Robotic surgery has applications in many medical specialties, including urology, general surgery, and surgical oncology. In the context of a widespread resource and personnel shortage in Low- and Middle-Income Countries(LMICs), the use of robotics in surgery may help to reduce physician burnout, surgical site infections, and hospital stays. However, a lack of haptic feedback and potential socioeconomic factors such as high implementation costs and a lack of trained personnel may limit its accessibility and application. Specific improvements focused on improved financial and technical support to LMICs can help improve access and have the potential to transform the surgical experience for both surgeons and patients in LMICs. This review focuses on the evolution of robotic surgery, with an emphasis on challenges and recommendations to facilitate wider implementation and improved patient outcomes.
Global healthcare fairness: We should be sharing more, not less, data
The availability of large, deidentified health datasets has enabled significant innovation in using machine learning (ML) to better understand patients and their diseases. However, questions remain regarding the true privacy of this data, patient control over their data, and how we regulate data sharing in a way that that does not encumber progress or further potentiate biases for underrepresented populations. After reviewing the literature on potential reidentifications of patients in publicly available datasets, we argue that the cost—measured in terms of access to future medical innovations and clinical software—of slowing ML progress is too great to limit sharing data through large publicly available databases for concerns of imperfect data anonymization. This cost is especially great for developing countries where the barriers preventing inclusion in such databases will continue to rise, further excluding these populations and increasing existing biases that favor high-income countries. Preventing artificial intelligence’s progress towards precision medicine and sliding back to clinical practice dogma may pose a larger threat than concerns of potential patient reidentification within publicly available datasets. While the risk to patient privacy should be minimized, we believe this risk will never be zero, and society has to determine an acceptable risk threshold below which data sharing can occur—for the benefit of a global medical knowledge system.
Effect of a model based on education and teleassistance for the management of obstetric emergencies in 10 rural populations from Colombia
Introduction: Pregnant women and health providers in rural areas of low-income and middle-income countries face multiple problems concerning high-quality obstetric care. This study was performed to identify changes in maternal and perinatal indicators after implementing a model based on education and telecare between a high-complexity hospital in 10 low-complexity hospitals in a southwestern region of Colombia.
Methods: A quasiexperimental study with a historic control group and without a pretest was conducted between 2017 and 2019 to make comparisons before and after obstetric emergency care through the use of teleassistance from 10 primary care centers to the referral center (Fundación Valle del Lili, FVL).
Results: A total of 470 patients were treated before teleassistance implementation and 154 patients were treated after teleassistance implementation. After program implementation, the maternal clinical indicators showed a 65% reduction in the number of obstetric patients who were referred with obstetric emergencies. The severity of maternal disease that was measured at the time of admission to level IV through the Modified Early Obstetric Warning System score was observed to decrease.
Conclusion: The implementation of a model based on education and teleassistance between low-complexity hospitals and tertiary care centers generated changes in indicators that reflect greater access to rural areas, lower morbidity at the time of admission, and a decrease in the total number of emergency events.
Telesurgery’s potential role in improving surgical access in Africa
An estimated five billion people worldwide lack access to surgical care, while LMICs including African nations require an additional 143 million life-saving surgical procedures each year.African hospitals are under-resourced and understaffed, causing global attention to be focused on improving surgical access in the continent. The African continent saw its first telesurgery application when the United States Army Special Operations Forces in Somalia used augmented reality to stabilize lifethreatening injuries.Various studies have been conducted since the first telesurgery implementation in 2001 to further optimize its application.In context of a relative shortage of healthcare resources and personnel telesurgery can considerably improve quality and access to surgical services in Africa.telesurgery can provide remote African regions with access to knowledge and tools that were previously unavailable, driving innovative research and professional growth of surgeons in the region.At the same time, telesurgery allows less trained surgeons in remote areas with lower social determinants of health, such as access, to achieve better health outcomes. However, lack of stable internet access, expensive equipment costs combined with low expenditure on healthcare limits expansive utilization of telesurgery in Africa. Regional and international policies aimed at overcoming these obstacles can improve access, optimize surgical care and thereby reduce disease burden associated with surgical conditions in Africa.
Virtual reality technology in linked orthopaedic training in Ethiopia
We describe the feasibility of delivering a live orthopaedic surgical teaching session with virtual reality (VR) technology simultaneously for trainee surgeons in Ethiopia and the UK.
