To achieve this, it is necessary to develop explicit clinical performance benchmarks in consultation with physicians. Furthermore, it will be necessary to collaborate in design with employers and health plans to ensure that there is a business case for use. Unless providers who use these systems are rewarded for doing so, widespread adoption will be hard to achieve.
Attempting to integrate HIT and comparative effectiveness research carries with it the unfortunate consequence of creating disillusionment. If we are able to build transparency about how little evidence base there is for much of our decisions into clinical decision support—and are transparent to patients on this risk, too—we risk considerable disillusionment. This could be ameliorated by integrating clinical research with the care encounter.
Bill Press has outlined a possible approach to this situation Press, In his model, when a patient comes to see a physician, the quality of evidence for different diagnostic and therapeutic options is arrayed as probabilities—the probability the diagnostic option will reveal, or the therapeutic option will resolve, the problem at hand.
When the patient makes a decision—which, it should be noted, will be a shared and informed decision—the clinical encounter becomes part of a rolling clinical trial. As a result, the probabilities evolve as the results of individual encounters and treatments are recorded and reported. The result is a learning system, where evidence is continually generated and refined, and then fed back to clinicians and patients to promote informed, shared decision making.
The level of patient-fostered engagement in this approach is crucial to promote innovation. If patients can be convinced of the benefits in such a system they will not only be eager to participate, but will begin to demand such capabilities from the healthcare system. With consumer demand we might be able to accelerate work on the technical, political, social, and economic dimensions of facilitating the rich exchange of data necessary to enable such a system.
This is an area of opportunity for academic medical centers because of informatics resources and large medical groups because of capitated care and large databases to design closed-loop learning systems that continually utilize data to evolve clinical understanding. Developing and refining this concept in small cases will begin to demonstrate the utility to the general public, stimulating larger efforts.
The third challenge facing health care is the reconfiguration of the health delivery system toward integrated care models such as accountable care organizations as a result of ACA. One of the defining characteristics of these new delivery system models will be the remote nature of care. Functions, not just data, will be liberated and redistributed. Furthermore, we will likely see the rise of long-distance—or remote—diagnosis, consultation, and treatment.
This will require advanced health information exchange between and among organizations. The evolution will be a fluid process, but it will also be rough. Considerable time and resources are being invested. The challenge will not be so much technical, as it will be political and economic. Consequently, the Office of the National Coordinator for Health Information Technology ONC should partner with the most advanced systems using telemedicine, tele-ICU, tele-emergency and telehealth technologies to understand how the structures, regulations, and processes that we are setting up now facilitate, or complicate, delivering networked care.
Addressing these challenges will test the limits of data integration with electronic health records that live inside separate enterprises and support learning and the dissemination of principles gleaned from data exchange. Ultimately, successful approaches to these challenges will emerge from treating them as complex systems.
Solutions will not involve rules and laws, but will be centered on processes for solving complex and evolving problems.
The topic of this paper—combinatorial innovation—comes from a concept introduced by Google chief economist Hal Varian. He postulates that there is currently enough innovation available such that we do not have to invent anything new to create disruption.
This paper will begin by addressing many elements that already exist today, but that in combination can be disruptive, and then move on to a discussion of work going on at the Institute for the Future as well as some priorities moving forward. Discussions on the digital infrastructure for a learning health system tend to focus on clinical information ecologies and the notion of standardized and interoperable electronic health records EHRs.
Much attention, not to mention recent legislation, concerns the linkage between evidence-based science and an interoperable EHR, but this is really only half the picture. Something that is commonly ignored is personal information ecologies. Citizens are constantly creating digital artifacts. These are not just health and fitness related, but come from their social life, shopping, media. There is enormous interest—not just in the healthcare community but across communities—in managing these digital artifacts.
Doing so will necessitate a holistic program which, fortunately, has been acknowledged by the current administration. The recently released a multiagency recommendation for a national identity and security 2 that advocates for a federated model for identity management as an important step in harnessing the potential of these data. When discussing patient engagement in health information technology, many argue that only a minority of people will collect their data and want to use it in their personal health record. Our indicators suggest that this is not going to be the case.
In the Silicon Valley, leading companies like Intel, Cisco, Google, and others are providing real incentives for people to get involved with their own data—body mass index, blood pressure, cholesterol—and take control over their own health. This is viewed as a corporate health issue, making it reasonable to assume that it can spread to populations at large.
