User Interfaces for Patient-Centered Communication of Health Status and Care Progress

The recent trend toward patients participating in their own healthcare has opened up numerous opportunities for computing research. This dissertation focuses on how technology can foster this participation, through user interfaces to effectively communicate personal health status and care progress to hospital patients. I first characterize the design space for electronic information communication to patients through field studies conducted in multiple hospital settings. These studies utilize a combination of survey instruments, and low- and high-fidelity prototypes, including a document-editing prototype through which users can view and manage clinical data to automatically associate it with progress notes. The prototype, activeNotes, includes the first known techniques supporting clinical information requests directly within a document editor. A usage study with ICU physicians at New York-Presbyterian Hospital (NYP) substantiated our design and revealed how electronic information related to patient status and care progress is derived from a typical Electronic Health Record system. Insights gained from this study informed following studies to understand how to design abstracted, plain-language views suitable for patients. We gauged both patient and physician responses to information display prototypes deployed in patient rooms for a formative study exploring their design. Following my reports on this study, I discuss the design, development and pilot evaluations of a prototype Personal Health Record application providing live, abstracted clinical information for patients at NYP. The portal, evaluated by cardiothoracic surgery patients, is the first of its kind to allow patients to capture and monitor live data related to their care. Patient use of the portal influenced the subsequent design of tools to support users in making sense of online medication information. These tools, designed with nurses and pharmacists and evaluated by cardiothoracic surgery patients at NYP, were developed using topic modeling approaches and text analysis techniques. Embodied in a prototype called Remedy, they enable rapid filtering and comparison of medication-related search results, based on a number of website features and content topics. I conclude by discussing how findings from this series of studies can help shape the ongoing design and development of patient-centered technology

Smart e-Health System for Real-time Tracking and Monitoring of Patients, Staff and Assets for Healthcare Decision Support in Saudi Arabia

Healthcare in Saudi Arabia has been lagging behind the developed countries of the world, due to the insufficient number of healthcare practitioners and the lack of applications of tracking and monitoring technology. These shortages have contributed to problems such as patient misidentification, long patient waiting times, and the inability to locate medical equipment efficiently. The country’s Vision 2030 plan outlines ways to solve the deficient workforce problem by promoting more local health-related educational outlets, and by funding this expanding sector. Consequently, Saudi Arabia needs to adapt to the demanding nature of modern healthcare, which presents major problems that this research aims to help solve. The literature has shown that Information Technology systems have begun to be implemented in some hospitals across Saudi Arabia, but even in those hospitals these technologies are being under-utilised. The intention of this thesis is to provide an appropriate choice for a real-time tracking and monitoring technology in healthcare, in the form of an integrated RFID/ZigBee system. This thesis develops a holistic framework for healthcare institutions, to be followed for customised solutions in improving staff efficiency and productivity, and for better patient care, while minimising long-term costs. This holistic framework incorporates contextual elements from both the Information System Strategy Triangle (ISST) and the Human, Organisation and Technology-fit factors (HOT-fit) frameworks, in a way that ensures the new framework addresses technology, organisational, human and business factors. The holistic model is refined through Communities of Practice (CoPs), one of which was developed and utilised for the research purposes of this thesis, and assisted in the creation of a questionnaire for assessing the requirements and challenges of the KSA healthcare system. This questionnaire was based on 220 usable responses. It also helped to refine the framework for its final version, which included all identified factors relevant to the decision a healthcare institution faces in choosing a health information technology system. Various cases were analysed to improve the hospitals workflow, using the proposed technology and including processes such as relocating staff and medical assets. This led to the need for visualisation and knowledge management, to support real-time data analysis for business intelligence decision making. The end goal of this analysis is to provide interactive platforms to healthcare staff for use in improving efficiency and productivity. The outcomes of these improvements will be to ensure better patient care, lower patient waiting time, reduced healthcare costs, and to allow more time for staff to provide improved patient-centric care in the Saudi healthcare sector. Keywords: e-Health, Health Information Technology, Tracking and Monitoring System, Kingdom of Saudi Arabia, Holistic Framework, Communities of Practice, Knowledge Management, Visualisation, KFM

Unwarranted variations modelling and analysis of healthcare services based on heterogeneous service data

