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Injecting Intelligence Into Health Care

Accenture's six-country survey among 180 C-level health executives says adoption of AI is measured, but real.
The survey assessed beliefs about market maturity, practical and clinical challenges to the adoption of AI in healthcare.
Enthusiasm for AI (artificial intelligence) is high among health executives, with people skills the most important implementation success factor.
Sufficient staff training/ expertise is rated the most important success factors for AI implementation (ranked in top three by 73 percent of execs).
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Injecting Intelligence Into Health Care

  1. 1. ACCENTURE EXECUTIVE SURVEY ON AI IN HEALTHCARE MULTI-COUNTRY INJECTING INTELLIGENCE INTOHEALTH CARE Survey analysis presented by Oxford Economics for Accenture
  2. 2. • Accenture commissioned Oxford Economics to do a six- country survey in Europe and Asia-Pacific, among 180 C-level health executives. • The goal was to assess current and planned use of AI in their organisations. They were also asked about benefits expectation, success factors, practical and clinical challenges related to AI adoption. • The survey included executives from each of the following markets: the UK, Spain, Finland, Norway, Singapore, and Australia. • Respondents were either in the roles of CIO, CTO, CDO, CExO, CEO or CCIO or direct reports. • Respondents were evenly split across health payer and health provider organisations, as well as public and private sectors. • Field work was conducted in summer 2018. DEMOGRAPHICS 2Copyright © 2019 Accenture. All rights reserved.
  3. 3. 3Copyright © 2019 Accenture. All rights reserved. AIADOPTION There is no doubt anymore – AI is finally being embraced by the healthcare industry. It is an encouragingly high priority among C-level health executives, as is adoption of AI technology. At the same time, executives are being appropriately selective about the types of AI they are choosing to implement. Our survey shows that an impressive 72 percent of health leaders polled say they are either piloting or planning AI adoption. Perhaps even more impressive: 93 percent of health execs confirmed they have AI projects on their agenda, with just seven percent saying they are minimally or not at all focused on AI.
  4. 4. THESTATEOFAIADOPTION • Although 11% say AI is well-integrated into operations, many may only be using one or a few applications of the technology. • Healthcare providers are more likely to say AI is well integrated into their organisation’s operations (19%, vs. 2% of payers); another 20% have implemented AI in some areas of the organisation, vs. 11% of payers. • Private organisations are more likely to say AI is well integrated (16%, vs. 6% of public organisations). • Numbers are directional, but larger organisations are much more likely to say AI is well integrated into operations (57%). • AI is more likely to be implemented in some areas or across the business among organisations headquartered in the UK (37%) or Singapore (33%); organisation in Norway (27%), Finland (23%), Australia (20%), and Spain (17%) are less likely to say so. n=180 Copyright © 2019 Accenture. All rights reserved. 4 Adoption: Which of the following statements best describes the current state of AI adoption in your organisation? 11% 16% 42% 30% 1% AI is well integrated into our organisation’s operations We have implemented AI in some areas of the organisation We are piloting our use of AI We are planning our adoption of AI We are not yet planning to adopt AI
  5. 5. MAJORITYAREINTHEPILOTINGAND PLANNINGPHASE Adoption: Which of the following statements best describes the current state of AI adoption in your organisation? Copyright © 2019 Accenture. All rights reserved. 5 n=180 * 3% of Norway respondents said they are not yet planning to adopt AI 17% 20% 37% 27% 7% 10% 47% 37% 10% 17% 30% 40% 10% 13% 60% 17% 13% 20% 37% 30% 7% 13% 47% 33% AI is well integrated into our organisation’s operations We have implemented AI in some areas of the organisation We are piloting our use of AI We are planning our adoption of AI UK Spain Norway Finland Singapore Australia
  6. 6. 6Copyright © 2019 Accenture. All rights reserved. WISDOMOFTHEMEASUREDAPPROACH While adoption rates are encouragingly high, health executives are taking an appropriately measured approach to AI. The popular narrative around benefits of Artificial Intelligence in Healthcare has largely been around been focused on making healthcare more effective: better decisions, better and smarter decisions for doctors, helping patients navigate their own healthcare. The reality is that healthcare organisations are being more focused internally and on cost. For now, healthcare AI is being implemented mainly in operational back offices areas, showing that the real transformative opportunity for Artificial Intelligence is actually on making healthcare more affordable.