Chest 95 , — Many organizations who are performing research are struggling to get financial support to conduct the research, much less invest that money in an informatics system that will not provide them any more income or improve the outcome of the research Embi, These action plans today are primarily based upon disease diagnoses, for diseases with systematic levels. Robust decision support systems are a pivotal tool for moving knowledge into routine practice and an important component of a NHII. Nursing assessment Nursing diagnosis Nursing care plan Nursing theory. Open Peer Review reports. Discussion CHI applications are widely available for consumers to leverage their potential for health self-management.
There are also growing stores of health-related information that many people do not normally consider. For example, since many people now carry GPS devices in their pocket, we can mine those data and forecast kinds of behaviors and activities in particular locations.
Furthermore, some individuals are beginning to wear sensors—not just for their health but for fitness. In fact, there is a lot of new research in the area of using mobile devices as hubs for a wearable network of sensors. All of these new technologies generate a surplus of information. We do not need all of the information, just the right information. Fortunately, we are well on our way to doing so. In online social networks, we are seeing the rise of social graphs—a schematic that visualizes the kinds of linkages and relationships between people on a dynamic and real-time basis.
This technique is based on a very common semantic web framework called Resource Description Framework RDF , a simple grammar for describing relationships in terms of the subject, predicate, and object. RDF is also the basis for almost all semantic web applications used for health information exchange. Soon, we will have a population of million people who have a semantic web description of their relationships,.
Of course there are problems with privacy and security if you put your data out there in a universal infrastructure—and there is a lot of work to be done on that front—but the opportunity is immense. In this new climate there are several major contact points that need to be kept in mind: the relationship of our personal health information and the public health commons, the relationship of our personal information and contextual health information, the relationship of our clinical information and contextual health, and the relationship of the scientific evidence base with the clinical information.
All of these pairs of relationships have to be explored as a coherent system, and at the Institute for the Future we are looking at what can be achieved with massive computing capability and an abundance of rich data. The examples discussed above are the types of technologies our teams have been working with—most recently in a project called Healthcare —to develop tools for precise clinical health information and adaptive health coaching.
The result would be that your mobile device would know, for example, that you are not supposed to drink and therefore advise you against going to a bar. With technologies like these we can optimize our health spans, not just prevent morbid conditions. As this field continues to grow, there will need to be a certification process for curating public health information on the web.
With so many individuals getting health information on the web from dubious sources, there is a new stewardship role that has to be fulfilled.
The importance of counseling and behavioral interventions is evident, given the influence on health of factors such as tobacco, alcohol, and illicit drug use; unsafe sexual behavior; and lack of exercise and poor diets. These risk behaviors are estimated to account for more than half of all premature deaths; smoking alone contributes to one out of five deaths McGinnis and Foege, Coverage of clinical preventive services has increased steadily over the past decade.
In , about three-quarters of adults with employment-based health insurance had a benefit package that included adult physical examinations.
Two years later, the proportion had risen to 90 percent Rice et al. The type of health plan is the most important predictor of coverage RWJF, Although the trend toward inclusion of clinical preventive services is positive, such benefits are still limited in scope and are not well correlated with evidence regarding the effectiveness of individual services. The U. Public Health Service, has endorsed a core set of clinical preventive services for asymptomatic individuals with no known risk factors.
However, the USPSTF recommendations have had relatively little influence on the design of insurance benefits, and recommended counseling and screening services are often not covered and, consequently, not used Partnership for Prevention, see Box 5—3. As might be expected, though, adults without health insurance are the least likely to receive recommended preventive and screening services or to receive them at the recommended frequencies Ayanian et al. Having any health insurance, even without coverage for any preventive services, increases the probability that an individual will receive appropriate preventive care Hayward et al.
Studies of the use of preventive services by Hispanics and African Americans find that health insurance is strongly associated with the increased receipt of preventive services Solis et al. However, the higher rates of uninsurance among racial and ethnic minorities contribute significantly. Counseling to address serious health risks—tobacco use, physical inactivity, risky drinking, poor nutrition—is least likely to be covered by an employer-sponsored health plan.
Yet about half of all pregnancies and nearly a third of all births each year are unintended. One out of five employer-sponsored plans does not cover childhood immunizations, and one out of four does not cover adolescent immunizations although these are among the most cost-effective preventive services. For example, African Americans and members of other minority groups who are diagnosed with cancer are more likely to be diagnosed at advanced stages of disease than are whites Farley and Flannery, ; Mandelblatt et al.
Preventive services are important for older adults, for whom they can reduce premature morbidity and mortality, help preserve function, and enhance quality of life.