Blog by Joe Babaian.
We know the devastation wreaked by the Big 5 Preventable Diseases – cancer, heart disease, stroke, unintentional injuries, and chronic lower respiratory diseases. We know what ‘preventable’ means, yet, we get bogged down figuring out how something is preventable and also continues to claim approximately 241K people annually (reported for 2014).
Preventable deaths occur based on factors that include the social determinants of health #SDoH, encompassing socio-economic status of the distributed population. Leading directly from this, we find the critical factor becomes the inherent cognitive biases in the healthcare system* itself. These cognitive biases are defined like this:
A cognitive bias refers to a systematic pattern of deviation from norm or rationality in judgment, whereby inferences about other people and situations may be drawn in an illogical fashion. Individuals create their own “subjective social reality” from their perception of the input.
Which people is this referring to? Patients? Yes. Clinicians? Yes. Systems? Yes. Everyone and every organization are impacted by cognitive biases. The *system is all of us and the organizations including their unique processes and flows. When decisions are colored by subjective social reality, we see the true nature of the #SDoH.
We shouldn’t fall into the trap of thinking that the nature alone of lower socio-economic classes is creating an environment where this population simply chooses poorly or has reduced/no access. As a result, this population fails to prevent many preventable diseases (for the #SDoH reasons we are familiar with). We should look at the higher end of socioeconomic status (SES) to see the other half of the cognitive biases in play. The same subjective social reality plays to full effect for the highly educated, powerful, skilled, and/or successful. For example, without even knowing it in most cases, practitioners are swayed by these biases, which in turn harm the recipients of care – including a magnified impact on those with low SES (they are affected by their SES status in itself – lower education/access resulting in poor choices plus the biases during evaluation/treatment).
Some excellent examples of biases are shared by the AHRQ Patient Safety Network:
Knowing yourself is one of the keys to counter cognitive biases in healthcare. Jennifer Blumenthal-Barby, an associate professor of medical ethics at Baylor College of Medicine in Houston notes:
If you want to make better health-related decisions, the first step is to cultivate awareness of various cognitive biases you may have so that you “can guard against them.”
Alice Domar, senior staff psychologist at Beth Israel Deaconess Medical Center in Boston sums it up in a difficult truth for both patients and caregivers:
[Ultimately] you need to have an openness to believing that you could be wrong or could be missing something important.
This applies to anyone, regardless of position, SES, or intent.
Behavior Change Science
Lygeia Ricciardi (@Lygeia), my #HCLDR friend, peer, and mentor, recently wrote, “Improve Your Health with Behavior Change Science” on Tincture. Her focus is on improving health via the understanding and application of behavior change science, and I recommend you read her concise article as soon as you can!
She begins with:
You influence your own health more than anyone else. Decisions you make every day about diet, exercise, taking medications (or not), and when to seek professional medical help matter much more, over the course of your lifetime, than anything a doctor or hospital does for you.
Lygeia goes on to focus on the ways to make these decisions the right ones for better health. Based on her research and from a panel she moderated at South by Southwest (SXSW), she describes options such as applying virtual reality to modify behavior and using effective financial incentives to meet the challenge.
Artificial Intelligence & Machine Learning
Using artificial intelligence and machine learning to combat bias is a technological solution that rests on a complete understanding of the cognitive biases present in the system from the start.
In “How AI Can End Bias,” we find the encouraging news that #AI and #MachineLearning are growing towards a strong role in overcoming cognitive bias in healthcare (and other fields). An important take away gives the ground rules for the expectation that AI will counter bias:
In other words, if we want AI to counter the effects of a biased world, we have to begin by acknowledging that the world is biased. And that starts in a surprisingly low-tech spot: identifying any biases baked into your own organization’s current processes. From there, you can determine how to address those biases and improve outcomes.
…the algorithms that drive AI are built by humans, and humans choose the data with which to shape and train the resulting models.
Even when using the depth of machine learning with further advanced AI, we cannot ignore the human factors that begin the process.
Digital Health Solutions & The Healthcare “Nudge”
#HCLDR regular and #DigitalHealth entrepreneur Megan Janas (@TextraHealth) of Textra Healthcare offers a seemingly lower-tech but no less innovative solution to countering cognitive biases: the humble text message. For patients discharged from the emergency department, Textra’s TextConnect automates selected post-discharge care management steps. With automation, patients are treated more equally in follow-up, and biases can be mitigated. Megan talks about:
…a follow up care system that will provide a series of automated text messages. These messages will engage a discharged patient and alert a doctor if care is being requested.
Textra’s system is one example of the emerging nudge paradigm in healthcare. Thaler and Sunstein’s book “Nudge” deals with decision making and the behavior science that supports the use of the nudge to improve decisions. They describe the nudge:
By a nudge we mean anything that influences our choices. A school cafeteria might try to nudge kids toward good diets by putting the healthiest foods at front. We think that it’s time for institutions, including government, to become much more user-friendly by enlisting the science of choice to make life easier for people and by gentling nudging them in directions that will make their lives better.
#DigitalHealth solutions that build in nudges such as interactive text-message follow-ups/reminders or haptic medication alerts create pathways for reducing or eliminating cognitive bias. All patients receive relevant notifications at the correct time regardless of provider view or the patient’s perspective.
Considering the digital divide as #hcldr did in December 2016, it’s valuable to note that many of these solutions are conducive to greater access. Texting works on a flip phone, and new generations of simple personal data trackers will see creative use of alerts such as haptics.
