Optimising healthcare ICT support for the care and management of elderly adults with mental illness in residential aged care facilities

Abstract

The global increase of adult mental illness (Dementia and Alzheimer) in the 65 years and older age group, pose unique treatment challenges to residential aged care facility staff. Current residential aged care mental illness treatment plans for elderly adults are not customised according to individual personal needs. Such customization requires inputs from multiple stakeholders i.e. mentally ill adults, carers, medical professionals and clinicians, community mental healthcare service providers, family/relatives and friends. This research aims to develop an integrated ICT framework that supports customised treatment plans for adults with mental illness in residential aged care facilities. This research-in-progress outlines initial stages of a novel methodology to better support and enable individual care and treatment of adults with mental illness in residential aged care facilities. Application of this methodology will 1) identify key information needs for individualised mental illness treatment plans, 2) integrate and consolidate multiple information sources, 3) enhance aged care facility carers’ experience and understanding of the impact of mental illness on human behaviour, and 4) implement innovative ICT solutions that support individualised care and treatment plans.

Keywords:

• Aged care
• design science
• healthcare delivery
• information sources
• mental illness and optimised healthcare

Introduction

The increasing growth in mental illness (Dementia and Alzheimer) in adults is a growing concern worldwide. Recent statistics indicate an estimated 47-million people living with Dementia in 2015 worldwide, with predictions that this number will increase every 20 years peaking 75-million in 2030 (Pearce et al., 2015). Predictions also indicate this increase in numbers will be in developing countries with the care and management of dementia posing unique challenges to residential aged care facility carers, medical practitioners, other residential care staff, friends and family members and countries’ healthcare systems. While there have been significant improvements in the care and on-going treatment plans of adults with mental illness, there is a need to customise treatments and care plans to better align with the specific symptoms of an individual. Customisation may not only alleviate financial and resource impact on residential aged care facilities but also ease the stress levels of carers and other aged care residents (McCabe et al., 2015).

Recent mental illness research concentrates on different areas that relate to the treatment of dementia such as: evidence-based practice (Lau et al., 2013; Nicholls et al., 2013; Ploeg et al., 2007; Stetler et al., 2009; Van Achterberg et al., 2008), educational aspects (Jeon et al., 2013), family involvement (Petriwskyj et al., 2014; Vreugdenhil, 2014), and funding impact and incentives (Jeon et al., 2013). However, there is a gap in research that focus on individual, customised aged care treatment care plans for mentally ill adults. More specifically there is a lack of evidence that indicates how key ‘patient-specific’ information from different stakeholder groups involved in the care and management of these adults (i.e. 1) aged care facility carers, 2) medical professionals, nurses and other professional staff, and 3) family and friends), can be consolidated to inform individual treatment plans. These stakeholder groups form an integrated care network each providing invaluable information about observable and changing symptoms and conditions of individuals’ progress through the mental illness cycle. In addition, there is a need to integrate this information with other existing medical sources of information for each individual (e.g. the Electronic Patient Record (EPR)) to better optimise the care of individuals and support decision-making at different levels.

A review of the most recent literature on mental illness treatment plans indicate that there is a lack of IS-related research that integrates IT artefacts and information sources with the aim to optimise individual treatment plans for adults with mental illness. Hence this research in progress poses the research question: what are key elements required to develop and implement an integrated ICT framework that optimises the individual care and treatment of adults with mental illness in residential aged care facilities?

Forming part of a larger research project, this paper reports only on the first of three research stages aiming to identify and analyse existing problems of current care and treatment Recovery Frameworks (AHMAC, 2014) for adults with mental illness residing in aged care facilities. The next sections outline background literature, the proposed methodology to analyse existing recovery framework and treatment plans and a presentation of the first cycle of the research methodology. A discussion then follows with a brief summary.

Background literature

The growing prevalence of mental illness (Dementia and Alzheimers) in the 65+ year age group places unique demands on care and treatment plans worldwide (Pearce et al., 2015). In addition to these, studies indicate that behavioural and psychological symptoms (BPSD) associated with mental illness such as dementia, cause high stress-levels to both carers and other aged care residents (McCabe et al., 2015). While the incidence of mental illness increases exponentially as age increases, manifestation in the 65 and older age group takes its own course of progress over time, which differs from patient to patient. Additionally, mental illness can co-exist with other health conditions e.g. diabetes and obesity that also increase with age. In the long term, no single generalised adult or patient treatment plan is fully effective in the treatment of mental illness in older adults. The World Alzheimer Report indicates that early diagnosis and early intervention are important mechanisms for effective treatment (Pearce et al., 2015). Therefore, early diagnoses of mental illness in the 65 years and older age group may significantly impact individual treatment plans and the care and management of this illness. Also, early mental illness diagnoses based on multiple sources of input might impact decision-making significantly in order to shape and customise individual treatment care plans for aged care facility residents.

