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21st Century Health Care and Wellness: Getting the Health Care Delivery System That Meets Global Needs
Chapter 26
21st Century Health Care and Wellness: Getting the Health Care Delivery System That Meets Global Needs Cost-Effective, Evidence Based, Meaningful Use Perfected, and Effective Delivery for a New “Personalized Prescriptive Medicine” Resulting from Rapid Decisions Derived from Accurate Predictive Analytics Models
Chapter Outline Introduction Overview Background and Need for Change Learning Objectives Trends Impacting Healthcare Industries Existing and Emerging Healthcare Organizations Health Start-Ups and Established Technology Firms Contributing to Health Care IBM Watson New Technology and 21st Century Health Care: Health Start-Up Firms Building the Star Trek Tricorder Wearable Computers for Doctors Explorys
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Technology Trends That Impact Health and Wellness Current Trends Outside Healthcare Facilities The eCAALYX Example Trends and Expectations for the Future of Health IT and Analytics The Next 4 Years by-2018 Predictions The Next 9 Years by-2023 Predictions Conclusions and Summary of Important Concepts Presented in This Book Technology for the Elderly Technology for Rural Areas Final Concluding Statements References Bibliography
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INTRODUCTION What is the value of the book you have just read? Minimally, your reading should have provided a framework and focus for the future in the following: 1. 2. 3. 4. 5.
Gaining new insights into disease pathways and progression Understanding the effectiveness of interventions Discovering “new knowledge” and the “real cause(s)” of diseases Speeding the pace of science Moving towards personalized medicine even patient-directed medicine.
Practical Predictive Analytics and Decisioning Systems for Medicine. DOI: http://dx.doi.org/10.1016/B978-0-12-411643-6.00054-5 © 2015 L.A. Winters-Miner, P.S. Bolding, J.M. Hilbe, M. Goldstein, T. Hill, R. Nisbet, N. Walton, G.D. Miner. Published by Elsevier Inc. All rights reserved.
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Overview This chapter attempts to summarize much of the preceding book and also focuses the reader on the future. There will be dramatic changes and improvements in the field of health care and wellness within the next 6 years: a new 21st century high-technology health care and wellness movement will shake the existing healthcare industry and institutions. By 2020, we expect that the healthcare industry will be reorganized into patient-centered wellness and medical teams supported by digital and mobile health technologies. By 2020 the first comprehensive health and wellness sensors and predictive analytics hardware tools will be common, embedded in eye glasses, clothing, architecture, facilities, and nanotechnologies in the human body. Our 21st century wellness movement will be focused on real-time analytics and feedback to achieve peak wellness through optimal nutrition, exercise, stress reduction, and measurements of continuous human health improvements and medical treatments, when needed. By 2020, science-based personalized medicine and wellness will become the standard: most (if not all) wellness and medical treatments will be based upon individual genome studies combined with a full array of digital health information with real-time analytics guiding individuals and medical support teams.
BACKGROUND AND NEED FOR CHANGE We started this book in its initial chapters (see particularly Chapter 2) with the need for changes in healthcare delivery, and especially the need for predictive analytics in medicine. We restate that need for change and point to the fact that change is happening already. To reiterate, in a recent online guide (AMA, 2013, p. 2) for medical doctors who are considering joining ACOs (Accountable Care Organizations), the American Medical Association (AMA) set out the business case for why the existing medical fee for service model must change in the United States. In that document it was said: “In 2008, health care expenditures in the US exceeded $2.3 trillion with costs per resident at $7,631 per year” (Henry J. Kaiser Family Foundation, n.d., cited in AMA, 2013). One other source gave the amount as $7,538 (Henry J. Kaiser Family Foundation, 2011, Exhibit 4A). “In 2009, the percentage of gross domestic product (GDP) spent on health care was 17.3 percent. In 2008, it was 16.2 percent, making the increase to 17.3 percent in 2009 the largest one-year increase since 1960 (Truffer et al., 2010, cited in AMA, 2013, p. 2). “The country closest to the United States in health care expenditures is Germany, where 11.1 percent of its GDP is spent on health care” (Truffer et al., 2010 cited in AMA, 2013). The United States has poor health outcomes compared with other developed countries: the effectiveness of healthcare delivery, despite the high costs, is far less than in other advanced economies. One of the primary needs to help correct this is the application of predictive analytics and decisioning, as eloquently stated by Dr. David Dimas of the University of California, Irvine (personal communications, 2012 and 2013): The Importance of Predictive Analytics in Healthcare: Current practice in healthcare is often based on more traditional statistical analysis (p-value), which can lead to treating a person as a “mean” of a population. An individual’s demographics, health history, comorbid conditions and genetics may cause him to react differently to a particular drug or treatment. As a result, predictive analytics applied to medical data can help develop treatments that are more in tune with the individual patient. (David Dimas, PhD).
Dr. Dimas, trained as an engineer, and seeing the value of predictive analytics, established one of the first graduate extension Predictive Analytics Certificate Programs in the USA. A course in Predictive Analytics for Healthcare & Medical Research will be added to this program, projected for 2014. The need for people to be trained in predictive analytics is so great that this program has a waiting list of students and the students are not only the “young twenties” generation; there are also MD and PhD holders and seasoned IT professionals with 20 years of experience taking this program. This speaks to the perceived need in our society for people trained in this field.
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LEARNING OBJECTIVES G
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To learn how Predictive Analytics and Digital & Mobile Health will change health care, and what these changes mean for your health and organization. To recognize the necessary steps a healthcare organization must undertake to successfully harness the power of analytics, digital, and mobile technologies. To understand emerging trends and best practices for the use and adoption of mobile technology, and particularly predictive analytics coupled with decisioning.
TRENDS IMPACTING HEALTHCARE INDUSTRIES Peter Drucker (2006) advised his students, when planning their careers in non-profits and business, to focus on the perceived major trends in society and the domain of interest, and in fact to consider major trends in all their strategic planning. We can see many of the major trends in medicine today based upon technology roadmaps and demographics that will impact 21st century health care: G
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Electronic medical record (EMR) systems, Health Information Exchanges (HIEs), and health and wellness applications making all forms of health digital data available. Big Data, use of large patient databases with patient-identifying information removed, combined with predictive analytics to guide effective decisions. Expansion of EMR systems and tools leading to consolidations of providers and payers; some hospital health systems are creating health insurance plans of their own. Finally, a growth of interest in scientific research support for clinical effectiveness: global movements towards comparative effectiveness research (CER) and heterogeneous treatment effects research (HTE) to guide decision-makers and healthcare providers regarding the scientific merit of clinical treatments.
A major trend in health systems in advanced economies is to remove the marketing and hype surrounding healthcare treatments and drug effectiveness, and move to scientific studies of clinical effectiveness that specifically target individuals and individual outcomes. Many examples of such scientific studies have been given in the previous chapters, such as that of the University of Pittsburg Medical Center (UPMC) reducing its readmissions dramatically by use of predictive analytics instead of techniques that regress to the mean (Mace, 2013). They were able to develop an algorithm based on “eight combinations of answers” to 5 questions on a 24-question survey, to target individuals who “were going to run about 300% more expensive than people who don’t hit those rules” (Mace, 2013, para. 7). Many of the developed world’s national healthcare systems use a structured process to synthesize scientific studies; many use comparative effectiveness research (CER) and heterogeneous treatment effect research (HTE) to help determine optimal courses of care and improve science-based medical treatments that have been proven to be effective. CER compares the benefits and harms of alternative methods to prevent, diagnose, and treat a clinical condition and monitor care; and HTE looks at different “groupings” of individuals based on genetics and other parameters that separate them into distinct groups; the focus is on methods to improve the delivery of care, including the effectiveness of drug treatments and dosage, for the individual patient. In 2010, the Patient Protection and Affordable Care Act established the Patient-Centered Outcomes Research Institute (PCORI, 2014a), a non-profit organization, to conduct research to provide information about the best available evidence to help patients and their healthcare providers make more informed decisions. PCORI has focused on comparative effectiveness research and heterogeneous treatment effects, and is building a national infrastructure to conduct CER and HTE research (PCORI, 2014b). PCORI’s targeted grants are helping to build this CER and HTE national infrastructure, and contribute to the overall PCORI Mission and Vision (PCORI, 2014a): Mission of PCORI The Patient-Centered Outcomes Research Institute (PCORI) helps people make informed health care decisions, and improves health care delivery and outcomes, by producing and promoting high integrity, evidence-based information that comes from research guided by patients, caregivers and the broader health care community. (PCORI, 2014a, para. 1).
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Vision of PCORI Patients and the public have the information they need to make decisions that reflect their desired health outcomes. (PCORI, 2014a, para. 2).
Obviously, PCORI emphasized the importance of patients in all aspects of their medical care. The addition of Steven Clauser, PhD, MPA, as the Director of Improving Healthcare Systems emphasizes PCORI’s commitment to patient-centered outcome research that encourages “self-management of their care” (PCORI, 2013, para. 2). The role of patient had shifted from passively receiving care to interactive involvement. Patient-directed medical care (see Chapter 14 in this book) is recognized as vital.
EXISTING AND EMERGING HEALTHCARE ORGANIZATIONS The existing healthcare industry consists of three major groups: G G G
payers health insurance firms or non-profits or governmental agencies providers hospitals and medical practices life science firms firms that manufacture medical devices, and pharmaceutical and biotechnology firms that manufacture drugs and other medicines.
Given grave concerns by both the general public and employers and employees in the United States regarding high healthcare costs and poor quality, citizens have called for reforms and new legislation. Passage of the Affordable Care Act in 2010 has greatly impacted the regulation and delivery of health care in the United States. Government incentives have helped move provider organizations to electronic health record (EHR) systems and other forms of digital data. Most if not all groups in the healthcare industry now have a focus on innovation and the use of analytics and scientific research to improve healthcare delivery. Some health systems are both payers and providers, such as Kaiser Permanente, which includes a HMO Health Insurance division and a health system of more than 40 large hospitals and integrated medical clinics and medical practices. Kaiser has invested heavily in its administrative, clinical, and data warehousing systems, reportedly investing more than $4 billion in its Epic clinical system, patient-provider portal, and a series of data warehousing systems, including a large Teradata clinical data warehouse for longitudinal data on its 8 million patients. The University of Pittsburgh Medical Center (UPMC, 2014) has established a regional health system and health insurance plans, and has started to offer health services internationally: With more than 3,200 physicians, 20 hospitals, and a myriad of community-based facilities and outpatient programs serving 29 counties in western Pennsylvania, UPMC is the leading provider of health care services in the region. (UPMC, 2014, para. 1).
