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Projects & Case Studies

Scaled Insights delivers innovative solutions to tackle the biggest healthcare challenges. We help private and public healthcare leaders make healthcare better, more affordable, and more accessible for millions of people around the world.

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Behavioral AI study to improve CPAP adherence and outcomes 
 

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Project partners:

Rush University Medical Center

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Behavioral Insights into groups identified as at high risk of COVID-19
 

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Project partners:

US Military

University of Leeds
UCL
University of Birmingham

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Personality as a predictor of healthcare experiences amongst people living with obesity

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Project partners:

PEP Health

University of Leeds 

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Behavioral AI study to improve CPAP adherence and outcomes 

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​Project partners:

Rush University Medical Center
PI: James Herdegen MD

Background:

Scaled Insights, Dr James Herdegen and the team at Rush University Medical Center, have agreed to work collaboratively on a research study to explore the use of Scaled Insights’ Behavioral Artificial Intelligence. The study will predict tolerance to CPAP device use, based on personality attributes of each patient, among patients diagnosed with sleep apnea.  

While acknowledged as a key factor affecting compliance rates, there is a paucity of interventions that have focused on personality as a facilitator of CPAP device use. 

Approach:

This collaboration will use Scaled Insights Behavioral AI to identify personality features of patients attending the Sleep Center at RUSH Medical Center. This data will be used to predict tolerance to CPAP device use as a treatment for sleep apnea among patients with this diagnosis.  

Language samples of 100-300 words will be gathered from consenting patients using an online survey, which will be matched with non-identifiable data relating to compliance with CPAP device treatment.  

Outputs:

RUSH University Medical Center will have an easy-to-use tool to rapidly identify likely compliance with CPAP devices as well as the likely barriers or concerns that may hinder compliance with CPAP devices. This tool will work by Identifying patient personality attributes as a predictor of tolerance to CPAP device. IT will do this by leveraging Scaled Insights Behavioral AI solution. 

Working collaboratively, Scaled Insights and Dr Herdegen will use the findings to design and test behavioral nudges to improve CPAP device treatment, compliance and outcomes. This work will develop an automated AI algorithm that will provide clinicians with real time information to support patients diagnosed with sleep apnea. It is anticipated that this algorithm will be employed in other clinical settings across the world and within digital platforms designed to support patients with sleep apnea. 

The findings will be disseminated in leading peer reviewed journals once released by the project’s funders. 

 

If interested in learning more about this project please contact bnagy@scaledinsights.com 

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Behavioral Insights into groups identified as at high risk of COVID-19

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​Project partners:

US Military
University of Leeds
UCL
University of Birmingham

Context:

Due to the unprecedented and rapidly changing impact of the COVID-19 outbreak, it is imperative to understand and appropriately support people who have and continue to manage health conditions. Responses to the outbreak have led to varied actions across the world to reduce infection and spread.

Challenge:

Help the UK Government, public health authorities and charities to understand how to communicate with people about protecting themselves from COVID

Aims:

To understand peoples' thoughts and behaviors relating to the coronavirus (COVID-19) outbreak, and how this has specifically impacted people identified as ‘vulnerable’.

Results:

In collaboration with our partner organizations, Scaled Insights conducted a large-scale online survey to assess awareness, attitudes and actions of UK adults identified as at risk of severe illness from COVID-19. Behavioral clustering based on Personality Traits obtained from language samples using Scaled Insights' Behavioral AI technology has identified distinct groups of respondents whose well being, depression, actions to manage their safety and health conditions during COVID-19 differed significantly. Our partners and the local Government across the UK are using these insights to tune behavior change and safety messaging to both specific groups as well as the public at large.


​This project was extended by our Partners and will now also assess longer term impacts of COVID-19 on these populations, making this an ongoing project with future data collection.

We have published the findings of this study in the BMJ Journals.

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Personality as a predictor of health care experiences amongst people living with obesity

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​Project partners:

PEP Health
University of Leeds 

Context:

People living with overweight or obesity report negative experiences including stigma and discrimination, and lower satisfaction with care provided in health care settings. As such, efforts to reduce stigma and discrimination within health care settings are needed, and in line with National Health Service (NHS) principles, addressing inequalities care are a high priority.

Challenge:

Help clinicians and health care professionals within the NHS to support people living with obesity to receive the same quality of care compared to anyone else.

Aims:

To examine the healthcare experiences of people living with overweight or obesity in England, and to assess whether difference in satisfaction with care exist.

