Demonstration of automated non-adherence and service disengagement risk monitoring with active follow-up for severe mental illness.

AIMS: Medication cessation and service disengagement often precedes relapse in people with severe mental illnesses but currently specialist mental health services only become involved after a relapse. Early detection of non-adherence is needed to enable intervention to avert relapse. This paper aims to demonstrate how digitally automated non-adherence risk monitoring from Medicare data with active follow-up can work and perform in practice in a real-world mental health service setting. METHODS: AI2 software is an automated risk monitoring tool to detect non-adherence using Medicare data. It was implemented prospectively in a cohort of 354 registered patients of a community mental health clinic between July 2019 and February 2020. Patients flagged as at risk by the software were reviewed by two clinicians. We describe the risks automatically flagged for non-adherence and the clinical responses. We examine differences in clinical and demographic factors in patients flagged at increased risk of non-adherence. RESULTS: In total, 46.7% (142/304) were flagged by the software as at risk of non-adherence, and 22% (31/142) received an intervention following clinician review of their case notes. Patients flagged by the software were older in age and had more prior mental health treatment episodes. More alerts were associated with patients who had been transferred from the mental health service to the care of their general practitioners, and those with more alerts were more likely to receive a follow-up intervention. CONCLUSION: Digitally automated monitoring for non-adherence risk is feasible and can be integrated into clinical workflows in community psychiatric and primary care settings. The technology may assist clinicians and services to detect non-adherence behaviour early, thereby triggering interventions that have the potential to reduce rates of mental health deterioration and acute illness relapse.

Office-based physical activity: mapping a social ecological model approach against COM-B.

BACKGROUND: There are growing concerns over the health impacts of occupational sedentary behaviour on office-based workers and increasing workplace recognition of the need to increase physical activity at work. Social ecological models provide a holistic framework for increasing opportunities for physical activity at work. In this paper we propose a social ecological model of office-based physical activity and map it against the Capability Motivation Opportunity (COM-B) framework to highlight the mechanisms of behaviour change that can increase levels of physical activity of office-based workers. DISCUSSION: The paper proposes a social ecological model of physical activity associated with office-based settings. The model considers opportunities for both incidental and discretionary activities, as well as macro and micro factors on both socio-cultural and physical dimensions. The COM-B framework for characterising behaviour change interventions is used to highlight the underlying mechanisms of behaviour change inherent in the model. The broad framework provided by social ecological models is important for understanding physical activity in office-based settings because of the non-discretionary nature of sedentary behaviour of office-based work. It is important for interventions not to rely on individual motivation for behaviour change alone but to incorporate changes to the broader social ecological and physical context to build capability and create opportunities for more sustainable change.

Total Knee Replacement and the Effect of Technology on Cocreation for Improved Outcomes and Delivery: Qualitative Multi-Stakeholder Study.

BACKGROUND: The growth in patient-centered care delivery combined with the rising costs of health care have perhaps not unsurprisingly been matched by a proliferation of patient-centered technology. This paper takes a multistakeholder approach to explore how digital technology can support the cocreation of value between patients and their care teams in the delivery of total knee replacement (TKR) surgery, an increasingly common procedure to return mobility and relieve pain for people suffering from osteoarthritis. OBJECTIVE: The aim of this study was to investigate communications and interactions between patients and care teams in the delivery of TKR to identify opportunities for digital technology to add value to TKR health care service by enhancing the cocreation of value. METHODS: A multistakeholder qualitative study of user needs was conducted with Australian stakeholders (N=34): surgeons (n=12), physiotherapists (n=3), patients (n=11), and general practitioners (n=8). Data from focus groups and interviews were recorded, transcribed, and analyzed using thematic analysis. RESULTS: Encounters between patients and their care teams are information-rich but time-poor. Results showed seven different stages of the TKR journey that starts with referral to a surgeon and ends with a postoperative review at 12 months. Each stage of the journey has different information and communication challenges that can be enhanced by digital technology. Opportunities for digital technology include improved waiting list management, supporting and reinforcing patient retention and recall of information, motivating and supporting rehabilitation, improving patient preparation for hospital stay, and reducing risks and anxiety associated with postoperative wound care. CONCLUSIONS: Digital technology can add value to patients' care team communications by enhancing information flow, assisting patient recall and retention of information, improving accessibility and portability of information, tailoring information to individual needs, and by providing patients with tools to engage in their own health care management. For care teams, digital technology can add value through early detection of postoperative complications, proactive surveillance of health data for postoperative patients and patients on waiting lists, higher compliance with rehabilitation programs, and reduced length of stay. Digital technology has the potential to improve patient satisfaction and outcomes, as well as potentially reduce hospital length of stay and the burden of disease associated with postoperative morbidity.

