New Health Systems using Network Information Technologies Bruce R. Schatz Department of Medical Information Science, College of Medicine Department of Computer Science University of Illinois at UrbanaChampaign, Urbana IL 61801 USA
[email protected] ABSTRACT A new health system is coming based on network information technologies. It consists of health measurement across the full spectrum of health determinants, using mobile devices such as smartphones. This measurement generates personal health records for each individual within a population, which can then be analyzed to identify cohorts, using statistical clustering on supercomputers. These cohorts determine “which persons have which outcomes”, so health management can be supported for higher quality at lower cost. This tutorial concisely summarizes the arguments in the recent book Healthcare Infrastructure: Health Systems for Individuals and Populations.
Keywords Healthcare infrastructure, health systems, health determinants, population cohorts, mobile devices, smartphones, supercomputers
1. INTRODUCTION This tutorial summarizes the arguments in the recent book Healthcare Infrastructure: Health Systems for Individuals and Populations [1]. This book is partially a textbook, based on 15 years of teaching graduate courses about medicine to engineering students at a large public university, and partially a research monograph, describing the future of health monitors for whole populations. It gives the appropriate technology for viable healthcare right now. Please see this book for many additional details with corresponding references, with samples at: http://www.springer.com/medicine/book/978-0-85729-451-7 .
interactions with this data after the infrastructure analyzes it automatically. This paradigm offloads the medical system with free labor from the actual persons being measured so that the economy of scale can take place for viable healthcare. All infrastructures make the transition from centralized services dominated by station-to-station routing staffed by experts to distributed services dominated by point-to-point routing staffed by amateurs. This transition is necessary to handle increasing volume with decreasing cost. The technology in the infrastructure enables the amateurs to simulate the experts so that they can handle most of the transactions themselves. While the technology has some cost, the cost of labor is so much greater that the amateurs being free labor enables the scaling to much larger volumes. For example, transportation made the transition from railroads to automobiles when the number of travels needed to greatly increase. Railroads could transport station to station with professional staff, while automobiles could transport point to point with only amateur drivers.
2. THE INEVITABLE EVOLUTION OF HEALTHCARE INFRASTRUCTURE A new health system is coming to save us from the economic crisis of our time. This new system will have higher quality and lower costs, being viable in a way that the existing system is not. The trends are already clear to certain communities, with terms like personal health records and mobile Health (mHealth), which symbolize moving from the centralized paradigm of physicians in hospitals to the distributed paradigm of patients in homes. This is the same transition that has taken place with the rise of the Internet, where personal computers interact in a client-server model with mainframe computers from a user interface to database record structure. But this paradigm has not yet passed into common usage with medicine and public health. We are still in the era of electronic medical records (EMR) and personally controlled health records (PCHR) still generated by physicians even when accessed by patients. With true personal health records (PHR), the patients themselves generate the status data and perform all the initial
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Figure 1. Provider Pyramid with Providers and Records. There is a provider pyramid from experts at the top to amateurs at the bottom. Scalable infrastructures emerge after the transition from mostly experts to mostly amateurs, when the technology at the bottom is good enough so that most transactions can be handled there at low cost compared to the higher cost for greater expertise at the top. For example, communications made the transition from telegraph to telephone when the number of message calls needed to greatly increase. Telephones could be used directly by persons by talking without needing to know telegraph codes as did the professional operators. Thus many more persons could make calls to “send messages”. In 1870, the operators began to transition from sending messages to routing calls. In 1900, researchers in the Bell System predicted that within 30 years, the increase in call volume would require
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that every person would be an operator for call routing. In fact, this is what happened, as the introduction of automatic switching machines into mass communications infrastructure meant that individual persons placed their own calls by looking up the number in the directory then dialing it, and the machines did the rest of the communication transaction. The same transition will take place in healthcare infrastructure, as the inevitable evolution. Most of the transactions will be handled directly by individuals at the bottom of the provider pyramid. They will require support from the infrastructure to measure their health and to manage their health. For simple transactions, they will support themselves. For more complex transactions, they will move up the provider pyramid to more expert professionals with higher cost but greater capability. Just as automobiles enabled ordinary persons to simulate a train driver and telephones enabled ordinary persons to simulate a telegraph operator, monitors will enable ordinary persons to simulate a clinic doctor. Health monitors are the front end of the new health system, while cohort analyzers are the back end. The monitors are implemented by sensors in everyday devices, such as cell phones, which provide health measurement, supplemented by personal messages. The analyzers are implemented by software in computer servers, which cluster measurements to discover related persons who can be managed in similar ways. Chronic illness can be effectively measured by a health system that tracks persons continually on a daily rather than on a yearly basis as at present with requiring medical professionals up the provider pyramid.
