Patients’ access to treatment and advice is already dramatically improving, as a result of mobile health, or “mHealth”. Now, when combined with internet-connected diagnostic devices, it offers novel ways to diagnose, track and control infectious diseases, and to improve the efficiency of the health system. A research team led by Imperial College London has investigated the opportunities and challenges of these technologies. Louise Thomas considers the implications for healthcare worldwide.
Rapid advances in technology have dramatically improved the speed and efficiency with which data can be processed and exchanged.
The advent of smartphones, in particular, and the networks needed to support them are rapidly reducing the costs of data gathering and transfer worldwide. This is particularly advantageous to resource-limited settings where there are often high barriers to care.
The ways in which disease is detected and responded to are also continually improving. The development of more sensitive, and specific, immunological and molecular-based diagnostics, and genetic sequencing, has facilitated the ability to diagnose an increasing number of diseases.
This in turn has enhanced the understanding of the burden and transmission of infectious agents as well as informing clinical decision-making, especially in the field of infectious diseases.
How mHealth devices offer potential for infectious disease response
Of course, the diagnosis and monitoring of diseases are key to clinical management.
Infectious diseases, however, represent a unique challenge because these can be transmitted to others and thus early detection and treatment are key to prevent outbreaks. In order to limit the spread of infection, mHealth tools must therefore be integrated with effective surveillance and control measures.
A 2019 paper published in Nature identified two main mechanisms that would allow these technologies to improve the efficiency, speed and interconnectedness of an integrated clinical and public health response – increased access to healthcare outside care settings and the real-time, or nearly real-time, reporting of diagnostic results to elicit rapid, and appropriate, clinical and public health responses to endemic infections and outbreaks of epidemic potential.
Most mHealth interventions have focused on the use of established mobile technologies to connect healthcare professionals and patients with each other and/or with test results. The use of portable diagnostic tools thus have huge potential to streamline these processes.
The current global risk of antimicrobial-resistant infections is huge and demands improved diagnostics to guide antimicrobial therapy. Connected diagnostics that can simultaneously detect a pathogen, and identify antimicrobial sensitivity and resistance, are thus hugely valuable because they are able to select appropriate treatments while reporting results to surveillance centres.
Benefits of mHealth devices include cost savings
In resource-limited settings, where health services are often already overwhelmed, mHealth approaches can also be hugely beneficial. Taking diagnostics outside formal health facilities and into the community in these situations could provide a cost-effective and user-friendly solution.
These interventions could improve patient access to precision medicine. In resource-rich settings, connected healthcare systems are already starting to stratify patients into remote-treatment and response-monitoring programmes.
The use of connected diagnostics and symptom-reporting apps, along with the electronic collection of epidemiological and clinical data, has huge potential to enhance the efficiency and speed of managing epidemic and endemic infections.
The real-time reporting of diagnostic test results can enable this surveillance through the geospatial mapping of infections via geotagged test results, social network and internet search analysis, providing new tools for effective outbreak control.
Despite the potential benefits and increasing number of diagnostic devices, it is still early days in terms of implementation of these technologies. For example, there is not yet an mHealth intervention featuring a connected diagnostic linked to a clinical care pathway and/or surveillance system for an infectious disease.
To achieve successful integration, systems need to be established for the secure transfer, analysis and storage of the data generated. Any conclusions made on the basis of this information must be reported to and acted upon by either the patient, healthcare professional or relevant institution, along with linkage to a suitable care pathway.
Of course, for this to run smoothly, measures must be in place to protect against the misuse of confidential health and personal data.
mHealth devices create a connected point of care
The World Health Organisation’s (WHO) “ASSURED” criteria outlines the key features for point-of-care diagnostics. Connected devices have additional requirements whereby the signal generated must be transduced into digital information ready for transmission.
Systems meeting these needs have already been developed and these technologies are being increasingly used to create connected point-of-care diagnostics. These devices tend to either use the sensors already in the phone, or use those external to the phone, and take advantage of its computational and connective power to create a diagnostic.
A smartphone camera could potentially take the place of advanced laboratory-based spectrometers, matching their quantitation and multiplexing capability via innovative engineering. Such developments would permit access to otherwise costly laboratory equipment and reduce the training required to interpret test results.
