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Similaire à Robotics, Machine Intelligence, and the Future of Hospitals Final
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Robotics, Machine Intelligence, and the Future of Hospitals Final
- 1. Robotics, Machine Intelligence, and the Future of Hospitals Donald G. Bellefeuille Healthcare Strategist April 24, 2015
- 2. Driving Forces Reduce cost and increase reliability
- 3. National Healthcare Expenditure Projections http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet.html http://kff.org/report-section/how-much-of-the-medicare-spending-slowdown-can-be-explained-issue-brief/ 17.40% 19.30% 2013 2023 NHE as a Percent of GDP 2013 Actual - 2023 Projected $936,900,000,000 $1,671,513,719,136 $1,963,100,000,000 $3,502,346,648,506 2013 2023 Hospital Expenditures as a Percent of Total Expenditures 2013 Actual - 2023 Projected Hospitals All Other NHE projected at 5.6% Y13-14 and 6% Y15-23; hospital remains as constant percentage of total – 32%
- 4. The costs are being passed on to you and me The Commonwealth Fund Issue Brief January 2015: State Trends in the Cost of Employer Health Insurance Coverage, 2003-2013
- 5. EnablingTechnologies http://www.wired.com/2014/10/future-of-artificial-intelligence/ Cheap parallel computation Data, lots of it Better algorithms Expanding imaging modalities and the ability to project in 3D and 4D holograms Micro electro-mechanical arrays that can feel heat and give haptic feedback http://www.bioopticsworld.com/articles/print/volume-6/issue-2/departments/biooptics- breakthroughs/wavelength-modulation-overcomes-drawbacks-of-raman-spectroscopy-.html http://www.memsnet.org/mems/what_is.html
- 6. AreWe Reaching Our Human Limits to Improve? Arnold Millstein, MD, Director, Stanford Clinical Excellence Research Center http://cerc.stanford.edu/about/team.html The pressure to improve value will force increasingly complex healthcare process specs. As the complexity of process specs expand, human cognitive limits will be exceeded. IT – enabled 3-layer control systems well- tailored to human factors will spread.
- 7. Some Definitions http://blogs.sas.com/content/subconsciousmusings/2014/08/22/looking-backwards-looking-forwards-sas-data-mining-and-machine-learning/ http://time.com/3641921/dont-fear-artificial-intelligence/?utm_source=Newsletter+list&utm_campaign=4f0dd73f67- Newsletter_2014_07_177_17_2014&utm_medium=email&utm_term=0_efd6a3cd08-4f0dd73f67-217135337&ct=t(Newsletter_2014_07_177_17_2014) • Passive robot: Human operator controlled • Active robot: Acts on own according to programming • Bounded intelligence: Cannot operate outside its programming • Unbounded intelligence: Can program itself
- 8. Present State of Logistical Robotics and Machine Intelligence 202520202015
- 9. Xenex Robot: Pulsed Ultraviolet Light Disinfection System http://www.xenex.com/xenex-robot/
- 10. http://www.aethon.com/wp-content/uploads/2013/10/TUGBrochureWeb.pdf RoboticTransport Systems TUG TransCar and Robocourier http://www.swisslog.com/en/Products/HCS/Automated-Material-Transport
- 16. Present State of Clinical Robotics and Machine Intelligence 202520202015
- 21. IGAR: Image Guided Autonomous Robot https://www.youtube.com/watch?feature=player_embedded&v=Q1RMUwRkxSc http://www.csii.ca/about_csii
- 22. http://www.artashair.com/artas-experience/how-it-works/https://www.youtube.com/watch?v=_kFCnudEoPs
- 26. Super intelligence induced psychosis http://www.dccomics.com/graphic-novels/watchmen
- 27. Emerging State of Robotics and Machine Intelligence 20252020
- 29. Sensors: Inside and Outside Sensor Use On-organ Data specific to organ function Ingestible Med management, heart rate, sleep, stress, physical activity Body-worn glucose Blood sugar monitoring for athletes Portable spectrophotometer Phone analysis of urine strips: diabetes, kidney disease, UTI Wearable UV Preventing skin cancer Pill bottle Medication adherence Stick-on health monitoring patch EKG, EEG Electronic skin Muscle disorder from Parkinson’s and epilepsy All-in-one skin sensor Heart rate, heart variability, respiratory rate, temperature, posture, and fall detection Blood monitoring patch Glucose, potassium, other levels Brain control sensor Controlling artificial limbs On-heart sensor Multiple cardiac markers: rhythm, pH, etc. Baby monitoring patch temperature Microfluidic home health sensor Inflammation, Vitamin D, fertility, influenza, etc. http://bionicly.com/sensor-innovations-in-digital-health/?utm_source=Newsletter+list&utm_campaign=6da742e30b-Newsletter_2014_07_177_17_2014&utm_medium=email&utm_term=0_efd6a3cd08-6da742e30b-
- 30. http://www.samsung.com/us/globalinnovation/innovation_areas/#digital-health Wearables: Moving from Wellness Devices to Medical Devices
- 31. QualcommTricorder Xprize & Nokia Sensing XChallenge http://tricorder.xprize.org/teams/zensor http://sensing.xprize.org/
- 32. Connecting all these devices https://validic.com/
- 35. Sense.ly - A DigitalAvatar and Much More http://sense.ly/
- 38. Robophobia
- 39. Future State of Robotics and Machine Intelligence 2025
- 40. Next gen soft tissue robotics is focusing on Soft Tissue Robotics – The Next Generation; MD Buyline, V3, #3, June 2014 Tactile feel Greater flexibility to robotic arms Smart systems
- 41. Active and passive microsurgery for long space voyages http://blog.sfgate.com/techchron/2014/04/04/nasa-outlines-plan-for-tiny-robotic-space-surgeons/ Design and Assembly of Parabolic Flight Payload to Evaluate Miniature in vivo Surgical Robots in Microgravity, Kearney M. Lackas, University of Nebraska-Lincoln, kearney.lackas@gmail.com, Spring 5-2014 http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1074&context=mechengdiss
- 42. Raven Surgical Robotic System http://citris-uc.org/telehealth/project/raven-surgical-robotic-system/
- 43. Advanced Machine Intelligence Amelia by IPsoft http://www.content-loop.com/meet-amelia-computer-thats- job/?utm_source=LinkedIn&utm_medium=status_update&utm_campaign=Capgemini_Yearlong_HMI_INT_2013 http://www.alicebot.org/chatbots3/Eugene.pdf
- 45. NanoTech SINGLE CELL PUNCTURE WITH OPTICALLY MANIPULATED HYBRID NANOROBOT, Takeshi Hayakawa* and Fumihito Arai Department of Micro-Nano Systems Engineering, Nagoya University, JAPAN http://www.rsc.org/images/loc/2013/PDFs/Papers/349_1037.pdf DARPA In Vivo Nanoplatform Program
- 46. Nano Tech ‘Flying Carpet’Technique Uses GrapheneTo Deliver One-Two Punch Of Anticancer Drugs https://news.ncsu.edu/2015/01/gu-graphene-2015/
- 48. The Future of Hospitals
- 49. Intense Acute Care Hospital Intense Ambulatory Care Center Telehealth Facility Urban and Rural Critical Access Hospitals The Hospital of the Future is MoreThan OneType of Facility
- 50. The Intense Acute Care Hospital Activity will be constantly monitored, measured, and adjusted using a variety of sensors and data streams. • Optical, sonic, and wavelength detectors and sensors will integrate with direct feeds from equipment. • Data will come from implanted/wearable patient biometric devices, autonomous machines, current volumes, staff locations, supplies, equipment locations, ambient conditions and energy use, and expected volumes from crowd-sourced data • The IACH’s machine intelligence will constantly adjust and ensure maximum efficiency in the use of all resources, anticipate future need, and ensure just in time resource consumption and staffing.
