Lead Healthcare’s Digital Future with Data Management — Or Risk Falling Behind

Determining and increasing value requires significant data analytics to identify, measure and improve patient outcomes.

Data can be used to improve resource efficiency, staffing levels, billing and revenue cycle management, and supply chain optimisation. For example, claim data analysis was used to demonstrate the cost-effectiveness of non-invasive telemedical intervention.

Collection of quality data allows identification of improvement areas in patient care, resource allocation and processes. For example, clinical registries share data regarding quality of care and benchmarking between healthcare organisations, with well-documented benefits.

Data can be used to provide information on population trends within a healthcare facility and emerging diseases and trends within the healthcare market.

An improvement in healthcare delivery by enhancing medical decisions with targeted clinical information or patient information that has grown in popularity with increasing healthcare digitalisation. However, poor data quality from non-integrated/interoperable systems or lack of data standards can lead to manual work arounds and compromise the quality of the decision support.

Utilisation of data analytics on detailed patient data and specific health information allows the tailoring of treatments.

Healthcare organisations manage diverse data from various sources, including structured data like lab results and unstructured data such as visit notes and discharge summaries. The challenge in data strategy arises from differing formats and communication standards, highlighting the importance of interoperability and integration, since the lack of these qualities between medical systems is a well-documented barrier to data-driven projects.

Choosing the right storage solution is crucial for organisational growth, allowing data management projects to focus on the data itself rather than the IT system architecture. Databases must be scalable, integrated with the organisation’s infrastructure, and support various data sources and structures.

The use of health data often leads to significant public distrust, especially when private institutions are involved in data sharing. Building trust requires addressing issues related to data privacy, security, and cybersecurity, while ensuring compliance with current laws and regulations.

The final consideration and arguably the most important. Once data is securely stored and managed, organisations can focus on extracting value from data to drive organisational decisions and improve quality of care. Analysis and critic of data is an important aspect and principles such as FAIR for data should be considered to improve the accessibility and usability of data collected.

Before data management can be considered, the current rate of digitalisation needs to be assessed. Digitalisation is often the first step in creating an environment which allows proper information extraction so that the best data can be used.

Knowledge of use cases can help align business objectives with your data management project. Use cases may include rejection of claims due to mismatched provider data, or the identification, measurement and improvement of outcomes in transitioning to value-based care.

Data management is not an IT project, and an assessment must be made as to whether an organisation has the right people with the right skills to carry out data management strategies. In healthcare, data management teams need to include data managers, IT, clinical staff and business owners. It may be necessary to hire external candidates with the required knowledge in the initial project and organise a systemic program of knowledge transfer to upskill internal teams.

A data protection and governance maturity assessment which includes privacy and protection should be performed to assess the readiness of an organisation to undertake a data management project in compliance to local regulations.

A DMP bridges the gap between specific data management projects and high-level data governance frameworks and acts as a “blueprint” to manage specific data sets throughout the data lifecycle. Systemwide DMPs can be automated to streamline data rules. Automation includes data collection, storage, access control and sharing procedures, ensuring consistency and reducing human error.