There is a growing demand worldwide to increase the quality and productivity of healthcare services thereby increasing the value of the healthcare services delivered. To deal with these demands, increasingly importance is being placed on analysing and reducing unwarranted variations in healthcare services to achieve significant savings in healthcare expenditure. Unwarranted variations are defined as the variations in the utilisation of healthcare services that cannot be explained by variation in patient illness or patient preferences. Current modelling and simulation approaches for healthcare service efficiency and effectiveness improvements in hospitals do not utilise multiple types of heterogeneous service data such as qualitative information about hospital services and quantitative data such as historic system data, electronic patient records (EPR), and real time tracking data for analysing unwarranted variations in hospital. Consequently, due to the presence of large amount of unwarranted variations in the service delivery systems, service improvement efforts are often inadequate or ineffective. Therefore, there is urgent need to: (i) accurately and efficiently model complex care delivery services provided in hospital; (ii) develop integrated simulation model to analyse unwarranted variations on a care pathway of a hospitals; and, (iii) develop analytical and simulation models to analyse unwarranted variations from a care pathway. Current process modelling methods to represent healthcare services rely on simplified flowchart of patient flow obtained based on on-site observations and clinician workshops. However, gathering and documenting qualitative data from workshops is challenging. Furthermore, resulting models are insufficient in modelling important service interactions and hence the resulting models are often inaccurate. Therefore, a detailed and accurate process modelling methodology is proposed together with a systematic knowledge acquisition approach based on staff interviews. Traditional simulation models utilised simplified flow diagrams as an input together with the historic system data for analysing unwarranted variations on a care pathway. The resulting simulation models are often incomplete leading to oversimplified outputs from the conducted simulations. Therefore, an integrated simulation modelling approach is presented together with the capability to systematically use heterogeneous data to analyse unwarranted variations on service delivery process of a hospital. Maintaining and using care services pathway within hospitals to provide complex care to patients have challenges related to unwarranted variations from a care pathway. These variations from care pathway predominantly occur due ineffective decision making processes, unclear process steps, their interactions, conflicting performance measures for speciality units, and availability of resources. These variations from care pathway are largely unnecessary and lead to longer waiting times, delays, and lower productivity of care pathways. Therefore, methodologies for analysing unwarranted variations from a care pathway such as: (i) system variations (decision makers (roles) and decision making process); (ii) patient variations (patient diversion from care pathway); are discussed in this thesis. A system variations modelling methodology to model system variations in radiology based on real time tracking data is proposed. The methodology employs generalised concepts from graph theory to identify and represent system variations. In particular, edge coloured directed multi-graphs (ECDMs) are used to model system variations which are reflected in paths adopted by staff, i.e., sequence of rooms/areas traversed while delivering services. A pathway variations analysis (PVA) methodology is proposed which simulates patient diversions from the care pathway by modelling hospital operational parameters, assessing the accuracy of clinical decisions, and performance measures of speciality units involved in care pathway to suggest set-based solutions for reducing variations from care pathway. PVA employs the detailed service model of care pathway together with the electronic patient records (EPRs) and historic data. The main steps of the methodology are: (i) generate sample of patients for analysis; (ii) simulate patient diversions from care pathway; and, (iii) simulation analysis to suggest set-based solutions. The aforementioned unwarranted variations analysis approaches have been applied to Magnetic Resonance (MR) scanning process of radiology and stroke care pathway of a large UK hospital as a case study. Proposed improvement options contributed to achieve the performance target of stroke services

