Decision technologies as normative instruments: exposing the values within

OBJECTIVE: Describe some of the implicit normative and value judgments made in decision technologies development and use. METHODS: Using conceptual analysis of published models, we first outline some of the background assumptions of the knowledge translation/evidence-based medicine view of decision technologies. We then describe how normative judgments are embedded in decision technology development and use, drawing from empirical normative analysis of qualitative interviews with clinical practice guidelines developers (n=18) and users (n=17) in Canada and the UK. RESULTS: Normative judgments are made in at least three stages of decision technologies' "life cycle": (1) in the identification of contexts where decisions are seen as requiring support; (2) in determining what type of information and options should be part of the content of decision technologies; (3) in the negotiation between different actors regarding how effectiveness of decision technologies should be judged. These findings contrast with the knowledge translation/evidence-based medicine picture of decision technologies as neutral carriers of facts, or 'pure' synthesis of research evidence. CONCLUSION: Normative judgments are at play throughout the life cycle of decision technology development and use. References to scientific notions of truth and validity in the knowledge translation/evidence-based medicine model tend to overlook the socio-political dimension of decision technology development and implementation, as well as the contested nature of what "good decision" these technologies aim to support. PRACTICE IMPLICATIONS: Empirical normative analysis is an important research tool to better understand the values, interests and power relationships embedded in decision technologies. Such lines of inquiry could foster a more open debate among stakeholders - including patients and members of the public - regarding the norms promoted by practice guidelines and patient decision aids. It also offers new insights in understanding the problem of implementing decision technologies in clinical practice.     

The evolution of a required research program for medical students at the University of Washington School of Medicine.

The concepts and tools clinicians use to understand disease and treat patients are the direct product of basic and applied scientific inquiry. To prepare physicians to participate in this tradition of medical science, the University of Washington School of Medicine (UWSOM) created a research requirement in 1981. The objective was to provide students, during their clinical years of medical school, with first-hand experience in hypothesis-driven inquiry and an understanding of the philosophies and methods of science integral to the practice of medicine. A comprehensive curriculum review in 1998-2000 identified several limitations of this requirement. Although many students completed it successfully, others struggled to find mentors, funding, or time as coursework became more demanding. Other students found they had no interest in or aptitude for the research process itself. Accordingly, UWSOM has reaffirmed its commitment to independent inquiry but expanded the ways in which students can meet the requirement. Three research options are now available under the Independent Investigative Inquiry (III) program, generally completed the summer after students' first year of medical school. These are the hypothesis-driven inquiry, a critical review of the literature, or an experience-driven inquiry in community medicine. The goal of UWSOM is to shape new physicians who can manage rapidly changing medical science, information technology, and patient expectations in clinical practice and/or laboratories. The role of III is to teach students to develop personal methods of acquiring new knowledge and integrate it into their professional lives. Faculty support, program oversight, and funding have been increased.     

Information system support as a critical success factor for chronic disease management: Necessary but not sufficient

Improvement of chronic disease management in primary care entails monitoring indicators of quality over time and across patients and practices. Informatics tools are needed, yet implementing them remains challenging. OBJECTIVE: To identify critical success factors enabling the translation of clinical and operational knowledge about effective and efficient chronic care management into primary care practice. DESIGN: A prospective case study of positive deviants using key informant interviews, process observation, and document review. SETTING: A chronic disease management (CDM) collaborative of primary care physicians with documented improvement in adherence to clinical practice guidelines using a web-based patient registry system with CDM guideline-based flow sheet. PARTICIPANTS: Thirty community-based physician participants using predominantly paper records, plus a project management team including the physician lead, project manager, evaluator and support team. ANALYSIS: A critical success factor (CSF) analysis of necessary and sufficient pathways to the translation of knowledge into clinical practice. RESULTS: A web-based CDM 'toolkit' was found to be a direct CSF that allowed this group of physicians to improve their practice by tracking patient care processes using evidence-based clinical practice guideline-based flow sheets. Moreover, the information and communication technology 'factor' was sufficient for success only as part of a set of seven direct CSF components including: health delivery system enhancements, organizational partnerships, funding mechanisms, project management, practice models, and formal knowledge translation practices. Indirect factors that orchestrated success through the direct factor components were also identified. A central insight of this analysis is that a comprehensive quality improvement model was the CSF that drew this set of factors into a functional framework for successful knowledge translation. CONCLUSIONS: In complex primary care settings environment where physicians have low adoption rates of electronic tools to support the care of patients with chronic conditions, successful implementation may require a set of interrelated system and technology factors.     

