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IBM Terkko Pop-up Presentation by Pekka Leppänen

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About Watson and Healthcare by Pekka Leppänen

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IBM Terkko Pop-up Presentation by Pekka Leppänen

  1. 1. 1 © IBM 2018 Pekka Leppänen IBM Healhcare +358 40 75 88 106 leppanen@fi.ibm.com About Watson and Healthcare
  2. 2. 2 © IBM 2018 1. Too much data, but not enough money 2. Data Science + Health = Watson Health 3. Examples • Watson for Oncology, Watson for Genomics, Watson for Clinical Trials Matching • Watson Conversation • Watson Content Analytics Discussion Topics
  3. 3. 3 © IBM 2018 We live longer – but cannot afford it Life expectancy increases by 2 months every year (2016: Boys -2 months, Girls: 0). Until 2012 over 1/2 of population was working, 2030 through 2040 only 1/3. We have run out of money and the situation will get worse until 2040. Healthcare needs to transform from labor intensive to information intensive. Fee-for-Service model is being replaced by Value Based Care, with focus on Population Health www.stat.fi/til/kuol/2016/01/kuol_2016_01_2017-10-27_tie_001_en.html www.thl.fi/en/tilastot/tilastot-aiheittain/sosiaali-ja-terveydenhuollon-talous/terveydenhuollon-menot-ja-rahoitus
  4. 4. 4 © IBM 2018 McGovern, L., Miller, G., Hughes-Cromwick, P., Mays, G., Lantz, P., Lott, R. (2014). The relative contribution of multiple determinants to health outcomes. Health Affairs (Robert Wood Johnson Foundation), 33, 2.
  5. 5. 5 © IBM 2018 It’s humanly impossible to keep up with the knowledge and the data… In medicine, there’s a gap between what we know and what we do… This rising tide of information contains insights critical to your success 24 months Frequency at which healthcare data doubles2 80% of medical data is invisible because it’s unstructured1 >1PB The amount of health- related data a person generates in their lifetime3 45% of medicine is not evidence based4 17 years Time it takes to translate science to practice5 A New Reality in Healthcare 1. ASCO Releases Its First-Ever Report on the State of Cancer Care in America. Available at: http://www.ascopost.com/issues/april-15-2014/asco-releases-its-first-ever-report-on-the-state-of-cancer-care-in-america/. Accessed November 15, 2016. 2. Marconi, Katherine and Lehmann, Harold. Big Data and Health Analytics. CRC Press, 2014. Available at: http://bit.ly/1UjEtLL. Accessed June 3, 2016 3. Managed Care, January 2011 and HealthAffairs Blog, July 2011 4. Elizabeth A. McGlynn, Ph.D., Steven M. Asch, M.D., M.P.H., John Adams, Ph.D., Joan Keesey, B.A., Jennifer Hicks, M.P.H., Ph.D., Alison DeCristofaro, M.P.H., and Eve A. Kerr, M.D., M.P.H. N Engl J Med 2003; 348:2635-2645 June 26, 2003. DOI: 10.1056/NEJMsa022615. Available at: http://www.nejm.org/doi/full/10.1056/NEJMsa022615#t=abstract 5. Slote Morris, Zoë & Wooding, Steven & Grant, Jonathan. (2011). The answer is 17 years, what is the question: Understanding time lags in translational research. Journal of the Royal Society of Medicine. 104. 510-20. 10.1258/jrsm.2011.110180. Available at: https://www.researchgate.net/publication/51897868_The_answer_is_17_years_what_is_the_question_Understanding_time_lags_in_translational_research6.
