The National Data Library (NDL) AI is a major element of the government’s opportunity action plan. The government in its heart is identifying five high-affected public datasets to enable government agencies, businesses and researchers to enable innovation in healthcare, policy development and public services.
The questions are how you build one and what are we making?
Chief Technology Officer, Quantaxa.
East for a national data library
Opinion is different on how to manufacture NDL. A school of idea is that it should be a decentralized, federated data platform that combines the existing database and facilitates data exchange, without storing data centrally. Another argument is that it should be centralized data repository.
The UK is an example of the latter. It has half a million health data that can be accessed by approved educational, enterprise, charitable and government researchers. Transparency in data use, strong safety measures and public belief in privacy security have been important for its success.
The X-road platform of Estonia is an example of the east. X-road is a technology underlining a National Digital Identification Scheme. It provides comfortable data exchange in government agencies while maintaining decentralized data storage. This system enables Estonian people to interact comfortably with public services, from healthcare to taxation, improves time saving and efficiency.
The lesson from the platform strengthens the need for interoperability, as well as the importance of ensuring that the data cannot be contaminated and it is safe. Importantly it is also a user-centric and operates on ‘only once theory’. Citizens of Estonia do not need to know that it works, just it does. And only provide their data once, which automatically update to all relevant systems.
Solid foundations are important for the construction of a library
The proposed National Data Library will be the largest data integration project made by any country. Despite what is involved in this and how it is used, the foundations are the same: high quality, reliable data.
Digital change efforts in NHS are an ideal example of the complexity of modern master data management. Our health system is the world’s largest healthcare dataset, which will create a fitting centerpiece of NDL collection. Although NHS is referred to as a unique – ‘NHS’ is a collection of departments, commissioning and provider organizations, areas and systems.
The complexity progresses as it requires coordination of care in local authorities and social care, especially as our population is aging and putting pressure on hospitals. The patient data currently becomes silent in heritage systems, often has an IT infrastructure without normal data formats and/or standards and is often incomplete, old and/or incorrect. All of which result in duplicate entries in regularly separate (silent) repository. The target for NHS is to create a view of truth by data matching from these several data sources.
Modern data management change challenge
Traditional master data management is an inherent data-quality problem. These model sources take an age to swallow the system feed that is often surrounded by data quality issues. They also rely on data matching, which compares each data string and apply a score on it to make a record-to-record match.
They use possible matching engine algorithms that evaluate and score matches. All of which are unilateral for the patient’s records that have many variations, as they may have many identification characteristics. Variations in personal data – such as name inconsistency (eg, Eliza vs Elizabeth) – can result in wrong matches, making it difficult for every citizen to make a single, accurate record.
A more powerful approach to the management of patient and civil data, which is required to integrate data in sillose, is an entity resolution (ER). ER data uses a skima-unknown model to save time and money from data conversion to engineering teams. ER takes advantage of all available records to create the most accurate possible representation of a person’s data, reduce errors and increase the reliability of government datasets.
Which is significantly added to public sector dataset, allows for continuous data refresh for all applications and services manufactured at the top of the ER platform.
Lesson from NHS for National Data Library
Federed data platform (FDP), which is currently being implemented by NHS England, provides a glimpse of how the national data library can function. The FDP collects local health care data to enable more coordinated care at a regional level, which reduces the disabilities caused by fragmented systems.
If expanded, a national scale data platform on a national scale can unite health records in NHS, allowing a single patient record to be accessible through the NHS app. This approach assumes that citizens are engaged in different fields with several public services, which requires a spontaneous data-sharing framework.
Enhance public services through relevant data sharing
As mentioned, the government’s approach to the National Data Library will focus on identifying the five major public datasets that can provide the most immediate effect. However, data alone is not valuable without a clear strategy for its application.
A promising example is to be used by NDL to enable cross-departal data sharing between NHS and NHS and department for work and pension. By adding healthcare and employment data, policy makers can achieve deep insights into relationships between health results and socio -economic factors. Additionally, integration of profit eligibility verification within health services can reduce fraud and ensure that resources are allocated to those with real needs.
The spontaneous difference between government systems will be required to maximize the benefits of NDL, allowing departments to communicate efficiently and reduce the need for manual data processing.
If you build it, they will come
The government’s vision is ambitious for a national data library, but its ability to change public services is unique. While the exact structure of NDL remains to be finalized, its travel towards its construction is important as results.
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