. The second light is a rim light.

Often a reflector is placed below the subject’s face to provide fill light and soften shadows.

This lighting may be recognized by the strong light falling on the forehead, the bridge of the nose, the upper cheeks, and by the distinct shadow below the nose that often looks rather like a butterfly and thus, provides the name for this lighting technique.

Butterfly lighting was a favourite of famed Hollywood portraitist George Hurrell, which is why this style of lighting is often called Paramount lighting.

From Wikipedia article which is released under the Creative Commons Attribution-Share-Alike License 3.0.

Windowlight Portraiture

Windows as a source of light for portraits have been used for decades before artificial sources of light were discovered. According to Arthur Hammond, amateur and professional photographers need only two things to light a portrait: a window and a reflector. Although window light limits options in portrait photography compared to artificial lights it gives ample room for experimentation for amateur photographers. A white reflector placed to reflect light into the darker side of the subject’s face, will even the contrast. Shutter speeds may be slower than normal, requiring the use of a tripod, but the lighting will be beautifully soft and rich.

The best time to take window light portrait is considered to be early hours of the day and late hours of afternoon when light is more intense on the window. Curtains, reflectors, and intensity reducing shields are used to give soft light. While mirrors and glasses can be used for high key lighting. At times colored glasses, filters and reflecting objects can be used to give the portrait desired color effects. The composition of shadows and soft light gives window light portraits a distinct effect different from portraits made from artificial lights.

From Wikipedia article which is released under the Creative Commons Attribution-Share-Alike License 3.0.

Why does everything feel so hard right now?

Submitted by Kasey Boehmer

In the midst of this COVID19 pandemic, those of us seeking to be responsible citizens, keep social distancing, while continuing to fulfill our obligations to family, community, and work, are feeling overwhelmed. It is all too much. Our research may help us understand that feeling and perhaps find ways forward. 

In 2016, we released a manuscript titled: “Patient capacity and constraints in the experience of chronic disease: a qualitative systematic review and thematic synthesis.” This review synthesized 110 published papers across a variety of chronic conditions seeking to understand what exactly gives people the capacity to handle chronic illness. We describe patient capacity as the abilities and resources that are mobilized to support the work of life and healthcare.

Our review uncovered five factors that a patient must interact with to generate their capacity: biography, resources, environment, patient work, and their social network (BREWS). We call this the descriptive Theory of Patient Capacity, and will demonstrate how each construct works when dealing with chronic illness. A biography is the narrative of normal life that we create; when chronic illness comes along and interrupts this narrative by way of bothersome symptoms and new treatment routines, one can experience biographical disruption. Resources are what are mobilized in an effort to support the work of managing life and healthcare. These may include things like finances, transportation, physical energy, time, knowledge, self-efficacy, etc. Environments are places of work, living, and healthcare. Patient work, when done in small segments, can generate new capacity for additional tasks through the experience of accomplishment; when work is given in an overwhelming fashion (all the tasks at once), capacity may be reduced. Finally, the social network can generate capacity through support, or be detrimental to capacity if unsupportive. For example, a patient who needs to eat at specified meal times could have social connections that subsequently adjust their meal times in an effort of togetherness or tells the patient it is no big deal to deviate from the plan that may be best for their condition’s management.

This theory was derived from published experiences of patients living with chronic illness, which is a population familiar with the sometimes massive disruption of life from diagnosis and treatment. However, it occurred to me yesterday on my walk that it can actually be a useful framework for what we are experiencing on a societal level right now amidst a pandemic. Almost everyone that I have spoken to by email, phone, or facetime, has indicated what seems to feel like the shrinking in their cognitive bandwidth. People are saying things like “I handle one tiny thing, and the next thing comes at me.” How might we use the BREWS framework to navigate the current climate? Let’s take a look at what is happening right now in each domain.