An example of this is smartphone-based microscopy, which is becoming increasingly used within parasitic infections. It is fast approaching the standard of laboratory-based microscopes but with a substantially lower upfront cost.
Smartphone-based microscopy is even yielding portable, handheld options for fluorescent imaging of viruses and DNA molecules.
Sensors in smartphones have also been explored in a broader context, including the accelerometer for monitoring the body’s motion, changes of which can
be linked with diseases such as Parkinson’s, and the microphone, which can be used to monitor lung function. As new capabilities continue to be added to smartphones, the full diagnostic applications of these technologies is yet to be seen.
External sensors are particularly valuable overcoming the issue of interoperability within healthcare, which hampers approval by regulators.
These can be engineered around any suitable biosensor or signal transduction system and connected to share data via mobile networks. Many manufacturers have started to integrate internet connectivity directly into their laboratory-based diagnostic equipment, providing faster access to results and enhanced integration into laboratory information management systems.
As these devices decrease in size, they are being increasingly deployed at or near to the point of care and have recently been applied in response to the recent Ebola epidemics.
Automated result analysis has the potential to dramatically reduce user error when interpreting, recording and transmitting results of diagnostic tests. Currently there are a number of methods to automate the visual interpretation of images, with suitability dependent on the type of data and resources available within a particular setting.
Cloud-based methods, for example, work best for more computationally expensive analysis, such as high-resolution image or video data, in situations where there is sufficient connectivity.
If connectivity is low, on-phone feature extraction to reduce the size of the images before their transmission and cloud-based interpretation can overcome this issue. Cloud-based systems are highly advantageous, as they allow connectivity to databases and algorithms to be updated centrally. They also remove the processing burden from mobile devices, thereby increasing the range of compatible devices.
Challenges in mHealth
On-phone analysis is most suited when less complex analysis is required or in remote settings with limited mobile network connectivity and bandwidth. These methods can reduce the amount of data that needs to be transmitted and allow results to be stored on-phone and subsequently uploaded once in range of mobile networks.
These capabilities are further enhanced by the continual improvements in mobile processing hardware. The use of dedicated neural-processing units and software frameworks for on-phone machine learning facilitate increasingly efficient and nuanced image classification, and could also improve automated inference when using defective equipment, or in poor lighting conditions.
Despite the huge potential of mHealth tools as a valuable source of data, they are associated with a number of challenges. Regulation is a key issue because it has not kept pace with the rate of technological advancement.
Authorities, such as the US Food and Drug Administration (FDA) and the UK Medicines and Healthcare Products Regulatory Agency (MRHA), have adopted a tentative approach to legislation around these applications.
In order to address this issue, there is a need for regulatory frameworks that are able to be applied to the wide range of technologies available, as well as harmonisation among different regulatory bodies so that legislation does not become a barrier to future innovation.
The devices themselves also pose issues. If a diagnostic test is designed to be used with a range of smartphones, then their variability in hardware and software makes it difficult to assess risk in the regulatory review process. This is causing companies to either develop individual devices with defined components or ship a standardised smartphone with the test while carefully monitoring the software environment, both of which increase cost.
The clinical governance of mHealth-based care pathways also demands consideration. In instances where patients provide data remotely, it is essential that there is escalation capability to a range of healthcare professionals and to face-to-face services when required. This requires quality assurance of clinical decision trees, care pathways and remote prescribing decisions, as well as a secure and user-friendly interface for remote use.
Cost and clinical effectiveness must also be comprehensively assessed on a large scale for the successful implementation of these tools into healthcare. Although these parameters have been assessed in point-of-care diagnostics and some mHealth strategies, connected diagnostics with associated mHealth interventions have yet to be analysed.
This is because of the inherent complexity in evaluating individual components of these devices, which consist of mutually dependent interactions.
While mHealth technologies provide the opportunity to broaden access to diagnosis for a range of health conditions, it is important to consider those who may be left behind. It is estimated that 35% of the world’s population do not have access to mobile technologies. This is primarily because of the lack of access to these tools in low and middle-income countries, although those of lower socio-economic status in resource-rich settings are also affected.
Evidence suggests the gaps are narrowing but more needs to be done to ensure that these technologies are available to all, particularly those most in need and with the largest barriers to overcome.
