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COVID-19 Analysis and Tools

Since the COVID-19 pandemic took hold in March 2020, Telstra Health UK have analysed available data relating to COVID-19, and created tools to help you provide the best care for your population. We provide support from monitoring current COVID statistics by region, to bespoke analysis designed to help you plan care delivery and recover from the elective backlog.

Services

COVID-19 Progression Dashboard

We have created a dashboard which shows the progression of Covid-19 as well as vaccinations in England by Clinical Commissioning Group (CCG). The data shows variation in the seven-day case rate across the CCGs, which has changed over time.

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COVID-19 Hotspot Indicator

Our analysts have developed a COVID-19 hotspot indicator to help local public health teams monitor any increases in COVID-19 infections in their region or upper tier local authority (UTLA) and help prevent lockdown situations.

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Activity Reduction Analysis

Our team has quantified the activity reduction for the majority of the most common diagnosis and procedure groups by comparing the observed spell volume for diagnosis/procedure groups in April 2020 with their expected volume.

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UK COVID-19 Tracker

Our analysts have created a graph tracking COVID -19 cases by Upper Tier Local Authority (UTLA) that is both intuitive to use and easy to interpret, updated recently to show cases since September 1st 2020 to highlight the effect of COVID-19 within the last few months.

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COVID-19 Patient Level Analysis

Our analyst team can analyse patient-level data to help hospitals easily understand which patient groups are likely to be severely affected by the disease and are therefore at higher risk of having to be admitted to an ICU and at higher risk of mortality.

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Elective Recovery Programme

We have joined forces with data automation experts, InSource, to deliver a comprehensive programme that gives command-level control of, and powerful insight into, accurate trust-wide or system-wide data for targeted elective care recovery.

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Our Expertise

Advanced Analytics and Data Science

Bespoke analytics and data science tailored to specific needs to identify clinical variation, efficiency savings, predict patient risk and improve patient outcomes.

Data curation and mining

Our data experts are able to assist you in organising and integrating health and care data from across the health and care system, together with important wider determinants of health. Our data scientists mine the data to find anomalies, patterns and correlations to predict outcomes.

BI and Visualisation

Developing innovative visualisations and analysis that enable organisations to gain a better understanding of their data. We utilise tools such Tableau and Power BI to provide bespoke analysis which makes it easier to identify trends, patterns, and outliers within large data sets.

AI and Machine learning

AI is the capability of a machine or system to imitate intelligent human behaviour. Machine Learning is a subset of AI that includes statistical techniques to enable systems to learn and improve at tasks with experience.  At Dr Foster, we are using techniques like supervised machine learning to predict the risk of an emergency admission, or to predict the risk of falls, as well as unsupervised machine learning to support current rules-based patient segmentation analysis.

Data visualisation and extraction

The visual language in InFocus is consistent, ensuring that once users know how to interpret one indicator, they know how to interpret all of them. Simple colour coding based on recognized statistical processes in conjunction with immediate access to the patient data that the indicators are reporting on, means the user can both see where issues are and pull the records to look in more detail.

Predictive modelling

Predictive modelling uses statistical techniques and supervised machine learning algorithms to predict future adverse outcomes. Predictions are made at patient-level to predict the risk of an adverse outcome and can further be aggregated into risk groups at provider or system-level where appropriate. Alternatively, forecasting techniques predict the future at an aggregate level, taking into account historical trends and seasonality.

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