Through advisory services, infrastructure consultation, and clinical management, UPMC is helping to transform health care throughout communities in: G G G G G G G
Ireland: Cancer centers and a full-service hospital Italy: Transplantation, radiotherapy, and biotechnology centers United Kingdom: Information technology and cancer care Kazakhstan: Oncology center consults Singapore: Transplantation and clinical management China: Pathology consults and health care collaboration Japan: Education in primary care and family medicine.
(UMPC, para. 4). Working with the Carnegie Mellon University, Software Engineering Institute (SEI) teams, and major technology partners, UPMC has announced a $100 million initiative to build a state-of-the-art predictive analytics data warehousing system, to develop new models of affordable, effective, patient-focused health care: PITTSBURGH, Oct. 1, 2012 What if a doctor could easily predict which treatment would be most effective and least toxic for an individual breast cancer patient, based on her genetic and clinical information? What if an intelligent electronic medical record could flag patients at risk for kidney failure, based on subtle changes in lab results? Or what if physicians could tell
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from the medical records of a large population of patients when the next outbreak of flu might occur and have the right kind and quantity of vaccine ready? These are just a few of the scenarios behind UPMC’s five-year, $100 million investment in a sophisticated enterprise analytics effort that will foster personalized medicine. Together with technology partners Oracle, IBM, Informatica and dbMotion, UPMC today announced that it intends to create a best-in-class data warehouse that brings together clinical, financial, administrative, genomic and other information that today is difficult to integrate and analyze. Advanced analytic and predictive modeling applications for clinical and financial decision-making are expected to produce better patient outcomes, enhanced research capabilities, continual quality improvements across UPMC, and reduced costs. With predictive analytics fully integrated with a decision support platform, hospital teams can quickly summarize and analyze patient information for the entire medical team, including doctors, nurses, specialists and other staff, so quick and informed decisions can be made. (UPMC, 2012).
HEALTH START-UPS AND ESTABLISHED TECHNOLOGY FIRMS CONTRIBUTING TO HEALTH CARE Both large corporations that have been around for a long time and many new small technology firms are contributing to the revolution in Big Data innovations in health care, including the following established companies: G G G G G
IBM, Oracle, and Teradata, supplying core database software and hardware DELL-StatSoft (STATISTICA Data Miner & Decisioning) IBM (SPSS i.e., IBM-MODELER formerly known as Clementine) SAS (SAS Enterprise Miner) SAP-KXEN, providing predictive analytics software.
In March of 2014, Dell acquired StatSoft STATISTICA in order to better supply all types of support for data analysis, from hardware to software to consulting services to cloud storage i.e., true “end-to-end” solutions where everything, from computers to servers to tablets to cloud to smart phones and any other e-communication formats yet to be developed, seamlessly communicates among all formats. (This seamless communication is one of the big problems within EMR systems and between EMRs and other medical data sources, as pointed out in the early chapters of this book). Predictive analytics for health care was a priority in that acquisition, providing the final major missing link in Big Data science analytics to fully enable this end-to-end solution for medicine. As the authors of this book write the final chapter in late May of 2014, we see an interesting phenomenon reminiscent of the Silicon Valley “bubble” of the 1990s: many MDs are leaving their practice or CMO (Chief Medical Officer) positions in major companies to start their own small “start-up” companies. These start-ups are pursuing various medical technology advanced products and processes, including mobile, genetic, and other avenues. Apparently venture capital is flowing into these start-ups, meaning that investors see a potential market. Only the best of these products and processes will likely prevail. One of this book’s authors (Gary Miner) predicts that within a short period of time (years) this bubble will burst, just like the Silicon Valley bubble burst several years ago, and then the best of these will become part of our routine healthcare procedures, making health care more cost-effective and individualized. There are many examples that could be cited, but for the purposes of this book, those below should give you, the reader, an idea of what is going on in this area of technology healthcare start-up endeavors.
IBM Watson IBM’s Watson machine, which is an integrated hardware and software process, won its initial fame on the TV show Jeopardy!, beating the leading human champions. Watson has started to use its natural language capabilities, hypothesis generation, and evidence-based learning to support medical professionals and organizations. The Memorial Sloan-Kettering Cancer Center (MSKCC) is the world’s oldest and largest private cancer center. MSKCC has partnered with IBM to put IBM Watson to work fighting cancer. Beginning with breast and lung cancers, MSKCC and IBM are using Watson to consolidate clinical expertise, molecular and genomic data, and its repository of cancer case histories to create evidence-based solutions. Physicians can use Watson to assist in diagnosing and treating cancer patients. Watson supports medical terminology by design by extending Watson’s natural language processing capabilities and data mining of patient data, such as family histories available in digital EMR records. Watson
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FIGURE 26.1 Multiple Analytic Services. Reproduced from IBM (2012, p. 4).
incorporates treatment guidelines, electronic medical record (EMR) data, and doctors’ and nurses’ notes. Further, Watson can include research and clinical studies, journal articles, and patient information into the data available for analysis. Watson’s natural language processing capabilities enable the system to leverage unstructured data. Watson can include genomic data and other insights to provide individualized, confidence-scored recommendations to physicians (Memorial Sloan-Kettering Cancer Center, 2013). According to IBM, “IBM Advanced Care Insights enables care providers to apply predictive and similarity analytics to confirm what is suspect, discover new information, flag what is being missed and anticipate change” (IBM, 2014) (Figure 26.1).
New Technology and 21st Century Health Care: Health Start-Up Firms Rock Health is a health start-up firm incubator that operates in San Francisco and Boston (Rock Health, 2013). Rock Health helps chosen start-up companies by providing office space, technology, and advertising to potential investors. New York, San Diego, Austin, and other areas have many health start-ups. Often, digital health start-ups use crowd funding platforms such as Kickstarter, Indiegogo, Fundable, and Medstartr. In 2013, Rock Health featured a list of 236 health start-up firms on its website (Rock Health, 2013). Many of the 236 health start-ups were part of the “Quantify Yourself” movement: health and wellness digital tracking devices and cloud software that incorporate analytics or predictive analytics.
Building the Star Trek Tricorder Remember the TV and movie series Star Trek, and how Dr. “Bones” McCoy used a tricorder device to help diagnose all medical problems? In 2012, the X Prize Foundation announced a long-term, worldwide contest in which $10 million would be awarded to the three teams that produced the best “tricorder type” device for detecting and monitoring a variety of diseases and conditions. Teams registered in 2013, and were to show their devices and test results in early 2014. By the summer of 2013, the Qualcomm Tricorder X Prize had drawn hundreds of competitor teams. In 2014, the original 300 teams had been whittled down to 10 (Dvorsky, 2014; see also Figure 26.2).
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FIGURE 26.2 Tricorder. Reproduced from Dvorsky (2014).
According to the contest website, the Qualcomm Tricorder X Prize would provide $7 million to the winning team and prize monies to the second- and third-placed teams: The Qualcomm Tricorder XPRIZE is a $10 million global competition to stimulate innovation and integration of precision diagnostic technologies, making reliable health diagnoses available directly to “health consumers” in their homes . . . Advances in fields such as artificial intelligence, wireless sensing, imaging diagnostics, lab-on-a-chip, and molecular biology will enable better choices in when, where, and how individuals receive care, thus making healthcare more convenient, affordable, and accessible. The winner will be the team whose technology most accurately diagnoses a set of diseases independent of a healthcare professional or facility, and that provides the best consumer user experience with their device. (Qualcomm, 2014, paras 2 and 3).
The winning tricorder device was to be a tool capable of capturing key health metrics that lead to diagnosing a set of 15 diseases; among the core set of screens and diseases that had to be diagnosed were the following: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
Anemia Urinary tract infection, lower Diabetes, type 2 Atrial fibrillation Stroke Foodborne illness Shingles Melanoma Strep throat Cholesterol screen HIV screen Osteoporosis Absence of condition. The Vital Signs Set Tricorder requirements included the following:
1. 2. 3. 4. 5.
Blood pressure Electrocardiography (heart rate/variability) Body temperature Respiratory rate Oxygen Saturation.
The winning tricorder device was to collect large volumes of data from various sources, from ongoing measurements of health data, using wireless sensors, and imaging technologies. Further, the tricorder was to use portable, non-invasive laboratory test replacements. Of the 300 teams, several leading medical sensor technology companies registered to join the contest to win the tricorder prize monies. For example, 10 teams of firms registered from the San Diego area.
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FIGURE 26.3 Scanadu Scout Tricorder. Reproduced from Dvorsky (2014).
In this competition, teams were to leverage technology innovation in areas such as artificial intelligence and wireless sensing. Much like the medical tricorder of Star Trek fame, doctors, nurses, and individual/ patients can use the tricorder devices and a smart phone to make preliminary medical diagnoses independent of a physician or healthcare provider. The goal of the competition is to drive development of devices that will give consumers access to their state of health using the new devices connected to smart phones to share data in the cloud. The 10 teams that were left were each working to improve medical care by taking the devices to the home and to the patients themselves. Diagnoses can be made regardless of the location of the individual (Dvorsky, 2014). Our future doctors could be robots! And predictive analytics should be at the center of those processes. Team Scanadu’s Scoutt is a $150 device that in 2014 was considered a front runner among the 10 entrants left in the contest (Dvorsky, 2014; see also Figure 26.3). The team was able to raise over $1 million by June 2013, and was then able to construct its Scout device that could gather key health metrics in under 10 seconds by holding it to one’s temple, recording vitals such as heart rate, blood pressure, temperature, heart activity, and oxygenation (Stenovec, 2013). Scanadu uses Bluetooth technology to upload information to a smart phone. Project ScanaFlo is a low-cost device that uses the smart-phone as a urine analysis tool. For pregnant women, Project ScanaFlo will be the first portable device to provide health data throughout the duration of a pregnancy. Project ScanaFlu is a low-cost tool that uses the smart-phone as a reader to assess cold-like symptoms. By testing saliva, the disposable cartridge can provide early detection for flu-like symptoms and Strep A, Influenza A, Influenza B, and other respiratory diseases. (Scanadu, 2013).
Wearable Computers for Doctors Google glasses were emerging in 2013 2014, and seemed a novelty. However, some physicians used them as they performed operations or interviewed their patients. The physician could be operating and intermittently glance at the patient’s charts or the patient’s results from tests (Hay, 2013).
