Downloaded 24 times
![[Using a new AI system], Johns Hopkins
can assign beds 30% faster. This has
reduced the need to keep surgery
patients in recovery rooms longer than
necessary by 80% and cut the wait time
for beds for incoming ER patients by 20%.
The new efficiencies also permitted
Hopkins to accept 60% more transfer
patients from other hospitals.
HARVARD BUSINESS REVIEW, HARVARD BUSINESS SCHOOL
NOVEMBER 2018](https://image.slidesharecdn.com/bioitworld2019-aiforhealthcare-simontaylor-190423222641/75/Bio-IT-World-2019-AI-For-Healthcare-Simon-Taylor-Lucidworks-32-2048.jpg)
Uploaded byLucidworks
Bio IT World 2019 - AI For Healthcare - Simon Taylor, Lucidworks
1) An AI system implemented at Johns Hopkins Hospital helped optimize hospital operations and bed assignment. It allowed beds to be assigned 30% faster. 2) This reduced the need to keep surgery patients in recovery rooms longer than necessary by 80% and cut wait times for ER patients to receive beds by 20%. 3) The efficiencies also allowed the hospital to accept 60% more transfer patients from other hospitals.
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Bio IT World 2019 - AI For Healthcare - Simon Taylor, Lucidworks
- 1. To AI orNot to AI, That Is the Question Simon Taylor, VP Global Partners & Alliances, Lucidworks
- 2. Concerns about AIin Healthcare? 1. Bad data in = bad recommendation out. False positives reducing confidence in machine learning 2. Longer term complacent dependencies on “infallible” machine learning 3. In ability for AI to take account of the wider context of patient needs / treatments HEALTHCARE IT NEWS - JANUARY 2019
- 3. Narrow “Weak” vs.General “Strong” AI Artificial Narrow Intelligence (ANI) is the AI that exists in our world today, programmed to perform a single task — whether it’s checking the weather, being able to play chess, or analyzing raw data to write journalistic reports. Artificial General intelligence (AGI) or refers to machines that exhibit human intelligence. In other words, AGI can successfully perform any intellectual task that a human being can being conscious, sentient, and driven by emotion and self- awareness. ANI systems are able to process data and complete tasks at a significantly quicker pace than any human being can, which has enabled us to improve our overall productivity, efficiency, and quality of life e.g. assist doctors to make data-driven decisions, making healthcare better AGI is expected to be able to reason, solve problems, make judgements under uncertainty, plan, learn, integrate prior knowledge in decision- making, and be innovative, imaginative and creative.
- 4. …agile organizations insist onfull transparency of information, so that every team can quickly and easily access the information they need... MCKINSEY, JANUARY 2018 Organizations possess lots of data...siloed inside disconnected applications, and unavailable to employees at their moments of need. FORRESTER, JULY 2018 85% of employees are not engaged or actively disengaged at work. GALLUP, 2017
- 5. Administrative and operational inefficienciesaccount for nearly one third of the U.S. health care system’s $3 trillion in annual costs. HARVARD BUSINESS REVIEW, HARVARD BUSINESS SCHOOL NOVEMBER 2018
- 6. 6 out ofevery 10 people who work in health care never interact with patients. Even those who do can spend as little as 27% of their time working directly with patients. The rest is spent in front of computers, performing administrative tasks. HARVARD BUSINESS REVIEW, HARVARD BUSINESS SCHOOL NOVEMBER 2018
- 7. The Time itTakes to Get things Done BigDataManagement &Analytics Data Scientist Driven Activity Business Alignment ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ Meet the “Last Mile Problem”
- 8. Facet, Topic & Cluster Query Rule Matching Natural Language Machine Learning Boosted Results Signals Content Index SystemGenerated Human Generated Application Generated Data Solution Digital Workplace What’s the Alternative?
- 9.
- 10. Let’s Focus onWhere AI is Helping Healthcare • MRI / CIT Image Diagnosis • Hospital Operations Decision Making • Exploring Clinical Pathways • Patient Risk Management • Automation & Exploration of Clinical Documentation • Ontology Base Search • Fraudulent Claim Detection • Competitive Drug Go-to-market MEDIACITY NEWS, JONATHAN MUSE JUNE 2018
- 11. Complex Oncology solutions for decisionbased personalized medicine & patient care LARGE VOLUMES OF LEGACY DATA OVER LONG TIMELINES Easier Optimization of hospital operations to predict demand for additional ED capacity CORRELATION OF REAL-TIME INPATIENT DATA AI Healthcare Decision Paths
- 12. Machine learning isa method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. SAS INSTITUTE INC. 2019
- 13. Many AI tools’designs mainly focus on data, not human. Thus, a great AI platform is usually solution based leveraging search rather than just tool based. AI AUTHORITY CHAO HAN, HEAD OF DATA SCIENCE, LUCIDWORKS SEPT 2018
- 14.
