1、Stanford Medicine 2020 Health Trends Report The Rise of the Data-Driven Physician 2 Since its inception, the Stanford Medicine Health Trends Report has examined the most consequential developments and technologies that are changing health care delivery. Our 2020 report describes a health care sector
2、 that is undergoing seismic shifts, fueled by a maturing digital health market, new health laws that accelerate data sharing, and regulatory traction for artificial intelligence in medicine. In 2020 and beyond, these trends have enormous implications for patients, influencing how they will experienc
3、e health care as well as the services they may soon have access to. But to truly understand how this future is taking shape for patients, we felt it was critical to start by asking those who will be directing their care. As a proxy for the health care delivery system, Stanford Medicine conducted a n
4、ational survey of physicians, residents, and medical students to understand how key health care trends will manifest themselves in the doctors office over the next decade. What we found boils down to one central idea: physicians expect new technology to transform patient care in the near term, and t
5、hey are actively preparing to integrate health data and the technologies that harness itinto the clinical setting. In other words, we are witnessing the Rise of the Data-Driven Physician. Physicians and medical students now pursue supplemental education in data-oriented subjects such as advanced sta
6、tistics, coding, and population health. And they express openness to using novel datasets, including those from health apps and wearables, as part of routine care. These developments have significant potential to advance patient care and empower tomorrows health care providers to predict, prevent, a
7、nd cure diseaseprecisely. And yet, the promise of this future is not assured. Among those surveyed, few feel “very prepared” to implement emerging technologies in clinical practice, especially for innovations that physicians and students say have the greatest potential benefit for patients. This Tra
8、nsformation Gap represents both a major challenge and opportunity for health care stakeholders. Certainly, we will need to heighten our focus on providing physicians and clinicians with adequate training. But we must also account for other issues. From our survey, we see a future health care workfor
9、ce that today is grappling with high levels of student debt, poor work-life balance, and administrative distractions issues that are influencing career choices, including whether to remain in medicine. Educators, health administrators, government officials, and the private sectornow enmeshed in heal
10、th care deliveryall have distinct opportunities to alleviate these burdens. The findings presented in this report are just the beginning of an important conversation. As we embark on a new decade, there is no better time to have a discussion about how we can prepare and support tomorrows health care
11、 providers to rise to their fullest potential. Lloyd B. Minor, MD Dean, Stanford University School of Medicine 2020 Health Trends Report Foreword from Dean Minor Table of Contents Introduction 4 Methodology 5 3 2020 Health Trends Report Findings 1. Transformation of Health Care 6 2. The Rise of the
12、Data-Driven Physician 10 3. The “Transformation Gap” 15 4. Under Pressure 17 Conclusion 21 4 2020 Health Trends Report Introduction The 2020 Stanford Medicine Health Trends Report identifies the Rise of the Data-Driven Physician as one of the industrys most consequential developmentsone that has sig
13、nificant implications for patients. To understand this trend, there are four factors to appreciate: 1. An industry under transformation. In 2020, it is clear that the heath care sector is being profoundly altered by a number of trends, from technology advancements, to consumer adoption of novel heal
14、th services, to policy and regulatory developments. Taken together, these trends place a high premium on new skills that fall outside the traditional domain of health care delivery. 2. Health care providers awake to new developments. The next generation of physicians is developing a broader skillset
15、. Our research found that nearly three-quarters of all medical students and nearly half of all physicians are planning to pursue additional education in data-oriented such as advanced statistics and data science. 3. A transformation gap in health care. Currently, physicians and medical students repo
16、rt low levels of readiness to implement the technologies they believe have the most transformative potential for health care and their patients. While physicians recognize the benefits of many new technologies, education and training currently lag in fully preparing them for these new developments.