  6. 6. 6 © IBM 2018 Value-based care presents a problem of scale FULL RISK Brings new responsibility for managing overall health Optimized health outcomes, maximized revenue OPTIMAL CLINICAL DELIVERY Requires transformative care management as the population expands Population under management Value-based reimbursement Scaling up to value-based care Max transformation Max risk
  7. 7. 7 © IBM 2018 1. Too much data, but not enough money 2. Data Science + Health = Watson Health 3. Examples • Watson for Oncology, Watson for Genomics, Watson for Clinical Trials Matching • Watson Conversation • Watson Content Analytics Discussion Topics
  8. 8. 8 © IBM 2018 Watson Health is forging a partnership between humans and machines Together, we can… Generate Remarkable Outcomes Help Accelerate Discovery Create Essential Connections Enable Heightened Confidence People excel at: Common sense Dilemmas Morals Compassion Imagination Dreaming Abstraction Generalization Cognitive systems excel at: Natural Language Pattern Identification Locating Knowledge Machine Learning Eliminate Bias Endless Capacity
  9. 9. 9 © IBM 2018 Cognitive systems are generally defined by the ability to understand, reason, learn, and interact UNDERSTAND Cognitive systems can understand unstructured information the same way humans do REASON They can reason, grasp underlying concepts, form hypotheses, and infer to extract ideas LEARN Each data point, interaction and outcome helps to continuously sharpen expertise INTERACT With abilities to see, talk and hear, cognitive systems interact with humans in a natural way
  10. 10. 10 © IBM 2018 What if a solution could help you derive the insights you need, when you need them? Turn information into insights Keep up with growing volumes of data Acquire information from many disparate sources
  11. 11. 11 © IBM 2018 In light of this disruption, digital technologies have become a source for creating new value in healthcare Artificial intelligence Simulation of human intelligence processes Robotics Conception, design, manufacture, and operation of robots Machine learning systems Ability to learn and improve without explicit instructions Natural language processing Ability to understand human speech as it is spoken Deep learning Machine learning with artificial neural network algorithms Predictive analytics Predicting outcomes using statistical algorithms and machine learning Recommendation engines Analyze data and suggest something as per user’s interest Cognitive Technologies Create new ways of interacting with customers Reveal more powerful on-demand business insights through real-time access to data Enable business model – and ecosystem – transformation Business Value
  12. 12. 12 © IBM 2018 Today, cognitive computing provides both strategic and financial value while improving quality care to a patient Re-invent client engagement Digitize and streamline processes Deploy disruptive business models § Customer satisfaction § Revenue growth (shorter sales cycle) § Cost reduction (headcount) § Customer satisfaction § Customer retention § Cost reduction (operational) § Revenue growth (new product development) § Revenue acceleration (speed to market entry) Scaling human expertise cost- effectively Natural language interactions Streamlining, standardizing, and improving decision processes Business Challenge Example of how Cognitive helps today Primary Strategic Value Primary Financial Value Client Impact Time- consuming search Evidence- based responses Yielding insights unattainable by humans ENGAGEMENT § Complex patient engagement scenarios with different past histories § Provide patients comprehensive evidence based answers to complex questions DISCOVER § Help patients with insights far above human levels § Finds insights and connections, understands the vast amounts of information available DECIDE § Offer evidence-based recommendations § Evolve continually towards more accuracy based on new information, outcomes, and actions