Explorys Based in Cleveland, Explorys is a Cleveland Clinic spin-off company, founded in 2009, that leverages Big Data to support clinical decisions. Explorys provides turnkey solutions for clinical integration, at-risk population management, cost of care measurement, and pay-for-performance solutions. Explorys’ solutions help clinicians analyze data from multiple clinical and administrative sources. Data mining can guide physicians to the personalized treatment plan called for by each patient’s unique case: analytic tools can show variations among patients and treatments influencing health outcomes. Several major hospital and health systems have joined to share clinical data; Explorys has created one of the largest clinical databases with predictive analytic, data mining, and other analytic tools available to support clinical decisions (Explorys, 2014).
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Explorys’ healthcare customers include some of the most prominent healthcare systems in the United States, together accounting for over $45 billion in care. With over 100 billion clinical, financial, and operational data elements, spanning 40 million patients, 200 hospitals, and over 100,000 providers, Explorys’ secure cloud-computing platform is being used by 14 major integrated healthcare systems to identify patterns in diseases, treatments, and outcomes. Our network includes Cleveland Clinic, Trinity Health, St Joseph Health System, Catholic Health Partners, and many others with patients in all 50 states. (Cleveland Biomedical Job Fair, 2014).
TECHNOLOGY TRENDS THAT IMPACT HEALTH AND WELLNESS Current Trends Outside Healthcare Facilities There is an increasing use of smart phones and tablets by clinicians and patients. Even a report back in 2011 (Boulos et al., 2011, para. 33) stated: According to a recent video report by Mobile Future, a Washington, DC, broad-based coalition of businesses and non-profit organizations, there has been a massive increase in the numbers of consumer smart phone apps (applications) downloaded over the past two years, with figures going up from 300 million apps downloaded in 2009 to five billion in 2010.
The eCAALYX Example As stated by Boulos et al. (2011, para. 11): The eCAALYX Mobile Application is being developed under the scope of the eCAALYX EU-funded project (Enhanced Complete Ambient Assisted Living Experiment, 2009 2012) which aims at building a remote monitoring system targeting older people with multiple chronic diseases. Patients, carers and clinicians’ involvement is extensive throughout the prototype design, deployment and testing, and clinical trial phases of the project. The main functionality of the eCAALYX Mobile Platform is to act as a seamless “informed” intermediary between the wearable health sensors (in a “smart garment”) used by the older person and the health professionals’ Internet site, by reporting to the latter (but also to the patients) alerts and measurements obtained from sensors and the geographic location (via smart phone GPS) of the user. Additionally, the mobile platform is also able to reason with the raw sensor data to identify higher level information, including easy-to-detect anomalies such as tachycardia and signs of respiratory infections, based on established medical knowledge. A user interface is also provided, which allows the user to evaluate the most recent medical details obtained from sensors, perform new measurements, and communicate with the caretakers.
Usability becomes a key issue for the group of individuals (patients) that are the target group for using eCAALYX, since this group may be an older population that is not necessarily familiar with using technology and may have physical impairments such as poorer eyesight and dementia. With that in mind, the eCAALYX mobile platform is designed to be as transparent as possible for the user. Other e-items in this “outside of healthcare facilities” category but within at least the partial control of patients include: G
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Mobile health and social media patients can (and are) form(ing) groups (support groups) based on either risk profiles or chronic diseases Mobile patient management with social components Digital health sensors and tracking health sensors can be put into buildings, homes, bathrooms, and other places to keep track of a patient’s symptoms Wearable computing machines, such as Google Glasses In-body sensors i.e., either ingested or implantable computerized devices.
TRENDS AND EXPECTATIONS FOR THE FUTURE OF HEALTH IT AND ANALYTICS The following “predictions” are based on the authors’ own “intuition” and from readings on eHealth and mHealth in various references, including the following: Wicklund (2014), in HIMSS mHealthNews; HIMSS14 (2014); HIMSS Analytics (2014); HIMSS Healthcare Global (2014); and HIMSS Transforming Health Through IT (2014).
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The Next 4 Years G
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by-2018 Predictions
More than 90% of US hospitals and larger medical practices will be using EMR systems with advanced analytics and predictive analytics. More than 70% of US doctors will be working in organizations that charge fees based on other than feefor-service models; ACOs (accountable care organizations) and HMOs will become the dominant model for health care in the USA. Integrated delivery organizations will use advanced Health IT systems with predictive analytics. New types of professional models for health care will emerge, such as Wal-Mart delivery of drop-in medical care; we will see rapid growth of comprehensive health plans with shared risk, and “medical tourism” with patients traveling to high quality overseas centers of excellence at lower costs. Globally, millions upon millions of patients will be remotely monitored (telehealth). Predictive analytics will combine with health IT systems, digital and mobile, and with comprehensive genome services studies on all high-risk patients and those with chronic diseases to create fully personalized medicine; personalized drug therapy will improve outcomes and reduce patient safety risks.
The Next 9 Years
by-2023 Predictions
Articles concerning changes in medicine are appearing daily in the popular press. For example, in one recent Wall Street Journal edition (Chernova, 2014; Marcus, 2014) there were two such articles. The first discussed baby clothing that can track vital signs of infants (Chernova, 2014); the second concerned the efforts of physicians currently tracking and analyzing patient data from routine check-ups (Marcus, 2014) that help to personalize advice given to patients, such as whether or not a sore throat is likely to be strep or whether the patient can simply stay home with chicken soup. Technological devices such as implants, data tracking tattoos, and nanotechnology are springing up like weeds (Hotz, 2014). By 2023: G
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Fully operational tricorder, non-invasive devices to help medical staff identify potential illness factors will be in use, including medical detector devices. Sensors embedded in clothing and architecture, and bathroom devices, will continuously monitor health risks and send alerts; monitoring will focus on the development of pre-cancer formations that may require early treatment; heart and stroke monitoring will reach an advanced state. Advances in health care, nutrition, and fitness, and health and wellness tracking and alerts, will boost life spans; many citizens will move to more vegetarian diets many days of the week, as well as taking moderate exercise such as averaging 30 minutes of walking daily. More than 80 90% of the population in advanced economies will have smart phones within 10 years; all will be using mobile medical or fitness applications. More than 90% of patients and doctors in advanced economies will be using health and wellness smart phone applications to maintain and improve their health and fitness. Advanced Patient & Provider portals will link health IT systems and mobile solutions. Patients will become more informed consumers of medical care, and more actively involved in their own health care, including using personalized predictive analytics. Google Glass and/or similar types of wearable computers will be used in all aspects of the delivery of medical care.
Some of the items listed above are happening already. For example, the Scanadu Scoutt (discussed earlier in this chapter), a sensor that measures various vital body functions such as heart rate, was initially released to a handful of people on March 31, 2014. This device is produced by a company on the NASA Ames Research Park Campus in Mountain View, California. The initial batch had some “bugs” and went back to the drawing board, but an updated version is expected to be released soon. Importantly, this means that Scanadu is bringing to life a vision, shared by the company, its backers, and people/patient supporters, of a health environment where each individual person is empowered through knowledge of their own health. People interested can become test volunteers during the full development of this device. (Scanadu, 2014).
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CONCLUSIONS AND SUMMARY OF IMPORTANT CONCEPTS PRESENTED IN THIS BOOK The evolution of data analytics, and what are the best practices for accuracy in 2014, include: G
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Bayesian data analysis, which was used for centuries prior to Fischerian Central Limit Theory statistics of the 20th century (for more detail, see Nisbet et al., 2009) Traditional p-value Fischerian statistics (population mean . . . group mean . . . central limit theory based) Data mining (statistical learning theory algorithms mostly machine learning/artificial intelligence based, and also some clustering and “rules based” methods) Predictive analytics, which is “broader” than data mining, including some DA (discriminant analysis), LR (logistic regression), and WoE-LR (weight of evidence combined with logistic regression); and the data mining algorithms, especially the statistical learning algorithms, but including CART, CHAID, K-E-means clustering, interactive trees, boosted trees, MARSplines, neural networks, Bayesian analysis, support vector machines, k-Nearest Neighbors analysis, association rules, SAL (Sequence, Association, and Link Analysis), and independent components analysis, among others)
The potential accuracy of a “predictive model” tends to increase as one goes from the lowest level with DA and LR, next to WoE-LR, and then finally to the “true” machine learning methods, as illustrated in Figure 26.4. The model in Figure 26.4 does not mean that any specific data set will only get the best predictive analytic model by using the AI machine learning algorithms, as some data sets, especially if the data are clearly linear (but this rarely happens with medical data), may get highest accuracy models using DA and LR. However, in general, for most data sets, in our experience, the best models with the highest “accuracy scores” are obtained by using the true statistical learning theory modern algorithms. Another way of describing this is to use the term “global learning” (i.e., a population-based learning where means of the population and corresponding t-tests, p values, and other traditional statistics are used) vs. “individual statistical learning theory” methods (i.e., the case-by-case learning approach of data mining algorithms). This distinction is important for the main message of this book, which is “personalized” and “person-centered” health care. This distinction is also important for CER (comparative effectiveness research) and HTE (heterogeneous treatment effects), in which some scientists are intent on looking at “groups” of people (groups classified by age, sex, etc.) but for which the ultimate goal is to look at “individuals” individuals that are genetically distinct, thus a finer grouping than that obtained by just looking at age, sex, and similarly classified groups. FIGURE 26.4 Potential increasing accuracy of a data analytics model.
Technology for the Elderly Consider this statistic: “In 2010, the total direct medical costs of fall injuries for people 65 and older, adjusted for inflation, was $30 billion” (CDC, 2014, para. 7). That same site predicted that, unless things change, the figure in 2020, using 2012 dollar values, will be a whopping $67.7 billion. One-third of adults over age 65 fall each year. The monetary costs are only a part of the issue there is also the matter of pain and suffering, and of income lost, family duties increasing to take care of the older member, and permanent losses that can result. Falls are a big problem in our country. Technologies already present are helping the elderly stay in their homes, and many medical scientists are beginning to find ways of collecting day-to-day data to use in predictive analytic individualized medicine (Wang, 2014). For example, Skubic et al. (2009) have been collecting data on stride length and speed of stride among residents of elderly apartments. They have been using 3D sensors, and with the data are developing a prediction model for falls. Demiris et al. (2008) installed various monitoring devices in nine seniors’ living quarters, validating the devices with independent observations, and found that the devices were accurate, accepted, and not considered intrusive by the residents after the initial phase of getting used to the devices. The authors did point out ethical concerns about privacy and control as the users learn to rely on automation. However, as if in answer, the residents reported they felt more of a sense of control over their lives and often wanted to see their data, reinforcing the concept that patients, if given the chance, would really like to become more involved in research. Predictive analytic techniques and projects can significantly impact the quality of lives of seniors and help reduce healthcare costs.