The increase in mental illness in the 65 and older age group places unique demands on existing Australian healthcare facilities, carers, medical professionals, clinicians and other healthcare professionals (Bell et al., 2005; Jeon et al., 2005; Mansah et al., 2014; Street et al., 2015). From a government perspective, the challenge is to make more effective use of these resources, while mental aged care patients need access to quality and integrated mental healthcare services. As early as the 90’s, Australia advanced its treatment of mental illness by moving this care into a community-based clinician care model. Since then, community care teams developed, with a strong focus on the adult services network (specifically the 18–65 age group) followed by models for other teams (e.g. aged care mental health services for the 65 and younger age group). Although this ‘recovery-oriented framework¹ model caters for functional illnesses such as depression and psychosis at the onset in people with psychiatric or severe behavioural difficulties associated with organic disorders such as dementia, it is concentrated on the 18–65 age group. Hence, there is a need to focus on the onset of recovery framework treatment plans that are geared towards mental illness in the 65 years and older age group.

Recent research in mental health diagnosis indicates that a recovery framework with different treatment resource inputs, i.e. support from others (carers, professionals, clinicians and family members or friends) and coping strategies, are instrumental in the road to recovery from, and treatment of mental illness (Daley, 2013). While current aged care support is not commensurate with the growing prevalence of mental disorders including depression (Katona et al., 2004) and psychosis (Kørner, Lopez et al., 2009), current Australian treatment frameworks (AHMAC, 2013) lack integrated, supportive tool and resource deployment for older aged care adults with mental illness. Four different role players are instrumental in the treatment and recovery care plans of these older clients; i.e. residential aged care facility carers, mental healthcare clinicians, other professionals (e.g. general practitioners, pharmacists and psychiatrists) and family/relatives and friends. In particular, current treatment and recovery plans do not include information from the latter group. Consequently, there is no supportive framework that: integrates the information stemming from those four key role players, optimises resource inputs to better support care plan processes and service delivery and decision-making with respect to these. Considering that the Recovery Framework (AHMAC, 2014) has been designed for the 18–65 adult age group, and not our target group of 65 years and older, the existing recovery framework therefore needs to be adapted into a new more comprehensive and effective framework that is customised to support the individual needs of older adults with mental illness.

In addition to the above mentioned problems associated with existing treatment frameworks, current Information Technology (IT) systems are fragmented, inefficient, acting as information silos that store but fail to capture and integrate information from multiple data sources and data points generated throughout the treatment care process, particularly for mentally ill adults (Armstrong et al., 2007; Ash et al., 2004; Schoen et al., 2005). The latter poses unique information access, knowledge sharing and decision-making problems and challenges between for example various clinicians during aged care facility visits and other healthcare parties involved in each mentally ill adult’s care and treatment plans (Ball et al., 2008). In addition, current IT system do not capture any family/relative and friends’ data that may significantly contribute to the customization of effective treatment plans for adults with mental illness (Petriwskyj et al., 2014). Notably, specialised treatment of mentally ill adults is reliant on continuous information inputs from multiple sources.

Apart from care plan and treatment challenges mentioned above, older adults with mental illness also often experience side effects arising from treatment and medication, while complex, challenging manifestations of illness symptoms often impact on individual ability to make rational decisions (Metz, 2004; Gibb et al., 2015). The latter becomes the responsibility of carers working in aged care facilities, which further augment facility care worker and clinician stress levels. It is therefore vital that aged care facility staff is up skilled and provided with supportive mechanisms and networks allowing for improved decision-making that facilitate the sharing of information with other mental healthcare service providers, medical professionals, clinicians, family/relatives and friends in order to overcome caring challenges.

Considering the collective of problems and lack of a comprehensive ICT support framework to support existing care and treatment of mentally ill adults in aged care facilities, this research proposes a novel methodology focused on developing customised care and treatment plans. This methodology may significantly contribute by identifying the data and information needs of multiple stakeholders and in developing and implementing ICT-enabled customised care and treatment recovery plans for adults with mental illness in aged care facilities.

