Closing Healthcare Gaps with AI: Challenges and Opportunities of Integration

1. Healthcare gaps

There are still general challenges in the European Union’s healthcare systems, such as a lack of access to healthcare services, low quality of care and poorer outcomes for vulnerable groups [1].

However, specific gaps in care for female patients cannot be denied, such as limited and less effective treatment options, delayed diagnosis and low investment in women’s health research and development [2].

2. Solutions for healthcare gaps

How can the identified gaps in healthcare be closed?

One possible solution is an appeal to politicians. Political interventions have the power to create real change within our society. For example, healthcare services for vulnerable population groups with higher needs could be expanded (redefining the benefits of statutory health insurance). Or further investments could be made to create productive, digital processes to reduce waiting times and regional inequalities and increase efficiency and quality in healthcare. [1]

3. Barriers in the healthcare system

‘According to the World Health Organisation, health equity means that everyone should have a fair chance to reach their full health potential and that no one should be disadvantaged in reaching that potential.’ [3]

In most cases, not everyone is given this opportunity to achieve their optimal health due to lack of physical availability of services (long distances to service providers, limited opening hours, waiting times and waiting lists), lack of ability of the person to advocate for obtaining the necessary care (inability of the person to formulate a request for care or apply for reimbursement) or due to the attitude of the service provider towards certain patients (discrimination, social stigmatisation). [1]

In addition, the reasons for gaps in care just listed are unfortunately also closely linked to the so-called social determinants of health of Dahlgren and Whitehead (1991). These social determinants are, for example, age and gender, individual lifestyle and behaviour, but also one’s own social network, living and working conditions and the general socio-economic and cultural environment in which one lives. [4]

Analysing and improving these determinants is also a fundamental element in closing gaps in care [3].

4. Potential of AI and big data

The most important question is probably how exactly can AI help to close the identified gaps in the healthcare system?

A successful AI system must be able to process structured data such as images or genetic data and analyse unstructured data such as text. Sophisticated algorithms are then trained using health data in order to ultimately support medical staff in making a diagnosis and finding the correct treatment option. [5]

With the help of AI systems customised for the healthcare sector, efficiency, labour productivity and service quality in medicine can be revolutionised and improved access to individual and affordable healthcare can be guaranteed [6].

AI, big data and robots have the potential to revolutionise the entire healthcare system by helping to reduce errors in diagnosis and treatment, support clinical decision-making, predict health outcomes, diagnose diseases, monitor adverse effects and provide real-time warnings of health risks. In addition, robots can support elderly care and the care of people with chronic diseases. [5]

5. Barriers to AI, big data and robotics in healthcare

There are barriers to AI, big data and robotics in the healthcare sector. These include the lack of implemented regulations to evaluate the safety and effectiveness of AI systems, the availability of health data and data sharing to continuously improve systems [5].

6. Minimising the risks of AI

How can the risks of AI and digital health applications be minimised?

Data protection measures, education about self-determined privacy and minimising discrimination are crucial for the safe use of AI in healthcare [6].

Only targeted, anonymised release of information to the public avoids stigmatisation and discrimination based on personal characteristics [6].

Conclusion

The integration of AI into the healthcare system offers immense opportunities, but also poses challenges that need to be addressed through targeted measures in order to create an equal healthcare system.

---

Takeaway points:

• In healthcare, common challenges such as lack of access to services, low quality of care and poorer outcomes for vulnerable groups persist.
• Policy interventions and investment in digital processes could help close gaps in care.
• Barriers such as the lack of regulations to assess the safety of AI systems and problems with data sharing hinder the implementation of AI, big data and robotics in healthcare.
• AI, big data and robotics could revolutionise the healthcare system by reducing diagnostic and treatment errors and improving access to healthcare.
• To minimise the risks of AI, data protection measures, privacy education and anti-discrimination are crucial.

For more Information

EQUAL CARE certifies medical intervention with a balanced gender representation in data and evidence. Join EQUAL CARE today and lead the health market with our certification. Together, we can set a new standard for healthcare excellence and create a future where everyone receives the care they deserve.

Let’s meet: https://calendly.com/thao_equalcare/30min

If you would like to find out more about the topics of gender-specific medicine and the related work of EQUAL CARE,
visit us on Instagram, X, LinkedIn or on our website www.equal-care.org.

Sources: