What is HealthTech?

As the demand for personalized healthcare skyrockets, software developers are faced with unprecedented opportunities. With a projected growth rate of 16.80%, the global digital health market is expected to reach a value of $586.59 billion by 2030. The industry’s on a growth spree and it’s not stopping soon. 

The advent of the AI revolution and machine learning plays a huge role in big data analytics. It’s allowing more efficient, personalized care than ever before. We’ve created this HealthTech x AI series to explore the current state of the industry and how your company can have a competitive edge.

As with all advancements, healthcare AI is a complex topic. Significant challenges face developers: data security, legal regulations, high costs, and pressing urgency. In such a dynamic industry, bringing new tech to market requires the perfect balance of speed and quality. 

As we enter this era of new potential, let’s ask the right questions. How is HealthTech being used right now? What solutions can your team build? And—most importantly—how can your company find its niche in a changing landscape?

First, let’s tackle the most fundamental question: what is HealthTech? There’s no easy answer, no single definition. 

In recent years, the World Health Organization’s Digital Health and Innovation team defined it thus: 

“Synonymous with ‘digital health…’ A health technology is the application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures and systems developed to solve a health problem and improve quality of lives.” 

In other words, HealthTech could be anything that helps improve health and patient care. This definition leaves a lot of room for interpretation, which makes sense in such a changing landscape. As the industry evolves, our understanding of HealthTech grows. 

But let’s condense it a little. At its most generic, HealthTech is the intersection of Healthcare and Technology: any way that tech enhances patient care. 

This casts a wide net. For software developers building innovative solutions, Diagnostics, Health Management Tools, and Biopharma are the most promising and impactful. In our HealthTechxAI series, we’ll touch on all of them. For now let’s focus on the role of Artificial Intelligence, or AI.

To understand AI’s role in HealthTech, we need to understand the difference between AI and machine learning. At its heart is a core concept: All machine learning is artificial intelligence, but not all artificial intelligence is machine learning. 

That is to say, machine learning is a subset of artificial intelligence. The two terms shouldn’t be used interchangeably. 

Machine learning enables machines to learn from existing data and experiences without being explicitly programmed. Machine learning can generate results, or make predictions, based on data analysis. The goal is to perform a specific, limited task and give an accurate result.

Artificial intelligence encompasses much more. The term “AI” describes the creation of machines that simulate human thought and behavior. This enables AI to use machine learning in its decision-making process. Instead of simply providing an accurate result or making a prediction, AI systems give intelligent advice. This includes learning, reasoning, and drawing its own conclusions. 

While machine learning focuses on accuracy and pattern recognition, AI works to maximize success, however “success” is defined.

It’s impossible to separate AI from modern HealthTech solutions. The AI industry is forecasted to grow by 37% per year, every year from 2022-2030. This means a jump in market value from $15.1 billion to $187.5 billion in only eight years. The future of AI in healthcare isn’t just secure, it’s crucial.

Leveraging AI and machine learning allows development companies to transform healthcare in crucial ways like…

• Automating Daily Tasks: Any medical professional will tell you one of the most important and tedious parts of the job is healthcare documentation. Data entry, insurance application and authorization, medical coding, and bill collection are all ripe for automation. Leveraging AI for automation in healthcare facilitates quick and efficient completion while limiting errors. This frees time and energy for more pressing tasks. 
• Data Analysis and Insight: AI has become a critical tool in data collection and analysis, allowing medical professionals to diagnose conditions accurately and efficiently. By analyzing scans, tests, and bloodwork, AI tools provide valuable insight and promote better patient treatment plans.

• Predictive Analysis / Risk Management: AI tools analyze risk factors alongside a patient’s data to predict their chances of developing certain diseases. This helps medical professionals catch illnesses before symptoms escalate. 
• Personalized Medicine: As AI tools can quickly process large amounts of data and detect patterns, they can predict effective treatment strategies. This allows doctors to better individualize care, taking all patient data into account. AI tools help offset the limited time that normally makes widespread personalized medicine impossible. 
• Drug Discovery: AI is enormously important in modern drug discovery because it can recognize hit and lead compounds, optimizing the chemical starting points that modulate a drug target. In other words, AI finds the best places for pharmaceutical developers to focus their work so they can refine compounds to reach the intended result. 

At Jobsity, our developers work with industry leaders to effect change. As we look to the future, we see niches where software development companies have the opportunity to impact HealthTech. Creating more efficient data analytics is the direct path to improving patient outcomes.

Health and healthcare are particularly complex topics as they sit at the exact intersection of “personal” and “public.” The two are inexorably linked when discussing artificial intelligence. In order to make the technological advances patients need, their data has to be used. AI and machine learning processes rely on personal health data and intimate patient histories for their databases. 

While every patient has the right to refuse healthcare AI treatment and diagnostic suggestions, their medical data rights remain a grey area. In the U.S., the Health Insurance Portability and Accountability Act (HIPAA) ensures that Protected Health Information (PHI) can’t be sold. However, whether the data itself can be sold and used to train AI systems is a separate question. 

The current compromise is PHI randomization. When using patient scans, metrics, trends, and genomes to train AI processes and broaden databases, all identifiable information is scrubbed. Patients are assigned a randomly generated “case number” that protects their identity. 

While AI simulates human thought, it can’t make judgment calls about patient privacy. The world of HealthTech and biomedical research is constantly evolving, and the legislation surrounding patient data protection must keep up. 

As we look to the future of artificial intelligence, machine learning, and healthcare, the opportunities are limitless. Developing the future of personalized medicine requires a variety of technical staff. 

Properly staffing your development team ensures the skills, creativity, and drive necessary to compete in a dynamic marketplace. The following roles are most in demand across artificial intelligence and HealthTech development.

• Software Engineers: Software engineers work on software infrastructure, including building the frameworks and platforms necessary for AI development. They also collaborate with machine learning engineers to integrate AI models into production systems.

• Artificial Intelligence Engineers: AI engineers train AI models using the algorithms and data created by machine learning engineers. There’s significant overlap between the two disciplines. Artificial intelligence engineers equip the AI models to extrapolate, make intelligent suggestions, and use language in a way natural to humans (NLP). 

• Machine Learning Engineers: Machine learning engineers specialize in developing and implementing machine learning algorithms and models. They’re responsible for data preprocessing, model training, and optimizing models for accuracy and performance. This is essential to building or integrating a functional AI program.

• DevOps Engineers: These specialists play a crucial role in AI software development by managing the deployment, monitoring, and scaling of AI models and applications. They ensure that AI systems are reliable and performant.

• Data Engineers: Data engineers manage the vast amount of data needed for the AI models, ensuring it's well-organized and in a suitable format for analysis. They standardize formats and remove inconsistencies, preprocessing the data to make it ready for the machine learning algorithms. They also oversee the design and maintenance of databases and infrastructure.

When you're working on creating new platforms, systems, and apps for the HealthTech industry, timing is crucial. You need a team you can rely on to help you bring projects to life and products to market.

That's where Jobsity comes in.

Jobsity is effective, convenient, and affordable. We handpick candidates who directly fit your goals, while saving you up to 40% on hiring costs. Even better? We offer a risk-free trial to make sure they're a great match.

Our AI experts make development and integration easy. Jobsity engineers have experience in languages like Java, Python, and other industry juggernauts. They'll fit in well with your in-house team: Jobsity developers are great communicators, proactive, and passionate about their work. Plus, anyone you hire from Jobsity will be in your time zone or close to it, so collaborating is a breeze.

Now is a great time to improve your product, expand your business, and be part of the healthcare revolution. Set up a call today to get started!