- 51. The Intense Acute Care Hospital Purpose designed autonomous robots and machine intelligence will replace routine functions. • Machines will do most of the housekeeping, dietary, pharmacy, care assistance, business, central sterile processing, and maintenance functions among other duties. • General labs will no longer exist. • Specialized robots will be used routinely in the operating room and other clinical areas replacing some physicians, nurses, and technical specialists. • Diagnosis and treatment plans will be developed by the IACH’s machine intelligence. • Doctors and nurses in the IACH will be experts at deploying the vast amount of data and robotic capabilities to cure and repair patients in the shortest amount of time.
- 52. The Intense Acute Care Hospital Most patient rooms will be multi-functional enabling a wide variety of procedures, imaging, and types of care to be conducted in the room. • The patient will use any number of features that connect with family or loved ones, the treatment plan and overall health record, active memory stimulation, and entertainment. • The room will monitor the patient’s condition, respond to requests, adjust the environment, and provide a machine intelligence generated ‘counselor’ (avatar or robot) to converse with, knows the patient’s condition and history, and is programmed to be empathetic. • That ‘counselor’ may have followed the patient from another place and, if not, could follow the patient to home care.
- 53. The Intense Acute Care Hospital Operating rooms, per se, won’t exist. • Individual rooms will give way to very large spaces where procedures are performed. • The room will reconfigure automatically to accommodate multiple scheduled and unscheduled procedures. • The operating space will accommodate autonomous or operator assisted robotic surgical equipment • Imaging equipment will be integrated directly into the space and not be hybridized. • Hologram images will be overlaid exactly onto the patient. Data read-outs will be within the hologram field. • Advances in sterilization and other methods to isolate multiple operations going on in the same space will accommodate these changes. • Necessary parts, biologics, implants, and surgical equipment will be custom made in the OR. • Anesthesia will move to electric cerebral cortex manipulation instead of drug injection. • Surgical times will be halved.
- 54. The Intense Acute Care Hospital Teaching and research will be dispersed to a variety of care settings. • Hospitals built on the triad of research, teaching, and care will struggle with maintaining that mission as teaching venues move to other types of facilities. • Many IACH’s will simply provide the most advanced care possible while research and some teaching are conducted elsewhere. • A new healthcare workforce will emerge, trained in the programming, deployment, and maintenance of machine intelligence and autonomous robotics. • Medical and nursing schools and GME programs will change curriculums to keep up with the demand for this specialized knowledge.
- 55. The Intense Acute Care Hospital Designs that accommodate machine intelligence, robotics, and efficiency while enhancing the human/machine interaction will be the norm. • New service pathways that keep machines out of public view will need to be created along with the necessary docking, storage, and maintenance spaces. • Grand hotel features, expansive lobbies, and other visitor spaces won’t be financially feasible or desirable because being admitted to an IACH will be a very unusual event in a person’s life • If admitted the length of stay will be very short • A new generation of staff, weaned on the integration of machines into their lives, will not object to co-working with machine intelligence however manifested. • Designs will need to ensure this co-existence in such a way that it preserves the humanity of the place. • This is more an imperative for the workforce than the patient.
- 56. Humans Need Not Apply https://www.youtube.com/watch?v=7Pq-S557XQU Ten DiseasesThat Might Afflict Us in the Future http://io9.com/10-diseases-that-might-afflict-us-in-the-future- 1666688319?utm_source=Newsletter+list&utm_campaign=bdd3ec26a1- Newsletter_2014_07_177_17_2014&utm_medium=email&utm_term=0_efd6a3cd08-bdd3ec26a1- 217135337&ct=t(Newsletter_2014_07_177_17_2014) A Roadmap for U.S. Robotics 2013 https://robotics-vo.us/sites/default/files/2013%20Robotics%20Roadmap-rs.pdf Cyborg GPs: healthcare in an imagined future http://www.theguardian.com/healthcare-network/2015/mar/04/gp-healthcare-imagined-future-nhs?utm_
- 57. Donald G. Bellefeuille 1 Beacon St., Suite 5200 Boston, MA 02108 617-378-4800 dbellefeuille@nbbj.com @dbellef thestratexcrossroad.blogspot.com
Notes de l'éditeur
- Good afternoon everyone. Thank you for attending this webinar. At no other time in the modern history of healthcare has change and innovation been occurring so fast. Hospitals, traditional laggards on the change curve, are finally beginning to adopt new technologies to meet the demand to deliver the lowest cost care at the highest level of reliability possible. This webinar explores one aspect of that change: The rapidly advancing field of robotics and machine intelligence and how it is impacting hospitals. We’ll examine the forces driving this change then take a look at the current state of robots being used for logistical and clinical purposes, the emerging state of clinical robotics and machine intelligence, and the future state of autonomous machines and intelligence. Along the way we’ll take a side trip into mobile and telehealth. Finally, I’ll summarize all this into a possible, provocative vision of what the hospital of the future might be. This is an area that is advancing rapidly with new innovations and technologies coming out almost every week. What I’ll be showing and discussing is meant to be indicative rather than comprehensive. What you'll learn: The forces driving change in hospitals What technology is being used now What technology will likely be in use 5 years from now What technology is emerging that could be in use 10 years from now The nature of active and passive robots and bounded and unbounded machine intelligence Who should attend: Healthcare strategists and planners, hospital architects and designers, nursing and clinical service line leaders, operational department leaders.
