Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions

Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions

Carmel M. Martin (Trinity College Dublin, Ireland), Rakesh Biswas (People’s College of Medical Sciences, India), Joachim P. Sturmberg (Monash University and the University of Newcastle, Australia), David Topps (Northern Ontario School of Medicine, Canada), Rachel Ellaway (Northern Ontario School of Medicine, Canada) and Kevin Smith (National Digital Research Centre, Ireland)
DOI: 10.4018/978-1-60960-561-2.ch810

Abstract

This chapter articulates key considerations for the translation of the concept of the Patient Journey Record Systems (PaJR) into real world systems. The key concept lies in the ‘discovery’ of the use of patient narratives to locate the phase of illness in a patient journey. We describe our developmental framework of in the context of Ambulatory Care Sensitive Conditions (ACSC) for older patients with multiple morbidity, who are at a high risk of hospitalizations and other adverse health outcomes. The framework addresses the feasibility and usability of an information technology based solution to avert adverse outcomes of hospitalization when this is potentially avoidable by activities in primary care. Key considerations in the PaJR knowledge systems are the design and implementation of robust expert knowledge and data support systems. The patient, caregiver, physician and care team perspectives drive clinical usability and functionality requirements. Experts from computer science domains in artificial intelligence, expert systems, and decision support systems ensure the requirements for the functionality of underlying systems architecture are met. We explore this transdisciplinary perspective and ways in which coherence might be achieved among the many practitioners and expert domains involved in a developmental framework for PaJR. We make a case for the implementation of PaJR systems as part of a universal move to electronic user driven health care.
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Translating Needs, Ideas And Knowledge For An Information Technology-Based Solution

Understanding Knowledge Translation: A Brief Introduction

Knowledge translation (KT) is the process of transferring research-based knowledge to daily practice. Moving knowledge between users, researchers, inventors, innovators and consumers should benefit society by improving the well being for its members, and enhancing the economic rewards for its goods and services (Graham & Tetroe, 2007). Knowledge comes in many forms and Lane and Flagg (2010) have identified three stages of development from the concept to operational design to implementation and marketing (Figure 1) (Lane & Flagg, 2010).

Figure 1

Stages of knowledge translation from discovery, to invention to innovation.(Lane & Flagg, 2010) with the PaJR knowledge translation steps

The development of a human information knowledge-based system (Kendal & Creen, 2007), involves: “Assessment of the problem; Development of a knowledge-based system shell/structure; Acquisition and structuring of the related information, knowledge and specific preferences for usability and functionality; Implementation of the structured knowledge into knowledge bases; Testing and validation of the inserted knowledge and Integration and maintenance of the system and Revision and evaluation of the system.” (“http://en.wikipedia.org/wiki/Knowledge_engineering#cite_note-3,” 2009).

Based on this theoretical framework and the PaJR conceptual framework, (Carmel M Martin, Biswas, Joshi, & Sturmberg, 2010)we are developing a prototype IT-solution to integrate patient, carer and clinician knowledge for ongoing close monitoring and more timely intervention for patients with chronic and/or unstable conditions.

A workable prototype implies the development of potential applications that form the basis for intellectual property and claims through patenting. Inventions are more tangible than discoveries, although inventions are still malleable and open to shaping in different ways (Lane & Flagg, 2010). A technology-based solution may be feasible and novel in a controlled setting, but utility is achieved only when the solution addresses the economic and operational constraints of the target user’s problem in the context of the marketplace.

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