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INTRODUCTION: The Pathways Community HUB Model provides a unique strategy to effectively supplement health care services with social services needed to overcome barriers for those most at risk of poor health outcomes. Pathways are standardized measurement tools used to define and track health and social issues from identification through to a measurable completion point. The HUB use Pathways to coordinate agencies and service providers in the community to eliminate the inefficiencies and duplication that exist among them. PATHWAYS COMMUNITY HUB MODEL AND FORMALIZATION: Experience with the Model has brought out the need for better information technology solutions to support implementation of the Pathways themselves through decision-support tools for care coordinators and other users to track activities and outcomes, and to facilitate reporting. Here we provide a basis for discussing recommendations for such a data infrastructure by developing a conceptual model that formalizes the Pathway concept underlying current implementations. REQUIREMENTS FOR DATA ARCHITECTURE TO SUPPORT THE PATHWAYS COMMUNITY HUB MODEL: The main contribution is a set of core recommendations as a framework for developing and implementing a data architecture to support implementation of the Pathways Community HUB Model. The objective is to present a tool for communities interested in adopting the Model to learn from and to adapt in their own development and implementation efforts. PROBLEMS WITH QUALITY OF DATA EXTRACTED FROM THE CHAP DATABASE: Experience with the Community Health Access Project (CHAP) data base system (the core implementation of the Model) has identified several issues and remedies that have been developed to address these issues. Based on analysis of issues and remedies, we present several key features for a data architecture meeting the just mentioned recommendations. IMPLEMENTATION OF FEATURES: Presentation of features is followed by a practical guide to their implementation allowing an organization to consider either tailoring off-the-shelf generic systems to meet the requirements or offerings that are specialized for community-based care coordination. DISCUSSION: Looking to future extensions, we discuss the utility and prospects for an ontology to include care coordination in the Unified Medical Language System (UMLS) of the National Library of Medicine and other existing medical and nursing taxonomies. CONCLUSIONS AND RECOMMENDATIONS: Pathways structures are an important principle, not only for organizing the care coordination activities, but also for structuring the data stored in electronic form in the conduct of such care. We showed how the proposed architecture encourages design of effective decision support systems for coordinated care and suggested how interested organizations can set about acquiring such systems. Although the presentation focuses on the Pathways Community HUB Model, the principles for data architecture are stated in generic form and are applicable to any health information system for improving care coordination services and population health.
RESUMEN
Michael Conrad was a pioneer in investigating biological information processing. He believed that there are fundamental lessons to be learned from the structure and behavior of biological brains that we are far from understanding or have implemented in our computers. Accumulation of advances in several fields have confirmed his views in broad outline but not necessarily in some of the strong forms he had tried to establish. For example, his assertion that programmable computers are intrinsically incapable of the brain's efficient and adaptive behavior has not received much examination. Yet, this is clearly a direction that could afford much insight into fundamental differences between brain and machine. In this paper, we pay tribute to Michael, by examining his pioneering thoughts on the brain-machine disanalogy in some depth and from the hindsight of a decade later. We argue that as long as we stay within the frame of reference of classical computation, it is not possible to confirm that programmability places a fundamental limitation on computing power, although the resources required to implement a programmable interface leave fewer resources for actual problem-solving work. However, if we abandon the classical computational frame and adopt one in which the user interacts with the system (artificial or natural) in real time, it becomes easier to examine the key attributes that Michael believed place biological brains on a higher plane of capability than artificial ones. While we then see some of these positive distinctions confirmed (e.g. the limitations of symbol manipulation systems in addressing real-world perception problems), we also see attributes in which the implementation in bioware constrains the behavior of real brains. We conclude by discussing how new insights are emerging, that look at the time-bound problem-solving constraints under which organisms have had to survive and how their so-called 'fast and frugal' faculties are tuned to the environments that coevolved with them. These directions open new paths for a multifaceted understanding of what biological brains do and what we can learn from them. We close by suggesting how the discrete event modeling and simulation paradigm offers a suitable medium for exploring these paths.
