using data to manage and predict social care pressures

TDX care analytics assists in the strategic management and planning of social care provision and ensures that it is optimised for service users within the constraints of local authority capacity. It provides robust and easy-to-use tools to import, clean and merge data from a variety of sources and uses Artificial Intelligence (AI) to provide strategic and operational insights, to perform impact analysis and to make recommendations in relation to service provision.

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core benefits

Secure

Our technology uses state-of-the-art digital certificate-based security to give organisations the confidence and assurance they need to share data.

Privacy

Fine grained permission levels ensure only the right people access sensitive information.

Smarter Planning

Tools to help plan capital and service investments reduce the costs and risks that are associated with both the over- and under-provisioning of services.

Resilience

Impact analysis is used to improve resilience and perform contingency planning, reducing the effects of emergency measures on cost and quality.

Service Optimisation

Optimising the configuration of care services and travel routes reduces the cost of care provision, improves operational efficiency and reduces CO2 emissions.

Better Outcomes

Optimising care package recommendations based on empirical outcome data, to improve the outcomes for service users and reduce the long-term cost of service provision.

Increased Carer Pool

By recommending walkable routes, the pool of carers can be expanded to include non-drivers. Reducing CO2 footprints and improving community cohesion.

Intervention Impact

Analysis of interventions over time to develop more effective care packages, leading to better outcomes and lower costs.

features

data import & integrity

The data import module enables the import of data from diverse sources into the standardised format that powers TDX care analytics. It is designed to work with new datasets with minimal configuration and the end schemas are published as open source to prevent vendor lock-in and to encourage the adoption of open standards within social care. The Data Integrity module provides fine-grained analytics on the completeness and quality of the data that was imported. It includes advanced filtering options that allow administrators to analyse the quality of their data and to identify areas where quality improvements would be beneficial.

status view

The status view provides high-level analytics based on health and social care data.

Granularity: Views based on how individual case histories change over time.
Real-time: Connected to live data sources giving a precise live view of the current situation or updated less often if preferred.
Dynamic cohorts: Sub-cohorts can easily be created in any view, making it easy to drill down and perform detailed context-specific analysis.
Advanced cohort definitions: Cohorts can be based on advanced combinations of the attributes of individuals as well as functions of them.
Integrated data: Providing information from multiple data sources.
Flow analysis: Model flows through the social care system (e.g. contact, referral, assessment, and commissioning).

service user view

The service user view facilitates detailed analysis of an individual service user’s care needs and provides a number of decision support tools. A complete list of service users can be refined to show only those that meet specific criteria. Selecting an individual service user provides a rich set of visualisations that combine information from multiple sources. The visualisations include information about the history and current status of the service user, as well as recommendations for changes to their care plan, predictions relating to future care needs and the likelihood of them requiring hospitalisation or movement to residential care.

wellbeing metrics

Many local authorities collect information on the wellbeing outcomes of their service users; however, the information is rarely analysed at scale and across multiple users. By performing long term analysis across a cohort of users, it is possible to infer the effectiveness of a care service, not just in terms of financial cost, but also in terms of the impact on the wellbeing of a service user. This enables a much more nuanced understanding of the quality of care being delivered.

scenario planner

The scenario planner makes it possible to investigate the impact of adverse events, such as extreme weather or a service provider exiting the market or discontinuing a specific service. It assesses the financial implications of an event, identifies the affected service users and suggests alternative providers or patch teams.

location analysis

In the location analysis view, it is possible to see the geographical efficiency with which services are supplied to users in terms of travel time, travel cost and distance, and to use AI to optimally allocate service users to service providers and patch teams.

service predictions

The service predictions module combines Office for National Statistics (ONS) population projections with local care usage data to forecast the future population in care and the cost of provisioning their care. Forecasts can be made for the entire cohort in care, or for specific subgroups that can be defined by gender, age, type of care and community to gain deeper insights.

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