The Digital Revolution: Leaving No One Behind

The global AI in healthcare market is growing fast, with an expected increase from $4.9 billion in 2020 to $45.2 billion by 2026. There are new solutions introduced every day that address all areas: from clinical care and diagnosis, to remote patient monitoring to EHR support, and beyond.

But, AI is still relatively new to the industry, and it can be difficult to determine which solutions can actually make a difference in care delivery and business operations.

59 Jan 2021 %

of Americans believe returning Jan-June 2019 to pre-coronavirus life poses a risk to health and well being. 11 41 % %

...expect it will take at least 6 The pandemic has greatly increased the 65 months before things get number of US adults reporting % back to normal (updated April and/or .5 2021).4

Up to of consumers now interested in telehealth going forward.

$250B 76 57% of providers view telehealth more of current US healthcare spend % favorably than they did before COVID-19.7 could potentially be virtualized.6

The dramatic increase in of Medicare primary care visits the conducted through 90% $3.5T telehealth has shown longevity, with rates in annual U.S. health expenditures are for people with chronic and conditions. since April 2020 0.1 43.5 leveling off % % Most of these can be prevented by simple around 30%.8 changes and regular health screenings9 Feb. 2020 Apr. 2020

OCCAM’S RAZOR • CONJUNCTION • DELMORE EFFECT • LAW OF TRIVIALITY • COGNITIVE FLUENCY • BIAS Digital health ecosystems are transforming• BIAS • STATUS medicineQUO BIAS • SOCIAL COMPARISONfrom BIASa rea• DECOYctive EFFECT • REACTANCEdiscipline, • REVERSE • BACKFIRE EFFECT • • PROCESSING DIFFICULTY EFFECT • PSEUDOCERTAINTY EFFECT • DISPOSITION becoming precise, preventive,EFFECT • ZERO-RISK personalized, BIAS • UNIT BIAS • IKEA EFFECT and • LOSS AVERSION participatory. • GENERATION EFFECT • ESCALATION OF COMMITMENT • IDENTIFIABLE VICTIM EFFECT • • HYPERBOLIC DISCOUNTING • RISK COMPENSATION • EFFORT JUSTIFICATION • • DEFENSIVE HYPOTHESIS • FUNDAMENTAL ATTRIBUTION ERROR • OF CONTROL • ACTOR- • SELF-SERVING BIAS • BARNUM EFFECT • • DUNNING-KRUGER EFFECT • HARD-EASY EFFECT • • THIRD-PERSON EFFECT • SOCIAL DESIRABILITY BIAS • • SELF-CONSISTENCY BIAS • • PROJECTION BIAS • PRO-INNOVATION BIAS • TIME-SAVING BIAS • PLANNING FALLACY • PESSIMISM BIAS • • DECLINISM • MORAL LUCK • • ILLUSION OF TRANSPARENCY • • SPOTLIGHT EFFECT • EXTRINSIC INCENTIVE ERROR • ILLUSION OF EXTERNAL AGENCY • ILLUSION Understanding how OF ASYMMETRIC INSIGHT • MENTAL ACCOUNTING • FALLACY • • ZERO SUM BIAS • • SUBADDATIVITY EFFECT • DENOMINATION EFFECT • MAGIC NUMBERS 7±2 • OUT-GROUP HOMOGENEITY BIAS • CROSS-RACE EFFECT • IN-GROUP BIAS • • CHEERLEADER EFFECT • POSITIVITY EFFECT • NOTTOO INVENTED MANY HERE • REACTIVE DEVALUATION • behavior-specific factors WELL-TRAVELED ROAD EFFECT • GROUP ATTRIBUTION ERROR • ULTIMATE ATTRIBUTION ERROR • STEREOTYPING • ESSENTIALISM • FUNCTIONAL FIXEDNESS • MORAL CREDENTIAL EFFECT • JUST-WORLD HYPOTHESIS • FROM FALLACYcontextual • • drive decisions and SOCIAL PROOF • PLACEBO EFFECT • • INSENSITIVITY TO SAMPLE SIZE • NEGLECT OF PROBABILITY • ANECDOTAL30+ FALLACY • ILLUSION OF VALIDITY • MASKED170 MAN FALLACY • GAMBLER’S FALLACY • HOT-HANDfactors FALLACY • actions is critical to this • PAREIDOLIApsychosocial • ANTHROPOMORPHISM • BIAS BLIND cognitiveSPOT • NAÏVE CYNICISM • NAÏVE REALISM • • POST-PURCHASE RATIONALIZATION • CHOICE-SUPPORTIVE BIAS • SELECTIVE •TO OBSERVER-EXPECTANCY COUNT EFFECT • EXPERIMENTER’S BIAS • OBSERVERbarriers EFFECT • EXPECTATION BIAS • OSTRICHbiases EFFECT • SUBJECTIVE VALIDATION • CONTINUED INFLUENCE EFFECT • SEMMELWEIS transformation. REFLEX • ANCHORING • CONSERVATISM • CONTRAST EFFECT • • FOCUSING EFFECT • FRAMING EFFECT • MONEY ILLUSION • WEBER-FECHNER LAW • • HUMOR EFFECT • • PICTURE SUPERIORITY EFFECT • SELF-RELEVANCE EFFECT • • AVAILABILITY • MERE EXPOSURE EFFECT • CONTEXT EFFECT • CUE-DEPENDENT • MOOD-CONGRUENT BIAS • FREQUENCY ILLUSION • • TIP-OF-THE-TONGUE PHENOMENON • GOOGLE EFFECT • NEXT-IN-LINE EFFECT • TESTING EFFECT • ABSENT-MINDED EFFECT • LEVELS OF PROCESSING EFFECT • SUFFIX EFFECT • SERIAL POSITION EFFECT • PART-LIST CUEING EFFECT • RECENCY EFFECT • PRIMACY EFFECT • MODALITY EFFECT • DURATION NEGLECT • LIST-LENGTH EFFECT • • LEVELING AND SHARPENING • PEAK-END RULE • FADING EFFECT BIAS • • IMPLICIT • SPACING EFFECT • SUGGESTIBILITY • • MISATTRIBUTION OF MEMORY • ACTIVE CHOICE • COMMITMENT - CONSISTENCY BIAS • TEMPORAL LANDMARKS • CURIOSITY