- There are a lot of forces at work in healthcare and the most fundamental is the growing cost. This chart from CMS shows that National Health Expenditures were $2.9 trillion in 2013, or $9,255 per person, and accounted for 17.4% of Gross Domestic Product (GDP). CMS projects that to grow to 19.3% in 2023. Hospital expenditures accounted for $937 billion in 2013 and a 32% share of NHE. Looking out ten years if total NHE growth occurs as CMS expects and hospitals maintain their share, hospitals will grow to $1.7 trillion of total NHE. Of course people are working very hard at slowing the rate of growth for hospital expenditures. Medicare wants half of its payments under alternative models like ACOs and bundled payments and 85% of its FFS payments tied to quality or value by 2018. And commercial insurers continue to develop limited networks and other ACO type plans. This will all have an effect on the rate of growth. Whether we end up in 2023 with 1.3, 1.5 or 1.7 trillion is probably moot. Hospital care is just too costly and not reliable enough for the product we get. Medicare spending grew 3.4% to $585.7 billion in 2013, or 20 percent of total NHE. Medicaid spending grew 6.1% to $449.4 billion in 2013, or 15 percent of total NHE. Private health insurance spending grew 2.8% to $961.7 billion in 2013, or 33 percent of total NHE. Out of pocket spending grew 3.2% to $339.4 billion in 2013, or 12 percent of total NHE. Physician and clinical services expenditures grew 3.8% to $586.7 billion in 2013, a faster growth than the 4.5% in 2012. Prescription drug spending increased 2.5% to $271.1 billion in 2013, faster than the 0.5% growth in 2012. The largest shares of total health spending were sponsored by households (28 percent) and the federal government (26 percent). The private business share of health spending accounted for 21 percent of total health care spending, state and local governments accounted for 17 percent, and other private revenues accounted for 7 percent. Only 2/3 of Medicare slowdown can be explained: Reductions in readmission penalties, hospital acquired conditions, sequestration, DME competitive bidding
- For those of us who have employer based insurance the amount we pay for out-of-pocket costs for premiums and deductibles as a share of our income is rising and is now at 9.6% nationally because employers are shifting the cost of care to us to keep their costs down. You can see from this Commonwealth Fund chart that nationally the percent of OOP costs rose from 5.3% to a 9.6% share of median family income. If you were making $100,000 that is $10,000 coming out of your pocket if you used all your deductibles and co-pays. And if you were spending up to your co-pay and deductible limits you’re probably cutting back on other things. On the other hand many people are avoiding using healthcare because they can’t afford the co-pays and deductibles or they‘re seeking alternatives that are cheaper. A lot of product development is going into capturing this cash client along with developing cheaper insurance policies. And that means hospitals have to change considerably to reduce costs. Out-of-pocket costs for insurance and cost-sharing—including workers’ premium contributions and deductibles—are accounting for higher percentages of incomes in all states compared with 2003 (Exhibit 7, Table 7). By 2013, the combined costs as a share of income ranged from about 6 percent to 7 percent in North Dakota, Hawaii and the District of Columbia, to 12 percent or more in Texas and Florida. Although the rate of increase has slowed in most states since 2010, the combination of higher premium shares and higher deductibles contribute to widespread public concerns about rising health care costs. Massachusetts is about 6.9% + -
- Developing cheaper healthcare requires technologies that enable it to happen. Here are five that play some part in all the tech I will be discussing. Cheap parallel computation started to evolve in the 2000’s and allows computers to perform related calculations at the same time compressing what could have taken several weeks into a day or less and it continues to speed up. This gives us the ability to crunch Big Data. We collect massive amounts of data everywhere and healthcare is one of the biggest generators of data. Add to that all the data that will come from wearable and implantable sensors and a new era is upon us. To take advantage of this data scientists are creating Better Algorithms. Deep-learning algorithms and their use has accelerated greatly since being tied to heavy parallel processing systems and it is an essential component of all current and future machine intelligence and it will get better and better. Imaging continues to expand and innovate. This is important because we use images to plan and guide surgeries. As this gets better, surgical robots will get better. Stay tuned for 4D ultrasound holograms., MRI activated and guided surgical tools, along with laser spectroscopy and electro magnetic radiation that can image abnormal cells at the molecular level. Expect advances in Haptic Feedback or the ability to feel pressure, heat, and other sensations. Feel is key for any surgeon and it will be key to any robot operating on soft tissue.
- For me the most important driving force is simply this: We are reaching our human limits to improve. This is what Arnold Milstein, MD, MPH a Professor of Medicine at Stanford who directs the Clinical Excellence Research Center told a small audience at a conference I attended last year. If you think about this at all our improvement efforts have only been getting incremental changes in productivity, efficiency, and quality despite everyone’s best efforts and its not enough to get where we need to go. We are finally realizing that we need to enlist the same technologies that other industries have used to make the kind of changes in cost and reliability that we need. Technologies like robotics and machine intelligence that can expand beyond our human limits. These technologies will use similar three-layered control that operate in other industries. The Local level is the sensing system where the work is actually being done and responding to sensory inputs. That feeds up to the… Control level which is the planning system: It’s developing the commands to act based on the inputs its receiving from the local level. When the plan is complete this level sends it to the… Supervisory level which is the execution system that gives the commands to act. In machine intelligence systems this happens almost simultaneously, faster than we can do it, while constantly correcting and adjusting. Human factors mean our strengths and limitations regarding interactions with people, equipment, systems, and the environment they operate in, to assure effectiveness, safety, and ease of use. Machine systems will have to integrate these human factors into their programming to assure lower cost and greater reliability. They need to adapt to us not us to them. Remember the landmark report “To Err is Human”? Fixing those errors will require machines. http://www.ncbi.nlm.nih.gov/books/NBK2666/ The Center is a collaboration of the Schools of Medicine, Engineering and Business to design and test new health care delivery models that both lower per capita health care spending and improve clinical outcomes.
- Let me set a framework for some of the terms I’ll be using and what I mean by machine intelligence . This graphic from SAS does a great job of establishing that framework. Machine intelligence or machine learning occupies a sweet spot taking advantage of data mining and pattern recognition. All this is enclosed within KDD or knowledge discovery in databases. This is what IBM’s Watson is all about. Machine Intelligence doesn’t create new knowledge so much as finds the knowledge hidden in the data. It needs programming and structure. It incorporates algorithms to capture, store, index, retrieve and merge data, and orchestrate multiple subtasks like those three-layered control systems I just spoke about. Most importantly, it can be bounded. It can be designed for specific purposes like any other machine. We use the word robot rather loosely. Technically a human operated machine like the DaVinci is not a robot but we call it that anyway. So to distinguish between true autonomous machines I’ll use the term passive robot for human operated systems like the DaVinci and active robot for fully autonomous machines. And as you’ll see there are blends of both. Artificial Intelligence on the other hand is the use of intelligent agents to perform tasks that require human intelligence, such as visual perception, speech recognition, and decision-making. In order to pass the Turing test, intelligence must be able to reason, represent knowledge, plan, learn, communicate in natural language and integrate all these skills towards a common goal. Machine Learning: The subfield of machine learning grew out of the effort of building artificial intelligence. Under the “learning” trait of AI, machine learning is the subfield that learns and adapts automatically through experience. It focuses on prediction, based on known properties learned from the training data. The origin of machine learning can be traced back to the development of neural network model and later to the decision tree method. Supervised and unsupervised learning algorithms are used to predict the outcome based on the data. Machine Learning focuses on the question of how to get computers to program themselves from experience plus some initial structure). Carnegie Mellon Professor Tom Mitchell, Chair of the CMU Machine Learning Department. In a 2006 article entitled “The Discipline of Machine Learning”, http://www.wired.com/2014/12/wearing-your-intelligence/?utm_source=Newsletter+list&utm_campaign=cfc3c5ff92-Newsletter_2014_07_177_17_2014&utm_medium=email&utm_term=0_efd6a3cd08-cfc3c5ff92-217135337&ct=t(Newsletter_2014_07_177_17_2014)
- Let’s take a look at what is happening between 2015 and 2020. I’ll start with logistics and then move to clinical innovations.
- You’ve probably heard of Xenex. It was made famous during the Ebola scare by a Texas hospital that used it to disinfect the room of the patient who had Ebola. It emits a pulsed xenon ultra violet light that can kill most microbes in about five minutes. As you can see on the side of the machine Xenex has trademarked it as the ‘germ-zapping robot’ but It’s really a passive robot. The room is prepared exposing all high touch surfaces like raising bed rails and toilet seats. The machine is rolled in, plugged in, and two safety cones are placed: One that detects motion to shut it off and one to warn folks not to enter and to let you know its done. The operator than starts it and has fifteen seconds to leave the room. After it’s done it may need to be repositioned to get to areas or equipment that might have been have missed. The active aspect is the raising of the light and the actual emission of it.