Biography – each one of us has a well negotiated set of routines, social roles, and normalcy. We may be, for example, employed by large organizations, run small businesses, or stay-at-home parents for kids. We all have routines of day to day life such as cooking breakfast, going to our favorite restaurants for lunch, friends we regularly socialize with, and extracurricular activities for ourselves or our children in the evenings. Suddenly all of our routines are being upended. We are rapidly evolving our daily routines to try to accommodate these life changes. Suddenly, we need to work from home, sometimes while simultaneously becoming homeschool teachers. Our typical jaunts from place to place are almost entirely restricted at the moment beyond what is absolutely necessary as we all work to minimize societal impact of the COVID-19 pandemic. These massive shifts in routine create a loud roar of cognitive dissonance in our brains, which find comfort in routine. Creativity is in short supply as our brains work to learn all the new tasks very rapidly. Put in short, we are all in the midst of a massive-scale biographical disruption.

Resources – These are the things we mobilize to deal with such shifts, and they seem uncertain in pandemic times. Many people are being faced with the reduced ability to work for income, especially if they are in a service industry or owners of a business. This straps our collective financial resources, which otherwise might be employed to cope with various aspects of disruption (e.g. hiring childcare). Our knowledge base is one we often mobilize to cope with new situations, and yet our knowledge base about the novel COVID-19 virus appears to change quite literally by the hour. Suddenly, we all need some knowledge of epidemiology to understand the “flatten the curve” lingo being used worldwide. Self-efficacy is a resource of confidence generated from doing or watching others do, and yet very few of us have experiential knowledge of such a situation to draw upon for confidence in this new one.

Environment – For many of us, our environments are now rapidly decreasing in scope as we hunker down in our homes. The places we seek care, an important part of our capacity in coping with healthcare matters, are rapidly trying to adapt from planned, routine care with some emergent services, to crisis response in a pandemic. As the ground we stand on feels shaky, so do we.

Work – To generate capacity, we actually have to accomplish some work, and it must be broken down in such a way that we can do so. Having such a volume of work that it feels too cumbersome to break down is a significant detriment to our capacity. Right now, in the span of seven to ten days, most of us have had new work thrown at such rapid-fire pace, we feel completely paralyzed to act.  

Social Network – In trying times, it is not uncommon for us to lean on our family, friends, and even acquaintances to get through. Often, we can even “borrow” some capacity from others to act in difficult times (e.g. asking someone to go to the pharmacy for you when you’re feeling too ill to go yourself). However, in such a time where every person’s capacity seems taxed, it feels uncertain who we might turn to in order to borrow anything, including toilet paper. Furthermore, even if not providing practical support, our social network often provides emotional support. We are mostly used to this happening during face-to-face interactions with physical displays of care, such as hugs. Yet, in a time of “social distancing” we find ourselves physically at a distance from those who may be our rocks. We are forced to think creatively about how to engage otherwise, through text, phone, or video. In a time where creativity is already taxed for reasons above, this may feel like too much.

This, my friends, is WHY it all probably feels like a little too much right now. If you’re feeling like your capacity is completely overwhelmed at the moment, know it is to be expected when we look at the current situation through the BREWS framework. So what are we to do? It seems we may be in for an indeterminate number of days ahead where the ground feels shaky, which means we need to build our capacity accordingly. We also don’t all have a personal capacity coach sitting on our shoulders to help us do this.

First, take heart in knowing that when working to build capacity with patients now living with chronic illness, we don’t start by tackling every single area of capacity all at once – that overwhelms overwhelmed people. What we would typically do is start by assessing where people are at – by taking stock, we can assess where we need to bolster our capacity, but also where we already possess strengths. Then, based on what one wants to work on, we would create small experiments to try. Note, I did not say goals, and I did not say assignments. Experiments. Each experiment is a new opportunity to learn something. Even if it didn’t go as planned, each time we can ask ourselves, what did I learn? We also don’t set up too many experiments. For example, we might have 1 – 3 in a week. Then, at the beginning of the next week, we would assess what worked, what didn’t work, and what we learned. Based on that information, we would keep the ways of working we liked and build on them. We would discard what we didn’t like after extracting the learnings, and choose new experiments. This process often continues for weeks or even months, so be patient with yourself. Reach out virtually to those in your social circle so you can be mutually supportive even while physically at a distance.

