Here are our entries for the category of Best Use of Data.
Seqera
Overview: Seqera makes complex data analysis accessible at any scale by providing the tools to drive innovation in genomics, global health and beyond.
Why? Scientific research can be quite complicated and time-consuming. Because of this, collaboration within the bioinformatics space is vital for increasingly complex and data-heavy life sciences to keep up with the demands of today’s world.
What happened? In a case study released in 2024, Seqera’s Nextflow platform helped researchers in Belgium contain a global mpox outbreak. The Institute of Tropical Medicine, a multidisciplinary team of researchers based in Antwerp, Belgium, was researching a global MPXV outbreak. Through Nextflow, the team was able to identify the MPXV variant responsible for the outbreak and subsequently deliver evidence of asymptomatic infections with the shared MPXV genome, preventing further infections. Today, Seqera consolidates fragmented data and diverse computing resources into a unified platform. Most recently, Seqera announced the launch of Seqera Containers, powered by Amazon Web Services in May 2024. The Containers solution enables the reproducibility, scalability, and collaboration within bioinformatics research, offloading the role of software management and minimising the burden of having to create custom containers. Seqera also launched Data Studios, which enables streamlined creation of collaborative notebook environments using cloud-native components coupled with data hosted in a secure environment.
Looking ahead. Seqera and its open source products want to enable life scientists to get more out of their data than ever before, at a faster pace, and with the support of other life scientists and all the benefits of open science.
Nottingham University Hospitals and Nervecentre
Overview: Nottingham University Hospitals (NUH) is accelerating the safe transfer of patients through its hospitals using a ‘Reverse Bed Chain’ model, enabled by system-wide data visibility via Nervecentre’s EPR.
Why? NUH has consistently operated at 98 percent bed occupancy and above, impacting flow through its emergency pathways, sometimes leading to crowding in ED.
What happened? After discussing requirements with the trust, Nervecentre developed AI functionality as an integrated part of its EPR that discovers and proposes bed moves in real-time, highlighting linked patients in colour-coded groups. RBC allows patients to be admitted from ED who would otherwise be blocked. This reduces the time for individual patients to be admitted by several hours. Crucially, the entire team is connected by Nervecentre, which means everyone, including porters and cleaners, is instantly alerted to tasks via their mobile device. The RBC model was rolled out as a pilot at the beginning of 2023, and NUH completed 899 reverse bed chains in the first three months at QMC, with 97 percent starting in ED and assessment wards. In 871 (94 percent) of those cases, the first move in the chain was to assessment wards, suggesting patients are being transferred to the correct clinical specialty; first move, right time, right place. Time taken to carry out a three-part sequence of transfers has reduced from around four hours to one hour – releasing an estimated 3 hours of ED staff time to focus on care.
Looking ahead. NUH and Nervecentre hope to continue their collaboration in helping NUH progress its digital maturity and confirm its position as a pioneer in using data to improve the quality, safety and efficiency of healthcare service provision.
Think Healthcare
Overview: A more localised and focused approach to data helped a surgery provide a blueprint that could be replicated nationwide.
Why? The surgery faced challenges including lack of detailed data on patient interactions and service demand, challenges managing appointment bookings and other functions at scale, and high call volumes.
What happened? The solution, tailored specifically for NHS primary care settings, integrates the advanced Think Healthcare NHS Cloud Telephony module with Virtual Care Navigator (VCN). VCN enables 24/7 access to essential services, allowing patients to book, check, and cancel appointments, order repeat prescriptions, and access other vital services via phone. With customised data collection, configuring an itemised menu system in combination with the custom call tagging feature allowed for collection of data on ‘why’ a patient was calling, and integrating the system with the practice total triage priorities. Results showed that 43 percent of patients wanted to request medication over the phone. Key results included 92 percent patient access satisfaction, with automated handling of prescription requests reduced calls by 30 percent, significantly decreasing wait times. The VCN sends a triage link to patients, with 80 percent filling the appointment request form immediately, reducing call queues. This allowed for the reallocation of 3.5 FTE of admin funding into clinical staff, and meant all calls were dealt with on the same day.