A logic model for precision medicine implementation informed by stakeholder views and implementation science

PurposePrecision medicine promises to improve patient outcomes, but much is unknown about its adoption within health-care systems. A comprehensive implementation plan is needed to realize its benefits.MethodsWe convened 80 stakeholders for agenda setting to inform precision medicine policy, delivery, and research. Conference proceedings were audio-recorded, transcribed, and thematically analyzed. We mapped themes representing opportunities, challenges, and implementation strategies to a logic model, and two implementation science frameworks provided context.ResultsThe logic model components included inputs: precision medicine infrastructure (clinical, research, and information technology), big data (from data sources to analytics), and resources (e.g., workforce and funding); activities: precision medicine research, practice, and education; outputs: precision medicine diagnosis; outcomes: personal utility, clinical utility, and health-care utilization; and impacts: precision medicine value, equity and access, and economic indicators. Precision medicine implementation challenges include evidence gaps demonstrating precision medicine utility, an unprepared workforce, the need to improve precision medicine access and reduce variation, and uncertain impacts on health-care utilization. Opportunities include integrated health-care systems, partnerships, and data analytics to support clinical decisions. Examples of implementation strategies to promote precision medicine are: changing record systems, data warehousing techniques, centralized technical assistance, and engaging consumers.ConclusionWe developed a theory-based, context-specific logic model that can be used by health-care organizations to facilitate precision medicine implementation.     

Issues of research in medicine

Research in medicine is liable to all rules and standards that apply to research in other natural sciences, since medicine as a science and service fully meets the general definition of science: it is a common, integrated, organized and systematized knowledge of mankind, whereby physician--being more or less aware of doing so-- in his daily activities applies scientific thinking and scientific methods. The procedure of problem solving in scientific work and in medical practice is characterized by many similarities as well as variation. In scientific research, the observation of some phenomenon that cannot be explained by the known facts and theories is followed by making a hypothesis, planning and carrying out experimental investigation resulting in some data. Interpretation of these data then provides evidence to confirm or reject the hypothesis. In medical practice, quite a similar procedure is followed; the initial examination of a patient, when his condition cannot be explained by the data thus obtained, is identical to the observation of a phenomenon which cannot be explained by the known facts; working diagnosis would correspond to making the hypothesis; and experimental investigation would compare to laboratory and other diagnostic studies. The working diagnosis is accepted or rejected depending on these results. Of course, there also are differences in the problem solving procedure between scientific research and daily medical practice. For example, in research a single hypothesis is posed, a single experiment with successive testing and/or repeats is performed, whereas in medical practice several hypotheses are made, multiple studies are concurrently performed to reject current hypotheses and to make new ones. Scientific investigation produces an abundance of systematic data, whereas in medical practice target data are being generated, yet not systematically. Definitive decision making also differs greatly, as in scientific research it only ensues from conclusive evidence, whereas in medical practice definitive decision is made and therapeutic procedures are performed even before reaching final evidence. The general strategy of work and research in medicine can be briefly described by four principles, i.e. good knowledge of one's own work; continuing upgrading of one's own work in collaboration with respective institutions (laboratories, university, and research institutes); implementation of standard, up-to-date and scientific methods most of the time; and publishing work results on a regular basis. This strategy ensures constant progress and treatment quality improvement while allowing due validation and evaluation of the work by the society. Scientific research is based on the pre-existing knowledge of the problem under study, and should be supervised, systematic and planned. Research produces data that may represent some new concepts, or such concepts are developed by further data processing. In research, scientific procedure includes a number of steps that have to be made to reach a new scientific result. This procedure includes (a) thinking about a scientific issue; (b) making a scientific hypothesis, i.e. the main objective of the study; (c) research ethics; (d) determination of sources and mode of data collection; (e) research performance; (f) collection and analysis of all research data; (g) interpretation of results and evidence; and (h) publications. The next section of this chapter brings an example of scientific research in the field of medicine, where the procedures carried out during the research are briefly described; other chapters of this supplement deal with statistical methodology used on processing the data obtained in the study, which is most frequently employed in scientific work in the field of medicine.     