Forty-three delegates from the Severn Deanery in the UK (n=30) and Bahir Dar in Ethiopia (n=13) attended a live training session in February 2021. During the session, participants watched a surgical operation (recorded earlier that week with a 360° VR camera) alongside live commentary. A qualitative questionnaire was distributed to gauge feasibility, connectivity and educational value of the session as well as its VR component.
The majority of delegates from both the UK and Ethiopia felt that the use of VR technology to aid surgical training is feasible, that it is useful for learning surgical approaches, that it aids surgical performance and that it is superior to conventional resources. Bahir Dar residents strongly agreed that VR simulation videos would allow trainees to supplement reduced learning opportunities as a result of the COVID-19 pandemic and help to counteract their reduced operating experience. For Bahir Dar trainees, a lack of a stable internet connection for large VR files was the predominant issue.
This study demonstrates that there are infrastructure challenges in low and middle income countries (LMICs) in terms of the reliable delivery of VR teaching in orthopaedics at the current time. Despite this, our findings better inform the potential role of VR technology in surgical education, and shed light on the possibility for it to feed into and enrich surgical training in both LMICs and high income countries.
Digital health and telemedicine in Pakistan: Improving maternal healthcare
Pakistan has not benefited significantly from telemedicine, despite the promise that it could overcome many of the barriers impeding maternal healthcare delivery in emerging markets. Due to a lack of a regulatory framework and a lack of government interest, new companies in Pakistan have a hard time establishing healthcare projects that will be cost-effective and innovative. A review of telemedicine adoption in the past and present for improving maternal healthcare standards is presented in this article. Furthermore, a discussion of the challenges associated with digital health adoption is provided, as well as possible and feasible policies for making the use of digital health in maternal health more effective.
Community-based adult hearing care provided by community healthcare workers using mHealth technologies
The rising prevalence of hearing loss is a global health concern. Professional hearing services are largely absent within low- and middle-income countries where appropriate skills are lacking. Task-shifting to community healthcare workers (CHWs) supported by mHealth technologies is an important strategy to address the problem.
To evaluate the feasibility of a community-based rehabilitation model providing hearing aids to adults in low-income communities using CHWs supported by mHealth technologies.
Between September 2020 and October 2021, hearing aid assessments and fittings were implemented for adults aged 18 and above in two low-income communities in the Western Cape, South Africa, using trained CHWs. A quantitative approach with illustrative open-ended questions was utilised to measure and analyse hearing aid outcomes. Data were collected through initial face-to-face interviews, telephone interviews, and face-to-face visits post-fitting. Responses to open-ended questions were analysed using inductive thematic analysis. The International Outcome Inventory – Hearing Aids questionnaire determined standardised hearing aid outcomes.
Of the 152 adults in the community who self-reported hearing difficulties, 148 were successfully tested by CHWs during home visits. Most had normal hearing (39.9%), 24.3% had bilateral sensorineural hearing loss, 20.9% had suspected conductive hearing loss, and 14.9% had unilateral hearing loss, of which 5.4% had suspected conductive loss. Forty adults met the inclusion criteria to be fitted with hearing aids. Nineteen of these were fitted bilaterally. Positive hearing aid outcomes and minimal device handling challenges were reported 45 days post-fitting and were maintained at six months. The majority (73.7%) of participants fitted were still making use of their hearing aids at the six-month follow-up.
Implementing a hearing healthcare service-delivery model facilitated by CHWs in low-income communities is feasible. mHealth technologies used by CHWs can support scalable service-delivery models with the potential for improved access and affordability in low-income settings.
mHealth Interventions to Improve Cancer Screening and Early Detection: Scoping Review of Reviews
Cancer screening provision in resource-constrained settings tends to be opportunistic, and uptake tends to be low, leading to delayed presentation and treatment and poor survival.
The aim of this study was to identify, review, map, and summarize findings from different types of literature reviews on the use of mobile health (mHealth) technologies to improve the uptake of cancer screening.
The review methodology was guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Ovid MEDLINE, PyscINFO, and Embase were searched from inception to May 2021. The eligible criteria included reviews that focused on studies of interventions that used mobile phone devices to promote and deliver cancer screening and described the effectiveness or implementation of mHealth intervention outcomes. Key data fields such as study aims, types of cancer, mHealth formats, and outcomes were extracted, and the data were analyzed to address the objective of the review.