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Technology for Rural Areas This is an area alluded to earlier in this chapter where mobile sensing devices were discussed. However, a specific example that came to our attention recently was the use of mobile phone technology for doing eye exams in rural Africa, where previously many people were going blind because of lack of access to eye doctors and eye clinics. In March 2014, Andrew Bastawrous, an eye surgeon and inventor, and a TED Fellow, filmed a description of this technology currently used in Kenya, Africa. It is called “Peek,” and is based on a smart phone that is given to community health workers who travel out to the field. In these rural areas this phone takes “snapshots” of a person’s inner eye conditions and sends it back to the diagnosing doctor at the clinic. Difficult cases can even be evaluated at major eye centers around the world, communicating the best diagnosis and treatment plan rapidly back to the health worker in the field. Currently, around the world, over 80 million people are blind. Most of this blindness occurs in underserved areas like Kenya. Most of this blindness is also easily preventable. So here, in this current development in Africa, we see not only a cost-effective solution, but also a saving of sight for potentially millions of people (Bastawrous, 2014; see also Box 26.1).
Box 26.1 We encourage the reader to watch the following video (http:// www.ted.com/talks/andrew_bastawrous_get_your_next_eye_ exam_on_a_smartphone?utm_campaign5&awesm5on.ted. com_quBe&utm_source5facebook.com&utm_medium5on.
ted.com-facebook-share&utm_content5awesm-publisher) as it demonstrates what is going on in mobile health technology in a much better way than we can put down here in words.
The Kenyan example is of special interest to one of the authors of this book, who had an eye condition that could eventually result in blindness. The author spent 18 months going to 5 different eye clinics, getting different diagnoses, but more concerning were the treatment plans (which were different at each facility). At the fifth, with the “renowned real expert,” the author was told that “this is a ‘no brainer,’ there is only one thing to do first”! Thus, the author finally had to take things into his own hands, Google, do research, and discover for himself what was the “best treatment plan” (it turned out that this fifth expert was right; the “no brainer” treatment was the first of five possible treatments, the least invasive, simplest, and least costly, and it in itself would also be “diagnostic” if it worked we would know the cause of the condition; if it did not work, then we knew that the cause was different and would move on to the number two treatment). Five different and conflicting medical opinions before a good “right” answer! If this author had taken some of the earlier “treatment plans” presented it would have been absolutely the wrong thing to do. Additionally, eye pressure checks were needed frequently (every 2 weeks) during this
period for safely monitoring the condition, involving considerable time and expense in attending the eye clinic. The author kept wondering, “Why is there not a ‘smart contact lens’ that can do this monitoring, sending continuous readings to the clinic?” If the author had had the smart phone mobile device current being used in Africa, he would have probably saved 17 of those 18 months of searching among different clinics, arrived at the “right answer” much faster, and received treatment earlier . . . all at considerably less medical cost! This example illustrates first-hand how “fragmented” our medical knowledge is in the US Health Care Delivery System, and what needs to be done to rectify the problems: G If predictive analytic models had been developed, the correct diagnosis could have been made on the first eye clinic visit. G If the professional readers of this book work toward the goals presented therein, then we will have this cost-effective, accurate diagnosis and treatment in health care in the future.
Final Concluding Statements This book has provided a background for predictive analytics; methodologies for predictive analytics; and examples of predictive analytics. It has also gone beyond that to the next step: decisioning systems that lead to action. In today’s world, data comprise the new gold. Acquiring appropriate data has become the supreme challenge to efforts at prediction. Those who are able to obtain permission to use the data have the best chance of producing individualized models, and generally those individuals likely are those closest to the data, such as physicians, medical groups, and teaching hospitals. It does no good if those data are unavailable, however, even if they are structured in EMRs that allow communication between departments and institutions. Patients are the closest to the data, but HIPAA generally stands in the
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way between the data and the research. Voluntary permission for one’s data to be used in research could be given in one of the many forms that new patients fill out. National groups such as PCORI can aid in joining patient data to researchers that need them. New IRB practices could include generalized informed consent to cover research both within and between institutions. New predictive techniques of analysis are imperative to the goal of individualizing practice, and reliance upon the older methodologies must be combined with newer techniques to produce better outcomes, which constitute the prize. In addition to the “futuristic ideas” discussed in this book, we have presented three phases in the development of accurate, non-error, cost-effective health care: G G G
Phase I Quality control/Six Sigma applied to medicine Phase II Predictive analytics applied to medicine Phase III Automatic decisioning systems applied to medicine. Will there be a fourth phase? Yes, most likely there will be
and that will probably be the topic of our next book!
REFERENCES AMA, 2013. ACOs and Other Options: A “how-to” Manual for Physicians Navigating a Post-Health Reform World. Fourth ed. Practice Management Center. American Medical Association, Chicago, IL. Available at: ,www.acponline.org/running_practice/delivery_and_payment_models/aco/ physician_howto_manual.pdf.. Bastawrous, A., 2014 TED: Get your next eye exam on a smartphone. Available at: ,www.ted.com/talks/andrew_bastawrous_get_your_next_eye_ exam_on_a_smartphone?utm_campaign5&awesm5on.ted.com_quBe&utm_source5facebook.com&utm_medium5on.ted.com-facebook-share& utm_content5awesm-publisher.. Boulos, M.N.K., Wheeler, S., Tavares, C., Jones, R., 2011. How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomed. Eng. Online. 10, 24. Available at: ,www.ncbi.nlm.nih.gov/pmc/articles/PMC3080339.. CDC (Centers for Disease Control and Prevention), 2014. Costs of Falls Among Older Adults. CDC, Atlanta, GA. Available at: ,www.cdc.gov/homeandrecreationalsafety/falls/fallcost.html. (last retrieved June 5, 2014). Chernova, Y., 2014. Oh, Baby. Wearables track infants’ vital signs. Wall Street J. 263 (111), B1. Demiris, G., Oliver, D., Dickey, G., Skubic, M., Rantz, M., 2008. Findings from a participatory evaluation of a smart home application for older adults. Technol. Health Care. 16 (2), 111 118. Drucker, P.F., 2006. What executives should remember. Harv. Bus. Rev. 84 (2), 144 152. Dvorsky, G., 2014. Meet the teams who are building the world’s first tricorder. Available at: io9.com/meet-the-teams-who-are-building-the-worldsfirst-medic-1543000639. Explorys, 2014. About Us: Who We Are. Explorys, Cleveland, OH. Available at: ,www.explorys.com/about-us/who-we-are.. Hay, T., 2013. Google glass could become a fixture in the operating room. Wall Street J. August 12, 2013. Available at: ,http://blogs.wsj.com/ venturecapital/2013/08/12/google-glass-could-become-a-fixture-in-the-operating-room.. HIMSS Analytics, 2014. Healthcare IT News. HIMSS, Chicago, IL. Available at: ,www.healthcareitnews.com/directory/himss-analytics.. HIMSS Healthcare Global, 2014. HIMSS 2014 Uncovers 5 New mHealth Trends. HIMSS, Chicago, IL. Available at: ,www.healthcareglobal.com/ healthcare_technology/himss-2014-uncovers-5-new-mhealth-trends.. HIMSS Transforming Health Through IT, 2014. Five Emerging Trends for State Public Policy & Mobile Health IT. HIMSS, Chicago, IL. Available at: ,www.himss.org/ResourceLibrary/NewsDetail.aspx?ItemNumber529708&navItemNumber518600.. HIMSS14, 2014. 7 Trends Healthcare Experts Anticipate in 2014. HIMSS, Chicago, IL. Available at: ,www.himssconference.org/GenInfo/ NewsDetail.aspx?ItemNumber526634.. Hotz, R.L., 2014. A future where bionics track your health. Wall Street J. 263 (93), D2. IBM, 2012. Solutions for Healthcare: IBM Content and Predictive Analytics for Healthcare. Available at: ,http://public.dhe.ibm.com/common/ssi/ ecm/en/zzb03009usen/ZZB03009USEN.pdf.. IBM, 2014. Analytics gives care providers new insights to improve individual outcomes. Available at: ,www.upmc.com/media/newsreleases/2012/ pages/upmc-personalized-medicine-investment.aspx. (last retrieved May 17, 2014). Kaiser Family Foundation, n.d. Health Care Costs. Available at: ,www.kaiseredu.org/Issue-Modules/US-Health-Care-Costs/Background-Brief.aspx.. Kaiser Family Foundation, 2011. Snapshots: Health Care Spending in the United States & Selected OECD Countries, Exhibit 4A. Available at: ,http://kff.org/health-costs/issue-brief/snapshots-health-care-spending-in-the-united-states-selected-oecd-countries. (last retrieved May 16, 2014). Mace, S., 2013. Readmissions drop like a rock with predictive modeling. Predictive Analytics Times. October 30, 2013. Available at: ,www. predictiveanalyticsworld.com/patimes/readmissions-drop-like-a-rock-with-predictive-modeling.. Marcus, A.D., 2014. A hidden data treasure trove in routine checkups. Wall Street J. 263 (111), D1. Memorial Sloan-Kettering Cancer Center, 2013. IBM Watson helps fight cancer with evidence-based diagnosis and treatment suggestions. Available at: ,www.03.ibm.com/innovation/us/watson/pdf/MSK_Case_Study_IMC14794.pdf.. Nisbet, R., Elder, J., Miner, G., 2009. Handbook of Statistical Analysis and Data Mining Applications. Elsevier, New York, NY.