Results:

In collaboration with our partners, we examined the personality characteristics of people living with overweight or obesity, using patient feedback relating to care experiences of people living with overweight or obesity across all NHS Acute and Specialist Trusts and GPPs in England from NHS UK, Google, Facebook and twitter.  We found that people living with overweight or obesity had a lower overall care, with ‘effective treatment’ and ‘Emotional Support’ two care metrics which were significantly lower across the country. Furthermore, we identified that based on personality, people living with overweight or obesity in a “negative behavioral cluster” had a lower perceived quality of care score compared to a “positive behavioral cluster”. Experiences indicated that speed of access, effective treatment, and emotional support, with stigmatizing healthcare experiences were particular issues reported by people living with overweight or obesity.

We have published the findings of this study in the BMJ Journal.

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Behavioral Insights into COVID-19

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​Project partners:

West Yorkshire Combined Authority
University of Leeds

Context:

The Covid-19 outbreak has had a huge impact on society. While certain aspects of everyday life were gradually returning to normal in the UK, local authorities needed to ensure the safe and healthy functioning of the society. West Yorkshire Combined Authority (WYCA) is responsible for ensuring economic prosperity supported by a modern, accessible transport network, housing and digital connections in the region.

Challenge:

Citizens' behaviors during the Covid-19 outbreak are influenced by a complex set of factors, making the strategic delivery of the 'return to normal' a challenge for local authorities. More people-centric insights are needed to inform policy and city planning.

Aims:

To better understand behaviors and attitudes of people in West Yorkshire as they relate to the Covid-19 outbreak, and thus enable WYCA to make more informed decisions about the next steps for West Yorkshire.

Results:

Scaled Insights in collaboration with the University of Leeds conducted a large-scale online survey on attitudes and behaviors relating to COVID-19. WYCA has co-designed a set of questions to inform their policy decisions. Behavioral clustering based on Personality Traits obtained from language samples using Scaled Insights'Behavioral AI technology has identified distinct groups of respondents whose actions and lifestyle behaviors during Covid-19 differed significantly. Innovative text analytics methods were also used to extract and categorize contextual factors influencing those actions.This project assesses longer term impacts of COVID-19, and thus, future data collection is currently underway.

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Personality insights to examine patients experiences and outcomes on the NHS Low Calorie Diet program

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​Project partners:

Leeds Beckett University
University of Leeds
University of Lancaster
University of York
University of Teesside
Sheffield Hallam University
Diabetes UK
National Institute for Health Research (Funder)

Context:

Research findings have indicated that a low calorie diet can lead to improved health amongst people who live with obesity and type 2 diabetes, including weight management, reduced risk of heart disease, and diabetes remission.

Challenge:

NHS England are delivering a Low Calorie Diet program in 10 areas of England. Patients eligible for the program receive low calorie meal replacement products, and support to manage eating behavior in the form of digital technology, and face to face support either one on one or as part of a group.

Aims:

To understand whether the low calorie diet program leads to improvements in health indicators, and whether there are differences in patients outcome and satisfaction based personality characteristics as assessed using Scaled Insights Behavioral AI.

Results:

This study is ongoing and as such, results are not available. Click "Read About the Study" for a link to the project page.

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Personalizing Nudges for Motivating Learners in Healthcare

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​Project partners:

Health Education England

University of Leeds 

Context:

Health Education England (HEE) is the national organization responsible for the education, training and workforce development in the health sector. HEE’s Technology Enhanced Learning (TEL) Program uses the most effective evidence informed technology and techniques to benefit health and care education. Their portfolio includes the design and development of e-learning resources for health and care professionals and the public.

Challenge:

Healthcare professionals at every level need to continuously develop and update their skills. This includes digital upskilling of the workforce that is necessary to realise the potential of state-of-the-art technologies like genomics and artificial intelligence in clinical practice. There is a need for effective and scalable e-learning solutions which caters to busy health and care professionals.

Aims:

To use Scaled Insights Behavioral AI technology to automatically infer user characteristics from a natural language sample collected by asking a person to answer some open ended questions. Then to use these characteristics to personalize motivational messages (‘nudges’) to increase e-learning take-up.

Project so far:

Scaled Insights has collaborated with academics from the School of Medicine and School of Computing at the University of Leeds to co-design two studies to investigate the feasibility of using Scaled Insight’s AI to infer user characteristics and to explore the relationships between these user characteristics and the effectiveness of different types of ‘nudges’ as motivational messages. To date we have we have established that the effectiveness of various types of ‘nudges’ differs between users with different characteristics that Scaled Insights’ AI has inferred. This means that Scaled Insights AI can be used for personalization in the health education space. 

 

We are currently submitting a publication of this study to a high impact, peer reviewed journal. The paper will be made available once published.

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