Automated Decision Support For Community Mental Health Services Using National Electronic Health Records: Qualitative Implementation Case Study.

BACKGROUND: A high proportion of patients with severe mental illness relapse due to nonadherence to psychotropic medication. In this paper, we use the normalization process theory (NPT) to describe the implementation of a web-based clinical decision support system (CDSS) for Community Mental Health Services (CMHS) called Actionable Intime Insights or AI2. AI2 has two distinct functions: (1) it provides an overview of medication and treatment history to assist in reviewing patient adherence and (2) gives alerts indicating nonadherence to support early intervention. OBJECTIVE: Our objective is to evaluate the pilot implementation of the AI2 application to better understand the challenges of implementing a web-based CDSS to support medication adherence and early intervention in CMHS. METHODS: The NPT and participatory action framework were used to both explore and support implementation. Qualitative data were collected over the course of the 14-month implementation, in which researchers were active participants. Data were analyzed and coded using the NPT framework. Qualitative data included discussions, meetings, and work products, including emails and documents. RESULTS: This study explores the barriers and enablers of implementing a CDSS to support early intervention within CMHS using Medicare data from Australia's national electronic record system, My Health Record (MyHR). The implementation was a series of ongoing negotiations, which resulted in a staged implementation with compromises on both sides. Clinicians were initially hesitant about using a CDSS based on MyHR data and expressed concerns about the changes to their work practice required to support early intervention. Substantial workarounds were required to move the implementation forward. This pilot implementation allowed us to better understand the challenges of implementation and the resources and support required to implement and sustain a model of care based on automated alerts to support early intervention. CONCLUSIONS: The use of decision support based on electronic health records is growing, and while implementation is challenging, the potential benefits of early intervention to prevent relapse and hospitalization and ensure increased efficiency of the health care system are worth pursuing.

Using algorithms to initiate needs-based interventions for people on antipsychotic medication: implementation protocol.

INTRODUCTION: Non-adherence to antipsychotic medications for individuals with serious mental illness increases risk of relapse and hospitalisation. Real time monitoring of adherence would allow for early intervention. AI2 is a both a personal nudging system and a clinical decision support tool that applies machine learning on Medicare prescription and benefits data to raise alerts when patients have discontinued antipsychotic medications without supervision, or when essential routine health checks have not been performed. METHODS AND ANALYSIS: We outline two intervention models using AI2. In the first use-case, the personal nudging system, patients receive text messages when an alert of a missed medication or routine health check is detected by AI2. In the second use-case, as a clinical decision support tool, AI2 generated alerts are presented as flags through a dashboard to the community mental health professionals. Implementation protocols for different scenarios of AI2, along with a mixed-methods evaluation, are planned to identify pragmatic issues necessary to inform a larger randomised control trial, as well as improve the application. ETHICS AND DISSEMINATION: This study protocol has been approved by The Southern Adelaide Clinical Human Research Ethics Committee. The dissemination of this trial will serve to inform further implementation of the AI2 into daily personal and clinical practice.

Characterising Routine Physical Activity in the Office.

Workplace health is of concern to management and employees alike. Managers are concerned about the impact of health and wellbeing on productivity, while employees are concerned about impacts on quality of life. In the past two decades, there has been growing concern about the impact of sedentary behaviour at work. Before any interventions can be made to encourage physical activity at work proactively, it is desirable to understand the baseline characteristics of office based physical activity at work. Consumer wearable technology has provided a new and convenient mechanism for using personal monitoring to achieve remote observation of lifestyle related health behaviours. This paper describes how this technology can potentially be used to characterize different levels of workplace physical activity.

Understanding Episodes of Physical Activity at Work Using Fitbit® Data.