Hang tough friends, we can do this! Tell us about what of this was helpful and what you’d like to see more of – we are here together for the ride.

Values-Based Care & Minimally Disruptive Medicine

REPOSTED WITH PERMISSION (https://www.thoughtarchitects.ca/blog )

Submitted by Margie Sills Maerov, BScOT, MBA, CHE

“Our group has come to understand that the challenge of evidence isn’t simply communicating what we know clearly to our patients—although that alone is a significant challenge. Instead, the real challenge is how to use evidence to discover what’s best for the particular patient in light of his or her circumstances and values.” (Hargraves et. al. 2016).

Ever felt incredibly lucky? I certainly have recently. In addition to my new role here at Thought Architects, I just started with the University of Alberta in the Faculty of Medicine and Dentistry in the Department of Lifelong Learning (or L3 as the “insiders” call it). The leaders there believe that a key piece of continuing education that physicians, dentists and their teams need is the ability to foster others’ thinking – and they want to bring Cognitive Coaching into their “pillars” of support. What I love about this new gig is that I am surrounded by passionate leaders, thinkers and doers who want to impact change. (As an aside, stay tuned for my next podcast on leadership – and how it can be truly great!)

As part of this new work I am taking on, I found myself at the Mayo Clinic in Rochester last week. Their conference on “Care That Fits” is the next iteration of the Minimally Disruptive Medicine (MDM) model. At the heart of the MDM model is the need to look at care and the burden of care differently. There is a subjective sense of capacity and capability that any patient has when balancing the demands of life and care, and how much capability the patient feels they might have. The premise is that in medicine we not only need to be attune to the medical condition, but also the real needs of the patient. For example, it is more about our need to “do our job” of providing medical advice when asking a patient to more frequently monitor blood sugars when they might have issues of food insecurity, or might be living in an abusive relationship. Instead, our jobs need to be about honouring what the patient values and needs, provide the “best medical advice”, and then help the patient make up their own mind on what makes sense for them. The challenge that providers have is that what we might want patients to do might not be what they want to do – and how to be OK with that.

How do we create the right conditions so that our goal of care evolves to fostering a greater the sense of self-directedness a patient has to manage his or her own condition, life circumstances and environment. In Cognitive Coaching, self directedness is defined as someone who is able to:

1. Self-Manage – I am in charge of me

2. Self-Monitor – I know how I am doing

3. Self-Modify – I know how to make changes in what I want to do

A nuanced shift in medical practice occurs when considering MDM. Building the resourcefulness of the patient to be self-directed is the ultimate outcome and goal of care – not necessarily adhering to best-practice guidelines. This requires providers to intervene not at the behaviour level, but instead at the “thinking level”. All behaviour is preceded by thoughts. Impact the thoughts, you can impact the behaviour.

At some level, a patient will have to decide to make change or not make change. A change in their lifestyle, how they live or the decisions they make. As providers, we hope that patients will make decisions that foster health and well-being (at least by our definition – a possible blind spot). Providing that definition is our role as a “consultant”. Fostering a patient’s sense of resourcefulness for change is our role as a coach.

One supportive approach to aiding providers is the use of shared decision making approaches (SDM). Changing workflows in practice to support SDM can be challenging at times. The brilliant Kasey Boehmer (@krboehmer) and her colleagues have developed the ICAN Discussion Aid through several iterations of user-centered design principles, interviews, and observations. It captures a patient’s subjective sense of burden and capacity, and helps shape a clinical encounter towards what is important to the patient in their care. It has been used not only to support patient-centred care practices, but also as a program planning and quality improvement tool. You can find the tool online:

My Life My Health Care

Interested in learning more? Reach out on Twitter (@msmaerov) or at margie@thoughtarchitects.ca and I can share what I learned, and see if the ICAN might be a fit in your clinic!