this is the big one like the 1918 flu that killed millions. But that turned out not to be the case, and hundreds of millions of vaccine doses simply sat in warehouses purchased but unused at the cost of billions of dollars in the US and the UK. The public health solution today is very similar to the prescription drug solution: either give it to everyone or give it to no one. The underlying difficulty is a fundamental lack of information. It is not possible to determine “which persons have which outcomes”. All or none is the strategy, since group differentiation requires more information. All of the factors that affect health have to be considered, to adequately place persons into groups, from the bodies of individuals to the societies of populations. For example, the risk factors for heart attacks range from genetics and metabolics of an individual’s body to diet and exercise in their everyday behavior to social pressures and societal conditions. Such determinants can be summarized by a series of concentric rings. For example, consider Figure 2, which is the framing diagram from the author’s book upon which this tutorial is based. This ring diagram is evolved from a classic government study sponsored by the Institute of Medicine entitled The Future of the Public’s Health in the 21st Century [2]. Their diagram has been modified to emphasize where information technologies could most strongly affect the viability of healthcare infrastructure.
3. PROBLEMS OF MEDICINE AND PUBLIC HEALTH (HEALTH DETERMINANTS) The fundamental problem can be understood by examining the withdrawal of a blockbuster drug, such as Vioxx, which nearly bankrupted Merck as its sales went from $3B to $0 in a year. Prescription drugs are approved via a long expensive development process involving a series of clinical trials increasing in size. The largest of these, the Phase III trials, will typically include hundreds of persons over a period of several months. If safety is validated and efficacy beats placebo, then the drug is approved by the FDA, entering the marketplace. A blockbuster drug will then be taken by millions of persons over periods of years. At this scale, there are often populations which are harmed by the drug. In fact, for most drugs, there is a typical computing 80-20 curve, where most of the persons are little affected by the drug, while a few (10%) are seriously helped and a few (10%) are seriously harmed. What typically happens with a withdrawn drug is that there are a few spectacular deaths, with resulting publicity, then the FDA requests the drug be withdrawn. For example, Vioxx increased bloodflow to ease arthritis pain, but this also increased risk for heart attacks. Deaths numbered in the tens, from a total population of millions taking it. After withdrawal, many more persons who were helped could no longer use it. The current health system has no technology to scale to PostMarketing Surveillance, so-called Phase IV trials, while testing millions of persons in clinical trials equivalents during uncontrolled everyday life. The same problem comes to the fore in public health, especially in the distribution of vaccines. When a potentially severe flu comes during the season, officials are faced with the decision of whether to require vaccination. For example, during the 2009 H1N1 epidemic, the decision was that
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Figure 2. Health Determinants for Five Rings of Human Status. In measuring health, it is important to include factors relating to the bodies of individuals. These include internal functioning considered by personal medicine, such as metabolism (blood pressure) and motion (gait analysis). But it is equally important to include external functioning considered by public health, such as social interaction (families and communities) and societal conditions (living and working). In-between the internal body and the external society is the behavior and stress of lifestyle, such as diet and exercise whose effects combine genes and environment into an integrated whole. See Table 1 for a summary of factors. 5 Societal conditions for Living and Working 4 Social networks for Family and Community 3 Stress and Behavior bridge (smoking and alcohol, diet and exercise). 2 Body for Physical (metabolism, motion) and Mental (cognition, memory) 1 Biology for Blood and Breath (acute system rather than chronic support) Table 1. Rings numbered to reflect power (hi) and speed (lo).