- 15. • Open sourcetech at its core: Apache Solr & Apache Spark • Personalizes work with applied machine learning • Hardened on the biggest corporate & government information systems
- 16. Integration requires: • Comprehensivedata access • Integrated end-to-end security • Classification & categorization The Integration Challenge: Securely access, ingest & synchronize data in real-time, and at massive scale
- 17. How to Eliminatedata silos and reducing tribal wisdom FEATURE BENEFIT IMPACT Entity Extraction Classify and categorize items such as people, places, and things from unstructured text Improve search precision so people can do their jobs better with less error 200+ connectors and advanced Connector SDK Real-time access to billions of documents across storage systems and file formats Increase: access to precise information and employee productivity. Decrease: frustration, time to market, and time to value. Integrated security Reduce risk by meeting global compliance requirements Securely administer access controls by integrating AD, SSO, and Kerberos with support for field, document, and user level restrictions 1 2 3
- 18. Curate the experienceby: • Understanding the way your analysts access and pivot around data • Automatically tuning relevancy through ML • Personalizing predictive outcomes and insights with AI
- 19. FEATURE BENEFIT IMPACT Self-learning,real-time recommendations engine Help users when they don’t know what they are looking for Increases employee productivity. Decreases: frustration, time to value and time to market Natural Language Processing and Rules based NLP suite Parse and process normal language queries so users don’t have to learn complex operators or functions Allows users to search like they speak augments individual and organizational intelligence Content and signal-driven automatic relevancy Personalized results improve with each click as the system learns from user activity Connects people to insights, content and each other, promoting collaboration, reducing frustration and increasing innovation What you need to improve user engagement 1 2 3
- 20. The Exploration Challenge: Connectusers to insights when they need them most Explore with: Superior user experience Real-time indexing Hyper-personal relevance Performance at scale
- 21. How to Accelerateorganizational agility FEATURE BENEFIT IMPACT Modern distributed architecture, built on Apache Solr and Spark Reliable performance at scale in real-time Deliver highly accurate, relevant results from billions of documents and thousands of queries per second (QPS) Signal-driven relevancy Personalized results increase productivity, reduce frustration Tailor results in real time with user info like role, dept, location and expertise Fusion App Studio Let developers quickly create apps in mins with prebuilt templates Rapid time to value 1 2 3
- 24. advanced connectors andAI enrichment, surfaced by one or more applications via App Studio AIAI AI Fusion Connect users to insights precisely at their moment of need any format, any platform System Generated Human Generated Application Generated Data Digital Workplace Solution
- 25. The Time itTakes to Get things Done BigDataManagement &Analytics Data Scientist Driven The Last Mile Problem Direct Alignment with Business NeedsQuantifiable & Faster ROI Reduced Time to Value Search,Discovery& OperationalAI ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❶ ❷ ❸ ❹
- 26. Surface the insightsthat matter most, with ML & NLP Recommenders give every user a customized experience Machine learning models pre- tuned and ready to use Classifiers for precise understanding of intent Clustering and anomaly detection for discovery Experiment more, code less Bespoke, data-anywhere search and discovery apps for all devices Data-driven apps created in hours, not weeks or months Modular, pre-built components for repeatable, predictable outcomes Tried, tested & proven modules allow iterative, prototype-led production Build applications more rationally by starting with real data Supports over 25 data platforms Highly scalable search engine and NoSQL datastore Trillions of data objects - any source, any type 1000s of queries per second from 1000s of concurrent users Full text search, SQL capabilities End-to-end inherited and embedded security Extensible
- 27. NLP: NER, Phrases,POS Document Classification Anomaly Detection Clustering Topic Detection Connectors ETL Pipelines Search Engine & Data Processing SQL Engine Rules Engine Scheduling & Alerting Query Pipelines Query Intent Detector Automatic Relevancy Signals & Query Analytics Recommenders A/B Testing Scalable Operations Extensible System Generated Human Generated Application Generated Data Modular Components Stateless Architecture User-focused Experience Geospatial Mapping Results Preview Rapid Prototyping Digital Workplace Solution CloudScalable CDCR Security
- 28. Don’t like math? Outof the box, automated clickstream relevance tuning Best-in-class recommendations simplify personalization, reduce system latency and simplify operations Data quality algorithms preemptively find issues in your data before your customers do Built-in query analytics automatically identify poor results and bad queries and suggest fixes Automated synonym generation simplifies management Let us do it…
- 29. Like math? Fusion usershave full and complete access to core search and ML algorithms Built-in support for most popular data science tools like Jupyter, Zeppelin, SQL, Python, R and SAS One stop shop for building and deploying machine learning models Common data prep functions for data science and engineering are out of the box, significantly speeding up model building Be our guest…
- 30. Being Data Drivenis not optional Experiment management that goes beyond A/B testing and is optimized for search Relentlessly track and measure relevance automatically Dissect the “why” through our in-depth query analytics workbench Capture, search, analyze and leverage user feedback all from within Fusion
- 31.
- 32. [Using a newAI system], Johns Hopkins can assign beds 30% faster. This has reduced the need to keep surgery patients in recovery rooms longer than necessary by 80% and cut the wait time for beds for incoming ER patients by 20%. The new efficiencies also permitted Hopkins to accept 60% more transfer patients from other hospitals. HARVARD BUSINESS REVIEW, HARVARD BUSINESS SCHOOL NOVEMBER 2018