Ambient assisted living systems for older people with Alzheimer’s

The older people population in the world is increasing as a result of advances in technology, public health, nutrition and medicine. People aged sixty or over were more than 11.5% of the global population in 2012. By 2050, this percentage is expected to be doubled to two billion and around thirty-three countries will have more than ten million people aged sixty or more each. With increasing population age around the word, medical and everyday support for the older people, especially those who live with Alzheimer’s who can't be trusted for consistence interaction with their environment, attract the attention of scientists and health care providers. Existing provisions are often deemed inadequate; e.g.; current UK housing services for the older people are inadequate for an aging population both in terms of quality and quantity. Many older people prefer to spend their remaining life in their home environment; over 40% of the older people have concerns about having to move into a care home when they become old and nearly 70% of them worry about losing their independence or becoming dependent on others. There is, therefore, a growing interest in the design and implementation of smart and intelligent Ambient Assisted Living (AAL) systems that can provide everyday support to enable the older people to live independently in their homes. Moreover, such systems will reduce the cost of health care that governments have to tackle in providing assistance for this category of citizens. It also relieves relatives from continuous and often tedious supervision of these people around the clock, so that their life and commitments are not severely affected. Hence, recognition, categorization, and decision-making for such peoples’ everyday life activities is very important to the design of proper and effective intelligent support systems that are able to provide the necessary help for them in the right manner and time. Consequently, the collection of monitoring data for such people around the clock to record their vital signs, environmental conditions, health condition, and activities is the entry level to design such systems. This study aims to capture everyday activities using ambient sensory II information and proposes an intelligent decision support system for older people living with Alzheimer’s through conducting field study research in the Kingdom of Saudi Arabia within their homes and health care centres. The study considers the older people, who live with Alzheimer’s in Kingdom of Saudi Arabia. Since Alzheimer’s is a special form of dementia that can be supported in early stages with the ambient assistive systems. Further, the results of the field study can also be generalized to societies, which are interested in the mental and cognitive behaviour of older people. This generalization is related to the existence of common similarities in their daily life. Moreover, the approach is a generalized approach. Hence it can also be utilized on a new society which is conducting the same field study. This study initially presents a real-life observation process to identify the most common activities for these patients’ group. Then, a survey analysis is carried out to identify the daily life activities based on the observation. The survey analysis is accomplished using a U-test (Mann-Whitney). According to the analysis, it has been found that these people have fourteen common activities. However, three of these activities such as sleeping, walking (standing) and sitting cover about 72% of overall activities. Therefore, this study focuses on the recognition of these three common activities to demonstrate the effectiveness of the research. The activity recognition is carried out using a common image processing technique, called Phase-Correlation and Log-Polar (PCLP) transformation. According to results, the techniques predicted human activities of about 43.7%. However, this ratio is low to utilise for further analysis. Therefore, an Artificial Neural Network (ANN)- based PCLP model is developed to increase the accuracy of activity recognition. The enhanced PCLP transformation method can predict nearly 80% of the evaluated activities. Moreover, this study also presents a decision support system for Alzheimer’s people, which will provide these people with a safe environment. The decision support system utilises an extended sensory-based system, including a vision sensor, vital signs sensor and environmental sensor with expert rules. The proposed system was implemented on an older people patient with 87.2% accuracy

Ambient assisted living systems for older people with Alzheimer’s

The older people population in the world is increasing as a result of advances in technology, public health, nutrition and medicine. People aged sixty or over were more than 11.5% of the global population in 2012. By 2050, this percentage is expected to be doubled to two billion and around thirty-three countries will have more than ten million people aged sixty or more each. With increasing population age around the word, medical and everyday support for the older people, especially those who live with Alzheimer’s who can't be trusted for consistence interaction with their environment, attract the attention of scientists and health care providers. Existing provisions are often deemed inadequate; e.g.; current UK housing services for the older people are inadequate for an aging population both in terms of quality and quantity. Many older people prefer to spend their remaining life in their home environment; over 40% of the older people have concerns about having to move into a care home when they become old and nearly 70% of them worry about losing their independence or becoming dependent on others. There is, therefore, a growing interest in the design and implementation of smart and intelligent Ambient Assisted Living (AAL) systems that can provide everyday support to enable the older people to live independently in their homes. Moreover, such systems will reduce the cost of health care that governments have to tackle in providing assistance for this category of citizens. It also relieves relatives from continuous and often tedious supervision of these people around the clock, so that their life and commitments are not severely affected. Hence, recognition, categorization, and decision-making for such peoples’ everyday life activities is very important to the design of proper and effective intelligent support systems that are able to provide the necessary help for them in the right manner and time. Consequently, the collection of monitoring data for such people around the clock to record their vital signs, environmental conditions, health condition, and activities is the entry level to design such systems. This study aims to capture everyday activities using ambient sensory II information and proposes an intelligent decision support system for older people living with Alzheimer’s through conducting field study research in the Kingdom of Saudi Arabia within their homes and health care centres. The study considers the older people, who live with Alzheimer’s in Kingdom of Saudi Arabia. Since Alzheimer’s is a special form of dementia that can be supported in early stages with the ambient assistive systems. Further, the results of the field study can also be generalized to societies, which are interested in the mental and cognitive behaviour of older people. This generalization is related to the existence of common similarities in their daily life. Moreover, the approach is a generalized approach. Hence it can also be utilized on a new society which is conducting the same field study. This study initially presents a real-life observation process to identify the most common activities for these patients’ group. Then, a survey analysis is carried out to identify the daily life activities based on the observation. The survey analysis is accomplished using a U-test (Mann-Whitney). According to the analysis, it has been found that these people have fourteen common activities. However, three of these activities such as sleeping, walking (standing) and sitting cover about 72% of overall activities. Therefore, this study focuses on the recognition of these three common activities to demonstrate the effectiveness of the research. The activity recognition is carried out using a common image processing technique, called Phase-Correlation and Log-Polar (PCLP) transformation. According to results, the techniques predicted human activities of about 43.7%. However, this ratio is low to utilise for further analysis. Therefore, an Artificial Neural Network (ANN)- based PCLP model is developed to increase the accuracy of activity recognition. The enhanced PCLP transformation method can predict nearly 80% of the evaluated activities. Moreover, this study also presents a decision support system for Alzheimer’s people, which will provide these people with a safe environment. The decision support system utilises an extended sensory-based system, including a vision sensor, vital signs sensor and environmental sensor with expert rules. The proposed system was implemented on an older people patient with 87.2% accuracy