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4. SOLUTIONS OF NETWORK INFORMATION TECHNOLOGIES (POPULATION COHORTS) Enabling technologies to support this full spectrum exist in research prototypes today. But they fail to be scalable enough for whole populations, existing in research laboratories rather than in health systems. On inner rings, data sensors can mimic pulse oximeters for blood and breath, as well as gait analysis for metabolism and mobility. On outer rings, data sensors can locate social interactions while text parsers can extract personal narratives for societal conditions. In the middle, combinations of data and text in mobile devices can effectively record diet and exercise from daily lifestyles of heterogeneous persons in heterogeneous environments. Practical systems measuring real populations have yet to be widely tested at this level of data. There are a wide variety of potential technologies to measure the health of populations at the level of individuals. As the ring diagram makes clear, factors across the full range of health must be measured. This health measurement must be actionable, which is not true of many technologies. Actionable means that the data changes quickly enough to be measured, for the tracking of chronic conditions. Note that “chronic” means “time”, where the changes are slower than for acute conditions currently supported. For example, simple devices can track body fat and walk gait, but the former changes slowly while the latter changes quickly. Actionable also means that the data can be analyzed with current understanding of medicine. For example, implantable sensors that measure blood flow at each small region of the body give more data than can be usefully interpreted, whereas single blood flow numbers could provide a chronic version of acute measurements. The fundamental solution is to measure as deeply as possible with actionable data. That is, to measure in scalable fashion across all the rings on a continuous basis. This measure process is performed by transforming the raw data from sensors into cooked data actionable from medicine viewpoint, then adding userspecified information to gather periodic record of health status. The manage process utilizes statistical clustering from information retrieval to generate equivalence cohorts, then the feature vectors across the health factors within a cohort cluster can be data mined to characterize that cohort. Information analysts, both human and machine, can use the common patterns from the cohort features to suggest treatment guidelines for that cohort. Different cohorts require different treatments. Similar persons within a cohort have similar outcomes so can use similar treatments. Figure 3 shows how population cohorts form an n-ary partition of the individuals, rather than a binary.
Traditional population measurement uses quality of life surveys to ask questions to measure health status. The largest of these is the Behavioral Risk Factor Surveillance Survey (BRFSS), developed by the CDC and administered to 350K persons annually, who are demographically representative of the US population. Adaptive questionnaires can expand the range of questions from a hundred to thousands across the entire range of lifestyle status. These questions can be supplemented, often replaced, by simply enabling users to type personal messages about health status. Outcomes can be automatically extracted from personal health messages, including standard categories such as in health questionnaires. Questions can be answered with phones, since they require only short display and numeric response, while messages can be typed on laptops or desktops since they require entry of several paragraphs of descriptive materials. Automatic sensors have the advantage of not requiring additional user effort to supplement manual descriptions. There has been significant effort to produce fitness monitors that can be worn to measure health, such as bands for the wrist or arm. To measure entire populations in the millions beyond those closely concerned with measuring fitness, something less invasive is needed to insure that persons actually wear the devices. There is significant active research on embedding sensors into clothes such as shirts or vests. These provide grids of sensors across the body with fully automatic continuous operation. However, they are not available on a mass scale, and the commercial costs are yet unknown. The only sensors available on population scale within the foreseeable future are those within mobile devices. These are carried rather than worn. The most common across all demographic groups is the cell phone, which can record voice and display messages. Younger persons often use music players instead, which are converging, with many of the same sensors. For example, an Apple iPod Touch is an inexpensive iPhone without calling capability. Smart phones in particular have significant penetration already among the middle class, the likely early adopters of health monitors, and the sensor features available in smartphones are rapidly transitioning into cellphones. If a smartphone is available, then automatic sensors can supplement manual descriptions with actionable data. In addition, the sensors may serve as health monitors to catch distress signals and request that the user provide text to describe the situation. For example, physical stress can be detected via gait analysis using the accelerometer while walking and mental stress can be detected via voice analysis using the microphone while talking. The user description could be entered via targeted questions or via free text including voice recording with automatic transcription. These sensors can approximate the data necessary for measuring within each of the rings of health status, as illustrated in Table 2. Ring 1 manual. Blood via camera and Breath via microphone. Ring 2 autodetect from gait analysis for physical and voice steadiness for mental stress. Ring 3 diet via camera and exercise via accelerometer. Stress Behavior from voice pitch. Ring 4 GPS for location and phone interact for social. Ring 5 customized questions for living/working conditions.
Figure 3. Curve of Population Cohorts with Different Outcomes for Different Persons.
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Table 2. Health Rings monitored with smartphone sensors.