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PCORI, 2013. Steven Clauser to Join PCORI as Improving Healthcare Systems Program Director. PCORI, Washington, DC. Available at: ,www. pcori.org/2013/steven-clauser-to-join-pcori-as-improving-healthcare-systems-program-director. (3 December 2013). PCORI, 2014a. About Us. PCORI, Washington, DC. Available at: ,http://pcori.org/about-us/mission-and-vision. (last retrieved May 17, 2014). PCORI, 2014b. Improving Our National Infrastructure to Conduct Comparative Effectiveness Research. PCORI, Washington, DC. Available at: ,http://pcori.org/funding-opportunities/improving-our-national-infrastructure-to-conduct-comparative-effectiveness-research.. Qualcomm, 2014. Qualcomm Tricorder Prize. X Prize Foundation, Culver City, CA. Available at: ,www.qualcommtricorderxprize.org/competitiondetails/overview.. Rock Health, 2013. Digital Health Startup list. Available at: ,http://rockhealth.com/resources/digital-health-startup-list.. Scanadu, 2013. Scanadu Secures $10.5 Million in Series A Funding From Relay Ventures, Tony Hsieh and Jerry Yang. Press Releases, Scanadu, Mountain View, CA. Available at: ,www.scanadu.com/pr. (November 11, 2013). Scanadu, 2014. Bloomberg Businessweek Kickstarter Lures Startups Seeking Guinea Pigs Over Cash. Scanadu Scout: Progress and Scanathon. Scanadu, Mountain View, CA. Available at: ,www.scanadu.com/blog/.. Skubic, M., Alexander, G., Popescu, M., Rantz, M., Keller, J., 2009. A smart home application to eldercare: current status and lessons learned. Technol. Health Care. 17 (3), 183 201. Stenovec, T., 2013. Scanudu Scout, medical tricorder, passes $1 million on Indiegogo, but doesn’t need the money. Huffington Post. June 20, 2013. Available at: ,www.huffingtonpost.com/2013/06/20/scanadu-scout_n_3468894.html.. Truffer, C.J., Keehan, S., Smith, S., Cylus, J., Sisko, A., Poisal, J.A., et al., 2010. Health spending projections through 2019: the recession’s impact continues. Health Aff. 29 (3), 522 529. UPMC (University of Pittsburgh Medical Center), 2012. UPMC Fosters “Personalized Medicine” with $100 Million Investment in Sophisticated Data Warehouse and Analytics. Available at: ,www.upmc.com/media/newsreleases/2012/pages/upmc-personalized-medicine-investment.aspx.. UPMC, 2014. About UMPC. UPMC, Pittsburgh, PA. Available at: ,www.upmc.com/about/partners/icsd/locations/Pages/default.aspx. (last retrieved May 16, 2014). Wang, S.S., 2014. New technologies to help seniors age in place. Wall Street J. D2, June 3, 2014. Wicklund, E., 2014. Editor, mHealthNews; 7 mHealth Trends at HIMSS 14. mHealthNews, March 12, 2014. Available at: ,www.mhealthnews.com/ news/7-mhealth-trends-himss14..
BIBLIOGRAPHY Additional books that discuss in further detail the needs of healthcare both in the USA and globally: Agus, D.B., 2011. The End of Illness. Free Press, New York, NY. Berry, L.L., Seltman, K.D., 2008. Management Lessons from Mayo Clinic. McGraw Hill, New York, NY. Boult, C., Giddens, J., Frey, K., Reider, L., Novak, T., 2009. Guided Care: A New Nurse Physician Partnership in Chronic Care. Springer, New York, NY. Brawley, O.W., Goldberg, P., 2011. How We Do Harm: A Doctor Breaks Ranks about Being Sick in America. St Martin’s Press, New York, NY. Carey, R.G., Lloyd, R.C., 2001. Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Applications. ASQ (American Society for Quality) Quality Press, Milwaukee, WI. Clifton, G.L., 2009. Flatlined: Resuscitating American Medicine. Rutgers University Press, New Brunswick, NJ. Duncan, I., 2011. Healthcare Risk Adjustment and Predictive Modeling. ACTEX Publications, Winsted, CT. Gawande, A., 2010. The Checklist Manifesto: How to Get Things Right. Picador (Henry Holt and Company), New York, NY. Goldhill, D., 2013. Catastrophic Care: How American Health Care Killed my Father And How We Can Fix It. Alfred A. Knopf, New York, NY. Kudyba, S.P. (Ed.), 2010. Healthcare Informatics: Improving Efficiency and Productivity. CRC Press, Boca Raton, FL. Makary, M., 2012. Unaccountable: What Hospitals Won’t Tell You and How Transparency Can Revolutionize Health Care. Bloomsbury Press, New York, NY. Pronovost, P., Vohr, E., 2010. Safe Patients. Smart Hospitals: How One Doctor’s Checklist Can Help Us Change Health Care from the Inside Out. Plume, New York, NY. Reid, T.R., 2010. The Healing of America: A Global Quest for Better, Cheaper, and Fairer Health Care. Penguin, New York, NY. Sheehan, B., 2013. Doctored. Jeff Hays Films, Sandy, UT. Available at: ,www.doctoredthemovie.com/.. Topol, E., 2012. The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care. Basic Books, New York, NY. Weinberg, J.E., 2010. Teaching Medicine: A Researcher’s Quest to Understand Health Care. Oxford University Press, New York, NY.
21st Century Health Care and Wellness: Getting the Health Care Delivery System That Meets Global Needs Cost-Effective, Evidence Based, Meaningful Use Perfected, and Effective Delivery for a New “Personalized Prescriptive Medicine” Resulting from Rapid Decisions Derived from Accurate Predictive Analytics Models
Chapter Outline Introduction Overview Background and Need for Change Learning Objectives Trends Impacting Healthcare Industries Existing and Emerging Healthcare Organizations Health Start-Ups and Established Technology Firms Contributing to Health Care IBM Watson New Technology and 21st Century Health Care: Health Start-Up Firms Building the Star Trek Tricorder Wearable Computers for Doctors Explorys
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Technology Trends That Impact Health and Wellness Current Trends Outside Healthcare Facilities The eCAALYX Example Trends and Expectations for the Future of Health IT and Analytics The Next 4 Years by-2018 Predictions The Next 9 Years by-2023 Predictions Conclusions and Summary of Important Concepts Presented in This Book Technology for the Elderly Technology for Rural Areas Final Concluding Statements References Bibliography
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INTRODUCTION What is the value of the book you have just read? Minimally, your reading should have provided a framework and focus for the future in the following: 1. 2. 3. 4. 5.
Gaining new insights into disease pathways and progression Understanding the effectiveness of interventions Discovering “new knowledge” and the “real cause(s)” of diseases Speeding the pace of science Moving towards personalized medicine even patient-directed medicine.
Practical Predictive Analytics and Decisioning Systems for Medicine. DOI: http://dx.doi.org/10.1016/B978-0-12-411643-6.00054-5 © 2015 L.A. Winters-Miner, P.S. Bolding, J.M. Hilbe, M. Goldstein, T. Hill, R. Nisbet, N. Walton, G.D. Miner. Published by Elsevier Inc. All rights reserved.
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Overview This chapter attempts to summarize much of the preceding book and also focuses the reader on the future. There will be dramatic changes and improvements in the field of health care and wellness within the next 6 years: a new 21st century high-technology health care and wellness movement will shake the existing healthcare industry and institutions. By 2020, we expect that the healthcare industry will be reorganized into patient-centered wellness and medical teams supported by digital and mobile health technologies. By 2020 the first comprehensive health and wellness sensors and predictive analytics hardware tools will be common, embedded in eye glasses, clothing, architecture, facilities, and nanotechnologies in the human body. Our 21st century wellness movement will be focused on real-time analytics and feedback to achieve peak wellness through optimal nutrition, exercise, stress reduction, and measurements of continuous human health improvements and medical treatments, when needed. By 2020, science-based personalized medicine and wellness will become the standard: most (if not all) wellness and medical treatments will be based upon individual genome studies combined with a full array of digital health information with real-time analytics guiding individuals and medical support teams.
BACKGROUND AND NEED FOR CHANGE We started this book in its initial chapters (see particularly Chapter 2) with the need for changes in healthcare delivery, and especially the need for predictive analytics in medicine. We restate that need for change and point to the fact that change is happening already. To reiterate, in a recent online guide (AMA, 2013, p. 2) for medical doctors who are considering joining ACOs (Accountable Care Organizations), the American Medical Association (AMA) set out the business case for why the existing medical fee for service model must change in the United States. In that document it was said: “In 2008, health care expenditures in the US exceeded $2.3 trillion with costs per resident at $7,631 per year” (Henry J. Kaiser Family Foundation, n.d., cited in AMA, 2013). One other source gave the amount as $7,538 (Henry J. Kaiser Family Foundation, 2011, Exhibit 4A). “In 2009, the percentage of gross domestic product (GDP) spent on health care was 17.3 percent. In 2008, it was 16.2 percent, making the increase to 17.3 percent in 2009 the largest one-year increase since 1960 (Truffer et al., 2010, cited in AMA, 2013, p. 2). “The country closest to the United States in health care expenditures is Germany, where 11.1 percent of its GDP is spent on health care” (Truffer et al., 2010 cited in AMA, 2013). The United States has poor health outcomes compared with other developed countries: the effectiveness of healthcare delivery, despite the high costs, is far less than in other advanced economies. One of the primary needs to help correct this is the application of predictive analytics and decisioning, as eloquently stated by Dr. David Dimas of the University of California, Irvine (personal communications, 2012 and 2013): The Importance of Predictive Analytics in Healthcare: Current practice in healthcare is often based on more traditional statistical analysis (p-value), which can lead to treating a person as a “mean” of a population. An individual’s demographics, health history, comorbid conditions and genetics may cause him to react differently to a particular drug or treatment. As a result, predictive analytics applied to medical data can help develop treatments that are more in tune with the individual patient. (David Dimas, PhD).
Dr. Dimas, trained as an engineer, and seeing the value of predictive analytics, established one of the first graduate extension Predictive Analytics Certificate Programs in the USA. A course in Predictive Analytics for Healthcare & Medical Research will be added to this program, projected for 2014. The need for people to be trained in predictive analytics is so great that this program has a waiting list of students and the students are not only the “young twenties” generation; there are also MD and PhD holders and seasoned IT professionals with 20 years of experience taking this program. This speaks to the perceived need in our society for people trained in this field.
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LEARNING OBJECTIVES G
G
G
To learn how Predictive Analytics and Digital & Mobile Health will change health care, and what these changes mean for your health and organization. To recognize the necessary steps a healthcare organization must undertake to successfully harness the power of analytics, digital, and mobile technologies. To understand emerging trends and best practices for the use and adoption of mobile technology, and particularly predictive analytics coupled with decisioning.
TRENDS IMPACTING HEALTHCARE INDUSTRIES Peter Drucker (2006) advised his students, when planning their careers in non-profits and business, to focus on the perceived major trends in society and the domain of interest, and in fact to consider major trends in all their strategic planning. We can see many of the major trends in medicine today based upon technology roadmaps and demographics that will impact 21st century health care: G
G
G
G
Electronic medical record (EMR) systems, Health Information Exchanges (HIEs), and health and wellness applications making all forms of health digital data available. Big Data, use of large patient databases with patient-identifying information removed, combined with predictive analytics to guide effective decisions. Expansion of EMR systems and tools leading to consolidations of providers and payers; some hospital health systems are creating health insurance plans of their own. Finally, a growth of interest in scientific research support for clinical effectiveness: global movements towards comparative effectiveness research (CER) and heterogeneous treatment effects research (HTE) to guide decision-makers and healthcare providers regarding the scientific merit of clinical treatments.
