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Noll Campbell,"Artificial Intelligence & Disabilities: Cognitive Impairment and Dementia"

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Noll Campbell,"Artificial Intelligence & Disabilities: Cognitive Impairment and Dementia"

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March 24, 2020

This event will highlight the challenges and opportunities in harnessing artificial intelligence (AI) technologies to serve the needs of individuals with disabilities and dependencies. AI can improve the lives of people with disabilities, such as smart devices supporting people with physical disabilities or sight loss. On the other hand, AI outputs can also reflect discriminatory biases present in the underlying data used to develop the algorithms. While this “garbage in, garbage out” principle is well documented in respect to AI and gender or race, it is understudied in respect to disability or dependencies.

Interdisciplinary panels of legal scholars, ethicists, AI developers, medical and service providers, and advocates with disabilities/ dependencies will explore best practices and guidelines for stakeholders, guided by ethical principles, legal considerations, and the needs of people with disabilities/ dependencies. Participants will seek to articulate clear criteria for developers and medical providers looking to harness the potential of AI to serve individuals with disabilities/ dependencies, including those whose disabilities/ dependencies are the result of aging, injury, or disease, and the caregivers -- including both professionals and unpaid friends and families -- who support some of these individuals.

This webinar was free and open the public.

For more information, visit our website at https://petrieflom.law.harvard.edu/events/details/artificial-intelligence-and-disability-dependency

March 24, 2020

This event will highlight the challenges and opportunities in harnessing artificial intelligence (AI) technologies to serve the needs of individuals with disabilities and dependencies. AI can improve the lives of people with disabilities, such as smart devices supporting people with physical disabilities or sight loss. On the other hand, AI outputs can also reflect discriminatory biases present in the underlying data used to develop the algorithms. While this “garbage in, garbage out” principle is well documented in respect to AI and gender or race, it is understudied in respect to disability or dependencies.

Interdisciplinary panels of legal scholars, ethicists, AI developers, medical and service providers, and advocates with disabilities/ dependencies will explore best practices and guidelines for stakeholders, guided by ethical principles, legal considerations, and the needs of people with disabilities/ dependencies. Participants will seek to articulate clear criteria for developers and medical providers looking to harness the potential of AI to serve individuals with disabilities/ dependencies, including those whose disabilities/ dependencies are the result of aging, injury, or disease, and the caregivers -- including both professionals and unpaid friends and families -- who support some of these individuals.

This webinar was free and open the public.

For more information, visit our website at https://petrieflom.law.harvard.edu/events/details/artificial-intelligence-and-disability-dependency

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Noll Campbell,"Artificial Intelligence & Disabilities: Cognitive Impairment and Dementia"

  1. 1. Noll Campbell, PharmD, MS Assistant Professor, Purdue University Faculty Associate, CALC, Purdue University Scientist, IU Center for Aging Research Scientist, IU Center for Innovation & Implementation Science Clinical Pharmacy Specialist, Eskenazi Health campbenl@iupui.edu Artificial Intelligence and Disabilities: Cognitive Impairment & Dementia
  2. 2. eCHAMP Study Justification • Improving the care of hospitalized older adults with acute or chronic cognitive impairment eCHAMP Khan, et al. JAGS 2011 Identify presence of CI ACh ↓ Referral to Geriatrics
  3. 3. Primitive AI: Provider-Focused CDS Notification Baseline variable CDSS N = 199 Usual Care N = 225 P Value Mean Age (SD) 76.8 (7.9) 77.6 (8.3) 0.32 % Female 60.3% 71.1% 0.02 % African American 61.8% 57.3% 0.37 Mean CCI (SD) 1.8 (1.8) 2.4 (2.1) <0.001 Mean SPMSQ (SD) 5.2 (2.6) 5.1(2.8) 0.67 % Delirium at Screening 30.2% 31.1% 0.83
  4. 4. eCHAMP Results CDSS N = 199 Usual Care N = 225 P value adjusted* Percent with any ACB order - First 48 hours of hospital stay - Entire hospitalization 13.6% 23.6% 14.7% 21.3% 0.91 0.33 Percent of ACB orders discontinued$ - First 48 hours of hospitalization - Entire hospitalization 7.4% 48.9% 3.0% 31.2% 0.46 0.11 Percent with any ACB discontinuation order - First 48 hours of hospital stay - Entire hospitalization 1.0 11.6% 0.4 6.7% 0.41 0.05 *Adjusted for Age, Gender, Race, SPMSQ, and Charlson comorbidity $ Denominator was number of orders eligible for discontinuation Boustani M, et al. J Gen Intern Med 2012 May;27(5):561-7
  5. 5. PMD Study Justification • Pharmacologic Management of Delirium to reduce short and long-term complications PMD Low dose haloperidol ACh ↓ BZD ↓ Khan, et al. JAGS 2011
  6. 6. Pre-Randomization Post-Randomization PMDa (N=170) Usual Care (N=176) P-value PMD (N=170) Usual Care (N=176) P-value Haloperidol Exposedb n (%) 29 (17.1) 32 (18.2) 0.888 116 (68.2) 56 (31.8) <0.001 Median daily Dose (IQR) 0 (0-0) 0 (0-0) 0.723 0.5 (0-0.9) 0 (0-0.3) <0.001 Benzodiazepinesc Exposedb n (%) 122 (71.8) 118 (67.0) 0.353 97 (57.1) 116 (65.9) 0.098 Median daily Dose (IQR) 1.3 (0 – 13.1) 1.0 (0-10.5) 0.466 0.1 (0-2.0) 0.3 (0-3.2) 0.079 Anticholinergic Burdend Exposedb n (%) 30 (17.6) 29 (16.5) 0.777 44 (25.9) 54 (30.7) 0.342 Median daily score (IQR) 0 (0-0) 0 (0-0) 0.706 0 (0-0.1) 0 (0-0.2) 0.248 Campbell NL, et al. J Am Geriatr Soc 2019 Apr;67(4):695-702 Khan BA, et al. J Am Geriatr Soc 2019 May;67(5):1057-1065
  7. 7. Brain Safety Lab R2D2 Funding: NIA R01AG061452 PI: Noll Campbell Cluster-randomized trial to determine whether deprescribing anticholinergics improves clinical outcomes in older adults Brain Safe Funding: NIA R01AG056926 PI: Rich Holden Randomized trial to determine if a mobile, scalable deprescribing intervention reduces use of anticholinergics among older adults PSLL Funding: AHRQ P30HS24384 PI: Chris Callahan Randomized trial of a system-wide dual provider and patient- focused deprescribing intervention in primary care
  8. 8. Noll Campbell, PharmD, MS Assistant Professor, Purdue University Faculty Associate, CALC, Purdue University Scientist, IU Center for Aging Research Scientist, IU Center for Innovation & Implementation Science Clinical Pharmacy Specialist, Eskenazi Health campbenl@iupui.edu Artificial Intelligence and Disabilities: Cognitive Impairment & Dementia

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