  13. 13. 13 © IBM 2018 Five pillars enabled through a platform that has data, knowledge, analytics and industry specific solutions supported on a secure cloud. Life Sciences Oncology/ genomics Imaging Value based care Government Analytics/Insights Platform Image Analytics Cognitive Knowledge Platform IBM Watson Health Cloud 200M+ lives 100M+ patient records Images 15M+ pages of medical literature 40+ M research documents
  14. 14. 14 © IBM 2018 1. Too much data, but not enough money 2. Data Science + Health = Watson Health 3. Examples • Watson for Oncology, Watson for Genomics, Watson for Clinical Trials Matching • Watson Conversation • Watson Content Analytics Discussion Topics
  15. 15. 15 © IBM 2018 “In 30% of the cases, Watson had found something new” “These were things that by our own definition, we would’ve considered actionable had we known about it” — Dr. Ned Sharpless, former Director of the Lineberger Cancer Center, on 60 Minutes Data & Evidence – Concordance & Additional Treatment Recommendations A case study with UNC Lineberger Comprehensive Cancer Center compared the human tumor board and Watson for Genomics’ analysis of tumor sequencing data: 1: Cancer Diagnostics and Molecular Pathology: Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing. The Oncologist first published on November 20, 2017; doi:10.1634/theoncologist.2017-0170. Accessed at: http://theoncologist.alphamedpress.org/content/early/2017/11/20/theoncologist.2017-0170.full.pdf+html?sid=0703cdfd- db36-45fb-b561-a81544688384 1,022 patients analyzed Watson was >99% accurate in identifying tumor board findings Watson identified additional options in 335 patients (33%) of the patients 42 patients with highly actionable mutations1
  16. 16. 16 © IBM 2018 Data & Evidence – Efficiency 16 In a recent comparison study by the New York Genome Center, researchers using a beta version of Watson to help scale the interpretation of whole genome sequencing found that: 10 minutes Watson provided a report of potential clinically actionable genomic insights. Whole genome sequencing identified more clinically actionable mutations than the current standard of examining a limited subset of genes, known as a targeted panel.1 9,600 minutes Human analysis and curation arrived at similar conclusions for this patient. 1: Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma. Neurology Genetics Aug 2017, 3 (4) e164; DOI: 10.1212/NXG.0000000000000164. Accessed at: http://ng.neurology.org/content/3/4/e165
  17. 17. 17 © IBM 2018 Data & Evidence — Operational Efficiency Cognitive technology addressing optimal cancer clinical trials: matching and protocol feasibility in a community cancer practice During a 16-week trial period, data from 2,620 visits by lung and breast care patients were processed in the Clinical Trial Matching (CTM) system. Watson for Clinical Trial Matching successfully demonstrated the ability to expedite patient screening for clinical trial eligibility, reducing processing time from 1 hour and 50 minutes to 24 minutes. Increased efficiency (Compared to manual work by a clinical trial coordinator at Highlands Oncology Group) 78% Reduced pre-screening wait time by 78% 94% Omitted 94% of non-matching patients automatically – reducing screening workload dramatically 2017 ASCO Annual Meeting. Cognitive technology addressing optimal cancer clinical trial matching and protocol feasibility in a community cancer practice. DOI: 10.1200/JCO.2017.35.15_suppl.6501 Journal of Clinical Oncology 35, no. 15_suppl (May 2017) 6501-6501. Accessed at: http://ascopubs.org/doi/abs/10.1200/JCO.2017.35.15_suppl.6501#affiliationsContainer
  18. 18. 18 © IBM 2018 Natural conversation is key to effective patient engagement Maija has been pondering how her new hobby – ultra running – impacts her nutritional needs. She decides to seek professional advise. Via her app, she accesses the chat function and initiates a conversation. MAIJA’S JOURNEY 1 2 I live in Ruoholahti Helsinki We have dieticians available at these locations. Which location do you prefer? Where can I get advise on nutrition? In that case I suggest Porkkalankatu. Make a booking here. Hi! How can I help you? Customer approaches with a specific intent. Conversational system identifies intent and relevant entities. 1 Through intents and entities conversational system is able to follow conversational logic, ask follow-up questions, and direct to correct pages. 2 UNDER THE HOOD