Join the #hcldr community of patients, clinicians, administrators, lurkers, counselors, social workers, designers, advocates, and everyone with an interest in improving healthcare. Please join us on Tuesday, March 28, 2017 at 8:30 pm Eastern (for your local time click here) as we discuss the following topics:
- T1: What do you see as the main factors allowing the Big 5 Preventable Diseases to proliferate?
- T2: For the Big 5, can we apply cognitive science/Digital Health to mitigate the effects of biases, #SDoH, and cost-containment?
- T3: What risks/benefits do you see with increasing reliance on #AI decision support in healthcare? Will the clinician role be impacted?
- T4: In addition to the solutions offered, how would you work to eliminate cognitive biases to give people the maximum chance for good health?
Resources for Further Study
Blair, Irene V, John F Steiner, and Edward P Havranek. “Unconscious (Implicit) Bias and Health Disparities: Where Do We Go from Here?” The Permanente Journal 15, no. 2 (2011): 71–78.
“CDC Press Releases.” CDC, May 1, 2014. http://www.cdc.gov/media/releases/2014/p0501-preventable-deaths.html
Cherry, Kendra. “How Cognitive Biases Influence How We Think and Act.” Verywell, May 9, 2016. https://www.verywell.com/what-is-a-cognitive-bias-2794963
Clyne, Wendy, Sarah McLachlan, Comfort Mshelia, Peter Jones, Sabina De Geest, Todd Ruppar, Kaat Siebens, Fabienne Dobbels, and Przemyslaw Kardas. “‘My Patients Are Better than Yours’: Optimistic Bias about Patients’ Medication Adherence by European Health Care Professionals.” Patient Preference and Adherence 10 (2016): 1937. doi:10.2147/PPA.S108827.
Colino, Stacey. “6 Cognitive Biases That May Hurt Your Health Care Decisions -.” Sott.net, October 7, 2016. https://www.sott.net/article/330414-6-cognitive-biases-that-may-hurt-your-health-care-decisions
“Diagnostic Errors | AHRQ Patient Safety Network.” Accessed March 28, 2017. https://psnet.ahrq.gov/primers/primer/12/diagnostic-errors
Ferriss, Timothy, and Arnold Schwarzenegger. Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers. Boston: Houghton Mifflin Harcourt, 2016.
García, Macarena C. “Potentially Preventable Deaths Among the Five Leading Causes of Death — United States, 2010 and 2014.” MMWR. Morbidity and Mortality Weekly Report 65 (November 18, 2016). doi:10.15585/mmwr.mm6545a1.
Hudson, Ed. “Trust Me, I’m a Robot | Artificial Intelligence in Healthcare.” Create Health, March 2, 2017. https://www.createmarketing.co.uk/blog/trust-me-im-a-robot-artificial-intelligence-in-healthcare/
Janas, Megan. “Digital Healthcare Communication and Patient Care. The New Networks Have Arrived.” Blog. Textra Healthcare, August 16, 2016. https://textrahealthcareblog.wordpress.com/
Mike Payne, M. B. A. “A Bundle of Nudges: Healthcare Payment in an Era of Behavioral Science.” American Journal of Managed Care 23, no. March 2017 SP4 (March 4, 2017). http://www.ajmc.com/journals/evidence-based-diabetes-management/2017/march-2017/a-bundle-of-nudges-healthcare-payment-in-an-era-of-behavioral-science/p-1
Novella, Steven. “NeuroLogica Blog » Cognitive Biases in Health Care Decision Making,” January 13, 2017. http://theness.com/neurologicablog/index.php/cognitive-biases-in-health-care-decision-making/
Pearson, Dave. “5 Cognitive Biases Common to Radiology—and How to Beat Them Back.” Health Imaging, January 30, 2017. http://www.healthimaging.com/topics/diagnostic-imaging/5-cognitive-biases-common-radiology%E2%80%94and-how-counter-them
Piette, John D, Sarah L Krein, Dana Striplin, Nicolle Marinec, Robert D Kerns, Karen B Farris, Satinder Singh, Lawrence An, and Alicia A Heapy. “Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: Protocol for a Randomized Study Funded by the US Department of Veterans Affairs Health Services Research and Development Program.” JMIR Research Protocols 5, no. 2 (April 7, 2016). doi:10.2196/resprot.4995.
“Potentially Preventable Deaths from the Five Leading Causes of Death — United States, 2008–2010,” May 2, 2014. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6317a1.htm
Ricciardi, Lygeia. “Improve Your Health with Behavior Change Science.” Tincture, March 24, 2017. https://tincture.io/improve-your-health-with-behavior-change-science-7c771571cb0#.o4xnzrbqy
Saposnik, Gustavo, Donald Redelmeier, Christian C. Ruff, and Philippe N. Tobler. “Cognitive Biases Associated with Medical Decisions: A Systematic Review.” BMC Medical Informatics and Decision Making 16 (2016): 138. doi:10.1186/s12911-016-0377-1.
Thaler, Richard H., and Cass R. Sunstein. Nudge: Improving Decisions About Health, Wealth, and Happiness. Revised & Expanded edition. New York: Penguin Books, 2009.
Thompson-Fields, Darcie. “The Conundrum of Machine Learning and Cognitive Biases.” Access AI, December 19, 2016. http://www.access-ai.com/blogs/the-conundrum-of-machine-learning-and-cognitive-biases/
Yvonne Baur, Brenda Reid, Steve Hunt, and Fawn Fitter. “How AI Can End Bias.” Digitalist Magazine, January 16, 2017. http://www.digitalistmag.com/executive-research/how-ai-can-end-bias
Photo Credit: http://imgur.com/a/BAwOD