HTN Now Awards Finalists 2022: Excellence in Data, AI and Automation

It’s January again, which means it’s time to unveil the finalists of the HTN Now Awards 2022. At the start of every new year, we share with our readers the entries, submissions and case studies from across NHS and industry.

It’s ultimately up to our panel of expert judges from the world of healthcare, technology and IT, to decide the winners, who will be announced at our dedicated awards evening on 20 January 2022.

As part of the build up towards the big day, we’re pleased to share with you the finalists from every award category, so you can hear all about the fantastic work that’s taken place across the past 12 months or so. Here, we take a look at the entries for Excellence in Data, AI and Automation, which is always one of our most exciting areas to cover…

NHS Arden and GEM CSU

Our first entry in this category focuses on generating actionable insight from population data for an Integrated Care System (ICS). NHS Bedfordshire, Luton and Milton Keynes CCG (BLMK CCG) wanted to better understand the healthcare needs and consumption of the local population. So, Arden & GEM’s Advanced Analytics Unit (AAU) developed SMITH, a bespoke linked dataset and segmentation model, to enable person-level analysis.

The Segmentation Model Individualised Towards Health (SMITH) is a needs-based model, bringing together secondary and primary care data, to divide the population into 12 segments. The linked dataset contains morbidity, sociodemographic, activity and spend information to generate a rich resource for advanced analysis.

To assign people to a segment identifying the correct level of need – high, medium or low – a clinically validated long term condition matrix was developed to classify needs based on impact on quality of life and the severity of treatment. Working closely with the Information Governance team, the team supported BLMK CCG to flow and link their primary care data into the SMITH dataset. This was followed by a calibration process, to check data quality and assumptions. All patients were then assigned to a segment based on common characteristics, health status and priorities.

Through use of the model, the BLMK system now has:

  • A better understanding of patient populations to improve care planning and delivery
  • Place-based comparisons and benchmarking so that intervention opportunities can be more easily and accurately identified
  • A framework to simplify analysis – giving a richer and quicker answer to questions
  • A complete view of their entire population, through the inclusion of primary care data.

“The CCG has already made progress by using the model to understand the health and wellbeing of the frail elderly and complex multimorbidity segments in Central Bedfordshire, and also in support of population profiles in BLMK’s four places – Bedford Borough, Central Bedfordshire, Luton and Milton Keynes local authority areas,” explained Charles Wheatcroft, PHM Programme Director at NHS BLMK CCG.


GreenPen is a free platform that provides pharmacy owners with business information and analytics by mining for intelligence and ingesting NHS and economic data. Key performance indicators are presented in an easy-to-digest format, and a bespoke AI algorithm calculates the value of the business at any given time.

The idea for GreenPen was to help community pharmacists find out much their business is worth when buying or selling a community pharmacy. According to GreenPen, the intangible value (known as ‘goodwill’) of a community pharmacy makes up the vast majority of its financial worth, and this is typically estimated by specialist accountants using financial multiples, namely the EBITDA or Turnover. However, the actual transaction price for a pharmacy business can deviate from industry-standard estimates, meaning pharmacists could undersell or overpay for a practice.

The solution utilised by GreenPen is based on proprietary research conducted with The Open University. The research models took over 35 indicators from a company’s accounts, NHS data on prescription and service levels, and used economic data to represent the broader financial and debt markets.

GreenPen has developed this research into an AI model that constantly evolves, ingesting NHS and economic data and actual transaction prices, to provide an up-to-date automated solution for valuing a business. The valuation algorithm changes based on the factors that are observed to be most important to actual transaction price, and as such the valuation model constantly updates itself.

GreenPen, which is free at the point of use, also provides business analytics such as: how a pharmacy performed last month; how a pharmacy has performed for the last 12 months; and how the last 12 months compares to the previous 12 months.


The Synopsis platform – comprised of the Synopsis iQ and Synopsis Home solutions – is supporting hospitals to increase the pace at which they can tackle the backlog through secure data sharing (enabling patient load balancing).

The PAS / EPR integrated pre-operative assessment solution is a fully digital tool that takes a hybrid approach to pre-operative data gathering and streamlines the assessment process. It allows hospital staff to access data from patients who complete a pre-op questionnaire from home (using Synopsis Home) directly alongside data from patients who attend hospital for their pre-op questionnaire (using Synopsis iQ) – all within a single digital dashboard. Staff then use Synopsis iQ to evaluate the data as they conduct a full, tailored risk assessment.

When Synopsis is applied as the pre-operative assessment platform across a trust, patient data can be viewed and accessed in real-time by any hospital within that trust or group – supporting a collaborative approach to tackling the backlog of operations. This approach to data gathering and sharing allows a hospital to accept a patient for a scheduled operation who may have been triaged elsewhere, thus allowing hospitals to transfer patients between them (based on theatre capacity and availability).

The technology is being used across the NHS at trusts including North Bristol NHS Trust, Hampshire Hospitals NHS Foundation Trust, Worcestershire Acute Hospitals NHS Trust, and King’s College Hospital NHS Foundation Trust to improve patient load balancing as part of their pandemic recovery programme.

Vikki Lewis, Chief Digital Officer at Worcestershire Acute Hospitals NHS Trust, said: “Synopsis iQ is another great example of how digital innovations within our organisation are having a profoundly positive impact on our patients, their families and their overall experience of care in our hospitals.”

Patients that used Synopsis Home during a three-month period at Worcestershire saved: 199 hours of travel time; 1,025 Kg CO2; 5,271 miles of travel; 722 hours inside the hospital. Synopsis has been used to complete over 200,000 pre-operative assessments across the NHS to date, delivering an average saving of £1.4m per NHS trust, per annum.

Bolton NHS Foundation Trust

Bolton NHS Foundation Trust designed an Electronic Medical eHandover List which allows clinicians to add, track and action outstanding clinical tasks and highlight acutely unwell patients throughout the hospital. The volume of clinical work and rise of clinical acuity meant that the risk of missed clinical tasks was growing, and necessitated a need to change the clinical handover process at the trust.

The trust saw an opportunity to exploit the flexibility within its EPR, following the implementation of the Acute Medicine Referral List, to design and implement a Medical eHandover clinical task list.

Configuration analysts worked with the Allscripts team to identify the best way to pull the data from the system and collated clinical tasks for patients. The clinical handover processes were mapped, with the electronic list automating the majority of the steps and minimising the risk of human error.

The list collates clinical tasks and information on acutely unwell patients from 24 medical wards, and includes useful clinical information such as patient demographics, clinical location, details regarding the outstanding clinical tasks, when they need to be actioned by and the grade of clinician needed to action them. The list can be updated in real-time and viewed by multiple clinicians simultaneously, improving continuity of care.

The list was implemented in September 2021 and three months later there had been 1,630 entries, equating to 1,755 clinical tasks for 934 patients, almost one-third of all the medical admissions during that time period. Compared with pre-implementation data from 2020 and 2021, there was a 65 per cent increase in the number of recorded clinical tasks, and a ten-fold increase in the number of outstanding investigations. The average time taken to complete a clinical task was 23 hours, with 98 per cent of all clinical tasks completed within 72 hours.

Dr Nithin Narayan, Clinical Lead in Acute Medicine, said: “Having a centralised digital list has truly transformed the standards of clinical care out of hours. It has introduced much needed visibility and traceability for the clinical tasks we hand over. The clinical environment as a result is much safer to both patients and clinicians.”