Assessing the capacity of the health services research community in Australia and New Zealand

BACKGROUND: In order to profile the health services research community in Australia and New Zealand and describe its capacity, a web-based survey was administered to members of the Health Services Research Association of Australia and New Zealand (HSRAANZ) and delegates of the HSRAANZ's Third Health Services Research and Policy Conference. RESULTS: Responses were received from 191 individuals (68%). The responses of the 165 (86%) who conducted or managed health services research indicated that the health services research community in Australia and New Zealand is characterised by highly qualified professionals who have come to health services research via a range of academic and professional routes (including clinical backgrounds), the majority of whom are women aged between 35 and 54 who have mid- to senior- level appointments. They are primarily employed in universities and, to a lesser extent, government departments and health services. Although most are employed in full time positions, many are only able to devote part of their time to health services research, often juggling this with other professional roles. They rely heavily on external funding, as only half have core funding from their employing institution and around one third have employment contracts of one year or less. Many view issues around building the capacity of the health services research community and addressing funding deficits as crucial if health services research is to be translated into policy and practice. Despite the difficulties they face, most are positive about the support and advice available from peers in their work settings, and many are actively contributing to knowledge through academic and other written outputs. CONCLUSION: If health services research is to achieve its potential in Australia and New Zealand, policy-makers and funders must take the concerns of the health services research community seriously, foster its development, and contribute to maximising its capacity through a sustainable approach to funding. There is a clear need for a strategic approach, where the health services research community collaborates with competitive granting bodies and government departments to define and fund a research agenda that balances priority-driven and investigator-driven research and which provides support for training and career development.     

Science policy, science funding, and the science community

The ongoing federal budget crisis has led to extensive debate over how much money the federal government should spend on scientific research. In the struggle for limited funds, many advocacy groups will covet the large sums proposed for science research, and the scientific community may be called upon to justify its large share of the discretionary funding, especially the large increases for some science agencies and "big science" projects. Many would instinctively support the assertion that scientific progress is a cornerstone of national well-being, but the connection between strong federal support of research and vigorous economic growth or societal vitality is not straightforward. Two variables--science and technology, not science alone, are basic to the larger issues of the economy and social welfare. Federal policy must facilitate development in coordination with support for basic science. Scientists, in turn, must help foster a perspective that encompasses research and development as a whole and that seeks to identify explicitly the connections between the nature of the R&D effort and economic vitality and quality of life. Furthermore, society needs a free flow of information between all members of the science and technology community and an end to the artificial and harmful barriers between them. The science community must focus on setting priorities, refining science and technology policy to maximize available resources, and convincing voters that science can make crucial contributions to the long-term welfare of the nation.     

The place of implementation science in the translational medicine continuum

There is a growing consensus that the transfer of knowledge from biomedical discoveries into patient and public benefit should be accelerated. At the same time there is a persistent lack of conceptual clarity about the precise nature of the phases of the translational continuum necessary to implement this. In this paper, we: (i) propose an integrated schema to understand the five sequential phases that link basic biomedical research with clinical science and implementation; (ii) discuss the nature of three blocks along this translational pathway; (iii) outline key issues that need to be addressed in removing such barriers. The five research phases described are: (0) basic science discovery; (1) early human studies; (2) early clinical trials; (3) late clinical trials; (4) implementation (which includes adoption in principle, early implementation and persistence of implementation). This schema also sets out three points at which communication blocks can occur. The application of 'implementation science' is in its early stages within mental health and psychiatric research. This paper therefore aims to develop a consistent terminology to understand the discovery, development, dissemination and implementation of new interventions. By better understanding the factors that promote or delay knowledge to flow across these blocks, we can accelerate progression along translational medicine pathways and so realize earlier patient benefit.     

Treatment Efficacy and Clinical Utility: A Guidelines Model Applied to Psychotherapy Research

The lack of integration of science and practice in clinical psychology is due largely to the inadequacy of our research. There is a pressing need to evolve models that give equal weighting to scientific rigor (i.e., treatment efficacy) and clinical utility (i.e., treatment effectiveness). Borkovec, Echemendia, Ragusea, and Ruiz (this issue) make an important contribution to bridging the split between science and practice. They have demonstrated the potential for a research model that balances efficacy and effectiveness considerations. Further evolution of such an approach depends on the availability of consensus-based criteria for treatment efficacy and effectiveness. The Criteria for Evaluating Treatment Guidelines is recommended as a model for this purpose.     