Our initial search identified 1981 titles, of which 12 (0.61%) reviews met the inclusion criteria (systematic reviews: n=6, 50%; scoping reviews: n=4, 33%; rapid reviews: n=1, 8%; narrative reviews: n=1, 8%). Most (57/67, 85%) of the interventions targeted breast and cervical cancer awareness and screening uptake. The most commonly used mHealth technologies for increasing cancer screening uptake were SMS text messages and telephone calls. Overall, mHealth interventions increased knowledge about screening and had high acceptance among participants. The likelihood of achieving improved uptake-related outcomes increased when interventions used >1 mode of communication (telephone reminders, physical invitation letters, and educational pamphlets) together with mHealth.
mHealth interventions increase cancer screening uptake, although multiple modes used in combination seem to be more effective.
Machine Learning in Diagnosing Middle Ear Disorders Using Tympanic Membrane Images: A Meta-Analysis
To systematically evaluate the development of Machine Learning (ML) models and compare their diagnostic accuracy for the classification of Middle Ear Disorders (MED) using Tympanic Membrane (TM) images.
PubMed, EMBASE, CINAHL, and CENTRAL were searched up until November 30, 2021. Studies on the development of ML approaches for diagnosing MED using TM images were selected according to the inclusion criteria. PRISMA guidelines were followed with study design, analysis method, and outcomes extracted. Sensitivity, specificity, and area under the curve (AUC) were used to summarize the performance metrics of the meta-analysis. Risk of Bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool in combination with the Prediction Model Risk of Bias Assessment Tool.
Sixteen studies were included, encompassing 20254 TM images (7025 normal TM and 13229 MED). The sample size ranged from 45 to 6066 per study. The accuracy of the 25 included ML approaches ranged from 76.00% to 98.26%. Eleven studies (68.8%) were rated as having a low risk of bias, with the reference standard as the major domain of high risk of bias (37.5%). Sensitivity and specificity were 93% (95% CI, 90%–95%) and 85% (95% CI, 82%–88%), respectively. The AUC of total TM images was 94% (95% CI, 91%–96%). The greater AUC was found using otoendoscopic images than otoscopic images.
ML approaches perform robustly in distinguishing between normal ears and MED, however, it is proposed that a standardized TM image acquisition and annotation protocol should be developed.
The Effect and Feasibility of mHealth-Supported Surgical Site Infection Diagnosis by Community Health Workers After Cesarean Section in Rural Rwanda: Randomized Controlled Trial
The development of a surgical site infection (SSI) after cesarean section (c-section) is a significant cause of morbidity and mortality in low- and middle-income countries, including Rwanda. Rwanda relies on a robust community health worker (CHW)–led, home-based paradigm for delivering follow-up care for women after childbirth. However, this program does not currently include postoperative care for women after c-section, such as SSI screenings.
This trial assesses whether CHW’s use of a mobile health (mHealth)–facilitated checklist administered in person or via phone call improved rates of return to care among women who develop an SSI following c-section at a rural Rwandan district hospital. A secondary objective was to assess the feasibility of implementing the CHW-led mHealth intervention in this rural district.
A total of 1025 women aged ≥18 years who underwent a c-section between November 2017 and September 2018 at Kirehe District Hospital were randomized into the three following postoperative care arms: (1) home visit intervention (n=335, 32.7%), (2) phone call intervention (n=334, 32.6%), and (3) standard of care (n=356, 34.7%). A CHW-led, mHealth-supported SSI diagnostic protocol was delivered in the two intervention arms, while patients in the standard of care arm were instructed to adhere to routine health center follow-up. We assessed intervention completion in each intervention arm and used logistic regression to assess the odds of returning to care.
The majority of women in Arm 1 (n=295, 88.1%) and Arm 2 (n=226, 67.7%) returned to care and were assessed for an SSI at their local health clinic. There were no significant differences in the rates of returning to clinic within 30 days (P=.21), with high rates found consistently across all three arms (Arm 1: 99.7%, Arm 2: 98.4%, and Arm 3: 99.7%, respectively).
Home-based post–c-section follow-up is feasible in rural Africa when performed by mHealth-supported CHWs. In this study, we found no difference in return to care rates between the intervention arms and standard of care. However, given our previous study findings describing the significant patient-incurred financial burden posed by traveling to a health center, we believe this intervention has the potential to reduce this burden by limiting patient travel to the health center when an SSI is ruled out at home. Further studies are needed (1) to determine the acceptability of this intervention by CHWs and patients as a new standard of care after c-section and (2) to assess whether an app supplementing the mHealth screening checklist with image-based machine learning could improve CHW diagnostic accuracy.