{"id":28126,"date":"2018-11-12T13:40:11","date_gmt":"2018-11-12T18:40:11","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/collaborative-autonomy-in-the-operating-room-verb-surgicals-quest-to-democratize-high-quality-surgical-care\/"},"modified":"2018-11-15T08:42:19","modified_gmt":"2018-11-15T13:42:19","slug":"collaborative-autonomy-in-the-operating-room-verb-surgical-and-democratized-surgery","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/collaborative-autonomy-in-the-operating-room-verb-surgical-and-democratized-surgery\/","title":{"rendered":"Collaborative autonomy in the Operating Room: Verb Surgical and Democratized Surgery"},"content":{"rendered":"

Machine Learning (ML) and Surgery 4.0<\/strong><\/p>\n

Recent advancements in ML for medicine have enabled physicians to leverage predictive tools for disease diagnosis (such as lung nodule detection [1]<\/a>; and diabetic retinopathy screening [2]<\/a>) and early intervention. However, most ML advancements have strayed away from interventional<\/em> care, such as surgery, because insufficient data exists to benchmark existing and gold standards of care [3]<\/a>. \u00ad<\/p>\n

This gap is alarming, as medical error is the third leading cause of death<\/em> in the US– upwards of 440,000 deaths occur annually [4]<\/a>, of which 17% are preventable<\/em> (operator-induced) complications that occur during surgery [5]<\/a>.<\/p>\n

Enter minimally invasive robotic surgery (MIRS), which promises to improve surgeon performance and patient safety — by enabling better anatomical access, instrument control and visualization through miniature incisions within the body.\u00a0 Robotics represents Surgery 3.0 (Figure 1<\/a>) – a leapfrog technology that promises safer, higher quality-of-life post-operative outcomes compared to open surgery or laparoscopy [6]<\/a>.\u00a0 However, MIRS is still competency\/skill dependent and complex to learn [7]<\/a>.\u00a0 Hence, the broader issue of surgeon-to-surgeon variability and its implications on patient outcome persist.<\/p>\n

\"\"<\/a>
<\/a>Figure 1: Innovation S-Curves for Surgery 1.0 — 4.0 [8]<\/a><\/figcaption><\/figure>What if there was a way to \u201cperfect\u201d surgery? Deal with adverse events? Deliver care in a safe, consistent (operator-agnostic) and<\/em> patient-specific manner? Enter Surgery 4.0<\/em> \u2013 a data-driven approach to operative performance and decision making (Figure 2<\/a>).<\/p>\n

\"\"<\/a>
<\/a>Figure 2: Surgery 4.0 — Digital Surgery [8]<\/a><\/figcaption><\/figure>Verb Surgical and the era of Digital Surgery<\/strong><\/p>\n

Verb Surgical aims to deliver surgery 4.0 to the 5 billion people [16]<\/a> worldwide who desperately need care.\u00a0 To push this new mode of surgery into the operating room, Verb aims to develop a digital surgery ecosystem (DSE) <\/em>around end-to-end patient care (Figure 3<\/a>).<\/p>\n

\"\"<\/a>
<\/a>Figure 3: An (Envisioned) Digital Surgery Ecosystem [9]<\/a><\/figcaption><\/figure>The biggest barrier to surgery 4.0 adoption is data<\/em> and annotation<\/em>. \u00a0\u00a0By developing a comprehensive data aggregation platform around DSE, Verb can start to derive contextual information on how a surgeon operates, who they\u2019re operating on, and technologies that can predict and prevent intraoperative adversities.\u00a0 \u00a0\u00a0How can Verb collect this data en masse<\/em>? How can they annotate this data to benchmark gold standards of care, so that ML models can reliably predict and sustain surgical quality metrics intraoperatively?<\/p>\n

Industry and Clinical Partners for Data and Annotation<\/strong><\/p>\n

The answer lies in partnerships<\/em>.\u00a0 Verb Surgical is a strategic partnership between Google and Johnson and Johnson (J&J).\u00a0 Google provides annotation frameworks that can be leveraged to train predictive models [1]<\/a>, while J&J provides the distribution channels within various hospital networks to acquire data at scale.\u00a0 However, no hospital or physician will adopt a system that does not provide clinical value today<\/em>.<\/p>\n