17、4. Under pressure. Even with a new skillset, tomorrows physicians face significant practice pressures today that are influencing their decision-making, including which specialties they choose and even whether they will remain in medical practice. Moreover, issues such as private industry getting inv
18、olved in health care loom over the medical profession. How these trends play out will ultimately determine the future success of the Data-Driven Physician. These trends and developments present important challenges and considerations for health care stakeholders. Whats clear is that, if they can be
19、successfully navigated, we will witness a revolution in health care, one that will bring significant innovation and data-driven insights to patient care. 5 2020 Health Trends Report Methodology In addition to conducting a secondary review of news articles, white papers, and peer-review research for
20、the 2020 Health Trends Report, Stanford Medicine worked with Brunswick Insight to conduct a comprehensive survey of 523 U.S. physicians and 210 medical students and residents. (The student and resident sample included 77 current medical students and 133 medical residents.) Respondents were contacted
21、 through a list of American Medical Association (AMA) verified physicians and survey sample panels of medical professionals. Respondents were compensated for their involvement in the survey and were informed that their responses will be used to inform public-facing research. Respondents were given t
22、he opportunity to opt- out of any and all questions in the survey. Respondents were contacted between September and October of 2019. Percentages are rounded up to the nearest whole number when calculating totals. 6 The Rise of the Data-Driven Physician is the direct consequence of data and technolog
23、y transforming the health care sector The Rise of the Data-Driven Physician is a sign that the entire health care market is now grappling with the practical application of data and new technologies. Specific developments that are driving these changes include: The maturation of the digital health ca
24、re market The availability of data Artificial intelligence moving from theory to practice The Digital Health Market Is Maturing The digital health market is showing signs of maturity as it moves from a realm of ambitious early-stage start-ups to more established companies with validated products. In
25、 part, this helped to fuel several high-profile IPOs of digital health companies in 2019. Last year, we also saw major corporations make multi-billion-dollar offers to acquire digital health companies that have, over time, amassed a significant consumer following. Notable health care companies that
26、went public in 2019 include Livongo and Health Catalyst, which both entered the market with billion-dollar valuations.i Also prominent was Googles announcement to acquire Fitbit for $2.1 billion dollars.ii Analysis from Rock Health indicates that investments in digital health care in 2019 are keepin
27、g pace with investments that were made in 2018. While the total number of deals seems likely to stay consistent with 2018, the average size of the deals has increased.iii Consumer behavior also demonstrates a growing maturation of digital health care. Analysis from Rock Health shows an increase in t
28、he use of digital health tools by consumers over time, including wearable health-tracking tools and provider reviews.iv Data Sharing Is Increasing Landmark legislation and regulatory priorities are pushing the health care sector to make data sharing a priority. The 21st Century Cures Act, passed in
29、December 2016, has led to new data sharing rules for Electronic Health Record (EHR) systemsthe technology now used by most medical practices to digitally document patient care. The rules, which are now under final review with the U.S. Office of Management and Budget, define and limit information blo
30、cking practices, greatly expand patients access to their own medical records, and promote standardized language and application programming interfaces (APIs) that encourage technical interoperability across EHR systems. 1. Transformation of Health Care 2020 Health Trends Report Findings 2020 Health
31、Trends Report 7 TOTAL VENTURE FUNDING AVERAGE DEAL SIZE # OF DEALS 2000172018H1 2019 $10B $8B $6B $4B $2B $0 1,000 800 600 400 200 $12.0M$10.6M$10.7M$14.0M$14.7M$13.5M$15.9M$21.9M$23.1M $1.1B $1.5B $2.1B $4.1B $4.7B $4.6B $5.8B $8.2B $4.2B $8.4B 360 93 142 196 291 320 340 363 3
32、76 180 LIVE VIDEO TELEMEDICINE WEARABLE OWNERSHIP DIGITAL HEALTH TRACKING ONLINE PROVIDER REVIEWS ONLINE HEALTH INFORMATION 200182019 100% 75% 50% 0% 7% 25% PERCENT OF ALL RESPONDENTS 22% 19% 34% 32% 13% 24% 24% 33% 33% 31% 33% 38% 42% 18% 50%51% 58% 64% 64% 71% 72% 79% 80% 76% Digital He
33、alth Funding 2011-H1 2019 Adoption of Digital Health Tools 2015-2019 Source: Rock Health (2019). 2019 Midyear Digital Health Market Update: Exits are heating up. Source: Rock Health (2019). Digital Health Consumer Adoption Report 2019. 8 For an industry that has long struggled with low levels of inf