Looking ahead. Think Healthcare hopes this case study will demonstrate a blueprint of how strategic use of the right data can address widespread NHS priorities and deliver meaningful benefits to both patients and healthcare providers.
C2-Ai
Overview: A digital health platform using clinical risk adjustment at scale to reduce harm, mortality and costs in hospitals globally, C2-Ai’s platform is designed to support patient safety.
Why? C2-Ai has developed its platform to help reduce deaths and life changing harms, reduce length of stay, free capacity across inpatient care and also get people through the waiting list faster.
What happened? C2-Ai reduces unsustainability/unaffordability of “reactive” healthcare delivery, and identifies and resolves the issues driving cost/clinical variation, waste (up to 30 percent of healthcare costs) and patient harm (hospitals today are only finding 10 percent of the issues that C2-Ai detects and helps resolve). The platform offers prioritisation of the waiting list effectively (NHS reports indicate an eight percent reduction in emergency admissions and 125 bed-days saved per 1,000 patients). It also identifies and helps resolve 900 percent more drivers of harm/mortality than today’s BI systems – e.g. reducing surgical complications by 56 percent and triggers of avoidable harm including AKI, pneumonia and sepsis by an average 83 percent in a leading hospital with all the best systems and processes). The #7 hospital in the World (Newsweek/Statista) states C2-Ai’s system helped them avoid 122 deaths and 345 harms in orthopaedics and trauma alone in one year, and achieved world-leading levels of quality and patient safety (clinically risk-adjusted OE ratios of 0.27 for mortality and 0.45 for morbidity).
Looking ahead. C2-Ai plans to continue to use its technology to save lives, reduce harm and increase capacity for healthcare organisations.
Sanius Health
Overview: Sanius Health’s AI-driven platform integrates data from wearables, symptoms data and medical records to enhance understanding and management of blood cancers including Waldenstrom’s Macroglobulinemia (WM).
Why? Individuals with rare and complex chronic blood cancers often encounter significant challenges in comprehending the intricacies of their condition. Current methods of assessing these diseases primarily rely on clinical trials and brief medical consultations, which frequently fail to provide a comprehensive picture.
What happened? Initially focusing on the WM patient cohort, Sanius Health has developed a ‘digital health ecosystem’ integrating electronic disease symptom data via a mobile application, real-time biometrics from clinically validated wearables, and medical records. The platform has provided a comprehensive view of patient health across both home and clinical settings, facilitated by a secure, integrated, real-time data dashboard. This is achieved through continuous monitoring of patients’ real-time biometrics, quality of life indicators, and healthcare utilisation. In addition to monitoring, this comprehensive approach has shown potential to enhance understanding of therapeutic effects, address unmet needs in WM, enable targeted interventions based on individual patient needs, and improve the prediction of disease progression. Through a 142-day snapshot of day-to-day monitoring, we uncovered significant correlations between increased activity, enhanced quality of life, and reduced symptom severity, as well as early differences between patients receiving specific treatments in comparison to the wider cohort.
Looking ahead. The technology has recently been launched in Myeloproliferative Neoplasms. This expansion showcases the potential to revolutionise the management of various complex chronic blood cancer conditions.
Sanius Health
Overview: Sanius Health’s predictive Al-driven solution for enhanced sickle cell disease management and improved patient care enhances quality of life and promotes self-care for sickle cell patients.
Why? Many SCD patients experience acute pain episodes, known as vaso-occlusive crises (VOC), which can result in severe end-organ damage in the long term, poor quality of life (QoL), and a shortened lifespan.
What happened? Sanius Health developed a machine learning (ML) algorithm capable of predicting the potential onset of a vaso-occlusive crisis (VOC) in patients with SCD. This was achieved by using longitudinal medical records and physiological data captured over time by a wearable smartwatch, combined with patient-reported outcomes entered via a specialised mobile phone application. This early warning system has enabled patients to make informed decisions and engage in self-care practices that reduce the risk of complications. Clinicians have also benefited from these alerts and curated data insights, supporting informed decision-making and treatment customisation. With a more complete picture of their patient’s health between appointments, healthcare providers could minimise monitoring burdens while ensuring effective patient care. The algorithm accurately predicted 84 percent (58 out of 69) of VOCs, enabling patients to take preemptive measures such as hydration and medication. This not only heightened patient awareness but also fostered proactive health planning.