- These two transport systems are active robots. The Aethon TUG is an autonomous mobile robot that uses a programmed map of the facility and sensors to get to where it needs to go. SwissLog is a European company that manufactures a similar system. They are used to transport medications, blood and tissue specimens, food and dirty tray return, clean and dirty linen, and trash using a variety of cart and cabinet attachments. Both are in use now and there growing. For example there are 25 TUGS deployed across the 600,000 sf of the new UCSF Medical Center in Mission Bay with back halls designed for their use as you can see in the picture on the lower left. The new Humber River Hospital in Toronto that is now under construction will be another good example of wide scale deployment. http://www.aethon.com/wp-content/uploads/2013/10/TUGBrochureWeb.pdf http://www.westerndailypress.co.uk/Robots-help-deliver-meals-patients/story-22867113-detail/story.html
- The Knightscope K5 Autonomous Data Machine, as its officially named, is an active robot. It can see, hear, feel, and smell. It has behavior analysis algorithms, predictive analytics, and can grab crowdsourced data if allowed or share its data to the crowd. It can send alerts or sound alerts if it is tampered with. Physically it weighs 300 pounds, stands 5 feet tall, is designed for the outdoors; it’s aimed at large parking lots, garages, and campuses. It is controlled through the companies central hub but it patrols on its own. The last time I checked it is deployed at Microsoft’s headquarters. The target market is companies and institutions with large campuses and parking areas, and security companies. I think hospitals won’t be far behind in adopting this. Many contract out to private security companies who always have a hard time retaining help especially during off-hours. At an entry price of $6.25 an hour this will be hard to resist. This data can be combined with existing business, government and crowdsourced social data sets so that it can predict where crime is likely to happen. If it detects crime it will send an alert. If it is tampered with it will sound an audible alert. People can use it to signal danger. If an alert is pushed, the K5 machine will turn on all of its sensors to not only allow the entire community to review the data, but to contribute important real-time information. The manufacturer claims to protect privacy concerns, engages the community on a social level to effectively crowdsource security, and provides an important feedback loop to the prediction engine.
- Healthcare is woefully behind other industries when it comes to enterprise logistics, running entire operations via computers, but we’re beginning to catch up. CareLogistics is a company dedicated to moving hospital logistics into a total enterprise management system. Right now they are focused on getting as much efficiency as possible in patient throughput. The patient’s care is managed through a control hub in the hospital like the one pictured here and all activity is monitored and updated constantly using care coordinators and live feeds. When ideally deployed the care coordination team knows the status of every patient, every bed, every procedure, and what is coming in through the ED resulting in LOS reductions of up to a day. While its not yet connecting to all systems I believe that will change as the system evolves.
- This is the active robotic pharmacy at UCSF’s main hospital. It’s made by SwissLog the same company that produces one of the transport systems I showed you. Since 2010 it has assembled more than 1.5 million pill packages of patient medications without an error. When an order is received, the robots pick, package and dispense individual doses of pills. It assembles the doses onto a plastic ring, each of which contains all the medications for a patient for a 12-hour period. Nurses receive each of their patients’ scheduled medications on the rings, which are barcoded and include the patient’s name, list of the medications and administration times. The other picture shows one of three of UCSF’s automated IV compounding stations for both regular IV’s and hazardous compounds. Robotic Pharmacy at UCSF
- Hospital lab automation has been around for awhile and many hospitals have deployed certain aspects, if not all, of it. These systems are configurable and scalable and I don’t think people view these systems as robotic but they are. This image is from BD Kiestra showing their total lab automation system and there are others. It’s designed to speed up the process and take the human element out. KIESTRA Lab Automation introduced the concept of Total Lab Automation for the clinical microbiology lab in 2006. Since then, many laboratories have chosen to implement this modular, scalable and open architecture solution. With the acquisition of KIESTRA Lab Automation B.V., BD now offers this proven technology as the BD Kiestra™ Total Lab Automation solution. It is designed to increase efficiency, streamline processes and to provide a new way to deliver high quality, consistent results with a fast turn-around time. Building on the experience and expertise developed over many years, BD is committed to becoming your thinking partner and delivering the right automated solution for your lab. We offer complementary and modular microbiology workflow automation solutions from small bench top units, all the way to fully automated track-based automation systems from medium-size to extra-large laboratories. As the BD Kiestra™ Total Lab Automation solution is customizable and forward compatible, we are able to tailor the system according to your needs — taking into account your lab’s space, specimen volumes, workflow and future ambitions. Specimen processing and microbiology. Installed in Europe and Canada
- Blood analysis is an $80 billion worldwide market and innovation and competition has heated up considerably in the last few years. Many of you have heard of Theranos, a new technology that performs a complete array of tests from a very small sample of blood and delivers its results in hours. They are being placed in and near many Walgreens. It has competition. The image on the right is Radisens Diagnostics’ Point of Care device. It uses one drop of blood to perform a range of tests including complete blood counts, A1c’s for diabetes, full lipids for heart disease, thyroid function, and renal function, among many other tests. They are aiming for the chronic disease market as managed in medical offices and ambulatory care centers. It’s an Irish company and their ambitions are not limited to Europe. Both technologies could make phlebotomy work stations and a major portion of hospital lab work obsolete.* *Our proprietary infrastructure allows us to perform our test analyses with unprecedented speed. So we can have results to you and your doctor in a matter of hours, not days. Which means a fast diagnosis to support better, more informed treatment.
- That was the survey of the present state of logistical robotics now we’ll turn our attention to clinical robotics and machine intelligence.
- Would it surprise you to know that true active robots and machine intelligence are providing care in hospitals right now? Whenever people are skeptical about healthcare robots I point out the CyberKnife. The machine and table work together automatically to position the patient for therapy and locks in when it registers the correct position. We probably don’t think too much about it because not a lot of people come in contact with it, the treatment lasts a minute to a minute and half, and nothing mechanical penetrates the body. The encounter is brief and unremarkable. But it is an active robot nonetheless. Created to make personalized treatments an option for your patients, the CyberKnife M6 Series offers a comprehensive set of clinical features. Indication-specific tumor tracking with automatic correction throughout treatment, true robotic mobility, and advanced collimation integrate seamlessly into the only system to automatically stay on target despite patient and tumor motion. It enables you to treat tumors anywhere In the body with confidence and without compromise. 3D image-guided, intensity modulated radiation therapy (IG-IMRT) for more advanced disease sites throughout the body. 700 machines installed.
- And here is DaVinci, the poster child of healthcare robotics. This machine is a passive robot that requires a surgeon with a lot of training to operate. It’s been controversial for some specific procedures but its effectiveness appears to be well accepted overall as long as the training is up to par. Many hospitals now have whole centers devoted to passive robotic minimally invasive surgery. The machine uses 3D imaging to better visualize the surgical field and can perform finer surgery than some current techniques and human ability allow. Most importantly DaVinci’s technology enables it to evolve to true active robotics. The latest version features automatic placement of the arms over the surgical site using a laser and the arms and instrument themselves are getting smaller. Going forward I believe it will integrate more active robotics into its system to stay ahead of the field and move to single port entry. Intuitive Surgical, the maker of DaVinci, is involved with the U of California and their efforts to advance active robotic surgical technology. I’ll go into that a little more later on. We also have an a active anesthesiology robot nicknamed McSleepy. It’s being developed at McGill University and already has completed eight operations including one with a DaVinci. The developers expect another two years of development and trials and then full commercialization within three years of that.
- This is a passive machine with an active feature that is key to its utility and it’s pretty scary looking. Based on “snake robotics” the active feature is that the endoscopic tube snake locks in each turn and passes that on to the next segment. In the image on the right each turn marked with a red dot is locked in and stays that way as each link progresses. Instruments are passed though the tube. It is joy stick driven, the surgeon is getting haptic feedback, and can see via an HD camera. No incisions are made and risk of infection is greatly reduced. Unlike current flexible endoscopes you can reach many areas that you couldn’t get to before because the endoscope would flex too much and not hold in place. You could turn this thing in a circle if you had to. Right now it’s being used in Europe on a limited basis for head and neck surgery with the aim to go after cardio-vascular surgery. Medrobotics is a US company located in Raynham, MA and the design is based on the work of Prof. Howie Choset, a Professor of Robotics at Carnegie Mellon University, Pennsylvania. Locks in each turn articulated multi-linked endoscope that will enable minimally-invasive procedures to replace open surgical procedures for many parts of the anatomy that are difficult or previously impossible to reach. On-board visualization The system enables physicians to operate through non-linear circuitous paths, self-supported, and through a single-site access into the body. The maneuverability of the endoscope is gained from its numerous mechanical linkages with concentric mechanisms. Each mechanism can be placed into a rigid or a limp state. By employing a patented "follow-the-leader" movement strategy with these alternating states, the endoscope can be directed into any shape through the relative orientations of its linkages.