34、ormation sharing and poor interoperability across its technology systems, in 2020 we expect to see the final rules create a seismic shift in how health care stakeholders share and interact with digital medical records. Writing for Health Affairs in June 2018, Don Rucker, the National Coordinator for
35、 Health Information Technology at the U.S. Department of Health and Human Services, explained the potential of using open APIs to encourage greater EHR data sharing, particularly on health care outcomes, to ultimately improve treatment for patients:v The Cures Act builds on the 2015 Edition of ONCs
36、health IT certification criteria by calling for the development of modern APIs that do not require “special effort” to access and use. APIs are technology that allow one software program to access the services provided by another software program. Today, payers and employers who purchase care have l
37、ittle information on health outcomes. Often times, contracts between providers and payers are negotiated on reputation of the provider rather than on quality care. Providers should compete on the entire scope of the quality and value of care they provide, not on how exclusively they can craft their
38、networks. Outcome data will allow payers to apply machine learning and artificial intelligence to have better insight on the value of the care they purchase. Population-level data transfer that is aligned with HIPAA is also central to having a learning health care system, advancing many research pri
39、orities and use cases, and modernizing public health reporting. Artificial Intelligence Moves from Theory to Practice The medical community is keenly aware of the power of artificial intelligence (AI). Our survey shows that almost 40% of physicians, students, and residents see the potential for AI t
40、o transform health care in the next five years. While technology developments in AI are a significant part of this story, an equally important component is the regulatory traction that AI has made in medical treatment. According to an analysis conducted by The Medical Futurist Institute, approval of
41、 medical algorithms by the FDA has seen rapid growth over the past several years. In a report released in June of 2019, the Institute found that the FDA had approved a total of 46 algorithms up to that point:vi The algorithms approved cover a broad spectrum of treatments and diagnoses. A common form
42、 consists of imaging algorithms designed to analyze medical scans and images to identify potential cancer and tumors. Examples include: AmCAD-US evaluates thyroid nodules and categorizes nodule characteristics. Arterys algorithm is able to spot cancerous lesions in liver and lungs on CT and MR image
43、s. iCAD classifies breast density and detects breast cancer as accurately as radiologists. ScreenPoint Medical assists radiologists with the reading of screening mammograms. However, current AI applications extend far beyond the domain of medical imaging. Increasingly, AI is being explored as a tool
44、 to support clinical workflows, including in the Intensive Care Unit (ICU). A recent study looked at how AI could be used to track patients while they are being treated in an ICU to potentially improve care efficacy and outcomes down the road:vii . 2020 Health Trends Report Year Total Number of FDA
45、Approved Algorithms 20141 20150 20164 20178 201825 2019 (as of June)8 Total (as of June 2019)46 Source: The Medical Futurist (6 June 2019) FDA Approvals For Smart Algorithms In Medicine In One Giant Infographic. 9 The study is the result of a six- year collaboration between AI researchers and medica
46、l professionals at Stanford University and Intermountain LDS Hospital in Salt Lake City, Utah. It used machine vision to continuously monitor ICU patients during day-to-day tasks. The goal was to test the feasibility of passively tracking how often they moved and for how long. Early studies of ICU p
47、atients have shown that movement can accelerate healing, reduce delirium, and prevent muscle atrophy, but the scope of those studies has been limited by the challenges of monitoring patients at scale. Depth sensors were installed in seven individual patient rooms and collected three-dimensional silh
48、ouette data 24 hours a day over the course of two months. The researchers then developed algorithms to analyze the footage helping them detect when patients climbed into and out of bed or got into and out of a chair, as well as the number of staff involved in each activity. The results showed prelim
49、inary success: on average, the algorithm for detecting mobility activities correctly identified the activities a patient was performing 87% of the time. The algorithm for tracking the number of personnel fared less well, reaching 68% accuracy. The researchers say that both measures would probably be improved by using multiple sensors in each room, to compensate for people blocking one another from a single sensors view. The demonstration suggests how these systems might augment the work of hospital staff. If a
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