Supporting Diabetes Self-Management with Ubiquitous Computing Technologies: A User-Centered Inquiry

Ubiquitous computing technologies offer opportunities to improve treatments for chronic health conditions. Type 1 diabetes is a compelling use-case for such approaches, given its severity, and need for individuals to make frequent care decisions, informed by complex data. However, current apps, typically based on effortful reflection on collected data, generally show poor adoption, lack vital cognitive and emotional support, and are poorly tailored to users’ actual diabetes decision making processes. This thesis investigates how diabetes apps can be improved from a user-centered perspective. An initial questionnaire-based study investigated how well existing diabetes apps meet user needs. Perceived benefits, limitations, and reasons for low adoption rates were identified. A talk-aloud study of detailed user interactions with diabetes logging apps was conducted to characterize the benefits and limitations of diverse UI elements for T1 diabetes management, and to more precisely identify wider problems with current interaction designs. This led to positing a refined version of Mamykina et al.’s model for diabetes self-management, to account for observed practices, whereby the previously accepted habitual and sensemaking cognitive states are augmented by a posited ‘fluid contextual reasoning’ (FCR) mode, which allows multiple contextual factors to be balanced for dynamic course correction when navigating complex situations, using previously learned knowledge. To investigate user perceptions of the levels and kinds of monitoring anticipated in next generation diabetes decision support systems, a 4-week technology probe, in which participants used multiple networked devices and external data aggregation, was used to frame requirements for user-centered development of such future systems. Integrating all of the above work, an iterative design process was undertaken to create DUETS, a card-based system to facilitate reflection by designers, users, and other stakeholders on diabetes support management systems. The resulting tool and method were then implemented and evaluated through structured sessions with stakeholder focus groups

The deferred model of reality for designing and evaluating organisational learning processes: A critical ethnographic case study of Komfo Anokye teaching hospital, Ghana

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The study proposed an evidence-based framework for designing and evaluating organisational learning and knowledge management processes to support continuously improving intentions of organisations such as hospitals. It demarcates the extant approaches to organisational learning including supporting technology into ‘rationalist’ and ‘emergent’ schools which utilise the dichotomy between the traditional healthcare managers’ roles and clinicians’ roles, and maintains that they are exclusively inadequate to accomplish transformative growth intentions, such as continuously improving patient care. The possibility of balancing the two schools for effective organisational learning design is not straightforward, and fails; because the balanced-view school is theoretically orientated and lack practical design to resolve power tensions entrenched in organisational structures. Prior attempts to address the organisational learning and knowledge management design and evaluation problematics in actuality have situated in the interpretivist traditions, only focusing on explanations of meanings. Critically, this is uncritical of power relations and orthodox practices. The theory of deferred action is applied in the context of critical research methods and methodology to expose the motivations behind the established organisational learning and knowledge management practices of Komfo Anokye Teaching Hospital (KATH) which assumed rationality design conceptions. Ethnographic data was obtained and interpreted with combined critical hermeneutics and narrative analyses to question the extent of healthcare learning and knowledge management systems failures and unveil the unheard voices as force for change. The study makes many contributions to knowledge but the key ones are: (i) Practically, the participants accepted the study as a catalyst for (re)-designing healthcare learning and knowledge management systems to typify the acceptance of the theory of deferred action in practice; (ii) theoretically, the cohered emergent transformation (CET) model was developed from the theory of deferred action and validated with empirical data to explain how to plan strategically to achieve transformative growth objectives; and (iii) methodologically, the sense-making of the ethnographic data was explored with the combined critical hermeneutics and critical narrative analyses, the data interpretation lens from the critical theory and qualitative pluralism positions, to elucidate how the unheard emergent voices could bring change to the existing KATH learning and knowledge management processes for improved patient care

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success