The socio-organizational age of artificial intelligence in medicine

The increasing pressure on Health Care Organizations (HCOs) to ensure efficiency and cost-effectiveness, balancing quality of care and cost containment, will drive them towards a more effective management of medical knowledge derived from research findings. The relation between science and health services has until recently been too casual. The primary job of medical research has been to understand the mechanisms of disease and produce new treatments, not to worry about the effectiveness of the new treatments or their implementation. As a result many new treatments have taken years to become part of routine practice, ineffective treatments have been widely used, and medicine has been opinion rather than evidence based. This results in suboptimal care for patients. Knowledge management technology may provide effective approaches in speeding up the diffusion of innovative medical procedures whose clinical effectiveness have been proved: the most interesting one is represented by computer-based utilization of evidence-based clinical guidelines. As researchers in Artificial Intelligence in Medicine (AIM), we are committed to foster the strategic transition from opinion to evidence-based decision making. Reviews of the effectiveness of various methods of guideline dissemination show that the most predictable impact is achieved when the guideline is made accessible through computer-based and patient specific reminders that are integrated into the clinician's workflow. However, the traditional single doctor-patient relationship is being replaced by one in which the patient is managed by a team of health care professionals, each specializing in one aspect of care. Such shared care depends critically on the ability to share patient-specific information and medical knowledge easily among them. Strategically there is a need to take a more clinical process view of health care delivery and to identify the appropriate organizational and information infrastructures to support this process. Thus, the great challenge for AIM researchers is to exploit the astonishing capabilities of new technologies to disseminate their tools to benefit HCOs by assuring the conditions of knowledge management and organizational learning at the fullest extent possible. To achieve such a strategic goal, a guideline can be viewed as a model of the care process. It must be combined with an organization model of the specific HCO to build patient careflow management systems. Artificial intelligence can be extensively used to design innovative tools to support all the development stages of those systems. However, exploiting the knowledge represented in a guideline to build them requires to extend today's workflow technology by solving some challenging problems.     

Values, Practices, and the Utilization of Empirical Critiques in the Clinical Triad

Empirical science is composed of a set of mutually reinforcing values and practices. A potential difficulty arises when empirical knowledge products are disseminated to other groups with an interest in research findings. The danger is that researchers' values and practices will be deemed superior to those of other parties, and codified across different contexts without consideration of their effectiveness in achieving broader goals of science (e.g., sharing important knowledge about treating problems in living). Alternatively, understanding and respecting how the values and practices of different groups are situated in local decision-making contexts can open up creative ways for enhancing collaboration between different members of the research and clinical practice community. Taking Scheel's (this issue) thorough and well-crafted critique of research on dialectical behavior therapy as an example, we explore the way values, practices, and local decision-making contexts affect researchers', practitioners', and clinical administrators' reactions to empirical knowledge products.     

Getting our act together: lessons on meaningful psychotherapy research from the philosophy of science

Psychotherapy research should concentrate on building up a scientifically validated, theoretical knowledge base by means of disciplined empirical research. The normal nested relationship between technology, theory, and research has not been the norm in the world of psychotherapy. Psychotherapy researchers should learn from the history of science and concentrate on building basic theory. Investigations into the causal relations underpinning psychotherapy is the best way that research can help us do psychotherapy better. These investigations must conform to the canons of inductive reasoning. Conventional use of clinical data to underwrite psychotherapeutic theory is vulnerable to Grünbaum's critique. A research proposal based on Langs' communicative approach to psychotherapy is presented predicting measurable unconscious responses to brief, time-limited psychotherapy.     

Implementation science approaches for integrating eHealth research into practice and policy

PurposeTo summarize key issues in the eHealth field from an implementation science perspective and to highlight illustrative processes, examples and key directions to help more rapidly integrate research, policy and practice.MethodsWe present background on implementation science models and emerging principles; discuss implications for eHealth research; provide examples of practical designs, measures and exemplar studies that address key implementation science issues; and make recommendations for ways to more rapidly develop and test eHealth interventions as well as future research, policy and practice.ResultsThe pace of eHealth research has generally not kept up with technological advances, and many of our designs, methods and funding mechanisms are incapable of providing the types of rapid and relevant information needed. Although there has been substantial eHealth research conducted with positive short-term results, several key implementation and dissemination issues such as representativeness, cost, unintended consequences, impact on health inequities, and sustainability have not been addressed or reported. Examples of studies in several of these areas are summarized to demonstrate this is possible.ConclusionseHealth research that is intended to translate into policy and practice should be more contextual, report more on setting factors, employ more responsive and pragmatic designs and report results more transparently on issues important to potential adopting patients, clinicians and organizational decision makers. We outline an alternative development and assessment model, summarize implementation science findings that can help focus attention, and call for different types of more rapid and relevant research and funding mechanisms.     