  7. 7. EARLYGAINSAREINTHEBACKOFFICE Copyright © 2019 Accenture. All rights reserved. Value of AI: To what extent have you realised value in the following areas as a result of your application of AI (i.e., the specific use of technology that can learn by itself without the need for any explicit programming)? "Substantial/Transformational value” responses 6% 10% 13% 13% 19% 20% 43% 50% 56% 56% n=124; those who have piloted or implemented AI 7 Increased cybersecurity Operational efficiency Improved analytical capabilities (e.g., patient identification and personalised treatment and management) Cost savings (e.g., appointments, financial management) Lower cost of care (with same or better outcomes) Expanded patient reach (e.g., serving broader population through digital tools) Patient satisfaction Labour savings/reallocation Better access to care (regardless of outcomes or cost) Better clinical outcomes • Healthcare payers are more likely than providers to say they have realised benefits from AI in terms of operational efficiency (63% vs. 50%) and cost (52% vs. 35%). Healthcare providers are more likely to say AI has improved their analytical capabilities (56% vs. 43%). • Larger organisations are more likely to have realised a range of benefits from AI, in terms of operational efficiency, increased cybersecurity, analytical capabilities, and cost savings. • Respondents from organisations headquartered in the UK are more likely than others to say they have realised value in terms of operational efficiency (73%, vs. 52% of others), cost savings (59% vs. 39% vs. 16%), and expanded patient reach. • Numbers are directional, but respondents who say AI is well integrated into their organisation’s operations are more likely to have realised benefits in operational efficiency and cost savings, increased cybersecurity, improved analytical capabilities, and expanded patient reach.
  8. 8. ROUTINETASKSANDCYBERSECURITYMAIN AIAPPLICATIONAREAS Copyright © 2019 Accenture. All rights reserved. n=180 Handling mundane administrative tasks like admissions, prescriptions, and regular vital-sign testing Linking AI to structured healthcare data to help transform care delivery models AI-assisted routing of healthcare records to patient- approved doctors Using AI-assisted alerts to improve cybersecurity AI-assisted robotic process automation Providing support to researchers for studies on new fields in medicine and disease diagnosis and management Applying AI to unstructured health data to order it & extract meaningful market intelligence and guide transformation Advanced analytics to reduce the cost of managing chronic patients An AI-assisted clinical coder (also referred to as an ‘Intelligent Chart Review Assistant’) Providing AI-assisted diagnosis and treatment recommendations to clinicians Audio-visual and biometric technology to monitor and report incidents such as falls or heart attacks among the elderly or infirm AI-assisted robotic surgery/surgery support Using chatbots at the patient interface to analyse patient needs and route them to appropriate clinicians In progress: Which of the following applications of AI are already in progress at your organisation? 8 44% 31% 29% 29% 26% 47% 53% 24% 22% 20% 18% 17% 3%
  9. 9. 9 PLANSANDBENEFITEXPECTATIONS Copyright © 2019 Accenture. All rights reserved. AI investments should increase over the next few years; 40 percent of health executives are quite or highly focused on increasing the use of AI-assisted applications, another 53 percent are moderately focused. While applications of artificial intelligence are clustered in the back office for now; patient-facing applications are expected to follow and are expect to show substantial benefits. The most transformative benefit of AI though they expect to lie in the operational application areas for the next three years.
  10. 10. GROWTHOFAIISAWIDESPREADIMPERATIVE • Healthcare providers are more likely to say they are highly focused (22%, vs. 3% of payers.) • Private organisations are more likely to say they are highly focused on increasing the use of AI (17%, vs. 9% of public organisations); 47% are quite or highly focused, vs. 33% of public. • Data on organisation sise are directional (i.e., the particular group of respondents is not large enough to be statistically meaningful), but larger organisations are much more likely to say increasing the use of AI is their primary goal (65%). • Respondents from organisations headquartered in Australia are least likely to say they are quite or highly focused on increasing their proportion of AI-assisted applications (23%, vs. 43% of all others). Importance: To what extent is your organisation currently focused on increasing its proportion of AI-assisted applications? 13% 27% 53% 6% 1% Highly focused, it is a primary/main goal Quite focused Moderately focused Minimally focused Not at all focused, it is not on our list of goals Copyright © 2019 Accenture. All rights reserved. 10 n=180
  11. 11. SOMECOUNTRIESMOREFOCUSEDONAI THANOTHERS? Importance: To what extent is your organisation currently focused on increasing its proportion of AI-assisted applications? 20% 10% 13% 10% 13% 10% 23% 40% 20% 33% 33% 13% 43% 50% 33% 43% 46% 23% UK Spain Norway Finland Singapore Australia Highly focused, it is a primary/main goal Quite focused Copyright © 2019 Accenture. All rights reserved. 11 n=180
  12. 12. VALUEEXPECTEDTOSHIFTTOWARDPATIENT CARE Dark violet bars: n=124; those who have piloted or implemented AI Light violet bars: n=180 Value of AI: To what extent have you realised value in the following areas as a result of your application of AI? To what extent do you expect to realise value from AI today? Over the next three years? Copyright © 2019 Accenture. All rights reserved. 12 54% 52% 61% 59% 58% 61% 86% 79% 89% 91% 6% 10% 13% 13% 19% 20% 43% 50% 56% 56% Today In three years Increased cybersecurity Operational efficiency Improved analytical capabilities (e.g., patient identification & personalised treatment & management) Cost savings (e.g., appointments, financial management) Lower cost of care (with same or better outcomes) Expanded patient reach (e.g., serving broader population through digital tools) Labour savings/reallocation Patient satisfaction Better access to care (regardless of outcomes or cost) Better clinical outcomes
  13. 13. 13 SUCCESSFACTORS The early successes for AI among health executives seem to beg the question: “Why aren’t even more AI projects underway?” Part of the answer may lie buried in a second major conclusion of the survey: people skills matter most to AI success, and insufficient skills within the health organisation workforce represent the top barrier to AI adoption and implementation. Clear guidelines and legislation, access to mature technology and sufficient funds for investing are a must, too. Copyright © 2019 Accenture. All rights reserved.