Looking ahead. The model was showcased at the 65th American Society of Hematology (ASH) Meeting as an oral presentation. We are continuing to refine and optimise the predictor to further improve its accuracy and effectiveness.
Diaceutics PLC
Overview: Diaceutics is harnessing data from hundreds of millions of patient testing records to improve disease testing and treatment for diseases such as cancer, multiple sclerosis and rheumatoid arthritis.
Why? The current diagnostics landscape means not all patients get access to the most appropriate medicine. Research from the US identified that over half of patients are not receiving the right treatment for them.
What happened? DXRX integrates multiple pipelines of real-world diagnostic testing data from a global network of laboratories to create the world’s largest repository of diagnostic testing data. The DXRX platform transforms this massive pool of huge raw data into insights to identify where patients are not receiving the optimal treatment and provide pharma and biotech organisations with the solutions to help address these opportunities. DXRX Signal, with the use of ML and AI, analyses large unstructured data from multiple sources on a daily basis to send daily alerts to pharmaceutical organisations when a patient has been diagnosed as being an appropriate candidate for their precision medicine. DXRX Signal had identified over 500,000 eligible patients in 2023. In March 2024, upgrades to DXRX including industry and technologically leading de-identification, generative AI (Diaceutics Large Lab Model, DLLM) and new comprehensive US data sets that include data on social determinants of health. DXRX can now break down siloed datasets and improve insights driven by the platform – providing greater longitudinal insight on the patient journey.
Looking ahead. Diaceutics has recently grown its lab network across Europe to increase its European data coverage, and can now accelerate the rollout of Signal across its European markets.
Quality Compliance Systems (QCS)
Overview: QCS Compliance Compass is a tool that can help businesses address the required improvements and get the rating they need to continue to operate as a business, providing quality care.
Why? Every care business at some point will have to be inspected by their regulator. Some 20 percent do not meet the key standards, with no time to look for new products or services, and no budget for costly consultants.
What happened? Assisted by AI technology and quality checked and qualified by experienced social care professionals, the team aims to take CQC inspection reports, summarise it and create an action plan, aligned to policies that will help navigate the areas requiring improvements. QCS Compliance Compass enables customers to create a plan to help address the required improvements, use the action plan, and then work off the back of this as evidence of continued improvements all aligned to the QCS Product suite for quick and easy implementation and instant addressing of required improvements. The mission is to help everyone who cares to do a great job, helping staff to focus on making a difference and caring for others whilst QCS Compliance Compass maintains business compliance every hour of every day.
Looking ahead. The first case study is in progress, and QCS looks forward to sharing the results from this.
North West London Pathology
Overview: Creating a unified pathology service through digital transformation.
Why? As a newly-formed pathology network built from the capabilities of three separate trusts, data visibility between the seven hospital sites was almost non-existent, with incompatible hardware and lab information management systems (LIMS).
What happened? The original system faced challenges such as multiple, incompatible LIMS running across seven lab sites, slow and ineffective paper-based processes, inconsistent site backups, siloed teams, and increasing costs associated with maintaining four end-of-life LIMS systems simultaneously. Aims included reducing touchpoints from multiple independent systems to a single harmonised system, enabling standardised training and staff interoperability between sites, and providing access to test results for primary to tertiary care providers, including for 200+ GP surgeries. Results are sent 24/7 to 280+ GP surgeries and tens of thousands of healthcare professionals, who have immediate access to patient results, improving timeliness and accuracy of medical responses. £1.1m has been saved by terminating legacy systems, and greater organisational savings stand at close to £17m to date since transformation efforts began. Pre-initiative our incident rate had peaked at 1 percent during downtime whilst migrating GP surgeries into our ecosystem. Today the incident rate stands at 0.02 percent – a 98 percent decrease.
Looking ahead. NWLP has already started sharing learnings with several organisations, and looks to continue this on a wider scale.
Next up: browse entries for the category of Best Solution for Clinicians here.
