In this blog we’ll examine healthcare software development in clinical and administrative settings and look at how it’s improving the health of the market.
‘Over the last five years, AI deal activity has grown faster in healthcare than in tech overall’, says a report on artificial intelligence in the sector. Investors have woken up to the potential of the technology to enable healthcare workers to – among myriad other things – diagnose more accurately, handle data more securely and treat patients more efficiently.
An injection of investment
The level of investment in recent years reflects the rate of healthcare software AI development and the increasing sophistication of solutions. From clinic mobile app development to optimise resources, to virtual assistants to support patients; new use cases are being tried, tested and deployed. These can be categorised into two areas: clinical and administrative.
Healthcare AI software development is having arguably the most visible impact in the clinical setting. It’s not difficult to see why. Augmenting human intelligence with technology with the aim of saving lives, tackling deadly diseases and improving patient care makes for great headlines. In the past few months we’ve heard, for instance, how an AI-enhanced blood test can enable early Parkinson’s diagnoses, how AI is being used to predict Malaria outbreaks, and how the first NHS AI-run physiotherapy clinic will be rolled out this year.
Regulating operations
Many healthcare AI software solutions are still in development or early trial stages. Healthcare is a highly regulated sector, yet regulations and data governance legislation for AI as a whole are arguably lacking. Furthermore, in the UK many healthcare AI software products are regulated as medical devices, presenting a further potential hurdle for developers.
Despite this, there are many examples of live deployments of clinical AI software which are aiding clinicians on a day-to-day basis. UK start-up Kheiron Medical Technologies’ software is being used to support cancer screening, for example. As well as screening and diagnoses, other types of tools in deployment include:
- Clinical decision support software, which assists healthcare workers in decision making
- Electronic medical record software, which manages data from a single healthcare provider
- Electronic health record software, which collates and manages data from multiple providers
The second area of healthcare AI software development is less hyped, but no less important. While journalists may be shouting about innovative AI deployment on the frontline of healthcare or at the forefront of research in a lab, we thought we’d also focus on its use behind-the-scenes: the administrative setting.
Administering a dose of AI
Healthcare staff are increasingly relying on software to drive efficiencies, reduce costs and improve patient care. Often used by healthcare facilities looking after hundreds or thousands of patients, these solutions have the potential to solve the kinds of issues which affect a large proportion of the general public. A successful trial by an NHS Foundation Trust of AI healthcare software that can reduce waiting times, for example, is set to be deployed by 10 further trusts.
In addition, we’re seeing AI embedded in software solutions to support with:
- Appointment scheduling
- Patient registration and data management
- Medical billing, for example in private clinics
- Resource allocation and staff management
- Virtual assistants and chatbots
- Fraud detection and prevention, such as highlighting potentially fraudulent medical insurance claims
- Patient flow management, such as how patients utilise and move through busy accident and emergency centres
A positive prognosis for healthcare software
Software solutions in both the clinical and administrative settings are being used to complement the judgements and increase the accuracy of frontline staff. Administrative teams are able to work more efficiently and handle increasingly large amounts of data, thanks to automation and data analytics built into many solutions. Researchers and clinicians on the other hand benefit from enhanced diagnostic accuracy, thanks to the ability of AI to analyse vast amounts of medical data.
On a broader scale, the healthcare sector as a whole also stands to benefit. By optimising staff schedules and the allocation of hospital resources, operational costs can be cut. This is an important point for private clinics and a critical one for the NHS.
Healthcare software AI development – and its regulation – will continue to evolve at pace. This will hopefully be mirrored by ongoing investment, meaning solutions will continue to enable a diverse range of applications. Ultimately though, these applications all support the same end goal: helping clinicians and admin teams focus less on technology and more on what matters most: their patients’ wellbeing.