Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

Modelling clinical goals: a corpus of examples and a tentative ontology

Knowledge of clinical goals and the means to achieve them are either not represented in most current guideline representation systems or are encoded procedurally (e.g. as clinical algorithms, condition-action rules). There would be a number of major benefits if guideline enactment systems could reason explicitly about clinical objectives (e.g. whether a goal has been successfully achieved or not, whether it is consistent with prevailing conditions, or how the system should adapt to circumstances where a recommended action has failed to achieve the intended result). Our own guideline specification language, PROforma, includes a simple goal construct to address this need, but the interpretation is unsatisfactory in current enactment engines, and goals have yet to be included in the language semantics. This paper discusses some of the challenges involved in developing an explicit, declarative formalism for goals. As part of this, we report on a study we have undertaken which has identified over 200 goals in the routine management of breast cancer, and outline a tentative formal structure for this corpus

Evaluation of a standard provision versus an autonomy promotive exercise referral programme: rationale and study design

Background The National Institute of Clinical Excellence in the UK has recommended that the effectiveness of ongoing exercise referral schemes to promote physical activity should be examined in research trials. Recent empirical evidence in health care and physical activity promotion contexts provides a foundation for testing the utility of a Self Determination Theory (SDT) -based exercise referral consultation. Methods/Design Design: An exploratory cluster randomised controlled trial comparing standard provision exercise on prescription with a Self Determination Theory-based (SDT) exercise on prescription intervention. Participants: 347 people referred to the Birmingham Exercise on Prescription scheme between November 2007 and July 2008. The 13 exercise on prescription sites in Birmingham were randomised to current practice (n=7) or to the SDT-based intervention (n=6). Outcomes measured at 3 and 6-months: Minutes of moderate or vigorous physical activity per week assessed using the 7-day Physical Activity Recall; physical health: blood pressure and weight; health status measured using the Dartmouth CO-OP charts; anxiety and depression measured by the Hospital Anxiety and Depression Scale and vitality measured by the subjective vitality score; motivation and processes of change: perceptions of autonomy support from the advisor, satisfaction of the needs for competence, autonomy, and relatedness via physical activity, and motivational regulations for exercise. Discussion This trial will determine whether an exercise referral programme based on Self Determination Theory increases physical activity and other health outcomes compared to a standard programme and will test the underlying SDT-based process model (perceived autonomy support, need satisfaction, motivation regulations, outcomes) via structural equation modelling. Trial registration The trial is registered as Current Controlled trials ISRCTN07682833

[email protected] - Agent Based Support of Clinical Processes

In this paper we present a system for agent-based support of clinical processes. We describe the basic engineering concept, along with specific simulation and testing scenarios for agent-based software engineering. Another important focus is the integration of existing agent or healthcare standards like FIPA, DICOM and HL7. Objectives of our research activities in this project are: a substantial increase of the efficiency of hospital process management as well as the development of a specific goal oriented requirements engineering methodology. As most important challenges of the healthcare domain we have identified on the one hand individualized, patient oriented processes in diagnostics, therapy, nursing and administration and on the other hand extremely distributed decision processes and strong local (individual) autonomy with a high degree of situational dynamics. The example scenario on „clinical trials“ illustrates how the system shall support distributed clinical processes and how it interacts with other multiagent systems within the Agent.Hospital Framework and hospital information systems in the eHealth Lab introduced in this paper. The system development is part of the German Priority Research Program (SPP) 1083 “Intelligent Agents and their application in business scenarios”

Evaluating follow- up and complexity in cancer clinical trials (EFACCT): an eDelphi study of research professionals’ perspectives.

Objectives: To evaluate patient follow-up and complexity in cancer clinical trial delivery, using consensus methods to: (1) identify research professionals’ priorities, (2) understand localised challenges, (3) define study complexity and workloads supporting the development of a trial rating and complexity assessment tool (TRACAT). Design: A classic eDelphi completed in three rounds, conducted as the launch study to a multiphase national project (evaluating follow-up and complexity in cancer clinical trials). Setting: Multicentre online survey involving professionals at National Health Service secondary care hospital sites in Scotland and England varied in scale, geographical location and patient populations. Participants: Principal investigators at 13 hospitals across nine clinical research networks recruited 33 participants using pre-defined eligibility criteria to form a multidisciplinary panel. Main outcome measures: Statements achieving a consensus level of 70% on a 7-point Likert-type scale and ranked trial rating indicators (TRIs) developed by research professionals. Results: The panel developed 75 consensus statements illustrating factors contributing to complexity, follow-up intensity and operational performance in trial delivery, and specified 14 ranked TRIs. Seven open questions in the first qualitative round generated 531 individual statements. Iterative survey rounds returned rates of 82%, 82% and 93%. Conclusions: Clinical trials operate within a dynamic, complex healthcare and innovation system where rapid scientific advances present opportunities and challenges for delivery organisations and professionals. Panellists highlighted cultural and organisational factors limiting the profession’s potential to support growing trial complexity and patient follow-up. Enhanced communication, interoperability, funding and capacity have emerged as key priorities. Future operational models should test dialectic Singerian-based approaches respecting open dialogue and shared values. Research capacity building should prioritise innovative, collaborative approaches embedding validated review and evaluation models to understand changing operational needs and challenges. TRACAT provides a mechanism for continual knowledge assimilation to improve decision-making

Randomised controlled trials of complex interventions and large-scale transformation of services

Complex interventions and large-scale transformations of services are necessary to meet the health-care challenges of the 21st century. However, the evaluation of these types of interventions is challenging and requires methodological development. Innovations such as cluster randomised controlled trials, stepped-wedge designs, and non-randomised evaluations provide options to meet the needs of decision-makers. Adoption of theory and logic models can help clarify causal assumptions, and process evaluation can assist in understanding delivery in context. Issues of implementation must also be considered throughout intervention design and evaluation to ensure that results can be scaled for population benefit. Relevance requires evaluations conducted under real-world conditions, which in turn requires a pragmatic attitude to design. The increasing complexity of interventions and evaluations threatens the ability of researchers to meet the needs of decision-makers for rapid results. Improvements in efficiency are thus crucial, with electronic health records offering significant potential

A model-driven method for the systematic literature review of qualitative empirical research

This paper explores a model-driven method for systematic literature reviews (SLRs), for use where the empirical studies found in the literature search are based on qualitative research. SLRs are an important component of the evidence-based practice (EBP) paradigm, which is receiving increasing attention in information systems (IS) but has not yet been widely-adopted. We illustrate the model-driven approach to SLRs via an example focused on the use of BPMN (Business Process Modelling Notation) in organizations. We discuss in detail the process followed in using the model-driven SLR method, and show how it is based on a hermeneutic cycle of reading and interpreting, in order to develop and refine a model which synthesizes the research findings of previous qualitative studies. This study can serve as an exemplar for other researchers wishing to carry out model-driven SLRs. We conclude with our reflections on the method and some suggestions for further researc