Decision technologies as normative instruments: Exposing the values within

ObjectiveDescribe some of the implicit normative and value judgments made in decision technologies development and use.MethodsUsing conceptual analysis of published models, we first outline some of the background assumptions of the knowledge translation/evidence-based medicine view of decision technologies. We then describe how normative judgments are embedded in decision technology development and use, drawing from empirical normative analysis of qualitative interviews with clinical practice guidelines developers (n = 18) and users (n = 17) in Canada and the UK.ResultsNormative judgments are made in at least three stages of decision technologies’ “life cycle”: (1) in the identification of contexts where decisions are seen as requiring support; (2) in determining what type of information and options should be part of the content of decision technologies; (3) in the negotiation between different actors regarding how effectiveness of decision technologies should be judged. These findings contrast with the knowledge translation/evidence-based medicine picture of decision technologies as neutral carriers of facts, or ‘pure’ synthesis of research evidence.ConclusionNormative judgments are at play throughout the life cycle of decision technology development and use. References to scientific notions of truth and validity in the knowledge translation/evidence-based medicine model tend to overlook the socio–political dimension of decision technology development and implementation, as well as the contested nature of what “good decision” these technologies aim to support.Practice implicationsEmpirical normative analysis is an important research tool to better understand the values, interests and power relationships embedded in decision technologies. Such lines of inquiry could foster a more open debate among stakeholders – including patients and members of the public – regarding the norms promoted by practice guidelines and patient decision aids. It also offers new insights in understanding the problem of implementing decision technologies in clinical practice.     

Mega-prizes in medicine: big cash awards may stimulate useful and rapid therapeutic innovation

Following Horrobin's suggestion of 1986, I argue that offering very large prizes (tens of millions of US dollars, or more) for solving specific therapeutic problems, would be an excellent strategy for promoting the rapid development of effective new treatments. The two mainstream ways of paying for medical research are funding the process with grants or funding the outcome via patent protection. When grants are used to fund the process of research the result tends to be 'pure' science, guided by intrinsic scientific objectives. Practical results, such as useful therapeutic advances, are a by-product. Patent-seeking research, by contrast, is more focused on technology than science. It seeks practical results; and aims to pay for itself (and make a profit) in the long term by generating a patentable product or procedure. Prize-seeking research is subject to different incentives and applicable to different situations than either process-funded or patent-seeking research. Prize seeking researchers have a strong incentive to solve the specified problem as rapidly as possible, but the problem may be solved using old ideas that are scientifically mundane or unpatentable technologies and methods. Prizes therefore seem to generate solutions which are incremental extensions, new applications or novel combinations of already-existing technologies. The main use of mega-prizes in medicine would be to accelerate therapeutic progress in stagnant fields of research and to address urgent problems. For example, medical charities focused on specific diseases should consider accumulating their resources until they can offer a mega-prize for solving a clinical problem of special concern to their patients. Prize money should be big enough to pay for the research and development, the evaluation of the new treatment in a clinical trial, and with a large profit left-over to compensate for the intrinsic risk of competing. Sufficiently large amounts of money, and the prestige and publicity derived from winning a mega-prize, could rapidly mobilize research efforts to discover a whole range of scientifically un-glamorous but clinically-useful therapeutic breakthroughs.     

