Technological advances are revolutionizing several sectors, including the healthcare sector. Digital health has allowed the development of a wide range of possibilities to improve the quality of healthcare in a variety of ways, such as promoting access to medical care, accelerating diagnosis and decision-making processes, improving patient outcomes and reduce costs.
Data is transforming healthcare by changing the way medical care is delivered and managed. The convergence of big data, artificial intelligence, wearables and advanced analytics is transforming the paradigm from a traditional reactive system into a more proactive, personalized and predictive healthcare approach. This article focuses on the different ways in which data and emerging technologies are transforming healthcare, including the Portuguese perspective on the digitalization of healthcare.
Big data
In recent decades, data collection and storage has been a common practice in healthcare, along with a growing interest in the secondary use of large amounts of data – big data – to improve clinical care and public health. Gartner proposed the “3V” definition of Big Data: “Big Data are high-volume, high-velocity, and high-variety information assets that require innovative and cost-effective ways of processing information for better insight and decision making” (Beyer et al).
Collected primarily from electronic health records, health insurance records, pharmacy data, medical devices and sensors and biomedical research, clinical data and real-world data help healthcare professionals better understand patient populations, comprehend the effectiveness of treatments, and support regulatory decisions, leading to:
• Personalized medicine: Based on genomic data, medical history, biomarkers, lifestyle information and patient health metrics, healthcare will become tailored to individual needs, increasing treatment effectiveness, reducing side effects and anticipating response to medications.
• Population health management: Aggregating and analyzing clinical data allows caregivers to identify populations at risk for certain diseases and create preventive public health interventions.
• Value-based healthcare: By analyzing patients' clinical indicators and costs, care providers and insurers can evaluate health outcomes and focus on delivering quality care that reduces hospital readmissions and promotes health in the long term, contributing to a value-based care approach.
Artificial intelligence
The consistent collection of clinical data facilitates the use of technologies that systematize not only the amount of health information collected and stored electronically, but also the detail of the conclusions that can be drawn from these data (Adamson et al.).
By identifying patterns and trends from large complex data sets, artificial intelligence (AI) has the ability to increase the accuracy and speed of disease diagnosis and drug development, optimize hospital workflows and manage healthcare systems, and strengthen disease surveillance and outbreak response.
The use of AI prediction models has widespread use in healthcare, whether for identifying patients at high risk of readmission or developing targeted interventions to reduce readmissions (Mohanty et al.), predicting patient admission rates and timing length of hospital stay through improving capacity planning and resource allocation (Davis et al.), forecasting hospital bed occupancy rates and demand (Tello et al.), as well as forecasting and diagnosing several diseases, such as cardiomyopathy (Xia et al.), diabetes (Lu et al.) or Alzheimer's (Uysal and Ozturk). Ultimately, AI can have a huge impact on:
• Predictive and preventive care: By analyzing patterns in patient data, algorithms can identify early warning signs in chronic diseases, enabling interventions and preventative measures, reducing healthcare costs and improving patient outcomes.
• Clinical decision support systems: Data-driven tools can provide clinicians with real-time case-based decision support systems and predictive models. These tools can help doctors make more informed decisions, reduce diagnostic errors, and choose the most effective treatment options.
• Unstructured data analysis: The collected health data can be structured and unstructured, as integrating data stored in both formats can add significant value to healthcare. Natural Language Processing can extract meaningful information from unstructured healthcare data such as medical notes, clinical reports, speech and research articles, helping to automate data entry, discover patterns in patient narratives and improve the quality of documentation in health systems.
Telemedicine and remote monitoring
The COVID-19 pandemic has been a catalyst for the development and adoption of a wide range of telehealth technologies by the health systems, such as virtual facilities and telecare platforms (Bouabida et al). The Internet of Things (IoT) and wearables have played a significant role in the expansion of telehealth, as data collected from these network of devices play a key role in enabling remote patient monitoring and remote care. Such devices can collect metrics – heart rate, blood pressure, glucose levels, activity levels and sleep patterns – which can be automatically transmitted to healthcare professionals. Thus, telemedicine and wearables enable continuous, real-time monitoring of patients, both for preventive healthcare and chronic disease management, reducing hospital admissions and enabling provision of care to remote populations. Additionally, telemedicine and wearables can significantly improve:
• Patient engagement: These technologies enable patients to take a more active role in managing their healthcare and monitor and track their progress, promoting self-care and adherence to treatment plans.
• Data-driven clinical trials: By aggregating data from real-world, wearable, and patient-reported outcomes and analyzing data from larger, more diverse populations, AI can help identify eligible patients for clinical trials, optimize trial designs, and generate insights about the efficacy and safety of drugs, creating virtual or hybrid clinical trials that will be faster, cheaper and more inclusive.
