Telemedicine, a method of healthcare service delivery bridging geographic distances between patients and providers, has gained prominence. This modality is particularly advantageous for outpatient consultations, addressing inherent barriers of travel time and cost.

We aim to describe economical outcomes towards the implementation of a multidisciplinary telemedicine service in a high-complexity hospital in Latin America, from the perspective of patients.

A cross-sectional study was conducted, analysing the institutional data obtained over a period of 9 months, between April 2020 and December 2020.

A high-complexity teaching hospital located in Cali, Colombia.

Individuals who received care via telemedicine. The population was categorised into three groups based on their place of residence: Cali, Valle del Cauca excluding Cali and Outside of Valle del Cauca.

Travel distance, time, fuel and public round-trip cost savings, and potential loss of productivity were estimated from the patient’s perspective.

A total of 62 258 teleconsultations were analysed. Telemedicine led to a total distance savings of 4 514 903 km, and 132 886 hours. The estimated cost savings were US$680 822 for private transportation and US$1 087 821 for public transportation. Patients in the Outside of Valle del Cauca group experienced an estimated average time savings of 21.2 hours, translating to an average fuel savings of US$149.02 or an average savings of US$156.62 in public transportation costs. Areas with exclusive air access achieved a mean cost savings of US$362.9 per teleconsultation, specifically related to transportation costs.

Telemedicine emerges as a powerful tool for achieving substantial travel savings for patients, especially in regions confronting geographical and socioeconomic obstacles. These findings underscore the potential of telemedicine to bridge healthcare accessibility gaps in low-income and middle-income countries, calling for further investment and expansion of telemedicine services in such areas.

The COVID-19 pandemic accelerated changes to clinical research methodology, with clinical studies being carried out via online/remote means. This mixed-methods study aimed to identify which digital tools are currently used across all stages of clinical research by stakeholders in clinical, health and social care research and investigate their experience using digital tools.

Two online surveys followed by semistructured interviews were conducted. Interviews were audiorecorded, transcribed and analysed thematically.

To explore the digital tools used since the pandemic, survey participants (researchers and related staff (n=41), research and development staff (n=25)), needed to have worked on clinical, health or social care research studies over the past 2 years (2020–2022) in an employing organisation based in the West Midlands region of England (due to funding from a regional clinical research network (CRN)). Survey participants had the opportunity to participate in an online qualitative interview to explore their experiences of digital tools in greater depth (n=8).

Six themes were identified in the qualitative interviews: ‘definition of a digital tool in clinical research’; ‘impact of the COVID-19 pandemic’; ‘perceived benefits/drawbacks of digital tools’; ‘selection of a digital tool’; ‘barriers and overcoming barriers’ and ‘future digital tool use’. The context of each theme is discussed, based on the interview results.

Findings demonstrate how digital tools are becoming embedded in clinical research, as well as the breadth of tools used across different research stages. The majority of participants viewed the tools positively, noting their ability to enhance research efficiency. Several considerations were highlighted; concerns about digital exclusion; need for collaboration with digital expertise/clinical staff, research on tool effectiveness and recommendations to aid future tool selection. There is a need for the development of resources to help optimise the selection and use of appropriate digital tools for clinical research staff and participants.

The PRESTIGIO Registry was established in 2017 to collect clinical, virological and immunological monitoring data from people living with HIV (PLWH) with documented four-class drug resistance (4DR). Key research purposes include the evaluation of residual susceptibility to specific antiretrovirals and the validation of treatment and monitoring strategies in this population.

The PRESTIGIO Registry collects annual plasma and peripheral blood mononuclear cell samples and demographic, clinical, virological, treatment and laboratory data from PLWH followed at 39 Italian clinical centres and characterised by intermediate-to-high genotypic resistance to ≥1 nucleoside reverse transcriptase inhibitors, ≥1 non-nucleoside reverse transcriptase inhibitors, ≥1 protease inhibitors, plus either intermediate-to-high genotypic resistance to ≥1 integrase strand transfer inhibitors (INSTIs) or history of virological failure to an INSTI-containing regimen. To date, 229 people have been recorded in the cohort. Most of the data are collected from the date of the first evidence of 4DR (baseline), with some prebaseline information obtained retrospectively. Samples are collected from the date of enrollment in the registry.

The open-ended cohort has been used to assess (1) prognosis in terms of survival or development of AIDS-related or non-AIDS-related clinical events; (2) long-term efficacy and safety of different antiretroviral regimens and (3) virological and immunological factors predictive of clinical outcome and treatment efficacy, especially through analysis of plasma and cell samples.

The registry can provide new knowledge on how to implement an integrated approach to study PLWH with documented resistance to the four main antiretroviral classes, a population with a limited number of individuals characterised by a high degree of frailty and complexity in therapeutic management. Given the scheduled annual updates of PLWH data, the researchers who collaborate in the registry can send study proposals at any time to the steering committee of the registry, which evaluates every 3 months whether the research studies can be conducted on data and biosamples from the registry and whether they are aimed at a better understanding of a specific health condition, the emergence of comorbidities or the effect of potential treatments or experimental drugs that may have an impact on disease progression and quality of life. Finally, the research studies should aim to be inclusive, innovative and in touch with the communities and society as a whole.

NCT04098315.

Fifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%–40% of patients in low-income and middle-income countries (LMICs) have access to it. A shortfall in specialised workforce has been identified as the most significant barrier to expanding radiotherapy capacity. Artificial intelligence (AI)-based software has been developed to automate both the delineation of anatomical target structures and the definition of the position, size and shape of the radiation beams. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to minutes) and human resources needed to deliver radiotherapy.

ARCHERY is a non-randomised prospective study to evaluate the quality and economic impact of AI-based automated radiotherapy treatment planning for cervical, head and neck, and prostate cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 990 patients (330 for each cancer type) has been calculated based on an estimated 95% treatment plan acceptability rate. Time and cost savings will be analysed as secondary outcome measures using the time-driven activity-based costing model. The 48-month study will take place in six public sector cancer hospitals in India (n=2), Jordan (n=1), Malaysia (n=1) and South Africa (n=2) to support implementation of the software in LMICs.

The study has received ethical approval from University College London (UCL) and each of the six study sites. If the study objectives are met, the AI-based software will be offered as a not-for-profit web service to public sector state hospitals in LMICs to support expansion of high quality radiotherapy capacity, improving access to and affordability of this key modality of cancer cure and control. Public and policy engagement plans will involve patients as key partners.