For office workers as much as much as 77% of time at work is spent in sedentary behaviour, increasing the risk of cardio vascular disease, cancer, weight gain, obesity and musculoskeletal pain. Given the amount of time spent at work, increasing opportunities for moderate to vigorous physical activity for office workers can not only reduce risk of disease but also actively contribute to better brain function and reduced anxiety at work. In this research we adopt an ecological approach to understanding opportunities for physical activity at work. Using 1373 Fitbit data points from two collocated office workers with identical job descriptions, we demonstrate that episodes of movement in the office are of very short or short duration. Results were less clear for sedentary behaviour because of the potential for spurious step counts and the fact that accelerometers cannot distinguish between sitting and standing. The paper shows the limitations and potential of data from Fitbit devices as a means of understanding the opportunities and constraints of the organisational and physical environment for physical activity at work for office workers.

Developing a Third-Party Analytics Application Using Australia's National Personal Health Records System: Case Study.

BACKGROUND: My Health Record (MyHR) is Australia's national electronic health record (EHR) system. Poor usability and functionality have resulted in low utility, affecting enrollment and participation rates by both patients and clinicians alike. Similar to apps on mobile phone app stores, innovative third-party applications of MyHR platform data can enhance the usefulness of the platform, but there is a paucity of research into the processes involved in developing third-party applications that integrate and use data from EHR systems. OBJECTIVE: The research describes the challenges involved in pioneering the development of a patient and clinician Web-based software application for MyHR and insights resulting from this experience. METHODS: This research uses a case study approach, investigating the development and implementation of Actionable Intime Insights (AI2), a third-party application for MyHR, which translates Medicare claims records stored in MyHR into a clinically meaningful timeline visualization of health data for both patients and clinicians. This case study identifies the challenges encountered by the Personal Health Informatics team from Flinders University in the MyHR third-party application development environment. RESULTS: The study presents a nuanced understanding of different data types and quality of data in MyHR and the complexities associated with developing secondary-use applications. Regulatory requirements associated with utilization of MyHR data, restrictions on visualizations of data, and processes of testing third-party applications were encountered during the development of the application. CONCLUSIONS: This study identified several processes, technical and regulatory barriers which, if addressed, can make MyHR a thriving ecosystem of health applications. It clearly identifies opportunities and considerations for the Australian Digital Health Agency and other national bodies wishing to encourage the development of new and innovative use cases for national EHRs.

Identifying Emerging Trends in Medical Informatics: A Synthesis Approach.

Medical informatics is a young and rapidly evolving field, influenced by and impacting on many different knowledge domains. Recent contributions on scoping the associated body of knowledge are confounded both by variations in popular use of terminology for established areas, and by the advent of new areas without yet established terminology. Determining the scope of a topic through online bibliographic search filters is a well-established approach in scientific research and has been developed as a human-directed task. Establishing the best approach and automating the process has proved a difficult problem. This paper explores the use of text analysis of bibliographic information using available search engines and NVIVO text analysis tools to test the potential for dynamic word based filters based on data mining. Results show that word searches of abstracts are more effective than topic searches for identifying health informatics papers, however more work is required to refine search terms to improve generalisability. Using data mining to track changes in word use in medical informatics journals, may make it possible to establish a more dynamic search filter to match the evolving nature of the field of health informatics.

Understanding Smart Home Sensor Data for Ageing in Place Through Everyday Household Routines: A Mixed Method Case Study.

BACKGROUND: An ongoing challenge for smart homes research for aging-in-place is how to make sense of the large amounts of data from in-home sensors to facilitate real-time monitoring and develop reliable alerts. OBJECTIVE: The objective of our study was to explore the usefulness of a routine-based approach for making sense of smart home data for the elderly. METHODS: Maximum variation sampling was used to select three cases for an in-depth mixed methods exploration of the daily routines of three elderly participants in a smart home trial using 180 days of power use and motion sensor data and longitudinal interview data. RESULTS: Sensor data accurately matched self-reported routines. By comparing daily movement data with personal routines, it was possible to identify changes in routine that signaled illness, recovery from bereavement, and gradual deterioration of sleep quality and daily movement. Interview and sensor data also identified changes in routine with variations in temperature and daylight hours. CONCLUSIONS: The findings demonstrated that a routine-based approach makes interpreting sensor data easy, intuitive, and transparent. They highlighted the importance of understanding and accounting for individual differences in preferences for routinization and the influence of the cyclical nature of daily routines, social or cultural rhythms, and seasonal changes in temperature and daylight hours when interpreting information based on sensor data. This research has demonstrated the usefulness of a routine-based approach for making sense of smart home data, which has furthered the understanding of the challenges that need to be addressed in order to make real-time monitoring and effective alerts a reality.

