Digital health solutions for reducing the impact of non-attendance: A scoping review

Selim, Shayma Mohammed; Kularatna, Sanjeewa; Carter, Hannah; Bohorquez, N.; McPhail, Steven

doi:10.1016/j.hlpt.2023.100759

Cited by 3 publications

(3 citation statements)

References 69 publications

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“…Other studies have also highlighted that vulnerable populations may be more susceptible to missing scheduled healthcare appointments [44,45]. Additionally, automated systems have drawbacks; where postal letters are susceptible to delivery delays and automated reminders have the potential of not being received by patients should there be a disruption to the technology that delivers these reminders [6].…”

Section: Discussionmentioning

confidence: 99%

“…Among the most common mitigation strategies used in healthcare systems are reminder systems. However, the success of reminders varies across different contexts [6]. The literature suggests that intervention success may be dependent on providers and consumer preferences, the needs of Given the evolution of digital communication tools such as reminder systems, there are a range of characteristics that could be integrated into such systems to help address patient non-attendance and improve patient engagement; however, little is known about patient preferences for aspects of these reminder systems in Australia.…”

Section: Introductionmentioning

confidence: 99%

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Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment

Mohammed Selim,

Senanayake,

McPhail

et al. 2024

Patient

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Background It is essential to consider the evidence of consumer preferences and their specific needs when determining which strategies to use to improve patient attendance at scheduled healthcare appointments. Objectives This study aimed to identify key attributes and elicit healthcare consumer preferences for a healthcare appointment reminder system. Methods A discrete choice experiment was conducted in a general Australian population sample. The respondents were asked to choose between three options: their preferred reminder (A or B) or a 'neither' option. Attributes were developed through a literature review and an expert panel discussion. Reminder options were defined by four attributes: modality, timing, content and interactivity. Multinomial logit and mixed multinomial logit models were estimated to approximate individual preferences for these attributes. A scenario analysis was performed to estimate the likelihood of choosing different reminder systems. Results Respondents (n = 361) indicated a significant preference for an appointment reminder to be delivered via a text message (β = 2.42, p < 0.001) less than 3 days before the appointment (β = 0.99, p < 0.001), with basic details including the appointment cost (β = 0.13, p < 0.10), and where there is the ability to cancel or modify the appointment (β = 1.36, p < 0.001). A scenario analysis showed that the likelihood of choosing an appointment reminder system with these characteristics would be 97%. Conclusions Our findings provide evidence on how healthcare consumers trade-off between different characteristics of reminder systems, which may be valuable to inform current or future systems. Future studies may focus on exploring the effectiveness of using patient-preferred reminders alongside other mitigation strategies used by providers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment

Mohammed Selim,

Senanayake,

McPhail

et al. 2024

Patient

Self Cite

View full text Add to dashboard Cite

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“…3 , 4 In hospital settings, the increasing adoption of electronic health records (EHRs), also referred to as electronic medical records, has contributed to the availability of large datasets to which ML can be applied. 5 ML models can potentially assist with diagnosis or prognosis of clinical conditions, 6 , 7 determine optimal treatment pathways and medication dosing, 8 improve healthcare efficiency, 9 , 10 and identify patients at risk of adverse outcomes. 11–13 As the science of ML further evolves, and more EHR data becomes available, health organizations need user friendly ML platforms that can efficiently develop and validate ML models.…”

Section: Introductionmentioning

confidence: 99%

Evaluating automated machine learning platforms for use in healthcare

Scott,

De Guzman,

Falconer

et al. 2024

JAMIA Open

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Objective To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models. Materials and Methods Evaluation criteria for selecting an Auto ML platform suited to ML needs of a local health district were developed in 3 steps: (1) identification of key requirements, (2) a market scan, and (3) an assessment process with desired outcomes. Results The final checklist comprising 21 functional and 6 non-functional criteria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case. Discussion A team of clinicians, data scientists, and key stakeholders developed a checklist which can be adapted to ML needs of healthcare organizations, the use case providing a relevant example. Conclusion An evaluative checklist was developed for selecting Auto ML platforms which requires validation in larger multi-site studies.

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“We’re here to help them if they want to come”: A qualitative exploration of hospital staff perceptions and experiences with outpatient non-attendance

Selim,

McPhail,

Carter

et al. 2024

Preprint

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BackgroundPatient non-attendance remains a major challenge for health services. Few studies have examined how health service providers think about, potentially address, and prioritise non-attendance within the scope of their practice. This study aimed to (1) explore healthcare professionals’ perspectives, beliefs, and opinions about the impact of patient non-attendance within a publicly-funded outpatient physiotherapy clinic context; (2) explore perceived barriers and facilitators associated with the implementation of non-attendance mitigation strategies; and (3) identify health service staff generated solutions to address perceived barriers and enhance facilitators.MethodsA focus group discussion and semi-structured interviews were conducted between June 2023 to January 2024 with 27 physiotherapy department clinic outpatient staff involved in operationalising clinic referral processing, appointment scheduling, or providing care to patients. Data was analysed using a hybrid inductive/deductive framework analysis approach.ResultsParticipants indicated that non-attendance had predominantly negative implications for the health service, healthcare provider, and patient. The interconnected issue of non-attendance encompassed multiple areas and were broadly categorised into five inductively identified themes: impact of non-attendance, perceptions of value, material deprivation, service delivery and built environment, and professional role and identity. Non-attendance mitigation strategies generated by participants were deductively mapped to the theoretical domains framework (TDF) to explore behavioural determinants that may influence successful implementation. This included knowledge, reinforcement, goals, optimism, memory, attention and decision-making, environmental resources and context, and emotions.ConclusionsStaff identified multiple strategies for reducing non-attendance; implementing many of these strategies would require additional resourcing. Research determining the effectiveness of such strategies both in the short-term and long-term following implementation into practice remains a priority for future investigation.

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Digital health solutions for reducing the impact of non-attendance: A scoping review

Cited by 3 publications

References 69 publications

Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment

Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment

Evaluating automated machine learning platforms for use in healthcare

“We’re here to help them if they want to come”: A qualitative exploration of hospital staff perceptions and experiences with outpatient non-attendance

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