To address this issue, Verb Surgical is targeting procedures that enable surgeons to visualize anatomy in novel ways, and robotically operate on these structures if they so desire [10]<\/a>.\u00a0 In many ways, they are benchmarking to Surgery 3.0, while delivering incremental DSE benefits to drive data-driven decision making for the surgeon – before, during and after a procedure [10]<\/a>.\u00a0 Surgeons have been receptive to this strategy [10]<\/a>, and to Verb\u2019s benefit, represent an early set of clinical adopters that will push data and annotation aggregation for broader surgery 4.0 adoption.<\/p>\n

Verb\u2019s biggest challenge to scale beyond early adopters is to overcome the heavy switching costs associated with acquiring DaVinci<\/em> surgical users in the US (a robotic system manufactured by Intuitive Surgical\u00ae, with 88.8% market penetration [11]<\/a>) and to sell their system to price-sensitive international users at a low capital cost [12]<\/a>.\u00a0 The latter customers are currently inaccessible to Intuitive Surgical\u00ae (each system costs upwards of $2M USD [13]<\/a>) but are high-value data opportunities for Verb (by surgical volume, these markets are 7x larger than the US [14]<\/a>).<\/p>\n

A Netflix Model for Surgery 4.0<\/strong><\/p>\n

How can Verb capture price-sensitive, high volume users quickly? Netflix may provide a clue.\u00a0 Hospitals typically take months to perform a cost-benefit-analysis before making a large capital purchase decision.\u00a0 What if hospitals got the robot for free, and paid an annual subscription after the first year of use<\/em>? This enables purchasers to develop an economic value proposition unique to their hospital system, with no capital downside.\u00a0 This is an emerging model garnering broader adoption across other device companies [15]<\/a>, and to Verb, presents a unique long-term monetization opportunity synergistic with surgery 4.0 (Data Products, Figure 3<\/a>).<\/p>\n

2030 and Beyond \u2013 Collaborative Autonomy<\/strong><\/p>\n

Fast forward a decade from now.\u00a0 Verb has every high-volume surgical market annotated, constantly refining predictive models that know how to execute the perfect procedure for any patient context.\u00a0 Surgeons in rural China, with the help of intelligent guidance systems (Figure 4<\/a>), can perform nephrectomies (removal of cancerous kidney) as well as their academic counterparts abroad.<\/p>\n

\"\"<\/a>
<\/a>Figure 4: Collaborative Autonomy and Intelligent Surgical Guidance<\/figcaption><\/figure>\n

Is this paradigm of care attainable? \u00a0Can sensors be placed within the body to characterize instrument and anatomic interactions with sufficient accuracy to warrant robust predictive power from a machine? (800 Words)<\/p>\n

References<\/strong><\/p>\n

[1]<\/a> Nam, Ju Gang, Sunggyun Park, Eui Jin Hwang, Jong Hyuk Lee, Kwang-Nam Jin, Kun Young Lim, and Thienkai Huy Vu et al. “Development And Validation Of Deep Learning\u2013Based Automatic Detection Algorithm For Malignant Pulmonary Nodules On Chest Radiographs”.\u00a0Radiology<\/em>, 2018, 180237. doi:10.1148\/radiol.2018180237.<\/p>\n

[2]<\/a> Gulshan, Varun et al. 2016. “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs”.\u00a0JAMA<\/em>\u00a0316 (22): 2402. American Medical Association (AMA). doi:10.1001\/jama.2016.17216.<\/p>\n

[3]<\/a> Fecso, Andras B. et al. 2017. “The Effect of Technical Performance on Patient Outcomes in Surgery”.\u00a0Annals of Surgery<\/em>\u00a0265 (3): 492-501. Ovid Technologies (Wolters Kluwer Health). doi:10.1097\/sla.0000000000001959.<\/p>\n

[4]<\/a> Makary, Martin A & Daniel, Michael. 2016. “Medical error\u2014the third leading cause of death in the US”.\u00a0BMJ<\/em>: i2139. BMJ. doi:10.1136\/bmj.i2139.<\/p>\n

[5]<\/a> La Pietra, L. 2005. “Medical errors and clinical risk management: state of the art.”.\u00a0Acta Otorhinolaryngol Ital.<\/em>\u00a025 (6): 339-346. PubMed. https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/16749601.<\/p>\n

[6]<\/a> Roh, Hyunsuk Frank; Nam, Seung Hyuk & Kim, Jung Mogg. 2018. “Robot-assisted laparoscopic surgery versus conventional laparoscopic surgery in randomized controlled trials: A systematic review and meta-analysis”.\u00a0PLOS ONE<\/em>\u00a013 (1): e0191628. Public Library of Science (PLoS). doi:10.1371\/journal.pone.0191628.<\/p>\n