- Here we have examples of hybrid passive and active machines used for hip and knee replacements. ThinkSurgical’s machine on the left autonomously removes the bone where the implant will go according to a preset plan done in a 3D planning work station. The burrer is placed over the site and registers itself via preset location markers that correspond to the plan. Once that is confirmed the surgeon starts the machine and it removes the bone exactly as planned. The surgeon always has start and stop control and there is a motion detection system that will stop it if the patient position changes. Everything else is done by the surgeon.* MAKO, on the right, is more like a robotic assistant. After planning and registering the the area to be burred, the surgeon uses the burring device to remove bone and create the place for the implant. The machine makes sure the surgeon does not exceed the pre-planned bone removal. It physically prevents any attempts to go beyond the bounds of the planned bone removal.** **Highly advanced RIO® robotic arm technology offers a new level of precision and accuracy in aligning and placing implants. These are important factors that may improve surgical outcomes. Patients who desire a restored lifestyle and seek the latest technology advancements in knee and hip replacement may benefit from learning more about MAKOplasty®. *TCAT is staged in the OR, followed by patient positioning, surgical incision and fixation. Using a patented process for bone registration, the surgeon uses a digitizer to collect points and locate the exact position of the patient’s anatomy for precise surgical implementation. Under direct control of the surgeon and using controlled, gentle pressure, TCAT mills the bone with sub-millimeter accuracy as specified by the plan. The specialized drill bits and other hardware have been developed for accurately preparing the bone to achieve optimal fit of the prosthetic implant. If bone motion occurs, a bone motion monitoring system halts the system. The registration system then allows the surgeon to quickly recover bone position and resume the surgery with no loss of precision.
- The Image-Guided Autonomous Robot (IGAR) was developed by Canada’s Centre for Surgical Invention and Innovation to autonomously sample and ablate breast cancer tumors. It’s planned for commercial release in 2016. It can accurately biopsy a small lesion identified under MRI. It can ablate the lesion. And it can implant radioactive seeds to tag lesions for surgical removal. The next version of the system will be capable of performing sentinel lymph node biopsy for the staging of breast cancer. And they plan on moving the tech forward to lung, liver, and kidney disease. Currently, few surgeons in larger sites can perform a sentinel node biopsy due to the need for a highly skilled surgical team to conduct the procedure. Image guided accuracy to secure this tissue therefore has a significant advantage over standard manual procedures. CSii will be able to further expand our future robotic offerings to improve patient care and surgeon practices via the creation of robotics in spinal surgery, ear/nose/throat (ENT) surgery, microsurgery and neurology.
- I couldn't resist including this machine. ARTAS is a robotic system that automatically harvests hair follicles for use in transplants. That is the active part; the surgeon does the actual transplant. The machine maps the hair removal area for what is needed for the transplant and then removes 1-4 individual hairs at a time eliminating plug removal and the need for suturing. This is very, very precise work. The surgeon still does the actual transplant but how far away can it be before it does the actual implant itself? Right now it’s only indicated for male pattern baldness. http://www.artashair.com/
- And here is RIBA and RI-MAN. RIBA stands for Robot for Interactive Body Assistance. It’s the first robot that can lift up or set down a real human from or to a bed or wheelchair up to a 135 pounds. Both can see, hear, feel, smell, and recognize emotion and they have a soft body feel. Note the anime style to soften the effect of robots looking too human and risk being rejected by us. This is the uncanny valley problem, when a robot looks so much like us but are not quite like us that we tend to reject them as just creepy. The green color you see on RI-MAN is deliberate; it’s supposed to evoke the color of medical scrubs. These robots are designed for use in elder care facilities and properly configured home environments but they can just as easily be used in hospitals. Commercial release is planned this year at an estimated price of $78,000. Japan, S. Korea, and Thailand are way ahead of us in these types of robots. China is planning a big push and so Japan and Korea are doubling their efforts in this area. There is a big caregiver robot race going on in Asia and I’m sure we’ll be seeing these in the US at some point.
- The eICU is great example of basic machine intelligence that proves its value everyday. The eICU receives input from the EHR, monitors, pumps, and visually if needed, into a central control hub. It’s algorithms continuously monitor all this data and alerts clinicians if a patient’s condition worsens so they can intervene earlier. I’ve seen this in action and it has proven itself time and again with reduced lengths of stays in ICU beds. And community hospitals are able to keep more acute patients rather than sending them to more expensive hospitals. Its capabilities continue to increase and can be used in other areas. The new hospital in Joplin, Missouri expanded its use to ten regular acute care beds in addition to the eICU.
- Arrayed here are a number of companies that are in the healthcare machine intelligence field in various ways. IBM’s Watson is the best example and probably the deepest and most extensive system operating in healthcare right now. It understands natural language, it generates hypotheses based on evidence, and learns as it goes by being taught by its users, from prior interactions, and by being presented with new information. But it is doing so within the bounds of its programming, not programming itself with new capabilities. It’s being used by Memorial Sloan Kettering, MD Anderson, New York Genome Center, Mayo Clinic, and the Cleveland Clinic among others to understand disease determinants, develop new treatments, and provide genome focused diagnostics. Sentrian is more modest than Watson but it has a big goal: Eliminate all preventable hospitalizations by being a wearable eICU. They’re targeting Medicare Advantage and managed Medicaid plans as first customers. The program identifies patients at risk for admission whose course can be influenced. It provides the appropriate low cost and less intrusive biosensors and wearable devices tailored to the patient’s condition. Clinicians create the rules that, based on deviations in the measured parameters, prompt automated notifications to the care managers. The rules address both short term and longer term changes using predictive analytics and machine learning since health deterioration patterns frequently develop as much as ten days before a patient becomes symptomatic. Twine Health is a collaborative platform for chronic disease management, shared planning and decision making, self-tracking, and monitoring. It’s goal is to provide an optimal outcome in three months. Remedy Partners is a bundled care payment solution. The software integrates data from a variety of sources, provides patient assessments, 90 day care plans, and site of care selection. It also provides quality measurement and reporting. HealthInfo Net is Maine’s EHR data gatherer. All hospital records are stored here. HBI is a data analytics firm out of CA and they were engaged to mine the records and predict emergency visits. So far they’ve been right 74% of the time. And let’s not forget about Google. Deepmind was bought by Google about two years ago. It combines the best techniques from machine learning and neurosciences to build powerful general purpose algorithms. I am assuming they will eventually get into healthcare.
- This is the first of three images that imagine diseases of the future. I’ve included a link to the whole series at the end of this webinar. Every action has a reaction and these images do a great job in capturing the potential untended disease consequences of the changes I’m talking about. This is a picture of Dr. Manhattan from the graphic novel The Watchmen. He is imagined to be suffering from super intelligence induced psychosis. In the graphic novel he suffered a nuclear physics accident and evolved from a regular human to something approaching pure intelligence freed from physical and temporal bounds and lost his humanity in the process. It’s conveys a serious point: We don’t compute like machines and machines don’t think like us. As we get more and more involved with using them what kind of psychological issues will we be creating for ourselves? I don’t have the answers, but plenty of folks are researching the subject. Trouble is, our culture is biased towards a very narrow band of intelligence — namely "IQ-type" intelligence, or what Mark Changizi calls chess-and-brain-teaser-like intelligence. But the acquisition of extreme cognitive abilities could prove to be maladaptive. Our evolutionarily-calibrated psychologies may not be able to handle such out-of-bounds intelligence. Should you choose to augment your brain, you may start to exhibit antisocial behaviors and outright insanity, including such behaviors and problems as pattern seeking (a la John Nash), seizures, information overload, anxiety attacks, existential crises, egomania, and extreme alienation.
