Hospital Information Systems (HIS): context, challenges, and opportunities in healthcare data

Healthcare institutions generate and store a vast amount of data. From the moment a patient enters a facility, they provide a significant amount of health-related information. Whether derived from therapeutic protocols, research, or simple questionnaires, this data contributes to a heterogeneous data environment. Systems must now handle the exponential growth of medical data, driven by advancements in genomics and proteomics, alongside laboratory data, patient history, clinical research, and health-related online content.

Assessing healthcare effectiveness follows strict regulations, often detached from a patient’s real-life experience. As a result, treatment adherence and follow-up can be inconsistent. The collection of real-world data (RWD) is emerging as a valuable new data source for healthcare institutions.

Today, the digitalization of the healthcare process is inevitable. Almost all hospitals operate with a hospital information system (HIS). A key benefit of such a system is the ability to maintain an electronic medical record (EMR), which can be accessed by healthcare professionals within the hospital.

Increasingly, hospitals are forming networks based on geographic proximity or similar medical specializations, sharing hospital information and moving toward unified information systems. These systems integrate laboratory and imaging results, medical records, treatment plans, prescriptions, and chemotherapy regimens.

Hospital information systems also have a logistical role, streamlining the management of operating rooms, hospital beds, medical appointments, and human resources (such as work schedules). Standardizing information sharing between hospitals and private healthcare professionals (such as general practitioners) further enhances patient care coordination.

The challenge of data growth and system interoperability

Data is now more abundant and faster than ever, with seemingly no limit to its growth. Various tools and systems attempt to interact and form increasingly complex platforms, further increasing data heterogeneity.

Processing this data manually is an immense task. Retrospective analysis of hospital information system data is a valuable resource, not only for hospital logistics but also for public health.

More and more, hospital information systems must evolve beyond being purely descriptive. They must learn from the collected data, supporting logistics, real-time decision-making, and medical diagnosis.

The role of Soladis in healthcare data management

Soladis, through its expertise in Clinical, Scientific, Engineering, and Data Science fields, helps integrate scientific knowledge, best practices, and advanced technologies. Achieving this goal requires mastering and learning from accumulated data, analyzing it effectively, and incorporating feedback from professionals to refine predictive models.

This learning process relies on the development of a standardized knowledge base capable of supporting various analyses and addressing unforeseen questions. Statistical analysis models help validate or refute hypotheses, detect changes in behaviors or processes, and track medical trends.

Characteristics of a learning Hospital Information System

An advanced HIS should have the following features:

• Integration of healthcare expertise and operational tools (such as EMRs, genomic data, and laboratory information).
• Support for best-practice identification by analyzing behavior patterns across different patient groups.
• Expansion of knowledge through health data exchanged on social networks.
• Data reconciliation across multiple sources (biological, clinical, social, and environmental) to drive personalized medicine innovations.
• Preparation of analyses using validated statistical and mathematical methods, especially for severe medical treatments. This could help mitigate the metabolic strain on patients by implementing severity scoring systems.
• Acquisition and standardization of patient medical data, clinical research, and specialized health content, ensuring continuous enhancement of the knowledge base.
• Real-time patient monitoring through comparison with similar patient groups, enabling early detection of symptoms or disease progression.
• Exploratory data analysis combined with advanced analytics, allowing users to uncover behaviors, trends, and correlations to develop new hypotheses.
• Preventive healthcare applications, including early symptom detection and lifestyle-based health recommendations through wellness plugins.
• Integration of connected devices, enabling a 360° patient view by incorporating non-medical factors influencing health.

Artificial Intelligence and predictive analytics in healthcare

Decision support is one of the primary objectives of a learning HIS. While AI will not replace medical practitioners, it will significantly impact their daily workflow. AI is already widely used in medical imaging, particularly for tumor detection.

Several AI and statistical models offer practical applications:

• Machine learning can help implement predictive analytics strategies to identify high-risk patients for adverse events or hospital readmissions.
• Time-series models can forecast events like ICU bed availability, emergency department congestion, or seasonal epidemics, allowing hospitals to optimize resource allocation.
• Healthcare consumption mapping can quantify common pathologies and track their evolution, incorporating demographic and environmental factors that may impact healthcare demand.
• Cost reduction through data-driven analysis, identifying key variables contributing to expenses and optimizing resource allocation without compromising care quality.
• Real-time hospital activity monitoring, using key performance indicators (KPIs) for interactive and data-driven decision-making.

Conclusion: the future of smart healthcare systems

The transformation of healthcare institutions hinges on three pillars: openness, coherence, and balance. These principles drive continuous improvement, ensuring quality management, minimizing waste, and mitigating modern healthcare risks before they become crises.

Soladis, as a consulting firm specializing in statistics and data analysis, remains vigilant in the pursuit of standardized hospital information systems. Our expertise contributes to the development of advanced HIS modules, exemplified by our EVIMERIA application.

This article was written during the COVID-19 pandemic.
Authors: Grégory Soler & Francis Destin