Consumer Perspectives on MyHealth Record: A Review.

The vision for Australia's national electronic health record system included empowering consumers to become active participants in their own health care. This paper aims to critically review the literature on consumer perspectives of Australia's My Health Record (formerly PCEHR). The review is based on a subset of articles (n=12) identified in the Australian EHR Repository (N=143), a repository of metadata of Australian Research on EHR located at Flinders University. Results show low levels of awareness and concerns about sharing records and equity of access for all Australians, which in view of the change from opt in to opt out raises concerns about explicit consent. Improved promotion and support, along with different models of access might lead to higher consumer engagement with, and use, of My Health Record, especially for populations at risk of digital exclusion.

Supporting the Delivery of Total Knee Replacements Care for Both Patients and Their Clinicians With a Mobile App and Web-Based Tool: Randomized Controlled Trial Protocol.

BACKGROUND: Total knee replacement (TKR) surgeries have increased in recent years. Exercise programs and other interventions following surgery can facilitate the recovery process. With limited clinician contact time, patients with TKR have a substantial burden of self-management and limited communication with their care team, thus often fail to implement an effective rehabilitation plan. OBJECTIVE: We have developed a digital orthopedic rehabilitation platform that comprises a mobile phone app, wearable activity tracker, and clinical Web portal in order to engage patients with self-management tasks for surgical preparation and recovery, thus addressing the challenges of adherence to and completion of TKR rehabilitation. The study will determine the efficacy of the TKR platform in delivering information and assistance to patients in their preparation and recovery from TKR surgery and a Web portal for clinician care teams (ie, surgeons and physiotherapists) to remotely support and monitor patient progress. METHODS: The study will evaluate the TKR platform through a randomized controlled trial conducted at multiple sites (N=5) in a number of states in Australia with 320 patients undergoing TKR surgery; the trial will run for 13 months for each patient. Participants will be randomized to either a control group or an intervention group, both receiving usual care as provided by their hospital. The intervention group will receive the app and wearable activity tracker. Participants will be assessed at 4 different time points: 4 weeks before surgery, immediately before surgery, 12 weeks after surgery, and 52 weeks after surgery. The primary outcome measure is the Oxford Knee Score. Secondary outcome measures include quality of life (Short-Form Health Survey); depression, anxiety, and stress (Depression, Anxiety, and Stress Scales); self-motivation; self-determination; self-efficacy; and the level of satisfaction with the knee surgery and care delivery. The study will also collect quantitative usage data related to all components (app, activity tracker, and Web portal) of the TKR platform and qualitative data on the perceptions of the platform as a tool for patients, carers, and clinicians. Finally, an economic evaluation of the impact of the platform will be conducted. RESULTS: Development of the TKR platform has been completed and deployed for trial. The research protocol is approved by 2 human research ethics committees in Australia. A total of 5 hospitals in Australia (2 in New South Wales, 2 in Queensland, and 1 in South Australia) are expected to participate in the trial. CONCLUSIONS: The TKR platform is designed to provide flexibility in care delivery and increased engagement with rehabilitation services. This trial will investigate the clinical and behavioral efficacy of the app and impact of the TKR platform in terms of service satisfaction, acceptance, and economic benefits of the provision of digital services. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12616000504415; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370536 (Archived by WebCite at http://www.webcitation.org/6oKES0Gp1).

Activity of Daily Living assessment through wireless sensor data.

Activity of Daily Living has become a clinical de facto instrument to assess daily functional status of older people living independently at home. Almost all ADLs scales are based on subjective assessment of clinical staff and self-reported responses of the elderly person. A great deal of variability in ADL assessment is likely due to the different cultural beliefs, language and education, and over-assessment of personal capability to potentially avoid negative consequences. This paper proposes automatic and objective ADLs assessment as key component of a technology platform that supports older people to live independently in their home, called Smarter Safer Homes. The objective ADL assessment is achieved through communicating data from simple non-intrusive, wireless sensors placed in a home environment. Pilot sensor data sets were collected over six months from nine independent living homes of participants aged 70+ year. The application of a clustering based, unsupervised learning method on these data sets demonstrates the potential to automatically detect five domains of activity contributing to functional independence. Furthermore, the method provides features that support elderlys self-monitoring of daily activities more regularly, that could provide the potential for timely and early intervention from family and carers.