With roots in Human Centred Software Engineering, and in User Centred Systems Design Åsa Cajander has lead research on Patient Accessible Electronic Health Records (PAEHR) for six years. This experience has made her revisit the core values of user centeredness, as the different user groups of PAEHR indeed describe conflicting user needs. In this key note she will present the patients' perspective of the system, and contrast that with the perspectives of physicians and nurses. The conflicting needs of the different user groups will be illustrated by a small role play, and she will relate and discuss the findings in relation to the design and development of eHealth systems.
This paper summarizes the accomplishments and recent directions of our medical safety project. Our process-based approach uses a detailed, rigorously-defined, and carefully validated process model to provide a dynamically updated, context-aware and thus, "Smart" Checklist to help process performers understand and manage their pending tasks . This paper focuses on support for teams of performers, working independently as well as in close collaboration, in stressful situations that are life critical. Our recent work has three main thrusts: provide effective real-time guidance for closely collaborating teams; develop and evaluate techniques for measuring cognitive load based on biometric observations and human surveys; and, using these measurements plus analysis and discrete event process simulation, predict cognitive load throughout the process model and propose process modifications to help performers better manage high cognitive load situations.
This project is a collaboration among software engineers, surgical team members, human factors researchers, and medical equipment instrumentation experts. Experimental prototype capabilities are being built and evaluated based upon process models of two cardiovascular surgery processes, Aortic Valve Replacement (AVR) and Coronary Artery Bypass Grafting (CABG). In this paper we describe our approach for each of the three research thrusts by illustrating our work for heparinization, a common subprocess of both AVR and CABG. Heparinization is a high-risk error-prone procedure that involves complex team interactions and thus highlights the importance of this work for improving patient outcomes.
The healthcare industry is driven by multiple stakeholders including health ministries, private and public hospitals, and vendors who provide technology to the patients, with each of them playing important health system roles in order to balance high quality and accessibility with cost-effectiveness and sustainability1. This becomes particularly important for chronic cardiovascular disease patients in emerging markets such as Malaysia where sophisticated advanced therapy are often too costly for the underserved patient population. These patients most often live very far from major hospitals and rely on subsidies from the Ministry of Health (MOH) for a significant portion of their healthcare. Thus worldwide, vendors face a unique challenge in delivering cost effective, affordable and accessible therapy to this patient demographic in emerging markets. This paper presents our findings on the challenges to delivering continuous care to the remote heart failure patients in Malaysia. We will also report the work in progress in applying a Double-Loop Learning approach as a collaboration model with the MOH, its cardiac centers, and a multinational medical technology company to mitigate the challenges faced by Malaysia healthcare providers by the design of a real-time embedded system applying artificial intelligence method to realize the Connected Health concept.
The continuous improvement in our understanding of the human genome is leading to an increasing viable and effective Precision Medicine. Its intention is to provide a personalized solution to any individual health problem. Nevertheless, three main issues must be considered to make Precision Medicine a reality: i) the understanding of the huge amount of genomic data, spread out in hundreds of genome data sources, with different formats and contents, whose semantic interoperability is a must; ii) the development of information systems intended to guide the search of relevant genomic repositories related with a disease, the identification of significant information for its prevention, diagnosis and/or treatment and its management in an efficient software platform; iii) the high variability in the quality of the publicly available information. This paper presents a conceptual framework for solving these problems by i) using a precise conceptual schema of the human genome, and ii) introducing a method to search, identify, load and adequately interpret the required data, assuring its quality during the entire process.
Complex patient health needs and care delivery models such as patient participatory medicine require the ability to share data across multiple touch points. Achieving systematic performance management of care processes require an infrastructure that addresses interoperability and data standardization while supporting data governance and privacy compliance. In this paper, we present a framework for operationalizing privacy compliance for correlated cloud-hosted data using Data Sharing Agreements (DSAs) in support of performance management of community healthcare. Our focus is to show how DSAs can be used to operationalize privacy compliance for a cloud-hosted surveillance and performance management infrastructure by leveraging selective anonymization based on both organizational and patient consents. This allows a cloud-computing infrastructure to configure processes and services, including anonymization to ensure privacy compliance and a systematic approach to data governance.
The aim of this paper is to provide a holon-based architecture for medical systems with the objective of increasing the flexibility, the agility, and the adaptability of the systems. Inherently, medical systems are safety-critical, thus their design requires a specific approach regarding solving the ever-rising problems and changes in the medical field. The proposed approach is inspired by holon-based manufacturing systems and is adequate for the construction of complex medical systems.
The verification of software intensive medical devices can largely benefit from the analysis of their execution traces. Trace points can easily be added to the software, and traces can be used at several stages of the development and maintenance process. In this paper we focus on the TKA system and identify 15 representative properties that should be fulfilled by its traces. We also identify several stages in the product lifecycle where these properties should be evaluated. These properties put requirements on what should be expressible in a trace property language for medical devices.
Healthcare clients are increasingly interested to be involved and informed of their healthcare delivery and status  . They need to be able to access, view, and analyze their health data easily and securely. Clients need one single gateway to their medical records. Some healthcare providers are creating portals for their clients to flow some data for them to view . In addition, clients can request a portion of their health data in paper format from their healthcare providers by filling in forms and manually submitting their requests. But, this is not sufficient for the average healthcare client. There is a need for a platform independent tool that can automatically gather and combine a client's health information from the various providers in their circle of care and provide the information securely and electronically without inconveniencing the client with multiple requests and sharing agreements . Healthcare providers can also benefit from such a tool in the sense of gaining insights from their colleagues' efforts automatically and without starting a separate quest for each piece of information. In this paper we propose framework and toolset that can provide a secure single point of access to a client's full picture of their personal health information. In particular, we delve into one of the key components of our framework which is our proposed ontology.
Modelling can be applied to all aspects of healthcare systems but one area in particular, clinical pathways, are of great interest currently due to many flow-oriented issues that are well-documented in the media. These pathways typically describe sequences of sorting and treatment activities such as surgical procedures or the care process for managing injuries, for example bone fractures. Previous efforts in using modelling languages have not been promoted as being highly generalizable nor have emphasised the inclusion of constraints defining rules for particular treatment activities. In this paper, we propose a workflow for building flexible models for healthcare systems by exploiting the combination of UML, OCL, and SMT solving. This paper serves as an exposition of an idea that can be developed into a more complete framework that could be used to create workflow models for improving the efficiency and safety of more complex clinical activities. A good application would be to tackle the prevalent problems of emergency departments and some of the challenges in this respect are discussed.
The field of Connected Health (CH) has emerged in response to growing demands for improved connectivity to deliver patient-centred care through innovative healthcare management systems. As a result, there is a growing need to develop new healthcare delivery models and new ways of thinking about how information and communication technology can support healthcare delivery. This paper examines the role of software engineering in CH and presents the CH Delivery Framework as a first step to encapsulate key factors software engineers need to consider.