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A smartphone today is both a telephone with a microphone and speaker, and a personal computer with a keyboard (sometimes virtual) and a display. It has communications to support both media, via the telephone network for voice and the wireless internet for data. Additional sensors typically include: camera for pictures, GPS for location, accelerometer for motion, gyroscope for orientation. Some smartphones specialized for healthcare also include body temperature for fitness evaluation and bodyfat measure for weight management. Health phones are more widely available in Japan than in America, but still uncommon. Continuous data for each individual can thus be collected and computed into actionable data for each ring. These individual personal health records can be collected into regional or national databases for population analysis or treatment guidelines. The analysis groups similar persons into similar cohorts that can be effectively managed with similar treatments. The feasibility of such computations can be shown by analogy with document processing. A community collection of 500K documents has about 30K different words, which can be utilized to statistically cluster the documents into 1000 groups comprising about 500 documents each. The health vectors are actually easier to cluster effectively than the document vectors, since the health factors have meaningful semantics to be weighted for different groups. The cluster analysis can be done on a hierarchy of machines. The initial cooking of raw data into actionable data could be done directly on the phone or on a remote server as done with fitness monitors for health phones commercially. The grouping of clusters into cohorts can be done interactively on a shared memory machine or batch for larger computations on a cloud computer. Finally, the pattern matching for data mining across all the cohorts and all the persons is a supercomputer computation, a suitable job for a national data center. Providing appropriate interaction for measurement and adequate scaling for management is a research question for cloud computing and supercomputing.
5. RESEARCH CHALLENGES FOR INFORMATION TECHNOLOGIES IN NEW HEALTH SYSTEMS There are many research challenges and opportunities that would utilize new computing and information technologies to enable better measurement and management for practical healthcare. The measurement technologies would focus on deeper monitoring of broader populations, thus getting the numbers up. The management technologies would focus on validating the new data against existing data, for example, comparing new personal health records to existing electronic medical records. This would enable effective predictive modeling for new health systems, such as which persons have which outcomes. Sample issues include: •
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What data should be recorded to measure everyday health? o For example, blood pressure to gait mobility to social interaction across the rings. How should this data be most helpfully collected? o For example, via smartphones in the short term, or smartclothes in the long term. How should the sensor data be classified into actionable data? o For example, identifying and categorizing temporal patterns in body monitors, or spatial patterns in social monitors, while dealing with missing and noisy data. How should the diverse sources be judged for quality?
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For example, evaluating provenance for physician supplied text versus patient supplied text, or for continuous sensor data versus episodic survey data. How should this data be mined and correlated? o For example, statistical methods such as data mining patterns, or information retrieval clusters. Deeper semantics are necessary to infer population cohorts. How is population data transformed into usable knowledge? o For example, computer generated classification may differ from human generated classification, requiring revisions to guidelines for diagnosis and for treatment. How should this data be used for practical health systems? o For example, outcomes can be generated from patient supplied text, which may require knowledge modeling for proper interpretation via new data logics. How can multiple knowledge sources be integrated for users? o For example, different sources have different uncertainties and different users have different preferences. Scalable inference systems must be developed to incorporate data provenance and user beliefs, to adequately capture context. How can existing data (medical records and clinical trials) be leveraged to refine predictive models? o For example, proactive detection and remediation of specific medical conditions may require inferences from physician records and patient sensors by using available knowledge for optimal interventions. What is the impact of this new data on health quality and cost? o For example, different cohorts have different outcomes, which likely cut across demographic boundaries in different ways than at present, requiring more intelligent systems for new personal health records. Potentially the cohorts can enable health systems that use treatments effective for the specific group and nothing else. o
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When researchers have developed sufficient answers to these questions, a new health system can be deployed using network information technologies to provide viable healthcare.
6. ACKNOWLEDGMENTS My book co-author Richard Berlin MD taught me useful medicine and closely collaborated on these arguments over many years.
7. REFERENCES [1] Schatz, B. and Berlin, R., Healthcare Infrastructure: Health Systems for Individuals and Populations, Springer-Verlag series in Health Informatics, 2011. [2] Institute of Medicine, The Future of the Public’s Health in the 21st Century, National Academies Press, 2003.
About the author: Bruce R. Schatz is Professor and Head of the Department of Medical Information Science in the University of Illinois at Urbana-Champaign. He is also Professor in the Department of Computer Science and the Institute for Genomic Biology. He was Principal Investigator of national flagship NSF projects in digital libraries and in bioinformatics. He won best paper at AMIA for the first semantic indexing of Medline, on supercomputers. He is AAAS Fellow for developing the first network browser for multimedia documents, which led directly to creation of the Web.
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