MindTick: Case Study of a Digital System for Mental Health Clinicians to Monitor and Support Patients Outside Clinics

Perry, Rosemary; Oakey-Neate, Lydia; Fouyaxis, John; Boyd-Brierley, S.; Wilkinson, Margaret; Baigent, Michael; Bidargaddi, Niranjan

doi:10.3233/shti210034

Cited by 4 publications

(6 citation statements)

References 28 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of Artificial Intelligence (AI) in healthcare has led to various success stories and challenges, as illustrated by several case studies. Perry et al (2021) present the MindTick case study, a digital system designed for mental health clinicians to monitor and support patients outside clinics. This system offered an app for patients to record symptoms and provided clinicians with a dashboard to tailor treatments and monitor progress.…”

Section: Case Studies: Success Stories and Limitationsmentioning

confidence: 99%

Ai-Driven Environmental Health Disease Modeling: A Review of Techniques and Their Impact on Public Health in the Usa and African Contexts

Nzubechukwu Chukwudum Ohalete,

Oluwatoyin Ayo-Farai,

Tolulope O Olorunsogo

et al. 2024

imsrj

View full text Add to dashboard Cite

This scholarly paper embarks on an exploratory journey into the realm of AI-driven environmental health disease modeling, with a keen focus on its implications in the diverse healthcare landscapes of the USA and Africa. The study's background delves into the historical evolution of disease modeling techniques, emphasizing the revolutionary role of AI in modern public health strategies. It meticulously examines the comparative effectiveness of AI models in these distinct regions, addressing the challenges and opportunities inherent in AI-driven health models. Aiming to unravel the multifaceted impact of AI in disease prediction and public health policy, the paper navigates through various thematic corridors. It critically analyzes the significance of data sources and quality, ethical considerations in AI health modeling, and the integration of AI models into public health policies. The scope of the paper encompasses a comprehensive review of AI's efficacy in predicting environmental diseases, its role in enhancing disease surveillance systems, and the geographic and socioeconomic variations affecting model accuracy. The main findings reveal that AI models, while effective in disease prediction and surveillance, encounter challenges related to data integrity and ethical complexities. The study concludes that the integration of AI in healthcare necessitates a balanced approach, advocating for policies that support the development of context-specific AI models and address ethical concerns. Recommendations include fostering interdisciplinary collaboration and continuous evaluation of AI models to align them with evolving healthcare needs and ethical standards. This paper serves as a beacon for understanding AI's transformative potential in environmental health disease modeling, offering insights that are crucial for shaping future public health strategies and interventions. Keywords: AI in Healthcare, Disease Modeling, Public Health Policy, Data Quality, Ethical Considerations, Geographic Variations.

show abstract

Section: Case Studies: Success Stories and Limitationsmentioning

confidence: 99%

Ai-Driven Environmental Health Disease Modeling: A Review of Techniques and Their Impact on Public Health in the Usa and African Contexts

Nzubechukwu Chukwudum Ohalete,

Oluwatoyin Ayo-Farai,

Tolulope O Olorunsogo

et al. 2024

imsrj

View full text Add to dashboard Cite

show abstract

“…To facilitate rapid development of digital health applications, a selected set of generic and reusable components from our previous projects 5,9 has been exposed as microservices and connected to DHLink as part of the “default package.” These components are considered either essential to new digital health applications, or common in health services. Application developers can rapidly develop a digital health application for conducting and managing interventions, collecting, and presenting patient data, process patient data and displaying statistical results by leveraging these microservices in combination with a custom‐built data analysis algorithm.…”

Section: The Dhlink Microservice Frameworkmentioning

confidence: 99%

“…From our previous experiences in developing digital health applications, 5,9,10 there are several reasons that could have limited the data and technology reuse. One reason is the difficulty in managing data and technology sovereignties.…”

