Publications

@article{nokey,

title = {Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review},

author = {Oliver Higgins and Brooke L Short and Stephan K Chalup and Rhonda L Wilson},

url = {https://onlinelibrary.wiley.com/doi/10.1111/inm.13114},

doi = {10.1111/inm.13114},

year  = {2023},

date = {2023-02-07},

urldate = {2023-02-07},

journal = {International Journal of Mental Health Nursing},

abstract = {An integrative review investigating the incorporation of artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health care settings was undertaken of published literature between 2016 and 2021 across six databases. Four studies met the research question and the inclusion criteria. The primary theme identified was trust and confidence. To date, there is limited research regarding the use of AI-based decision support systems in mental health. Our review found that significant barriers exist regarding its incorporation into practice primarily arising from uncertainty related to clinician's trust and confidence, end-user acceptance and system transparency. More research is needed to understand the role of AI in assisting treatment and identifying missed care. Researchers and developers must focus on establishing trust and confidence with clinical staff before true clinical impact can be determined. Finally, further research is required to understand the attitudes and beliefs surrounding the use of AI and related impacts for the wellbeing of the end-users of care. This review highlights the necessity of involving clinicians in all stages of research, development and implementation of artificial intelligence in care delivery. Earning the trust and confidence of clinicians should be foremost in consideration in implementation of any AI-based decision support system. Clinicians should be motivated to actively embrace the opportunity to contribute to the development and implementation of new health technologies and digital tools that assist all health care professionals to identify missed care, before it occurs as a matter of importance for public safety and ethical implementation. AI-basesd decision support tools in mental health settings show most promise as trust and confidence of clinicians is achieved.},

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pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Artificial intelligence: An eye cast towards the mental health nursing horizon},

author = {Rhonda L Wilson and Oliver Higgins and Jacob Atem and Andrea E. Donaldson and Frederik Alkier Gildberg and Mary Hooper and Mark Hopwood and Silvia Rosado and Bernadette Solomon and Katrina Ward and Brandi Welsh},

url = {https://onlinelibrary.wiley.com/doi/10.1111/inm.13121},

doi = {10.1111/inm.13121},

year  = {2023},

date = {2023-02-01},

urldate = {2023-02-01},

journal = {International Journal of Mental Health Nursing},

abstract = {Abstract There has been an international surge towards online, digital, and telehealth mental health services, further amplified during COVID-19. Implementation and integration of technological innovations, including artificial intelligence (AI), have increased with the intention to improve clinical, governance, and administrative decision-making. Mental health nurses (MHN) should consider the ramifications of these changes and reflect on their engagement with AI. It is time for mental health nurses to demonstrate leadership in the AI mental health discourse and to meaningfully advocate that safety and inclusion of end users' of mental health service interests are prioritized. To date, very little literature exists about this topic, revealing limited engagement by MHNs overall. The aim of this article is to provide an overview of AI in the mental health context and to stimulate discussion about the rapidity and trustworthiness of AI related to the MHN profession. Despite the pace of progress, and personal life experiences with AI, a lack of MHN leadership about AI exists. MHNs have a professional obligation to advocate for access and equity in health service distribution and provision, and this applies to digital and physical domains. Trustworthiness of AI supports access and equity, and for this reason, it is of concern to MHNs. MHN advocacy and leadership are required to ensure that misogynist, racist, discriminatory biases are not favoured in the development of decisional support systems and training sets that strengthens AI algorithms. The absence of MHNs in designing technological innovation is a risk related to the adequacy of the generation of services that are beneficial for vulnerable people such as tailored, precise, and streamlined mental healthcare provision. AI developers are interested to focus on person-like solutions; however, collaborations with MHNs are required to ensure a person-centred approach for future mental healthcare is not overlooked.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Commercial determinants and therapeutic chatbots: A mental health nursing perspective},

author = {Oliver Higgins and Rhonda L Wilson},

url = {https://doi.org/10.1111/inm.13199},

doi = {10.1111/inm.13199},

year  = {2023},

date = {2023-08-03},

journal = {International Journal of Mental Health Nursing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Editorial: The continued importance of mental health nurses engaging with social media and related emerging technologies},

author = {Rhonda L Wilson and Oliver Higgins},

url = {https://doi.org/10.1111/inm.13129

},

doi = {10.1111/inm.13129},

year  = {2023},

date = {2023-02-14},

urldate = {2023-02-14},

journal = {International Journal of Mental Health Nursing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Interpretations of Innovation: The Role of Technology in Explanation Seeking Related to Psychosis},

author = {Oliver Higgins and Brooke L Short and Stephan K Chalup and Rhonda L Wilson},

url = {https://doi.org/10.1155/2023/4464934},

doi = {10.1155/2023/4464934},

year  = {2023},

date = {2023-02-08},

urldate = {2023-02-08},

journal = {Perspectives in Psychiatric Care},

volume = {2023},

abstract = {Background. The prominence of technology in modern life cannot be understated. However, for some people, these innovations or their related plausible advancements can be associated with perceptual misinterpretation and/or incorporation into delusional concepts. Objective. This paper aims to explore the intersection of technological advancement and experiencing psychosis. We present a discussion about the explanation seeking that incorporates the concept, that for some people, of technological innovation becoming intertwined with delusional symptoms over the past 100 years. Methods. A longitudinal review of the literature was conducted to synthesize and draw these concepts together, mapping them to a timeline that aligns computing science and healthcare expertise and presents the significant technological changes of the modern era charted against mental health milestones and reports of technology-related delusions. Results. It is possible for technology to be incorporated into the content of delusions with evidence supporting a link between the rate of technological change, the content of delusions, and the use of technology as a way of seeking an explanation. Moreover, analysis suggests a need to better understand how innovations may impact the mental health of people at risk of psychosis and other mental health conditions. Conclusions. Clinical experts and lived experience experts need to be informed about and collaborate with future research and development of technology, specifically artificial intelligence and machine learning, early in the development cycle. This concurs with other artificial intelligence research recommendations calling for design attention to the development and implementation of technological innovation applied in a mental health context.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Higgins_2022,