Battling Bias and Psychological Barriers to Care

Many of these barriers have been heightened during the pandemic and create significant challenges to effective communication and engagement.

Social & Cultural Context Threat Perception Highlight positive norms tailored Crisis Communication Avoid fear-based strategies in to communities and shared groups Promote trust in communication favor of optimism and positive or identities with the use of credible sources framing to reduce defensive and easily-processed content, responses or helplessness structure, and imagery Individual and Alignment Encourage prosocial behaviors by highlighting the self-gain in protective measures of others

Understanding and overcoming these barriers takes hyper-personalized communications that can learn and evolve with your population and environment. Move your population to better health with proactive engagement and Precision Nudging.TM

Lirio Helps You Drive Behavior Change and Improve Population Health.

In all circumstances, even the most unique like COVID-19, we develop person-centered communication that leads your community to better health

IOR CHAN AV GE EH A B I

Behavior Change AI & Your Communications

Behavior Change AI is the marriage of behavioral science and artificial , TM scaled through Precision Nudging . E B C E N H E A IG V L L IO E R T A IN L L SC IA IEN FIC CE + ARTI

How to Get Started With Behavior Change AI

Start with a Return 1 desired behavior. to care

Profile the population Return and what gets in Start Step Step Step to care 2 their way.

Use behavioral science to create Return content to overcome Start Step Step Step to care 3 barriers to action.

AI goes to work building effective cluster messages 4 from content libraries.

Behavioral reinforcement learning responds to reactions from the population and the 5 clustered groups within.

Over time, messaging is Return hyper-personalized to the to care 6 person within the population.

Schedule a consultation to see how Lirio can www.lirio.com | 877.819.2188 improve your communications and processes.

1https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html 2https://www.healthaffairs.org/do/10.1377/hblog20200716.620294/full 3https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/insights-on-racial-and-ethnic-health-inequity-in-the-context-of-covid-19 4https://www.ipsos.com/en-us/news-polls/axios-ipsos-coronavirus-index 5https://www.medicaleconomics.com/view/covid-19-s-impact-on-americans-mental-health?page=2 6https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19- 7ibid 8https://aspe.hhs.gov/system/files/pdf/263866/hp-issue-brief-medicare-telehealth.pdf 9https://www.cdc.gov/chronicdisease/about/costs/index.htm