Differing approaches to COVID-19 contact-tracing apps
As the coronavirus pandemic continues, one set of tools increasingly mooted as offering a potential way out of lockdown are contact-tracing mobile apps.
Contact tracing itself is nothing new as a fundamental component of outbreak control, albeit usually done in a manual manner. This is achieved, for instance, by researching the movements of a confirmed infected individual, and contacting all those who may have come into contact with her.
What’s new is automating this process via the use of the ubiquitous mobile phone. An application installed on the phone will track the user’s movements while Bluetooth signals emitted periodically will be able to determine the length of proximity to other people. The central idea is that an individual who contracts COVID-19 can mark themselves as such, which will then send out an alert to the phones of people who have been near the infected person for a certain period of time.
Such an activity raises a host of ethical and security questions, however, particularly in the security of the data detailing people’s movements.
The large American tech companies such as Apple and Google favour a decentralised approach, which keeps data about user movements on an individual’s phone, thus maximising user privacy.
Some nations, however, favour a centralised model whereby such data will be processed in a central server, potentially allowing for better data processing and the gleaning of insights about how the virus itself spreads.
While the world is currently split, it seems likely that one form will prevail over the other - think Blu-ray vs HD DVD or Betamax vs VHS. The UK, which has been developing an app on the centralised model, has reportedly commissioned a feasibility study into moving in a decentralised direction. That development came after the UK’s app was trialled on the Isle of Wight, where some 40,000 people have been testing the application.
Introducing Dosis - the AI powered dosing platform
Cloud-based platform Dosis uses AI to help patients and clinicians tailor their medication plans. Shivrat Chhabra, CEO and co-founder, tells us how it works.
When and why was Dosis founded?
Divya, my co-founder and I founded Dosis in 2017 with the purpose of creating a personalised dosing platform. We see personalisation in so many aspects of our lives, but not in the amount of medication we receive. We came across some research at the University of Louisville that personalised the dosing of a class of drugs called ESAs that are used to treat chronic anaemia. We thought, if commercialised, this could greatly benefit the healthcare industry by introducing precision medicine to drug dosing.
The research also showed that by taking this personalised approach, less drugs were needed to achieve the same or better outcomes. That meant that patients were exposed to less medication, so there was a lower likelihood of side effects. It also meant that the cost of care was reduced.
What is the Strategic Anemia Advisor?
Dosis’s flagship product, Strategic Anemia Advisor (SAA), personalises the dosing of Erythropoiesis Stimulating Agents (ESAs). ESAs are a class of drugs used to treat chronic anaemia, a common complication of chronic kidney disease.
SAA takes into account a patient’s previous ESA doses and lab levels, determines the patient’s unique response to the drug and outputs an ESA dose recommendation to keep the patient within a specified therapeutic target range. Healthcare providers use SAA as a clinical decision support tool.
What else is Dosis working on?
In the near term, we are working on releasing a personalised dosing module for IV iron, another drug that’s used in tandem with ESAs to treat chronic anaemia. We’re also working on personalising the dosing for the three drugs used to treat Mineral Bone Disorder. We’re very excited to expand our platform to these new drugs.
What are Dosis' strategic goals for the next 2-3 years?
We strongly believe that personalised dosing will be the standard of care within the next decade, and we’re honored to be a part of making that future a reality. In the next few years, we see Dosis entering partnerships with other companies that operate within value-based care environments, where tools like ours that help reduce cost while maintaining or improving outcomes are extremely useful.
What do you think AI's greatest benefits to healthcare are?
If designed well, AI in healthcare allows for a practical and usable way to deploy solutions that would not be feasible otherwise. For example, it’s possible for someone to manually solve the mathematical equations necessary to personalise drug dosing, but it is just not practical. AI in healthcare offers an exciting path forward for implementing solutions that for so long have appeared impractical or impossible.