Science and technology for the new millenium

The arrival of the new millennium move us to make a series of reflections about the status of Science and Technology in Venezuela and consequently, in Latin America. It seems that we have not understood yet, that having strong Science and Technology put us in a more advantageous position to cope with the problems related to our underdevelopment. Decisive and expeditious actions are required in Venezuela and Latin America to reduce the scientific and technological gap between developed and underdeveloped countries. These actions can be portrayed in two possible scenarios. The first one, dealing with scientific funding of basic and applied research focused on areas of critical need. Central, regional and local governments must share responsibilities for scientific development and hence, to provide appropriate funding for scientific research. The financing of personnel and the acquisition of equipment and materials for qualified scientists and research centers should be guaranteed. The second scenario deals with universities and superior education institutions which should match their curricula to contemporaneous knowledge, and to assume their role as science promoters through a creditable scientific leadership. Also, they must provide affordable opportunities for teachers and scientists to catch up with the continuing and progressive advance of science, through the establishment of inter-institutional agreements in Venezuela and abroad which would allow Venezuelan scientists to participate in joint research ventures, and therefore, to be in touch with knowledge and technologies not available in our countries.     

Steps toward a clinically relevant science of interventions in pediatric settings: introduction to the special issue

OBJECTIVE: To describe methods and strategies to advance the science of interventions in pediatric psychology. METHODS: We consider the advantages of various strategies to develop and extend the applications of intervention research in pediatric practice settings. RESULTS: Strategies are needed to enhance application of empirically supported interventions to pediatric settings, including testing the generalizability of empirically supported interventions in clinical samples, developing interventions based on clinical experience and tested in controlled clinical trials, designing program evaluations in the context of practice settings, and conducting case studies and series. Critical next steps in intervention research include documenting the clinical significance of interventions, conducting multisite research concerning interventions, including interventions conducted in clinical settings, and implementing integrated clinical intervention and research. Training in empirically supported treatments and intervention research and developing policy related to intervention research would also promote a clinically relevant scientific agenda concerning intervention research with pediatric populations. CONCLUSIONS: Pediatric psychologists have the opportunity to develop a clinically relevant science of interventions in pediatric settings by using multiple methods and strategies.     

Adoption of Technology‐Enhanced Treatments: Conceptual and Practical Considerations

As the efficacy of technology‐enhanced mental health service delivery models (i.e., supportive or adjunctive technological tools) is examined, we must inform and guide clinician decision making regarding acceptance and, in turn, uptake. Accordingly, this review aimed to move beyond traditional discussions of geographic barriers by integrating, reconciling, and extending literatures on dissemination and implementation, as well as technology uptake, in order to anticipate and address organizational and clinician barriers to adoption of technology enhancements. Specifically, a five‐stage model is proposed to address organizational readiness for and clinician acceptance of technology enhancements to evidence‐based treatments, as well as the relevance of current adoption strategies for technology‐enhanced services. Our aim was to provide a guiding framework for future research and practice.     

Effects of performance-based compensation and faculty track on the clinical activity, research portfolio, and teaching mission of a large academic department of medicine.

PURPOSE: Academic departments of medicine must compete effectively for extramural research support and access to patients while preserving their teaching mission. There is not much literature describing plausible mechanisms for ensuring success. The authors describe the design, implementation, and testing of a performance-based compensation plan in a department of medicine that is closely linked to the faculty appointment track. METHOD: Over a three-year period, the changes this plan effected in research portfolio, clinical enterprise, and faculty satisfaction as well as the teaching perceptions of students and housestaff were measured. RESULTS: The compound annual growth rate (CAGR) for clinical work grew 40% faster after plan implementation. Federal funding increased at a CAGR that was 170% greater than before. The department halved its award rankings at the National Institutes of Health and faculty satisfaction improved compared with the former method of compensation. Faculty who better understood the plan were more satisfied with the conversion. High measures of teaching quality were maintained by faculty with no apparent change in satisfaction among students or housestaff. CONCLUSIONS: This performance-based compensation plan with its emphasis on the objectives of career orientation and faculty track assignment strengthened the opportunity to grow both clinical productivity and the funded research portfolio.     

Signaling molecules as therapeutic targets in allergic diseases

A molecular understanding of physiologic and pathologic processes requires complete knowledge about the signal transduction mechanism of involved cells. Signal transduction research is a rapidly growing field in basic science. Unlike intercellular inflammatory mediators, signaling molecules show less functional redundancy. This allows inhibition of multiple cytokines/mediators by blocking one common signaling molecule. Interference with signaling pathways has shown significant potential for inhibition of fundamental processes as well as clinical phenotype of allergic diseases. The purpose of this review was to provide a theoretical classification of signaling molecules based on their function and to analyze various strategies for developing effective signaling inhibitors for allergic diseases.