Interoperability and data security
Healthcare data is challenging. On the one hand, it is sensitive and require a high level of privacy and security to be shared. On the other hand, the inability to access health data can cause substantial harm to individuals and populations. Given the increasing in life expectancy, sharing health data will be critical. As healthcare systems generate more data, there is an increasing need for interoperability between different healthcare information technology systems. Interoperability allows healthcare providers in hospitals, clinics and laboratories to access patient data from multiple sources. Some interoperability standards, such as Fast Healthcare Interoperability Resources (HL7 FHIR) and Electronic health information exchange (HIE), are making it possible to exchange health data between different players, allowing for better coordination of care and reducing redundancies in medical exams or treatments.
Furthermore, technologies such as blockchain are revolutionizing the way health data is stored and shared, ensuring that patient information is secure, immutable and accessible only by authorized parties, eliminating issues related to fraud and missing information, and ensuring accuracy in health records while giving patients more control to grant access to their records.
On the other hand, ensuring compliance with regulations such as the General Data Protection Regulation (GDPR) is critical to protect patient data and maintain trust. As privacy concerns grow, synthetic data is being used to simulate real-world patient data without exposing sensitive information. AI can generate synthetic datasets that reflect real patient behaviors for research and model training purposes, enabling the development of new treatments and public health strategies without exposing sensitive patient information, ensuring compliance with privacy laws.
Implications of data intelligence in healthcare management
The implications of data intelligence in healthcare management represent a critical advancement that leverages big data, AI, and data analytics to improve decision-making, patient care, and operational efficiency. This underlies the growing dependence of contemporary healthcare systems on effective data management to improve hospital performance, facilitate real-time analytics, and ensure ethical implementation of AI technologies.
Recent studies have described the decisive role that data governance, integration and quality play in transforming healthcare operations, which directly impacts care outcomes, patient safety and research possibilities (Tang et al.). The ability to plan ahead through predictive analytics allows hospitals to use this tool to reduce waiting times, proactively schedule follow-up appointments, identify areas of inefficiency, and monitor the allocation of human and logistical resources. This ability depends heavily on two main factors that cannot be dissociated: (i) comprehensive data collection, including patient admission records, demographic and seasonal information, and even external indicators such as public health data and local events, and (ii) high quality and accuracy of data, especially real-time data, which become crucial in emergency scenarios such as pandemic waves (Klein et al.).
Digitalization of healthcare: the Portuguese perspective
Portugal has been creating conditions to accelerate digitalization through an action plan for digital transition, which comprises a pillar related to the digitalization of public services and the national health system, including the adoption of a data-based approach in hospitals, from central clinical activities to critical management operations.
As part of the National Strategy for the Health Information Ecosystem – ENESIS 2020-2022, the Advanced Analytics and Intelligence Unit of the Shared Services of the Portuguese Ministry of Health (SPMS) launched the data strategy for the next generation of the National Health Service (NHS) (Pinto et al.). It contains the vision, key areas and principles of secondary data use, advanced analytics and artificial intelligence to improve the health of the Portuguese population and calls for a comprehensive approach to reinforce the digital transformation of the NHS through a strong data governance model and including AI-supported systems to add value.
More recently, Portugal stood out as the second European country that most increased its maturity in terms of access to digital health data in the report ‘Digital Decade 2024: Study of Digital Health Indicators’ (Winkel et al.). The study assessed the progress towards the Digital Decade's key objective of ensuring that 100% of EU citizens have access to their electronic health record by 2030 and covered four main areas: (i) electronic access services for citizens, (ii) healthcare data categories, (iii) technology and coverage, and (iv) access opportunities. In terms of citizens' ability to access their electronic health record, Portugal scored 100%, ranking 5th in the technology and access coverage indicator. In the indicators of availability of categories of electronic health data and opportunity for access to the electronic health record by specific groups such as children, elderly people or citizens with disabilities, Portugal improved 17% and 38%, respectively, compared to 2022.
These results are promising and reveal the continuous improvement of digital health over the last years. Data CoLAB's mission is aligned with this purpose of empowering the healthcare sector with the skills necessary to achieve a sustainable digital transformation, and to contribute to the digitalization of the sector by developing innovative data acquisition, interoperability, intelligence and data visualization solutions. and data security.
Conclusion
In summary, data has the ability to profoundly transform healthcare, making it more predictive, personalized, proactive and efficient. From optimizing clinical flows to increasing patient engagement and improving health indicators, data and emerging technologies are on track to become key in reshaping the healthcare landscape.
References
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Adamson, B., et al. (2023). Approach to machine learning for extraction of real-world data variables from electronic health records. Frontiers in Pharmacology, 14, 1180962.
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Bárbara Patrício
Program Manager – Health and Pharma