Clinician Developed AI and CDS - Built in real time, case by case A SHANE BROWN PHD MEDICAL AND SCIENTIFIC LIAISON ABBOTT DIAGNOSTICS DIVISION September | 2018 Proprietary and confidential — do not distribute ADD-00065154
Value in Laboratory Data Laboratory data is unquestionably of immense value : • 1984 – ICU data included 41% of total patient record 1 • 2000 - Mayo Clinic 94% of data in enquiry system pre digital radiology 2 • 2011 – Aurora Health 82% of stored data generated in pathology 3 • 70% often quoted but more accurately “is integral to many clinical decisions providing . . [HCP] . . With often pivotal information” 4 1) Bradshaw, KE et al. Int J Clin Monit Comput 1984;1:81-91 2) Forsman R Clin Leadership Manag Rev 2000: 14:292-5 3) Feist. K http://www.executivewarcollege.com/2010/PDFs/Feist.pdf 4) The Value of Laboratory Medicine to Health Care. Chapter 1. In: The Lewin Group: Laboratory Medicine – A National Status Report . May 2008:19 – 65. See http://www.ascls-sd.org/sitebuildercontent/sitebuilderfiles/laboratory_medicine_-_a_national_status_reportmay08.pd Proprietary and confidential — do not distribute
Contribution of AI in Financial Services • Transactions involve only a single item – currency or derivatives representing currency • International standardisation of currency values • Global regulation of activities • Consumers predictable - mostly Proprietary and confidential — do not distribute
Contribution of AI in Business Intelligence • Transactions involve multiple items – however centred around individual entities e.g. retail, production, logistics • Standard and controlled operations within each entity • Local control of activities • Transactions / activity controlled Proprietary and confidential — do not distribute
Contribution of AI in Health Management • Transactions involve multiple items in multiple entities across multiple sectors • Practices vary widely entity to entity and sector to sector • Limited control of activities • Imprecise predictability of individual health needs Proprietary and confidential — do not distribute
AI In Health Case Study: IBM Watson/MD Anderson • Project commenced in June 2012 – budget $2.4M • Project terminated in September 2016 – expense $39.2M • Goal was to “help community oncologists provide MD Anderson -quality cancer care to patients who cannot seek treatment directly from MD Anderson physicians” • Outcome: it “never guided the treatment of any community - based patients” https://www.medscape.com/viewarticle/876070 Proprietary and confidential — do not distribute
AI in Health (2) Case Study: IBM Watson/MD Anderson • IBM claimed that Watson would “continually ingest patient and research data, medical literature, and treatment options, to offer care advice” • However, its recommendations are not based on computed insights from this data. • Instead recommendations rely exclusively on supervised training from clinical experts • At the end of the MD Anderson trail with work from computer engineers and doctors, Watson was able to deal with only seven types of cancer https://www.medscape.com/viewarticle/876070 https://www.statnews/2017/09/05/Watson-ibm-cancer Proprietary and confidential — do not distribute
What is different about the questions we ask in Health and Medicine Q1 : When was the Battle of Hastings? 1066 Q2: Who discovered insulin? Banting and Best (Sharpey-Schafer) Q3 : Why is the sky blue? Rayleigh scattering Q4: Why do I have a headache? ????? Proprietary and confidential — do not distribute
Why do I have a headache? • Context • Multiple data sources • Query ability • Assimilation capability • Experience in the specific domain to interpret the information i.e. An Expert is required Proprietary and confidential — do not distribute
Expert Systems • Developed extensively in the 1990’s, were the first truly successful form of AI. • Solve complex problems by application of a corpora of knowledge through rules rather than attempting to classify via coded algorithms. • Sophisticated expert systems expose their user interface such that non- programmers create the rules. • Have been adopted by many application suite vendors as integral components of their AI technology. Proprietary and confidential — do not distribute
Expert Systems Advantages Disadvantages • Rules development provided by a • System development relies heavily domain expert so delivery of on domain experts. system outputs is independent of Supervised training sets with • an IT specialist. defined end points are needed. • Easy to maintain since no • Needs on-line system integration. conventional code. • Systems are built incrementally. • Training data sets are provided incrementally. Proprietary and confidential — do not distribute
Ripple Down Rules (RDR) • Developed by Prof Paul Compton and colleagues at Garvan Institute of Medical Research in 1988. • Uses “case based reasoning” to incrementally acquire knowledge with each creation of a new rule. • Built on the premise that at any point in time more WILL be known about any given topic – poorly classified cases are used to improve the quality of the knowledge. • Assumes that a) knowledge is only correct in context and b) over time that knowledge will be modified – eg the atom. Proprietary and confidential — do not distribute
RDR Expert technology Proprietary and confidential — do not distribute
Expert Controlled Rules Engine 8 rules added 7 rules added Proprietary and confidential — do not distribute
Rapid, Consistent Rule Building – In production Environments No Potential Conflict Write a rule Conflicting ? case? Yes Yes No Conflict or No more cases - Finish rule session Add conditions AlinIQ-CDS Knowledge Update rules Base Proprietary and confidential — do not distribute
Rapid, Consistent Rule Building – Case Studies OhioHealth USA Dr Eugenio Zabaleta Proprietary and confidential — do not distribute
Proprietary and confidential — do not distribute
Rapid, Consistent Rule Building – Case Studies Lancet Laboratories South Africa Cumulative Number of Kbase 35 30 25 Dr Peter Cole 20 15 10 5 Dr Emma Wypkema 0 2013 2014 2015 2016 2017 2018 2019 18 Proprietary and confidential — do not distribute
Organisational use of Expert System in Lancet Pathologists • Specimen reception • Workflow • Data entry • Laboratory alerts • Accounts • Appropriate testing (intelligent reflex) • Appropriate testing (MTF) • Patient centred reporting • Customer service (Referrers and Funders) • Patient / Referrer tailored reports • Marketing Proprietary and confidential — do not distribute
Clinical Decision Making is Complex 1 2 3 1. Graber ML, Franklin N, GordonR. Arch Intern Med 2005; 165: 1493-9. 2. Singh H et al, JAMA Intern Med 2013; 173(6); 418-25. 3. Kachalia A, et al. Ann Emerg Med 2007; 49: 196-205. Proprietary and confidential — do not distribute
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