A major trend in health systems in advanced economies is to remove the marketing and hype surrounding healthcare treatments and drug effectiveness, and move to scientific studies of clinical effectiveness that specifically target individuals and individual outcomes. Many examples of such scientific studies have been given in the previous chapters, such as that of the University of Pittsburg Medical Center (UPMC) reducing its readmissions dramatically by use of predictive analytics instead of techniques that regress to the mean (Mace, 2013). They were able to develop an algorithm based on “eight combinations of answers” to 5 questions on a 24-question survey, to target individuals who “were going to run about 300% more expensive than people who don’t hit those rules” (Mace, 2013, para. 7). Many of the developed world’s national healthcare systems use a structured process to synthesize scientific studies; many use comparative effectiveness research (CER) and heterogeneous treatment effect research (HTE) to help determine optimal courses of care and improve science-based medical treatments that have been proven to be effective. CER compares the benefits and harms of alternative methods to prevent, diagnose, and treat a clinical condition and monitor care; and HTE looks at different “groupings” of individuals based on genetics and other parameters that separate them into distinct groups; the focus is on methods to improve the delivery of care, including the effectiveness of drug treatments and dosage, for the individual patient. In 2010, the Patient Protection and Affordable Care Act established the Patient-Centered Outcomes Research Institute (PCORI, 2014a), a non-profit organization, to conduct research to provide information about the best available evidence to help patients and their healthcare providers make more informed decisions. PCORI has focused on comparative effectiveness research and heterogeneous treatment effects, and is building a national infrastructure to conduct CER and HTE research (PCORI, 2014b). PCORI’s targeted grants are helping to build this CER and HTE national infrastructure, and contribute to the overall PCORI Mission and Vision (PCORI, 2014a): Mission of PCORI The Patient-Centered Outcomes Research Institute (PCORI) helps people make informed health care decisions, and improves health care delivery and outcomes, by producing and promoting high integrity, evidence-based information that comes from research guided by patients, caregivers and the broader health care community. (PCORI, 2014a, para. 1).
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Vision of PCORI Patients and the public have the information they need to make decisions that reflect their desired health outcomes. (PCORI, 2014a, para. 2).
Obviously, PCORI emphasized the importance of patients in all aspects of their medical care. The addition of Steven Clauser, PhD, MPA, as the Director of Improving Healthcare Systems emphasizes PCORI’s commitment to patient-centered outcome research that encourages “self-management of their care” (PCORI, 2013, para. 2). The role of patient had shifted from passively receiving care to interactive involvement. Patient-directed medical care (see Chapter 14 in this book) is recognized as vital.
EXISTING AND EMERGING HEALTHCARE ORGANIZATIONS The existing healthcare industry consists of three major groups: G G G
payers health insurance firms or non-profits or governmental agencies providers hospitals and medical practices life science firms firms that manufacture medical devices, and pharmaceutical and biotechnology firms that manufacture drugs and other medicines.
Given grave concerns by both the general public and employers and employees in the United States regarding high healthcare costs and poor quality, citizens have called for reforms and new legislation. Passage of the Affordable Care Act in 2010 has greatly impacted the regulation and delivery of health care in the United States. Government incentives have helped move provider organizations to electronic health record (EHR) systems and other forms of digital data. Most if not all groups in the healthcare industry now have a focus on innovation and the use of analytics and scientific research to improve healthcare delivery. Some health systems are both payers and providers, such as Kaiser Permanente, which includes a HMO Health Insurance division and a health system of more than 40 large hospitals and integrated medical clinics and medical practices. Kaiser has invested heavily in its administrative, clinical, and data warehousing systems, reportedly investing more than $4 billion in its Epic clinical system, patient-provider portal, and a series of data warehousing systems, including a large Teradata clinical data warehouse for longitudinal data on its 8 million patients. The University of Pittsburgh Medical Center (UPMC, 2014) has established a regional health system and health insurance plans, and has started to offer health services internationally: With more than 3,200 physicians, 20 hospitals, and a myriad of community-based facilities and outpatient programs serving 29 counties in western Pennsylvania, UPMC is the leading provider of health care services in the region. (UPMC, 2014, para. 1).
Through advisory services, infrastructure consultation, and clinical management, UPMC is helping to transform health care throughout communities in: G G G G G G G
Ireland: Cancer centers and a full-service hospital Italy: Transplantation, radiotherapy, and biotechnology centers United Kingdom: Information technology and cancer care Kazakhstan: Oncology center consults Singapore: Transplantation and clinical management China: Pathology consults and health care collaboration Japan: Education in primary care and family medicine.
(UMPC, para. 4). Working with the Carnegie Mellon University, Software Engineering Institute (SEI) teams, and major technology partners, UPMC has announced a $100 million initiative to build a state-of-the-art predictive analytics data warehousing system, to develop new models of affordable, effective, patient-focused health care: PITTSBURGH, Oct. 1, 2012 What if a doctor could easily predict which treatment would be most effective and least toxic for an individual breast cancer patient, based on her genetic and clinical information? What if an intelligent electronic medical record could flag patients at risk for kidney failure, based on subtle changes in lab results? Or what if physicians could tell
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from the medical records of a large population of patients when the next outbreak of flu might occur and have the right kind and quantity of vaccine ready? These are just a few of the scenarios behind UPMC’s five-year, $100 million investment in a sophisticated enterprise analytics effort that will foster personalized medicine. Together with technology partners Oracle, IBM, Informatica and dbMotion, UPMC today announced that it intends to create a best-in-class data warehouse that brings together clinical, financial, administrative, genomic and other information that today is difficult to integrate and analyze. Advanced analytic and predictive modeling applications for clinical and financial decision-making are expected to produce better patient outcomes, enhanced research capabilities, continual quality improvements across UPMC, and reduced costs. With predictive analytics fully integrated with a decision support platform, hospital teams can quickly summarize and analyze patient information for the entire medical team, including doctors, nurses, specialists and other staff, so quick and informed decisions can be made. (UPMC, 2012).
HEALTH START-UPS AND ESTABLISHED TECHNOLOGY FIRMS CONTRIBUTING TO HEALTH CARE Both large corporations that have been around for a long time and many new small technology firms are contributing to the revolution in Big Data innovations in health care, including the following established companies: G G G G G
IBM, Oracle, and Teradata, supplying core database software and hardware DELL-StatSoft (STATISTICA Data Miner & Decisioning) IBM (SPSS i.e., IBM-MODELER formerly known as Clementine) SAS (SAS Enterprise Miner) SAP-KXEN, providing predictive analytics software.
In March of 2014, Dell acquired StatSoft STATISTICA in order to better supply all types of support for data analysis, from hardware to software to consulting services to cloud storage i.e., true “end-to-end” solutions where everything, from computers to servers to tablets to cloud to smart phones and any other e-communication formats yet to be developed, seamlessly communicates among all formats. (This seamless communication is one of the big problems within EMR systems and between EMRs and other medical data sources, as pointed out in the early chapters of this book). Predictive analytics for health care was a priority in that acquisition, providing the final major missing link in Big Data science analytics to fully enable this end-to-end solution for medicine. As the authors of this book write the final chapter in late May of 2014, we see an interesting phenomenon reminiscent of the Silicon Valley “bubble” of the 1990s: many MDs are leaving their practice or CMO (Chief Medical Officer) positions in major companies to start their own small “start-up” companies. These start-ups are pursuing various medical technology advanced products and processes, including mobile, genetic, and other avenues. Apparently venture capital is flowing into these start-ups, meaning that investors see a potential market. Only the best of these products and processes will likely prevail. One of this book’s authors (Gary Miner) predicts that within a short period of time (years) this bubble will burst, just like the Silicon Valley bubble burst several years ago, and then the best of these will become part of our routine healthcare procedures, making health care more cost-effective and individualized. There are many examples that could be cited, but for the purposes of this book, those below should give you, the reader, an idea of what is going on in this area of technology healthcare start-up endeavors.
IBM Watson IBM’s Watson machine, which is an integrated hardware and software process, won its initial fame on the TV show Jeopardy!, beating the leading human champions. Watson has started to use its natural language capabilities, hypothesis generation, and evidence-based learning to support medical professionals and organizations. The Memorial Sloan-Kettering Cancer Center (MSKCC) is the world’s oldest and largest private cancer center. MSKCC has partnered with IBM to put IBM Watson to work fighting cancer. Beginning with breast and lung cancers, MSKCC and IBM are using Watson to consolidate clinical expertise, molecular and genomic data, and its repository of cancer case histories to create evidence-based solutions. Physicians can use Watson to assist in diagnosing and treating cancer patients. Watson supports medical terminology by design by extending Watson’s natural language processing capabilities and data mining of patient data, such as family histories available in digital EMR records. Watson
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FIGURE 26.1 Multiple Analytic Services. Reproduced from IBM (2012, p. 4).
incorporates treatment guidelines, electronic medical record (EMR) data, and doctors’ and nurses’ notes. Further, Watson can include research and clinical studies, journal articles, and patient information into the data available for analysis. Watson’s natural language processing capabilities enable the system to leverage unstructured data. Watson can include genomic data and other insights to provide individualized, confidence-scored recommendations to physicians (Memorial Sloan-Kettering Cancer Center, 2013). According to IBM, “IBM Advanced Care Insights enables care providers to apply predictive and similarity analytics to confirm what is suspect, discover new information, flag what is being missed and anticipate change” (IBM, 2014) (Figure 26.1).
New Technology and 21st Century Health Care: Health Start-Up Firms Rock Health is a health start-up firm incubator that operates in San Francisco and Boston (Rock Health, 2013). Rock Health helps chosen start-up companies by providing office space, technology, and advertising to potential investors. New York, San Diego, Austin, and other areas have many health start-ups. Often, digital health start-ups use crowd funding platforms such as Kickstarter, Indiegogo, Fundable, and Medstartr. In 2013, Rock Health featured a list of 236 health start-up firms on its website (Rock Health, 2013). Many of the 236 health start-ups were part of the “Quantify Yourself” movement: health and wellness digital tracking devices and cloud software that incorporate analytics or predictive analytics.
Building the Star Trek Tricorder Remember the TV and movie series Star Trek, and how Dr. “Bones” McCoy used a tricorder device to help diagnose all medical problems? In 2012, the X Prize Foundation announced a long-term, worldwide contest in which $10 million would be awarded to the three teams that produced the best “tricorder type” device for detecting and monitoring a variety of diseases and conditions. Teams registered in 2013, and were to show their devices and test results in early 2014. By the summer of 2013, the Qualcomm Tricorder X Prize had drawn hundreds of competitor teams. In 2014, the original 300 teams had been whittled down to 10 (Dvorsky, 2014; see also Figure 26.2).
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FIGURE 26.2 Tricorder. Reproduced from Dvorsky (2014).