- Let’s move on to the five year period beginning in 2020 keeping in mind that this timeline is my best estimate of when these technologies are coming on line.
- The developments in telehealth are important because they have implications concerning what the hospital of the future is and is not. I’ve placed this section in the 2020 timeline because there are a lot of issues that need to be resolved regarding the technical integration of all the data that will come out of mobile and the acceptance and viable use of it by the medical establishment. Some believe it will all come together by 2020. Progress is being made: Intermountain Health is in the process of equipping all their hospital rooms to be able to handle virtual visits so they can increase efficiency and convenience. Medicare is paying for an expanding list of indications and has a code for telemedicine. Forty three state Medicaid programs and private insurance in 20 states provide some form of reimbursement. And accountable care organizations, given their flexible model, can also pay. And the Federation of State Medical Boards wrote model legislation for interstate licensure of tele-medicine. Topol also believes that “one of the most important factors that will drive digital health forward in the next five years will be recruiting data scientists into the field”. It’s a key point and will affect how we educate and train our doctors going forward. For example, the U of Illinois recently announced a new school that integrates engineering and medicine. http://media.bizj.us/view/img/2213181/harrisrpweb.jpg Dr. Topol’s quote on the screen bears paying attention to. As we go forward with ever more sophisticated robotic and machine intelligence systems will the human being in all us get lost? That is exactly the same point being made in the Dr. Manhattan picture. Let’s look at some examples of mobile and telehealth and keep in mind that these are just a sample of everything that is going on.
- The key to mobile health is the ability to sense and monitor all sorts of conditions and behaviors. On this slide is a list of various types of sensors and what they can measure and monitor. The list will keep growing along with our technical capabilities. Expect sensors that are drawn on you with enzyme based inks among other developments. There is another aspect of this we have to keep in mind. As the data streams grow most of the data we will use for research and development will come from individual human beings, not from animal studies in a lab. This will change how AMC’s go about their research missions and will free many future researchers from the hospital and lab. It also raises questions about ownership of data but that’s a subject for another webinar to consider.
- Wearable sensors are now mainstream. The big companies in this field know they need to move up from providing wellness apps to being fully capable medical monitoring businesses if they are to make money. On the lower right is Fitbit’s new Charge HR and Surge: The smaller Charge HR provides heart rate monitoring. The larger Surge has GPS, text viewing, and can control music from your smartphone. It’s a step up for them but they are lagging far behind if they intend to get into the medical monitoring business. Samsung’s Simband is a developer platform and not a consumer device. Based on their Gear line, it has an accelerometer, gyroscope, ECG monitor, a galvanic skin response sensor, optical sensors for pulse and heart rate, and a skin temperature thermometer. The Simband is an open data and open sensor platform. Samsung wants to be a medical data broker not a device manufacturer. Apple has moved forward with its HealthKit and ResearchKit services. HealthKit is a repository for patient-generated health information like blood pressure, weight, heart rate or anything the application developer can think to monitor and ResearchKit is open source app development for anything that a researcher wants to do that can be done via a phone or the watch. There are a lot of hospitals that are working with Apple on pilot programs and probably more will join in. Unlike Samsung, Apple wants to in both the device and data game. Coupled with Apple’s partnerships with Epic and IBM’s Watson the possibilities here are vast. The FDA does not appear to be an obstacle for either device or algorithm approvals and has issued guidance on what can be advertised as a wellness device or a medical device. They are holding in reserve their powers to regulate, though, and that has gotten the ire of the device developers and there are bills in congress to give developers more freedom without FDA oversight. The agency to watch is the FTC. They are looking into issues on who will hold monopolies on data, data generation, and transmission because data is the currency of the realm. It’s a question that will become increasingly important to answer as this sector evolves. Gartner is projecting that By 2020, the developed world’s life expectancy will increase by a half year due to widespread adoption of wireless health monitoring technology. They also think that by 2017 costs for diabetic care will be reduced by 10 percent through the use of mobile monitoring devices operating through smartphones. http://www.gartner.com/newsroom/id/2866617
- Will the Tri-Corder trump all these devices? The rHealth sensor pictured here is made by DMI right here in Cambridge MA. It won the second round in the Nokia challenge and is a finalist in the Quallcomm challenge. There are several other worthy devices competing for the prizes. Both challenges are seeking to create the ultimate sensor, the Tri-Corder, as we’ve come to know it from Star Trek. According to DMI the rHEALTH sensor is: a universal health sensor with capabilities to assess hundreds of different clinical lab tests in a single drop of blood or bodily fluid. And it has a detachable wireless vitals sensor module for monitoring all vital signs. They’re working on a version for routine consumer and clinical use.
- Of course there is the issue of connecting all these devices and the data coming from them into existing medical records and making sense of it all. Validic is an example of a company that wants to be the collector, integrator, and transmitter of all this data. This chart from their website demonstrates how they work in this space to try and bring it all together. Despite the big push in the last five years to digitize hospital and physician office medical records there are still plenty of issues regarding interoperability between the two and adding mobile data and devices complicates it even more. As we introduce more and more wearable and implantable monitors for medical use keep in mind what Dr. Topol said: Interoperability needs to be enhanced, standards set, and interventions proven.
- There’s a lot of interest in telemedicine kiosks and it has gotten traction. The basic idea is you enter a kiosk and it directs you to use an array of diagnostic devices. Typically they have a pulse oximeter, BP cuff, magnascope, thermometer, otoscope, stethoscope, and scale. Self check-in is done via touchscreen and you connect via video to a doctor or NP. Rite Aid is piloting the Healthspot kiosks in selected Ohio stores that connect to Cleveland Clinic and other hospitals in Ohio. Florida Blue Cross is also using them in their branded stores. Healthspot is attended, CSI is not and both treat cleaning a little differently. What’s interesting about this is that there is an ongoing lawsuit (As Dec. 8, 2014) regarding CSI’s patent that transmits data to the cloud. CSI is trying to protect that technology and Healthspot is trying to co-opt it. That gives you an idea on how important data and data transmission is to all things in mobile and tele-health.
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- Finishing off this trip into mobile and telehealth is ‘Molly’, a virtual nurse avatar that is the face of Sense.ly’s telemedicine solution. It’s a program that is targeted to chronic care management in between doctor’s visits. It connects to medical devices in the home like a BP cuff and collects data from the patient through image capture and responses to questions that the avatar asks. The program integrates all this into the medical record for action and the patient can interact with real clinicians in real time if needed. The best way to understand this is to go to their website and watch it in action. In the interest of time I won’t be playing it but you can imagine that avatar asking you questions, recording your answers and asking additional questions based on your answers while the camera records you the whole time.
- Vanderbilt University has invented a unique robotic device designed right now for epilepsy surgery but its has possibilities for other types of brain procedures or anything a needle is used for. It uses a MRI compatible nickel-titanium needle that operates like a mechanical pencil, with straight and curved concentric tubes that allows the tip of the needle to follow a fixed curved path to reach the target area of the brain. The whole thing is powered by compressed air and is steered and advanced a millimeter at a time by the robotic platform and tracked by the MRI. The needle is inserted though the cheek and avoids drilling through the skull and using straight needles. But the straight needles they use can't reach the source region, so they must drill through the skull and insert the needle used to destroy the misbehaving neurons through the top of the head. "The systems we have now that let us introduce probes into the brain -- they deal with straight lines and are only manually guided," Neimat said. "To have a system with a curved needle and unlimited access would make surgeries minimally invasive. We could do a dramatic surgery with nothing more than a needle stick to the cheek."