  19. 19. 19 © IBM 2018 Creating the intent model starts with identifying probable utterances expressed by chatbot users. Utterances can be collected from historical customer service data and new data from real end users. The solution is taught to detect correct intents and entities from the utterance, and trigger a matching action. Intents are extracted from the utterances. Intent definition and categorization is a critical part of intent model creation. Intent definitions should be aligned with the purpose of the chatbot. In addition to intents, conversational system recognizes entities, which are classes of objects or data types relevant to a user's purpose. Entities help the system to select the most appropriate action to be triggered. Finally, intent model creation includes defining correct actions for the chatbot to retrieve based on the detected intent and entities. Functionalities like response variation randomization and slot filling help creating a more human-like experience. Response quality can be scored by users (e.g. thumbs up/down or 1-3 stars). Collected feedback can be used to improve accuracy through re-training. Design and build of the intent model is a fundamental element of creating natural conversation with Watson Conversatio Where can I get advise on nutrition? #find_location @nutrition We have dieticians available at these locations. Which location do you prefer? UTTERANCE → INTENT → ENTITY → ACTION → FEEDBACK
  20. 20. 20 © IBM 2018 Natural conversation expands and integrates to cover both pre and post care processes As Maija is running, suddenly she feels pain in her knee. Frustrated, she is forced to stop. Maija reaches for her phone, opens the app, logs in with touch id, and opens the chat function. As the customer logs in, the conversational system is aware of her profile. For instance whether she is an occupational or private customer. 1 2 Dr. Mallikas has an appointment time on Tuesday 3.10. 9:00 am at Porkkalankatu. Would you like to reserve it? The intent and entities in the message are identified by the conversational system. 2 Integration to the patient information system and business logic enable managing of appointments without human operators. 3 Maija is able to make a booking by answering the questions in the conversation. The night before her appointment, she gets a reminder message. Despite the unfortunate circumstances Maija is happy to see Dr. Mallikas. 1 I’m sorry to hear that Maija. Would you like to see the same orthopedist as previously? That sounds like a good idea. My knee hurts. I need to see a doctor. Is she available around noon? She is free at 12:15 pm. Should I book it? 3 UNDER THE HOOD
  21. 21. 21 © IBM 2018 Kela – Specific official support in a demanding domain in Finnish language FINNISH LANGUAGE IMPLEMENTATION1 OFFICIAL SUPPORT IN A CHALLENGING DOMAIN2HIGHLIGHTS Key considerations: • Solid Finnish language implementation in a juridically challenging domain • Very simple UI utilizing pre-build functionalities fitted to KELAs official public image. • Provides “Official and Legally checked” answers to questions related to students benefits. • Links easily to additional information and functionalities like calculators etc. LINKS CUSTOMERS TO ADDITIONAL SOURCES OF INFORMATION 3
  22. 22. 22 © IBM 2018 Single log in Healthcare service providers and other stake holders Patient Information Systems Kanta Data lake Patient-360° crawl, index and integrate to a chosen viewpoint