According to the contest website, the Qualcomm Tricorder X Prize would provide $7 million to the winning team and prize monies to the second- and third-placed teams: The Qualcomm Tricorder XPRIZE is a $10 million global competition to stimulate innovation and integration of precision diagnostic technologies, making reliable health diagnoses available directly to “health consumers” in their homes . . . Advances in fields such as artificial intelligence, wireless sensing, imaging diagnostics, lab-on-a-chip, and molecular biology will enable better choices in when, where, and how individuals receive care, thus making healthcare more convenient, affordable, and accessible. The winner will be the team whose technology most accurately diagnoses a set of diseases independent of a healthcare professional or facility, and that provides the best consumer user experience with their device. (Qualcomm, 2014, paras 2 and 3).
The winning tricorder device was to be a tool capable of capturing key health metrics that lead to diagnosing a set of 15 diseases; among the core set of screens and diseases that had to be diagnosed were the following: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
Anemia Urinary tract infection, lower Diabetes, type 2 Atrial fibrillation Stroke Foodborne illness Shingles Melanoma Strep throat Cholesterol screen HIV screen Osteoporosis Absence of condition. The Vital Signs Set Tricorder requirements included the following:
1. 2. 3. 4. 5.
Blood pressure Electrocardiography (heart rate/variability) Body temperature Respiratory rate Oxygen Saturation.
The winning tricorder device was to collect large volumes of data from various sources, from ongoing measurements of health data, using wireless sensors, and imaging technologies. Further, the tricorder was to use portable, non-invasive laboratory test replacements. Of the 300 teams, several leading medical sensor technology companies registered to join the contest to win the tricorder prize monies. For example, 10 teams of firms registered from the San Diego area.
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FIGURE 26.3 Scanadu Scout Tricorder. Reproduced from Dvorsky (2014).
In this competition, teams were to leverage technology innovation in areas such as artificial intelligence and wireless sensing. Much like the medical tricorder of Star Trek fame, doctors, nurses, and individual/ patients can use the tricorder devices and a smart phone to make preliminary medical diagnoses independent of a physician or healthcare provider. The goal of the competition is to drive development of devices that will give consumers access to their state of health using the new devices connected to smart phones to share data in the cloud. The 10 teams that were left were each working to improve medical care by taking the devices to the home and to the patients themselves. Diagnoses can be made regardless of the location of the individual (Dvorsky, 2014). Our future doctors could be robots! And predictive analytics should be at the center of those processes. Team Scanadu’s Scoutt is a $150 device that in 2014 was considered a front runner among the 10 entrants left in the contest (Dvorsky, 2014; see also Figure 26.3). The team was able to raise over $1 million by June 2013, and was then able to construct its Scout device that could gather key health metrics in under 10 seconds by holding it to one’s temple, recording vitals such as heart rate, blood pressure, temperature, heart activity, and oxygenation (Stenovec, 2013). Scanadu uses Bluetooth technology to upload information to a smart phone. Project ScanaFlo is a low-cost device that uses the smart-phone as a urine analysis tool. For pregnant women, Project ScanaFlo will be the first portable device to provide health data throughout the duration of a pregnancy. Project ScanaFlu is a low-cost tool that uses the smart-phone as a reader to assess cold-like symptoms. By testing saliva, the disposable cartridge can provide early detection for flu-like symptoms and Strep A, Influenza A, Influenza B, and other respiratory diseases. (Scanadu, 2013).
Wearable Computers for Doctors Google glasses were emerging in 2013 2014, and seemed a novelty. However, some physicians used them as they performed operations or interviewed their patients. The physician could be operating and intermittently glance at the patient’s charts or the patient’s results from tests (Hay, 2013).
Explorys Based in Cleveland, Explorys is a Cleveland Clinic spin-off company, founded in 2009, that leverages Big Data to support clinical decisions. Explorys provides turnkey solutions for clinical integration, at-risk population management, cost of care measurement, and pay-for-performance solutions. Explorys’ solutions help clinicians analyze data from multiple clinical and administrative sources. Data mining can guide physicians to the personalized treatment plan called for by each patient’s unique case: analytic tools can show variations among patients and treatments influencing health outcomes. Several major hospital and health systems have joined to share clinical data; Explorys has created one of the largest clinical databases with predictive analytic, data mining, and other analytic tools available to support clinical decisions (Explorys, 2014).
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Explorys’ healthcare customers include some of the most prominent healthcare systems in the United States, together accounting for over $45 billion in care. With over 100 billion clinical, financial, and operational data elements, spanning 40 million patients, 200 hospitals, and over 100,000 providers, Explorys’ secure cloud-computing platform is being used by 14 major integrated healthcare systems to identify patterns in diseases, treatments, and outcomes. Our network includes Cleveland Clinic, Trinity Health, St Joseph Health System, Catholic Health Partners, and many others with patients in all 50 states. (Cleveland Biomedical Job Fair, 2014).
TECHNOLOGY TRENDS THAT IMPACT HEALTH AND WELLNESS Current Trends Outside Healthcare Facilities There is an increasing use of smart phones and tablets by clinicians and patients. Even a report back in 2011 (Boulos et al., 2011, para. 33) stated: According to a recent video report by Mobile Future, a Washington, DC, broad-based coalition of businesses and non-profit organizations, there has been a massive increase in the numbers of consumer smart phone apps (applications) downloaded over the past two years, with figures going up from 300 million apps downloaded in 2009 to five billion in 2010.
The eCAALYX Example As stated by Boulos et al. (2011, para. 11): The eCAALYX Mobile Application is being developed under the scope of the eCAALYX EU-funded project (Enhanced Complete Ambient Assisted Living Experiment, 2009 2012) which aims at building a remote monitoring system targeting older people with multiple chronic diseases. Patients, carers and clinicians’ involvement is extensive throughout the prototype design, deployment and testing, and clinical trial phases of the project. The main functionality of the eCAALYX Mobile Platform is to act as a seamless “informed” intermediary between the wearable health sensors (in a “smart garment”) used by the older person and the health professionals’ Internet site, by reporting to the latter (but also to the patients) alerts and measurements obtained from sensors and the geographic location (via smart phone GPS) of the user. Additionally, the mobile platform is also able to reason with the raw sensor data to identify higher level information, including easy-to-detect anomalies such as tachycardia and signs of respiratory infections, based on established medical knowledge. A user interface is also provided, which allows the user to evaluate the most recent medical details obtained from sensors, perform new measurements, and communicate with the caretakers.
Usability becomes a key issue for the group of individuals (patients) that are the target group for using eCAALYX, since this group may be an older population that is not necessarily familiar with using technology and may have physical impairments such as poorer eyesight and dementia. With that in mind, the eCAALYX mobile platform is designed to be as transparent as possible for the user. Other e-items in this “outside of healthcare facilities” category but within at least the partial control of patients include: G
G G
G G
Mobile health and social media patients can (and are) form(ing) groups (support groups) based on either risk profiles or chronic diseases Mobile patient management with social components Digital health sensors and tracking health sensors can be put into buildings, homes, bathrooms, and other places to keep track of a patient’s symptoms Wearable computing machines, such as Google Glasses In-body sensors i.e., either ingested or implantable computerized devices.
TRENDS AND EXPECTATIONS FOR THE FUTURE OF HEALTH IT AND ANALYTICS The following “predictions” are based on the authors’ own “intuition” and from readings on eHealth and mHealth in various references, including the following: Wicklund (2014), in HIMSS mHealthNews; HIMSS14 (2014); HIMSS Analytics (2014); HIMSS Healthcare Global (2014); and HIMSS Transforming Health Through IT (2014).
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The Next 4 Years G
G
G
G G
by-2018 Predictions
More than 90% of US hospitals and larger medical practices will be using EMR systems with advanced analytics and predictive analytics. More than 70% of US doctors will be working in organizations that charge fees based on other than feefor-service models; ACOs (accountable care organizations) and HMOs will become the dominant model for health care in the USA. Integrated delivery organizations will use advanced Health IT systems with predictive analytics. New types of professional models for health care will emerge, such as Wal-Mart delivery of drop-in medical care; we will see rapid growth of comprehensive health plans with shared risk, and “medical tourism” with patients traveling to high quality overseas centers of excellence at lower costs. Globally, millions upon millions of patients will be remotely monitored (telehealth). Predictive analytics will combine with health IT systems, digital and mobile, and with comprehensive genome services studies on all high-risk patients and those with chronic diseases to create fully personalized medicine; personalized drug therapy will improve outcomes and reduce patient safety risks.
The Next 9 Years
by-2023 Predictions
Articles concerning changes in medicine are appearing daily in the popular press. For example, in one recent Wall Street Journal edition (Chernova, 2014; Marcus, 2014) there were two such articles. The first discussed baby clothing that can track vital signs of infants (Chernova, 2014); the second concerned the efforts of physicians currently tracking and analyzing patient data from routine check-ups (Marcus, 2014) that help to personalize advice given to patients, such as whether or not a sore throat is likely to be strep or whether the patient can simply stay home with chicken soup. Technological devices such as implants, data tracking tattoos, and nanotechnology are springing up like weeds (Hotz, 2014). By 2023: G
G
G
G
G
G
G
Fully operational tricorder, non-invasive devices to help medical staff identify potential illness factors will be in use, including medical detector devices. Sensors embedded in clothing and architecture, and bathroom devices, will continuously monitor health risks and send alerts; monitoring will focus on the development of pre-cancer formations that may require early treatment; heart and stroke monitoring will reach an advanced state. Advances in health care, nutrition, and fitness, and health and wellness tracking and alerts, will boost life spans; many citizens will move to more vegetarian diets many days of the week, as well as taking moderate exercise such as averaging 30 minutes of walking daily. More than 80 90% of the population in advanced economies will have smart phones within 10 years; all will be using mobile medical or fitness applications. More than 90% of patients and doctors in advanced economies will be using health and wellness smart phone applications to maintain and improve their health and fitness. Advanced Patient & Provider portals will link health IT systems and mobile solutions. Patients will become more informed consumers of medical care, and more actively involved in their own health care, including using personalized predictive analytics. Google Glass and/or similar types of wearable computers will be used in all aspects of the delivery of medical care.
Some of the items listed above are happening already. For example, the Scanadu Scoutt (discussed earlier in this chapter), a sensor that measures various vital body functions such as heart rate, was initially released to a handful of people on March 31, 2014. This device is produced by a company on the NASA Ames Research Park Campus in Mountain View, California. The initial batch had some “bugs” and went back to the drawing board, but an updated version is expected to be released soon. Importantly, this means that Scanadu is bringing to life a vision, shared by the company, its backers, and people/patient supporters, of a health environment where each individual person is empowered through knowledge of their own health. People interested can become test volunteers during the full development of this device. (Scanadu, 2014).