- The goal of this robotic arm is to help doctors perform certain procedures many times faster and with increased accuracy than if they were only using their hands. Right now this device can do three to five sutures autonomously. This is a development of the Centre for Image-Guided Innovation & Therapeutic Intervention (CIGITI) in Toronto, Canada. It’s connected to Canada Arm the company that develops the arms on the space station and MacDonald, Dettwiler and Associates the company that builds them. The first image is an early prototype with a biopsy instrument attached, the second image is a third generation device that is demonstrating suturing. It’s being tested at SickKids, the Hospital for Sick Children in Toronto, specifically around connecting veins and arteries to each other and to other surfaces. The testing involves camera-based tracking of tissue and desired suture points and automated positioning and application of sutures. It can suture ten times faster than a surgeon. It’s hard to tell in these pictures but it is really small. http://www.asc-csa.gc.ca/eng/canadarm/kidsarm.asp http://www.parabolicarc.com/2015/01/30/csa-partners-sickkids-centre-develop-kidsarm/
- This is the second imagined future diseases and a very basic one: Fear of robots. Notice that it bears a striking resemblance to ARTAS and Cyberknife. The question is, Will we reject active robots as part of the care team? We have easily accepted Cyberknife and almost no one has a problem going into a CT or MRI bore. We are also comfortable with DaVinci. How far will we humans allow ourselves to go when it comes to autonomous robotic testing, diagnosis, and treatment? How we design them, like the Japanese are doing, will matter, but more importantly how we introduce them as part of the care team will matter even more. They will have to adapt to our physical and emotional human factors.
- According to a 2014 Pew Research study on the future of the internet the vast majority of the 1900 experts Pew surveyed think that robotics and artificial intelligence will affect wide segments of our lives by 2025 especially in health care, transport and logistics, customer service, and home maintenance. Many believe this would be job creating but our educational system is unprepared to educate and train the workforce that will be needed to take advantage of these technological developments. http://www.pewinternet.org/2014/08/06/future-of-jobs/
- These three items: increasing tactile feel, getting greater flexibility in robotic arms, and developing even smarter systems will be the key to increasing the use of active and minimally passive surgical robots.
- This micro-surgical device is being developed by NASA and the U of Nebraska in anticipation of long voyages to Mars. It’s planned to be controlled by a surgeon on earth in an active/passive arrangement but there is no reason it could not work autonomously on earth. The image on the right compares the robot arms to a quarter. The surgical approach they are planning is to inflate the abdomen and slide it in to the astronaut’s body through a small incision in the belly button and perform emergency appendectomies, emergency cholecystectomies, emergency perforations of gastric ulcers, and repair intra-abdominal bleeding due to trauma. The surgeon interface will have haptic devices and a monitor to provide visual feedback. It will have to do certain things by command rather than direct surgeon control because of the inherent lag of remote operation over such long distances.
- The UC Davis and Santa Cruz Center for Information Technology Research in the Interest of Society (CITRUS)and UC Berkeley’s Center for Automation and Learning for Medical Robotics are working on similar technology. Ignore the actual physical technology in the picture on the left because it is admittedly old and just a test bed for the software they are developing. Like the previous example they’re trying to overcome the time delays inherent in very long distance tele-surgery by giving robots autonomous functions. They do this by teaching particular surgical subtasks. The picture on the right shows it tying a suture on its own. Ideally, the remote surgeon orders the robot to perform each step of a surgery by specifying subtasks and parameters that are then performed autonomously by the robot, thereby avoiding the instabilities of long distance operator control. They are using a new statistical approach to robot learning called Apprenticeship Learning that has the potential to allow robots to execute specific subtasks with superhuman performance in terms of speed and smoothness. The robot’s sensors record a set of human-guided surgical motions and it’s software smooth's out and refines the motions and then gradually increases the speed of the motions. This type of learning is also being used in industrial robots like Baxter. So far they have demonstrated this on a figure eight motion and a two handed knot-tie after being trained by non experts. At Berkeley they have demonstrated autonomously cutting out a circular patch designed to resemble a cancerous lesion. You can see from these last two examples and KidsArm that robotic soft tissue surgery is becoming faster, more reliable, and more autonomous. And it’s all controlled by that three layered IT system we talked about with the supervising surgeon exercising the highest level of control and the robot responsible for the two lower levels. Intuitive Surgical gave Berkeley a DaVinci machine so to some extent they are working with them and obviously very interested in it because it DaVinci’s future. Chasing all of this are magnetically controlled instruments that are more powerful and even smaller.
- Both these programs shown here aspire to be Artificial Intelligence Eugene Goostman is an animated artificial intelligence that was able to fool the 2014 Turing Test judges 33% of the time; but that remains controversial, some dismissing it, some thinking it’s a huge turning point for technology. IPsoft calls Amelia an artificial intelligence that can read and understand text, follow processes, solve problems and learn from experience. It is designed to replace humans in a wide range of low-level jobs. That still sounds like machine intelligence to me but they really, really want to cleanly pass the Turing test. I may have placed Amelia too far out in the timeline because it’s already been field tested. It’s been tried out on help desks, procurement processing, financial trading, operations support, and providing expert advice for field engineers. During the help desk trials it was able to go from solving very few queries independently to 42 percent of the most common queries within one month. By the second month it could answer 64 percent of those queries independently According to IPsoft it can follow the same written instructions as humans but absorb it in a matter of seconds; understand the full meaning of what it reads rather than simply recognizing individual words; quickly apply knowledge to solve queries in a wide range of business processes; learn from watching others; and create its own 'process map' of the information it is given and work out what actions to take depending on the problem. IPsoft believes these technologies will enable companies to have digital workforces that comprise a mixture of human and virtual employees. There are a lot of functions in healthcare that an Amelia like machine intelligence could replace Gartner projects that by 2017 managed services offerings that make use of autonomics like Amelia will drive a 60 per cent reduction in the cost of services. IPsoft has further plans to embed Amelia into robots allowing it to take advantage of their mechanical functions. Alan Turing was a founder of computer science and devised a test to determine if computers could think like humans or more precisely if they could imitate thinking like humans. It’s important because our interactions with robots will depend very much on the machine’s ability to connect with us on our own terms and no more so than in healthcare. If you want to know more go see The Imitation Game.
- Now we begin to get into sub-miniature and nano level robotics. Johns Hopkins has developed micro-grippers to perform biopsies and be more effective in accessing narrow conduits in the body to detect early signs of cancer and other disease. It is autonomously activated by the body’s heat which causes the fingers to close on cells. The grippers and their samples are taken out through an existing body opening with a magnet. Typically 30 to 40 pieces of tissue are removed for biopsy. These would be introduced in swarms and increase the accuracy of discovering disease and the extent of the disease. It’s been successfully tested on animals.
- On the left is a picture of an actual nano robot. It was created by researchers at Nagoya U in Japan who successfully performed a single cell puncture using that needle like device extending from the main structure. The objective is to enable manipulations and surgeries of small cells. DARPA, the Department of Defense’s Research Arm is very involved with nano tech and their focus is continually monitoring a soldier’s condition and to be able to intervene using nano technologies. The picture on the right is a rendering of their In Vivo (within the body) nano platform. They have two research programs going on. The first is focused on diagnostics and launched in 2012. Ideally they want to monitor the various components of the chemistry of small and large molecules that have diagnostic interest to them and be able to communicate that information without any implanted electronics basically creating a data stream of a soldier’s health. The second program, launched in 2013, is focused on treatment. They are looking at treating infections caused by multi-drug resistant organisms and conditions due to traumatic brain injury.