[7]<\/a> Mazzon, Giorgio et al. 2017. “Learning Curves for Robotic Surgery: a Review of the Recent Literature”.\u00a0Current Urology Reports<\/em>\u00a018 (11). Springer Nature. doi:10.1007\/s11934-017-0738-z.<\/p>\n

[8]<\/a> Huennekens, Scott. 2018. “Verb Surgical”.\u00a0Twitter.com<\/em>. https:\/\/twitter.com\/verbsurgical\/status\/1008674056546242561<\/a>.<\/p>\n

[9]<\/a> Vedula, S. Swaroop & Hager, Gregory D. 2017. “Surgical data science: the new knowledge domain”.\u00a0Innovative Surgical Sciences<\/em>\u00a02 (3): 109-121. Walter de Gruyter GmbH. doi:10.1515\/iss-2017-0004.<\/p>\n

[10]<\/a> Ezez. 2018. “Johnson and Johnson Q2 Analyst Presentation”.\u00a0Edge.media-server.com<\/em>. https:\/\/edge.media-server.com\/m6\/p\/trmhmyqo<\/a>.<\/p>\n

[11]<\/a> Webb, Marionn. 2018. “Market Intel: Rivals Catching Up To Intuitive Surgical In Fast-Growing, Fast-Innovating Robotic-Assisted Devices Market”.\u00a0MedTech Insight | Pharma Intelligence<\/em>. https:\/\/medtech-pharmaintelligence-informa-com.ezp-prod1.hul.harvard.edu\/MT121751\/Market-Intel-Rivals-Catching-Up-To-Intuitive-Surgical-In-FastGrowing-FastInnovating-RoboticAssisted<\/a>.<\/p>\n

[12]<\/a> Udwadia, TehemtonE. 2015. “Robotic surgery is ready for prime time in India: Against the motion”.\u00a0Journal of Minimal Access Surgery<\/em>\u00a011 (1): 5. Medknow. doi:10.4103\/0972-9941.147655.<\/p>\n

[13]<\/a> Ho, Chuong Thanh. 2011.\u00a0Robot-assisted surgery compared with open surgery and laparoscopic surgery<\/em>. Ottawa, Ont.: Canadian Agency for Drugs and Technologies in Health = Agence canadienne des m\u00e9dicaments et des technologies de la sant\u00e9.<\/p>\n

[14]<\/a> Rose, John et al. 2015. “Estimated need for surgery worldwide based on prevalence of diseases: a modelling strategy for the WHO Global Health Estimate”.\u00a0The Lancet Global Health<\/em>\u00a03: S13-S20. Elsevier BV. doi:10.1016\/s2214-109x(15)70087-2.<\/p>\n

[15]<\/a> Chandrasekaran, Brahadeesh. 2018. “Transformation Of The Medical Device Industry”.\u00a0Frost And Sullivan<\/i>. https:\/\/ww2.frost.com\/frost-perspectives\/transformation-medical-device-industry\/https:\/\/ww2.frost.com\/frost-perspectives\/transformation-medical-device-industry\/.<\/p>\n

[16]<\/a>\u00a0Alkire, Blake C, Nakul P Raykar, Mark G Shrime, Thomas G Weiser, Stephen W Bickler, John A Rose, and Cameron T Nutt et al. 2015. “Global Access To Surgical Care: A Modelling Study”.\u00a0The Lancet Global Health<\/i>\u00a03 (6): e316-e323. doi:10.1016\/s2214-109x(15)70115-4.<\/p>\n","protected":false},"excerpt":{"rendered":"

Can Verb Surgical use advanced robotics and machine learning to deliver high quality surgical care to the 5 billion [16] people around the world who desparately need it?<\/p>\n","protected":false},"author":11330,"featured_media":28148,"comment_status":"open","ping_status":"closed","template":"","categories":[5059,5075,1909,5177,1045,2062,4055,5176,41,5174,346,1685,344,5173,2235,745,5178,4310,3528,5175],"class_list":["post-28126","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artfiical-intelligence","category-artificial-inteligence","category-artificial-intelligence","category-autonomy","category-children-healthcare","category-digital-health","category-digital-healthcare","category-engineerng","category-healthcare","category-intuitive","category-machine-learning","category-medicine","category-product-development","category-robotic-surgery","category-robotics","category-surgery","category-surgical","category-surgical-robotics","category-value-based-healthcare","category-verb","hck-taxonomy-organization-verb-surgical","hck-taxonomy-industry-medical-devices-and-supplies","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nCollaborative autonomy in the Operating Room: Verb Surgical and Democratized Surgery - 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