Organisations are more reliant than ever before on information and data to make decisions, improve performance, create transparency, and ensure safety. When talking about digitising paper records, two terms are important: structured and unstructured data. These are both categories of collectible data and understanding the differences will help you make the most of enterprise and care records. This is particularly important for quality and compliance managers, registered managers and tech leaders in the care sector.
Structured data refers to information that is highly organised, information that fits neatly into data tables represented, typically by rows and columns in a database. Structured data typically includes quantitative or discrete information such as categories, numbers and values. This allows efficient interpretation of the data collected, so that means it can be searched, analysed and processed. The quality of the data allows it to be easily searchable and sortable, therefore it can be analysed by traditional tools such as spreadsheets and business intelligence software. It also allows for trends to be identified, particularly negative trends which need to be addressed.
Unstructured data is the opposite, which means it does not fit neatly into a data table or categorisation making this form of data challenging to use. Unstructured data includes qualitative information such as scanned documents, text files, audio files, video files and sensor data. The lack of consistency frequently requires advanced tools and that’s where artificial intelligence (Ai) has a role to play in extracting and transforming raw data into discrete values.
The Social Care sector is one of the few sectors where adoption of technology has been slower than other sectors like financial services. However, in the last couple of years, the need to look for further operational efficiencies has increased the pace of adoption of new technology. In social care settings like care homes, a significant amount of sensitive data is collected day and night, from resident’s vital signs, food and fluid consumption, incidents/accidents, care observations and more. Data collection takes place in various forms (both structured and unstructured) and is used to monitor health trends, and quality of care. The objective of this data collection is to assure the residents health and wellbeing by providing personalised care that is targeted for their needs. Users of social care settings often receive complex care, and to optimise care, data plays a key role. It’s important therefore that the data is accessible, whether its person centred or aggregated.
Despite the benefits, adoption of digital social care records (DSCRs) by Care Quality Commission (CQC) registered providers has been slower than expected. However, it has increased from 40% in December 2021, to at least 63% in February 2024. The Department of Health and Social Care (DHSC) had pledged that by March 2024 it would ensure that at least 80% of social care providers would have digitised care records in place. As this target has been missed, the DHSC has extended its timeframes to March 2025 and has allocated another £25 million to ensure many providers benefits from the Digital Social Care Records (DSCR) grants.
Compared to many other countries, care providers in the UK collect significant amounts of information in data sets dictated by the mature sector and stringent regulatory environment. This data is collected in many forms, but often the focus has been on electronic health records or care records, over enterprise data. Even though there is improvement in the adoption rate of digital care records, there are still many opportunities for care organisations when it comes to good data management and we explore why your recording systems may be holding you back.
1. Paper-based records
Handwritten notes in free text format are challenging to interpret by AI due to varying writing styles and presents risk of errors due to language barriers presented by a diverse and international workforce.
2. Legacy software
Digital systems on aged tech stacks present cybersecurity risks, accessibility issues and may not integrate with APIs or sensors.
3. Niche single point solutions
Working on disparate systems can overwhelm care professionals and digital fatigue reduces the impact of going digital. There are multiple benefits of using all-in-one platforms which you can explore on our previous blog: The Power of Comprehensive Care Management Systems.
Transitioning to digital data management is imperative. Structured data provides clear advantages in terms of analysis of standard records. However, unstructured data is equally important, offering valuable insights in specific scenarios. Beyond resident data, social care settings handle various types of information, including financial, marketing, rostering, maintenance, and quality assurance. Currently, much of this data remains underutilised. This is where AI under a single ecosystem can make a significant impact. AI algorithms can harness both structured and unstructured data to generate real-time recommendations, alerts and predict potential issues, improving care operations and delivery.
PredicAire is one such comprehensive cloud-based care management platform which collects both structured data and unstructured data to build proprietary models that will be able to analyse multi-variant data to deduce and prevent unnecessary outcomes. In order for this to work, it will be important that data is managed well as the quality of output from an AI model, depends directly on the quality of the input data.
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