- NC State is using graphene strips that are one atom thick to deliver anticancer drugs The research team bound a cancer drug labeled Dox in this image and the TRAIL protein on a graphene strip. When they inject it into laboratory mice with human lung cancer tumors, the graphene compound sticks to cancer cells because the cancer cells are attracted by the TRAIL protein. The cancer cells then swallow up the graphene sheets with the cancer drug still attached. This way the drug can attack the nucleus of the diseased cells, where it is most effective, while the TRAIL protein works on destroying the membrane. Another nano tech example, not pictured here, is a 20 micrometer long polymer tube that is the width of a human hair. It’s coated with zinc and is orally ingested. The zinc reacts with stomach acid and produces bubbles that propels it into the lining of the stomach and attaches. It then dissolves and whatever medication it is carrying is released into the tissue. This was developed at UC San Diego. https://news.ncsu.edu/2015/01/gu-graphene-2015/ Dox doxorubicin and a membrane-associated cytokine (tumor necrosis factor-related apoptosis-inducing ligand, TRAIL) to tiny sheets of graphene, either directly or by using chains of peptides.
- Here is the last of my featured diseases of the future. This one depicts nano toxicological shock where normally healthy human beings pick up nano particles, react badly with them, and go into shock. The question this raises is How will we prevent nano tech from entering the environment and what affect will it have if it does escape? Nano materials are being developed for all sorts of industrial and commercial uses not just for healthcare. Our track record of preventing any sort of chemical from entering the environment is not good so we need to be acutely aware of how we prevent the same from happening with nano tech. We simply don’t know what could happen on a wide scale if these emerging materials were released into the environment. Nanotechnology has the potential to reshape virtually every aspect of the human condition, both for better and for worse. Already today, scientists are concerned about the impact that nanotechnological materials and devices will have on the environment. There's considerable debate as to what extent industrial and commercial use of nanomaterials will affect organisms and ecosystems. Because these technologies involve the production of materials at the molecular scale, it's conceivable that particulate matter will begin to bioaccumulate in the environment. Humans will eventually come into contact with these nanopollutants, causing all sorts of serious health problems, including damage to our cells and DNA. On a related note, nanotechnological devices that are deliberately infused into the human body could cause serious problems as well. Poorly designed nanobots could deliver medicines to the wrong area, or degrade in unpredictable ways. And if their programming goes awry, they could physically damage tissue, or replicate uncontrollably, leading to an internal grey goo catastrophe. And like cybernetic implants, they could also trigger exaggerated immune responses resulting in anaphylactic shock.
- What does this all mean for the future of hospitals? Here’s my take on it and I’m sure there will be plenty of debate about it so don’t be shy to email me your thoughts because it’s only hearing from you that these ideas can develop further and advance.
- My view is that the hospital of the future will be more than one type of facility. I’m positing at least four kinds: The intense acute care hospital, the intense ambulatory care facility, the telehealth facility, and urban and rural critical access hospitals. The intense acute care hospital will be characterized by expertise, efficiency, and information via robotics and machine intelligence. It will have very short lengths of stay, the minimum number of beds to do the job, and a 24/7 working environment. I’ll focus on the intense acute care hospital in the next set of slides but let me very briefly describe the three other types first. The intense ambulatory center will have many of the same features as the intense acute care hospital except you won’t stay overnight. The telehealth facility will be the receiver and integrator of all wearable, implantable, and mobile data streams of patients. Doctors and data scientists will work here coordinating care and doing research. Finally, I believe we will have to maintain an inventory of urban and rural critical access hospitals for emergency and disaster response. All four types can come together in various combinations depending on the market and the system.
- The IACH will constantly monitor, measure, and adjust activity in the facility from a variety of optical, sonic, and wavelength detectors and sensors and direct feeds from equipment. The information includes data from implanted/wearable patient biometric devices, data from autonomous machines, current volumes, staff locations, supplies, equipment locations, ambient conditions and energy use, and projected volumes. In short, all the data the IACH’s machine intelligence needs to constantly adjust and ensure maximum efficiency in the use of all resources, anticipate future need, move the patient efficiently through the treatment plan, and ensure just in time resource consumption and staffing.
- Routine functions will be replaced with purpose designed autonomous robots and machine intelligence. These machines will do most of housekeeping, dietary, pharmacy, care assistance, central sterile processing, and business and maintenance functions. General labs will no longer exist. Specialized robots will be used routinely in the operating room and other clinical areas replacing some physicians, nurses, and technical specialists. Diagnosis and treatment plans will be developed by the IACH’s machine intelligence. Doctors and nurses in the IACH will be experts at deploying the vast amount of data and robotic capabilities to cure and repair patients in the shortest amount of time.
- Most patient rooms in the IACH will be multi-functional enabling a wide variety of procedures, imaging, and levels of care to be conducted in the room. The patient, when not undergoing active treatment, will be able to access any number of room features that can connect him or her with family or loved ones, the treatment plan and overall health record, active memory stimulation, and entertainment. The room will monitor the patient’s condition, respond to requests, adjust the environment, and provide a machine intelligence generated ‘counselor’ to converse with that knows the patient’s condition and history and is programmed to be empathetic. That ‘counselor’ may have followed the patient from a home care environment and, if not, could follow the patient to home care.
- Operating rooms, per se, won’t exist. The notion of individual rooms will give way to very large spaces where procedures are performed. This wide open space will configure and reconfigure automatically to accommodate multiple scheduled and unscheduled procedures. The operating space will accommodate autonomous or operator assisted robotic surgical equipment. Imaging equipment will be integrated directly into the space and not be hybridized. One of the biggest developments will be the use of hologram images of the operating field. These will be overlaid exactly onto the patient and will guide the surgery. Data read-outs will be within the hologram field. Advances in sterilization and other methods to isolate multiple operations going on in the same space will accommodate these changes. Necessary parts, biologics, implants, and surgical equipment will be custom made in the OR space through a variety of machines. Anesthesia will move to electric cerebral cortex manipulation instead of drug injection. Surgical times will be halved.
- Teaching and research will be dispersed. Singular enterprises built on the triad of research, teaching, and care will struggle with maintaining that mission as teaching venues move to other types of facilities. Many IACH’s will simply provide the most advanced care possible while research and some teaching are conducted elsewhere. Medical and nursing schools and GME programs will have to change curriculums to keep up with the demand for this specialized knowledge. A new healthcare workforce will emerge, trained in the programming, deployment, and maintenance of machine intelligence and autonomous robotics.
- Facility designs will change substantially to accommodate machine intelligence and autonomous robotics. New service pathways that keep machines out of public view will be created along with docking, storage, and maintenance spaces. Designs focused on grand hotel-like features, expansive lobbies, and other visitor spaces will no longer be financially feasible or desirable because being admitted to an IACH will be a very unusual event in a person’s life and if they are admitted their length of stay will be very short. The number of beds and public spaces will be greatly reduced. Designs that feature efficiency and enhance the human/machine interaction will be the norm. A new generation of staff, digital natives who were weaned on the integration of machines into their lives, will not object to co-working with machine intelligence however manifested. Designs will need to ensure this co-existence in such a way that it preserves the humanity of the place. This is more an imperative for the workforce than for the patient.
- Thank you all for taking the time to attend this webinar. The next slide has my contact information and don’t be shy about reacting to this or asking questions. I’m leaving you with four additional references that I think you will find fun, enlightening, and perhaps a little disturbing. Enjoy.