Section: Reasons To the Problemmentioning

confidence: 99%

Section: Default Microservices For Rapid Application Developmentmentioning

confidence: 99%

“…Among all digital health innovations, a group of digital health applications has gained significant interest both in research and health services, which leverages electronic health records, mobile apps, monitoring sensors, and other digital traces as sources of patient data to provide clinicians real‐time insights, such as patient behavior, experiences of symptom and side effects, attendance of appointments, and medication adherence. For example, there are popular mobile‐based patient interaction applications for tracking personal health and conducting medical interventions, 3–5 data analysis applications for studying disease symptoms, 6,7 medical record management applications for tracking, monitoring and alerting abnormal medical activities, 8 and many more.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A framework for developing digital health applications with efficiency and reuse principles: DHLink case study

Bidargaddi

Fouyaxis

et al. 2022

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Summary A common issue among digital health applications is that they are based on project‐specific solutions and are developed from scratch, which results in redundant development and lack of data and technology reuse. This can be seen in, for example, building study specific websites and mobile frontends, deploying customized infrastructures, and collecting data that may have been collected in other studies and projects. However, existing data and technology are not easily shareable between projects due to large investment required to address data exchanging mechanism, data and technology sovereignty, data security and resource discovery, while the benefit for the resource owners is uncertain. In this article, we present a supporting framework, named DHLink, to address this issue on two fronts. First, the DHLink framework securely connects multiple digital health applications, facilitates real‐time data sharing, and supports rapid application development by reusing data and technology. Second, to aid in rapid development of new digital health applications, a set of highly generic and reusable microservices has been developed as the initial resources available in the DHLink ecosystem. Two proof‐of‐concept use cases outlined show the effectiveness of DHLink for both data sharing between existing applications, and rapid development of a new application.

show abstract

Examining clinician choice to follow-up (or not) on automated notifications of medication non-adherence by clinical decision support systems

Thorpe

Bidargaddi

2023

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

Background Maintaining medication adherence can be challenging for people living with mental ill-health. Clinical decision support systems (CDSS) based on automated detection of problematic patterns in Electronic Health Records (EHRs) have the potential to enable early intervention into non-adherence events (“flags”) through suggesting evidence-based courses of action. However, extant literature shows multiple barriers—perceived lack of benefit in following up low-risk cases, veracity of data, human-centric design concerns, etc.—to clinician follow-up in real-world settings. This study examined patterns in clinician decision making behaviour related to follow-up of non-adherence prompts within a community mental health clinic. Methods The prompts for follow-up, and the recording of clinician responses, were enabled by CDSS software (AI2). De-identified clinician notes recorded after reviewing a prompt were analysed using a thematic synthesis approach—starting with descriptions of clinician comments, then sorting into analytical themes related to design and, in parallel, a priori categories describing follow-up behaviours. Hypotheses derived from the literature about the follow-up categories’ relationships with client and medication-subtype characteristics were tested. Results The majority of clients were Not Followed-up (n = 260; 78%; Followed-up: n = 71; 22%). The analytical themes emerging from the decision notes suggested contextual factors—the clients’ environment, their clinical relationships, and medical needs—mediated how clinicians interacted with the CDSS flags. Significant differences were found between medication subtypes and follow-up, with Anti-depressants less likely to be followed up than Anti-Psychotics and Anxiolytics (χ2 = 35.196, 44.825; p < 0.001; v = 0.389, 0.499); and between the time taken to action Followed-up0 and Not-followed up1 flags (M0 = 31.78; M1 = 45.55; U = 12,119; p < 0.001; η2 = .05). Conclusion These analyses encourage actively incorporating the input of consumers and carers, non-EHR data streams, and better incorporation of data from parallel health systems and other clinicians into CDSS designs to encourage follow-up.

show abstract

MindTick: Case Study of a Digital System for Mental Health Clinicians to Monitor and Support Patients Outside Clinics

Cited by 4 publications

References 28 publications

Ai-Driven Environmental Health Disease Modeling: A Review of Techniques and Their Impact on Public Health in the Usa and African Contexts

Ai-Driven Environmental Health Disease Modeling: A Review of Techniques and Their Impact on Public Health in the Usa and African Contexts

A framework for developing digital health applications with efficiency and reuse principles: DHLink case study

Examining clinician choice to follow-up (or not) on automated notifications of medication non-adherence by clinical decision support systems

Contact Info

Product

Resources

About