  23. 23. 23 © IBM 2018 Medical report TYÖTERVEYSHUOLLON KÄYNTI KIRJAUS : ”... 58-vuotias nainen saapui vastaanotolleni 8.2.2017 sen jälkeen kun hän oli kärsinyt useamman päivän huimauksesta, ruokahaluttomuudesta, anoreksiasta, kuivasta kurkusta, lisääntyneestä janon tunteesta sekä toistuvasta virtsaamisen tarpeesta. Hänellä oli ollut aiemmin kuumetta ja kertoi nielemisvaikeuksista ja ruuan jäämisestä helposti kiinni kurkkuun. Hän ilmoitti myös kivusta vatsassaan, selässsä sekä kyljessä. Ei yskää, hengenahdistusta, ripulia tai dysuriaa. Hänellä on suvussa ilmennyt suu sekä virtsarakkosyöpää äidin puolelta, gravesin tautia kahdella sisaruksella, hemochromatosis yhdellä sisaruksella ja idiopaattinen thrombocytopenic purpura yhdellä sisaruksella. Hänen potilashistoriassaan on mainintoja huomattavasta cutaneous lupuksesta, hyperlipidemiasta, osteoporoosista, usein toistuvuvista virstatien tulehduksista, kolmesta keisarin sektiosta jossa ei ilmennyt komplikaatiota, vasemman ophorectomyn hyvän laatuisesta kystasta, primääri kilpirauhasen vajaatoiminnasta joka oli diagnisoitu vuosi kystan löytämisen jälkeen. Hänellä on määrätty lääkitys levotyroksiiniin, hydroksiklorokiiniin, pravastatiiniin ja alendronaattiin. Virtsanäyteestä ilmeni positiivisa leukosyyttiesteraasia ja nitriittejä. Potilaalle oli määrätty resepti fo ciprofloxacinia virtsatieinfektiota varten. Kolme päivää myöhemmin potilas kertoi heikkoutta ja huimausta. Hänen verenpaineensa oli 120/80 mmHg, ja pulssi oli 88...” SAIRAALAN KEUHO-OSASTON YÖ VUORO KIRJAUS : HAPETUS : Tavoitteena normoventilaatio hengityskoneessa, limaa nousee jnkv. sekä trakeasta että nielusta HEMODYNAMIIKKA: Tavoitteen RR-syst 140-120 mmHg -> Noradrenaliinaa titrattu. 0 sen mukaisesti CVP- tavoite 6-10 mmHG. Eks:ssä *null* 0 siisti SR. DIUREESI : OK Tajunta : Sedaatiotauolla nopeasti pintaan vetää. Mielekkäästi kädet kohti intubaatioputkea. Avaa silmät puheelle. OMAISET : Mies keskustellut tilanteesta EA- pkl:llä lääkärin kanssa. TAJUNTA : Potilaan tajunta ennen koilausta tauon aikana hyvä ; liikutteli kaikkia raajojaan pupillat keskikokoiset ja valolle reagoivat. Kädet menee mielekkäästi kohi putkea. Pyyntöjä ei noudata / kielimuuri? Hoitamaton lienee vuotava aneurysma. Nimotop ja Caprilon jatkuvatKOTIHOITO KONTROLLI KIRJAUS : Vas jalkapöydän kipua ja huimausta. RR 169/92, p. 80, SpO2 96.5%, lämpö 36,1.Lab+ Tytär käynyt illalla. Kontrolloitu natiivirtg eikä mielestäni kipua selittävää kuvassa. crp 38, Hb alhainen, 90, mikä ei uutta, mutta etiologia ilmeisesti avoinna
  24. 24. 24 © IBM 2018 Text Mining Watson Content Analytics UIMA CRAWL & IMPORT PARSE & INDEX SEARCH & ANALYTICS Data is ingested from more than 40 datasource R represents a C1-12 alkyl group or alkoxyl group, C2-12 alkenyl group or alkoxylalkyl group ... Z represents a fluorine atom, chlorine atom, bromine atom, cyano group, --OCF3, --OCF2 H, -- CF3, -- ... alkoxyl group, C2-12 alkenyl group or alkoxylalkyl group or C3-12 alkenyloxy group, which comprises ... the following general formula (IV): [Figure] wherein Z represents a fluorine atom, chlorine atom, ... H, --CF3, --OCH2 CF3, C1-12 alkyl group or alkoxyl group, C2-12 alkenyl group or alkoxylalkyl group ... group which may be substituted by fluorine atom, trans- 1,4-cyclohexylene group, pyrimidine-2,5-diyl ... -1,3-dioxane-2,5- diyl group; and m represents an integer of 0 or 1 and a compound represented by the ... R represents a C1-12 alkyl group or alkoxyl group Natural Language Processing • Tokenization • Morphological Analysis • Part-of-Speech Detection • Entity Extraction • Semantic Analysis • Sentiment Analysis • Clustering • etc. Key information is extracted using NLP Toimii myös suomenkielellä Business user obtains insight using intuitive and unique mining application What products are increasing in recent problems? What are the requests and claims that stand out for specific products? What is the correlation between products and defects? Other data source via API Export to RDB Deep Inspection Alerts Business User text index Linguistics – WCA Studio Linguistic rules based Watson Knowledge Studio Machine Learning based DEVELOP – TEACH MACHINE Analyzed Content & data Analyzing Natural Language with UIMA
  25. 25. 25 © IBM 2018 Finnish Language Support
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