title = {Interpretations of Innovation: The intersection of technological advancement and psychosis (Preprint)},

author = {Oliver Higgins and Stephan K Chalup and Brooke L Short and Rhonda L Wilson},

url = {https://doi.org/10.2196%2Fpreprints.37033},

doi = {10.2196/preprints.37033},

year  = {2022},

date = {2022-02-01},

urldate = {2022-02-01},

journal = {Journal of Medical Internet Research},

publisher = {JMIR Publications Inc.},

abstract = {ABSTRACT

Background:



The prominence of technology in modern life cannot be understated. However, for some people these innovations or their related plausible advancements, can be associated with perceptual misinterpretation and/or incorporation into delusional concepts.



Objective:



This paper aims to explore the intersection of technological advancement and experiencing psychosis. We present a discussion about the explanation seeking that incorporates the concept, that for some people, of technological innovation becoming intertwined with delusional symptoms over the past 100 years.



Methods:



A longitudinal review of the literature was conducted to synthesise and draw these concepts together, mapping them to a timeline that aligns computing science and healthcare expertise and presents the significant technological changes of the modern era charted against mental health milestones and reports of technology-related delusions.



Results:



It is possible for technology to be incorporated in the content of delusions with evidence supporting a link between the rate of technological change, the content of delusions and the use of technology as a way of seeking an explanation. Moreover, analysis suggests a need to better understand how innovations may impact the mental health of people at risk of psychosis and other mental health conditions.



Conclusions:



Clinical experts and lived experience experts need to be informed about and collaborate with future research and development of technology, specifically artificial intelligence and machine learning, early in the development cycle. This concurs with other artificial intelligence research recommendations calling for design attention to the development and implementation of technological innovation applied in a mental health context.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid28233407,

title = {Evaluation of a general practitioner-led cardiometabolic clinic: Physical health profile and treatment outcomes for clients on clozapine},

author = {Dominiek Coates and Patricia Woodford and Oliver Higgins and Deborah Grover},

doi = {10.1111/inm.12321},

issn = {1447-0349},

year  = {2018},

date = {2018-02-01},

urldate = {2018-02-01},

journal = {Int J Ment Health Nurs},

volume = {27},

number = {1},

pages = {303--310},

abstract = {The present study is a review of a cardiometabolic clinic for consumers taking clozapine. This clinic was recently established and co-located with the clozapine clinic at a regional hospital in New South Wales, Australia, to enhance engagement and improve the physical health outcomes of consumers taking antipsychotic medication. A descriptive analysis of clients' (n = 73) information collected during routine care for the first 6 months of the clinic's operation, from January 2016 to July 2016, was conducted. First-visit data were analysed to establish a client profile, consisting of weight, height, blood pressure, pulse, a range of blood measurements, smoking status, alcohol consumption, and eating and exercise habits. Data collected for clients who had three or more visits with the general practitioner (n = 40) were analysed separately for outcomes. Two case studies are used to depict the service received and client profile. At the first appointment, the majority of clients had metabolic syndrome that was mostly left untreated; many of these clients were commenced on metformin. The outcomes are positive, and show that the majority of clients lost weight (82.5%) and had a reduction in body mass index (84.6%); nearly half (44.4%) had a reduction in waist circumference. The majority of clients self-reported increased physical activity (72.5%, n = 29) and positive dietary changes (77.5%, n = 31) since their first appointment. The model trialled by the cardiometabolic clinic integrated a specialist mental health and primary care service, and demonstrates success in engaging clients with severe mental illness in physical health care. Co-location is conceptualized as critical for positive patient outcomes and high levels of engagement.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Blinkhorn2013,

title = {The Impact of Dental Disease on a sample of Aboriginal adults living in the Central Coast Region of New South Wales Australia},

author = {Leeann Mullen and Oliver Higgins and Brian Redmayne and Loran Keegan and Anthony Blinkhorn and Fiona Blinkhorn},

url = {https://www.stephenhancocks.com/view.php?article_id=1275&journal_id=147},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

journal = {Journal of Social Science and Dentistry},

volume = {2},

issue = {2},

pages = {73-79},

abstract = {Objective: To record the impact of dental disease on the quality of life of Aboriginal* adults. Setting: An Aboriginal dedicated dental clinic in the Central Coast Region of NSW, Australia. Methods: Adult patients attending the clinic were interviewed about the impact of dental disease on their day to day lives.



Results:

Fifty adults were interviewed of the 61 who attended for care, giving a response rate of 82%. The major impacts were an aching, painful mouth 76%, self conscious about their teeth 62%, embarrassed about their teeth 62%, and diet impacted by poor oral health 64%. Nearly half required the extraction of at least one tooth. Cost was a major barrier for not seeking care for 62% of the participants. Conclusion: Poor oral health impacted on the lives of the Aboriginal respondents, but the offer of free dental care in an Aboriginal dedicated clinic encouraged attendance for treatment, especially amongst women.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}