Research methodology

In line with the abovementioned research challenges and the need to improve human performance and decision-making in an Information Systems (IS) context, we propose a Design Science approach for this research (Hevner, 2007, Van Aken, 2004). Design Science is a valuable research approach in Information Systems (IS) particularly in instances where the focus of the research process is the creation of IT artefacts (Peffers et al., 2007). In the context of this study, the emphasis is on learning through artefact design, which will significantly build a comprehensive knowledge-base of the information required for optimised care and treatment of older residential aged care adults affected by mental illness.

The Design Science research methodology provides three interacting research cycles: analysis of existing human behaviour and systems that operate in a given context (the Relevance cycle), the design, building and evaluation of new artefacts (the central Design Cycle) and processes to assess that designs produced, are innovative research contributions to the problem at hand (the Rigour cycle). Figure 1 presents the three interwoven Design Science cycles.

Figure 1. Design Science research cycles (from: Hevner, 2007).

In this paper we report only on the first cycle – the Relevance Cycle (Figure 1), which comprises the four sub-steps outlined below in Table 1 that follows. This cycle will aim to get a comprehensive understanding of the most pertinent information sharing and knowledge management problems associated with existing recovery framework practices in view of existing workflows, artefacts used and interaction between different stakeholder groups.

Table 1. Design Science cycles (Relevance cycle) sub-steps and focus of this study.

Design Science (DS) cycle 1
Relevance cycle for the care and treatment of adults with mental illness in aged care facilities
DS sub-step focus	Aim	Data collection & analysis/representation techniques
1) People	Analyse existing work practices, information and knowledge sharing tasks, of the four key stakeholder groups involved: (i) Aged care facility carers, (ii) Mental healthcare clinicians, (iii) Medical professionals (general practitioners, pharmacists and psychiatrists), and (iv) Family/relatives and friends	Data collection: One-on-one 30 min interviews with 4 people from each of the listed stakeholder groups
		Data analysis: qualitative data analysis techniques using coding to identify key themes and cluster these themes into meaningful categories (Miles & Huberman 1994; Neuman, 2003)
2) Organisational Systems	Understanding of existing organisational systems in terms of workflows related to the stakeholders (People) and Recovery Framework tasks and practices	Data collection/representations: use sub-step 1 information and Business Process Modelling Notation (BPMN) to model as-is information flows between (i) key stakeholder groups, and (ii) stakeholder groups and adults with mental illness (OMG, 2015).
3) Technical Systems	Understanding of existing IT artefacts used to store data, facilitate information flows, decision-making and stakeholder tasks	Data representation: using BPMN diagram of sub-step 2 to tabulate existing IT artefacts (tools, applications and systems) that capture data and process data into information
4) Problems & Opportunities	Identification of key information, knowledge sharing and decision-making problems and IT support associated with: (i) stakeholder collaboration & net-working and different types of relationships and networks, (ii) IT support that enables and support access to content and (iii) IT support that enables and support improved interaction and decision-making	Data analysis & representation: (i) A Soft Systems Method (SSM) approach to analyse & present the core stakeholder problems in Rich Pictures (Checkland, 1981) (ii) A Social Network analysis of interaction and communication between stakeholders, (Cross & Prusak, 2002), and (iii) A to-be set of BPMN diagrams to model new opportunities for networking, communication, information, knowledge sharing and decision-making between stakeholders.

The activities associated with the Relevance Cycle will occur iteratively and incremental to build a comprehensive set of requirements required for the second critical Design Cycle. During the first cycle a deeper awareness of the information and knowledge gaps will be acquired through extensive data gathering and analysis activities. This will give the research team the opportunity to propose and discuss improvements with the different stakeholders, which in turn will support the iterative refinement of the set of requirements.

Forming part of the first Relevance Cycle, a series of four consecutive sub-steps are proposed in this paper to gain a deeper understanding of the specific environment associated with recovery framework care and treatment plans in addition to the different key role-player agency and interaction in this environment. This first cycle also gives the opportunity for researchers to gain a deeper understanding of the key organisational and technical systems that operate and interact in this environment. Table 1 outlines the sub-steps of this cycle and indicate for this context the key aims associated with each sub-step and corresponding data collection analysis/representation techniques to be used to gain a deeper understanding of the key challenges and problems associated with the care and treatment of older mentally ill adults in residential aged care facilities.