  14. 14. PEOPLEMATTERMOSTTOAISUCCESS Success factors: Which of the following factors are most important to successful AI project implementation at your organisation? 1% 1% 2% 1% 3% 2% 4% 26% 19% 23% 21% 1% 1% 7% 2% 9% 8% 24% 23% 27% 1% 1% 1% 3% 4% 7% 10% 7% 19% 18% 25% Staff buy-in C-suite leadership Patient/customer buy-in Clinician buy-in Ability to free up clinician time Clear benefit articulation Access to successful healthcare case studies Integration into business as usual/workflows Budget to hire consultants/acquire technology Maturity of technology Clear guidelines and legislation Sufficient staff training/expertise Rank 1 Rank 2 Rank 3 n=180 Copyright © 2019 Accenture. All rights reserved. 14 • Healthcare providers are more likely to rank maturity of the technology as important to successful AI project implementation (69%, vs. 54% of payers); payers are more likely to rank integration into business as usual/workflows (29% vs. 17%). • Public organisations are more focused on clear guidelines and legislation (69%, vs. 59% of private organisations) and maturity of technology (70% vs. 53%); private organisations are more focused on the budget to hire consultants or acquire technology (57% vs. 26%).
  15. 15. 15 BARRIERS Copyright © 2019 Accenture. All rights reserved. In addition to being the most important success factor, skills (or the absence thereof) also constitute the biggest obstacle to effective AI projects in healthcare. Ethical and data privacy concerns are ranked high too. Too a much lesser extent it was feared that distrust from clinicians or distrust from patients can hinder AI take off in healthcare organisations.
  16. 16. WORKERSKILLSTOPROADBLOCKSTO ADOPTION n=180 Obstacles: To what extent are the following factors obstacles to the broad-scale implementation of AI at your organisation? "Meaningful/Major obstacle" responses – Part 1 63% 44% 40% 37% 29% 29% 26% An insufficiently skilled workforce Ethical concerns Data privacy concerns Insufficiently developed/ tested technology Lack of clear legislative framework Lack of strategic alignment within the organisation Lack of focus on innovation/ new technologies Copyright © 2019 Accenture. All rights reserved. 16
  17. 17. 18% 17% 16% 15% 15% 11% 11% 8% Systemic structural/ organisational factors (e.g., lack of available data) Distrust from clinicians about effectiveness of technology Overly ambitious early goals (e.g., focusing on complex tasks rather than mundane functions) Clinician concerns over potential job loss or job changes The absence of structured healthcare databases Legal liability/ risk to health outcomes Distrust from patients A lack of industry standards PATIENT,PROFESSIONALCONCERNSNOT (YET?)AMAJORROADBLOCK • Payers are more likely to cite ethical concerns (50%, vs. 39% of providers), lack of clear legislative framework (38% vs. 21%), and lack of strategic alignment within the organisation (37% vs. 21%) as obstacles to AI implementation. n=180 Obstacles: To what extent are the following factors obstacles to the broad-scale implementation of AI at your organisation? "Meaningful/Major obstacle" responses – Part 2 Copyright © 2019 Accenture. All rights reserved. 17
  18. 18. CONCLUSIONS 18 The survey has shown that executives have a strong focus on AI, and are already realising concrete value from implementations mainly in the back office. Executives absolutely need to focus on increasing AI skills among staff, and will likely need to turn to external talent to make rapid progress and exploit AI’s huge potential. With this measured approach, health organisations will position themselves well to lead the market with respect to AI in healthcare. AI´s most transformative aspects lie in making healthcare more affordable and productive, as opposed to just making healthcare better.
  19. 19. FRODE GJENDEM Managing Director, Accenture Digital NIAMH MCKENNA Managing Director, Health and Public Services UK IAN MANOVEL Managing Director, Innovation Principal Director Health Click here to know more. CONTACT US

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