Healthcare

By leveraging patient data, AI and Machine Learning can effectively detect patterns, identify potential risks, and even predict patient outcomes. This capability holds great potential in supporting healthcare professionals with accurate diagnoses, treatment planning, and medication dosage optimization.

To ensure the responsible and ethical use of this technology, the EU is developing an act that will have a profound impact on the application of AI in healthcare, particularly in the field of diagnostics. The act specifically focuses on diagnosing conditions that significantly influence treatment decisions and post-care, categorizing them as high-risk activities. This classification necessitates rigorous risk analysis, documentation, and stringent requirements for algorithm transparency.

7N's AI specialists assist our clients in how to take advantage of the full potential of AI and obtain the benefits of implementing the technology correctly, regardless of the objective. With expertise in healthcare domain knowledge, advanced analytics, and statistical modeling, our specialists ensure responsible and ethical use of AI technology.

Sustainability is essential amidst climate and biodiversity crises, for both private and public actors.

It encompasses ESG (Environment, Social, Governance) considerations. Environmentally, we must minimize our climate and biodiversity impact by analyzing our supply chain, resource usage, and waste management. Socially, we focus on treating employees, patients, and stakeholders well, promoting diversity and reducing absenteeism. Governance ensures a sustainable focus, including risk management and anticipating business risks related to climate, biodiversity, and diversity.

IT plays a vital role in achieving ESG goals. It enables environmentally friendly practices through energy-efficient infrastructure and sustainable procurement, and fosters social inclusivity through improved communication, remote work, and equitable healthcare access. Additionally, IT ensures governance by monitoring ESG performance, protecting data, and supporting adherence to standards. Ambitiously, we strive for a positive difference, not just reducing negative footprints.

Both HIPAA and GDPR impose stringent requirements on healthcare organizations to safeguard patient privacy and data security. Ensuring compliance with these regulations is crucial for organizations operating in the United States and the European Union. Robust data protection measures must be implemented to handle healthcare data securely.

In Denmark, the NIS2 directive will have a significant impact on digital healthcare organizations. Compliance with NIS2 will define the standards and practices for ensuring cybersecurity and resilience in the healthcare sector. Organizations must stay updated on the requirements and take proactive measures to mitigate cybersecurity risks and protect sensitive information.

By prioritizing regulatory compliance in healthcare, organizations can build trust, protect patient confidentiality, and maintain the integrity of their operations. Effective data protection measures and adherence to regulatory frameworks are essential for providing secure and reliable healthcare services in the digital age.

EHRs is one of the key components to the digital transformation of health, enabling public and private healthcare actors to store and share information of patients digitally and safely. This enables more seamless communication, reduces the risks of errors and eliminates the need for paper based records and requires rigid data management and cybersecurity standards.

Important data, such as laboratory results, medical imaging, and bookings, is exchanged through integrations between the electronic patient record and various clinical and paraclinical systems. To ensure high-quality integrations, it is vital to have the right experts involved in the project. Understanding and correctly applying integration patterns (Enterprise Integration Patterns) is key, along with conducting a thorough analysis of integration needs, documenting interfaces, and prioritizing data encryption and protection. Standardized protocols and data models like HL7 and FHIR facilitate efficient and error-free data exchange in the healthcare domain.

In recent years, many companies and organizations, including regions and municipalities, have adopted a centralized approach to procurement and supply chains to reap the benefits of economies of scale. Significant efforts are being made to optimize logistics by utilizing advanced technologies such as artificial intelligence (AI) to help optimize route planning, packaging patterns, and delivery models, as well as different types of automation like robots, cranes, conveyor belts, etc.

Within individual hospitals, there is a growing trend of internalizing logistics functions to enhance the flow of goods from the point of receipt to various departments and kitchens, etc. This involves analyzing the frequency of item usage and determining which items to locate closer to patients, reducing the travel distance for staff, and which can be stored in depots and transported locally.

A successful logistics system relies heavily on accurately tracking locations, items, and events, such as when an item is observed at a specific location. Proper labeling and adherence to standards, such as GS1, are pivotal in ensuring efficient logistics operations. Here, data management is a critical aspect of logistics, as it enables the availability of information to personnel, management, and algorithms, facilitating informed decision-making and improved operational efficiency.

Robotic Process Automation (RPA) in healthcare involves automating rule-based and repetitive tasks to streamline processes and increase operational efficiency. RPA utilizes software robots or "bots" to mimic human actions and interact with various systems and applications. By automating these manual and time-consuming processes, healthcare organizations can reduce errors, improve accuracy, and free up staff to focus on higher-value activities.

While RPA itself does not encompass AI and machine learning, it can be integrated with these technologies to enhance its capabilities. For example, RPA can be combined with AI algorithms to extract relevant information from unstructured data sources, such as medical records or clinical notes, enabling more advanced data processing and analysis. By leveraging RPA alongside AI and machine learning, healthcare providers can achieve a more comprehensive automation strategy that optimizes resource utilization, improves data accuracy, and enhances overall operational efficiency.

Telemedicine and remote patient monitoring are revolutionizing how patients are monitored, receive medication and visit doctors. It allows healthcare professionals to virtual consultations with patients, leveraging video conferencing and other communication technologies.

Remote patient monitoring utilizes connected devices, such as wearables and sensors, to collect and transmit patient data in real-time. This enables healthcare providers to monitor patients' vital signs, medication adherence, and other health indicators remotely, through IoT.

7N specialists understand the transformative impact of telemedicine and remote patient monitoring in healthcare, and can assist healthcare organizations in implementing and optimizing these technologies to enhance patient care and improve operational efficiency.