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CONCLUSIONS AND SUMMARY OF IMPORTANT CONCEPTS PRESENTED IN THIS BOOK The evolution of data analytics, and what are the best practices for accuracy in 2014, include: G
G G
G
Bayesian data analysis, which was used for centuries prior to Fischerian Central Limit Theory statistics of the 20th century (for more detail, see Nisbet et al., 2009) Traditional p-value Fischerian statistics (population mean . . . group mean . . . central limit theory based) Data mining (statistical learning theory algorithms mostly machine learning/artificial intelligence based, and also some clustering and “rules based” methods) Predictive analytics, which is “broader” than data mining, including some DA (discriminant analysis), LR (logistic regression), and WoE-LR (weight of evidence combined with logistic regression); and the data mining algorithms, especially the statistical learning algorithms, but including CART, CHAID, K-E-means clustering, interactive trees, boosted trees, MARSplines, neural networks, Bayesian analysis, support vector machines, k-Nearest Neighbors analysis, association rules, SAL (Sequence, Association, and Link Analysis), and independent components analysis, among others)
The potential accuracy of a “predictive model” tends to increase as one goes from the lowest level with DA and LR, next to WoE-LR, and then finally to the “true” machine learning methods, as illustrated in Figure 26.4. The model in Figure 26.4 does not mean that any specific data set will only get the best predictive analytic model by using the AI machine learning algorithms, as some data sets, especially if the data are clearly linear (but this rarely happens with medical data), may get highest accuracy models using DA and LR. However, in general, for most data sets, in our experience, the best models with the highest “accuracy scores” are obtained by using the true statistical learning theory modern algorithms. Another way of describing this is to use the term “global learning” (i.e., a population-based learning where means of the population and corresponding t-tests, p values, and other traditional statistics are used) vs. “individual statistical learning theory” methods (i.e., the case-by-case learning approach of data mining algorithms). This distinction is important for the main message of this book, which is “personalized” and “person-centered” health care. This distinction is also important for CER (comparative effectiveness research) and HTE (heterogeneous treatment effects), in which some scientists are intent on looking at “groups” of people (groups classified by age, sex, etc.) but for which the ultimate goal is to look at “individuals” individuals that are genetically distinct, thus a finer grouping than that obtained by just looking at age, sex, and similarly classified groups. FIGURE 26.4 Potential increasing accuracy of a data analytics model.
Technology for the Elderly Consider this statistic: “In 2010, the total direct medical costs of fall injuries for people 65 and older, adjusted for inflation, was $30 billion” (CDC, 2014, para. 7). That same site predicted that, unless things change, the figure in 2020, using 2012 dollar values, will be a whopping $67.7 billion. One-third of adults over age 65 fall each year. The monetary costs are only a part of the issue there is also the matter of pain and suffering, and of income lost, family duties increasing to take care of the older member, and permanent losses that can result. Falls are a big problem in our country. Technologies already present are helping the elderly stay in their homes, and many medical scientists are beginning to find ways of collecting day-to-day data to use in predictive analytic individualized medicine (Wang, 2014). For example, Skubic et al. (2009) have been collecting data on stride length and speed of stride among residents of elderly apartments. They have been using 3D sensors, and with the data are developing a prediction model for falls. Demiris et al. (2008) installed various monitoring devices in nine seniors’ living quarters, validating the devices with independent observations, and found that the devices were accurate, accepted, and not considered intrusive by the residents after the initial phase of getting used to the devices. The authors did point out ethical concerns about privacy and control as the users learn to rely on automation. However, as if in answer, the residents reported they felt more of a sense of control over their lives and often wanted to see their data, reinforcing the concept that patients, if given the chance, would really like to become more involved in research. Predictive analytic techniques and projects can significantly impact the quality of lives of seniors and help reduce healthcare costs.
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Technology for Rural Areas This is an area alluded to earlier in this chapter where mobile sensing devices were discussed. However, a specific example that came to our attention recently was the use of mobile phone technology for doing eye exams in rural Africa, where previously many people were going blind because of lack of access to eye doctors and eye clinics. In March 2014, Andrew Bastawrous, an eye surgeon and inventor, and a TED Fellow, filmed a description of this technology currently used in Kenya, Africa. It is called “Peek,” and is based on a smart phone that is given to community health workers who travel out to the field. In these rural areas this phone takes “snapshots” of a person’s inner eye conditions and sends it back to the diagnosing doctor at the clinic. Difficult cases can even be evaluated at major eye centers around the world, communicating the best diagnosis and treatment plan rapidly back to the health worker in the field. Currently, around the world, over 80 million people are blind. Most of this blindness occurs in underserved areas like Kenya. Most of this blindness is also easily preventable. So here, in this current development in Africa, we see not only a cost-effective solution, but also a saving of sight for potentially millions of people (Bastawrous, 2014; see also Box 26.1).
Box 26.1 We encourage the reader to watch the following video (http:// www.ted.com/talks/andrew_bastawrous_get_your_next_eye_ exam_on_a_smartphone?utm_campaign5&awesm5on.ted. com_quBe&utm_source5facebook.com&utm_medium5on.
ted.com-facebook-share&utm_content5awesm-publisher) as it demonstrates what is going on in mobile health technology in a much better way than we can put down here in words.
The Kenyan example is of special interest to one of the authors of this book, who had an eye condition that could eventually result in blindness. The author spent 18 months going to 5 different eye clinics, getting different diagnoses, but more concerning were the treatment plans (which were different at each facility). At the fifth, with the “renowned real expert,” the author was told that “this is a ‘no brainer,’ there is only one thing to do first”! Thus, the author finally had to take things into his own hands, Google, do research, and discover for himself what was the “best treatment plan” (it turned out that this fifth expert was right; the “no brainer” treatment was the first of five possible treatments, the least invasive, simplest, and least costly, and it in itself would also be “diagnostic” if it worked we would know the cause of the condition; if it did not work, then we knew that the cause was different and would move on to the number two treatment). Five different and conflicting medical opinions before a good “right” answer! If this author had taken some of the earlier “treatment plans” presented it would have been absolutely the wrong thing to do. Additionally, eye pressure checks were needed frequently (every 2 weeks) during this
period for safely monitoring the condition, involving considerable time and expense in attending the eye clinic. The author kept wondering, “Why is there not a ‘smart contact lens’ that can do this monitoring, sending continuous readings to the clinic?” If the author had had the smart phone mobile device current being used in Africa, he would have probably saved 17 of those 18 months of searching among different clinics, arrived at the “right answer” much faster, and received treatment earlier . . . all at considerably less medical cost! This example illustrates first-hand how “fragmented” our medical knowledge is in the US Health Care Delivery System, and what needs to be done to rectify the problems: G If predictive analytic models had been developed, the correct diagnosis could have been made on the first eye clinic visit. G If the professional readers of this book work toward the goals presented therein, then we will have this cost-effective, accurate diagnosis and treatment in health care in the future.
Final Concluding Statements This book has provided a background for predictive analytics; methodologies for predictive analytics; and examples of predictive analytics. It has also gone beyond that to the next step: decisioning systems that lead to action. In today’s world, data comprise the new gold. Acquiring appropriate data has become the supreme challenge to efforts at prediction. Those who are able to obtain permission to use the data have the best chance of producing individualized models, and generally those individuals likely are those closest to the data, such as physicians, medical groups, and teaching hospitals. It does no good if those data are unavailable, however, even if they are structured in EMRs that allow communication between departments and institutions. Patients are the closest to the data, but HIPAA generally stands in the
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way between the data and the research. Voluntary permission for one’s data to be used in research could be given in one of the many forms that new patients fill out. National groups such as PCORI can aid in joining patient data to researchers that need them. New IRB practices could include generalized informed consent to cover research both within and between institutions. New predictive techniques of analysis are imperative to the goal of individualizing practice, and reliance upon the older methodologies must be combined with newer techniques to produce better outcomes, which constitute the prize. In addition to the “futuristic ideas” discussed in this book, we have presented three phases in the development of accurate, non-error, cost-effective health care: G G G
Phase I Quality control/Six Sigma applied to medicine Phase II Predictive analytics applied to medicine Phase III Automatic decisioning systems applied to medicine. Will there be a fourth phase? Yes, most likely there will be
and that will probably be the topic of our next book!
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BIBLIOGRAPHY Additional books that discuss in further detail the needs of healthcare both in the USA and globally: Agus, D.B., 2011. The End of Illness. Free Press, New York, NY. Berry, L.L., Seltman, K.D., 2008. Management Lessons from Mayo Clinic. McGraw Hill, New York, NY. Boult, C., Giddens, J., Frey, K., Reider, L., Novak, T., 2009. Guided Care: A New Nurse Physician Partnership in Chronic Care. Springer, New York, NY. Brawley, O.W., Goldberg, P., 2011. How We Do Harm: A Doctor Breaks Ranks about Being Sick in America. St Martin’s Press, New York, NY. Carey, R.G., Lloyd, R.C., 2001. Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Applications. ASQ (American Society for Quality) Quality Press, Milwaukee, WI. Clifton, G.L., 2009. Flatlined: Resuscitating American Medicine. Rutgers University Press, New Brunswick, NJ. Duncan, I., 2011. Healthcare Risk Adjustment and Predictive Modeling. ACTEX Publications, Winsted, CT. Gawande, A., 2010. The Checklist Manifesto: How to Get Things Right. Picador (Henry Holt and Company), New York, NY. Goldhill, D., 2013. Catastrophic Care: How American Health Care Killed my Father And How We Can Fix It. Alfred A. Knopf, New York, NY. Kudyba, S.P. (Ed.), 2010. Healthcare Informatics: Improving Efficiency and Productivity. CRC Press, Boca Raton, FL. Makary, M., 2012. Unaccountable: What Hospitals Won’t Tell You and How Transparency Can Revolutionize Health Care. Bloomsbury Press, New York, NY. Pronovost, P., Vohr, E., 2010. Safe Patients. Smart Hospitals: How One Doctor’s Checklist Can Help Us Change Health Care from the Inside Out. Plume, New York, NY. Reid, T.R., 2010. The Healing of America: A Global Quest for Better, Cheaper, and Fairer Health Care. Penguin, New York, NY. Sheehan, B., 2013. Doctored. Jeff Hays Films, Sandy, UT. Available at: ,www.doctoredthemovie.com/.. Topol, E., 2012. The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care. Basic Books, New York, NY. Weinberg, J.E., 2010. Teaching Medicine: A Researcher’s Quest to Understand Health Care. Oxford University Press, New York, NY.