A variety of qualitative data collection and analysis techniques (i.e. data gathering through interviews, document analysis, and normal qualitative coding techniques that include open, axial and selective coding) will be used for the sub-steps. To complement these activities, process and workflow modelling diagrams will also be created to deepen the qualitative analysis. The aim will be to attain a deeper understanding of the associated information needs, tasks, and interactions between the different collaborative elements. This includes: people (i.e. various stakeholders including patients, carers, family/friends, nurses, and other healthcare professionals), organisational systems (i.e. functions, roles and responsibilities that collaborate and are dependent on each other), and technical systems (i.e. existing repositories and content systems, databases, manual forms and systems as well as automated tools and applications). The overall aim of including all these components, is to identify the diversity of existing challenges, problems and gaps associated with information needs, decision-making and treatments plans of aged care adults in an attempt to identify solutions to these and new opportunities that may arise from this.

Sub-step 1 outlined in Table 1 will gather comprehensive rich data from people (stakeholders) in terms of their tasks, workflow, information and knowledge sharing tasks. Followed by an extensive qualitative analysis of the interview data to identify information and knowledge sharing themes for each stakeholder type, a series of workflow modelling diagrams (using BPMN notation) will be used to represent individual and collective information flows between the different stakeholders and existing IT systems (sub-steps 2 and 3). Once a deeper understanding of the as-is information and knowledge flows has been acquired, the final sub-step (4) will represent the unique problems associated with the care and treatment of adults from the viewpoint of the different stakeholders. A comprehensive view will therefore be built of areas that impact on decision-making and associated care plan and treatment problems. Checkland’s Rich Picture (1981) will diagrammatically depict the unique problems from a human activity model perspective, which will serve as the basis for the development of a set of comprehensive to-be workflow modelling diagrams, which will capture new information and knowledge sharing flows and interactions between the various stakeholders. BPMN will be used to represent these, which will serve as the basis for the subsequent Design Science phases to follow (i.e. critical Design Cycle which precedes the final Rigour Cycle). (Note these two phases and accompanying sub-steps are not represented in this paper).

Discussion

The focus of this research on mentally ill adults in the 65-year and older group will significantly impact on existing healthcare recovery framework care and treatment plans. More specifically a focus on residential aged care facilities will contribute to key decision-making criteria that impact on improving healthcare service delivery and deployment. The proposed Design Science methodology will provide a number of significant advantages:

•	Develop and implement care and recovery treatment plans customised to individual needs and progress of mental illness,
•	Identify information and knowledge sharing problems associated with current recovery framework care and treatment plans,
•	Reduce current risks associated with the treatment of mental illness in aged care adults (e.g. medication errors),
•	Provide better support to residential aged care facilities and staff who manage and treat clients with behavioural problems,
•	Improve communication, decision-making and networking between different healthcare providers, carers, family and other relatives,
•	Provide more targeted ICT support for carers in the form of: access to customised care plans, diagnoses, and access to vital information from the four key stakeholder groups,
•	Support aged care facility managers in their decision-making activities that relate to treatment plans and resource allocation of carers and staff for treatment, and
•	Yield overall economic and financial benefits at the aged care facility and national healthcare levels.

Summary

The aim of this research is to optimise the care and treatment of mentally ill adults in the 65 and older age group who reside in residential aged-care facilities. The first stage of a novel Design Science methodology is proposed to tackle the unique challenges and information needs associated with current care and treatment plans for the specific age group. IT-support shortcomings for residential aged care facility treatment will be analysed in more depth to identify new innovative ICT solutions for the identified challenges. The second and subsequent stages of this research will design a comprehensive Information System in the form of a portal with centralised information storage allowing for continuous uploading of key data about individual patients by different stakeholders. Uploaded data will be processed by a backend server and allow for the visual representation of a comprehensive patient profile, while patient data will be tailored into individual treatment and care plans for each individual patient. This research aligns with the IFIP Conference theme, as it emphasises the need for: improved IS systems that capture data and information from multiple stakeholders, and comprehensive analysis of captured data and information using big data analytics and prediction activities to identify data patterns, which in turn will enable improved decision-making for patient-centred healthcare solutions and outcomes.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. A recovery-oriented framework model describes high-quality recovery-oriented service delivery and practices in the form of key capabilities for mental health workforce operations.