MEMORI

An AI-enabled Clinical Decision Support tool seamlessly integrated with your hospital's EPR, empowering clinical teams to detect deteriorations in patients with acute neurological injuries and/or neurological conditions earlier.

Problem

1 in 5 patients will develop a hospital acquired infection during their stay, increasing LOS and adding pressure to overstretched clinical teams

¹ NICE (2016). Healthcare-associated infections
Healthcare-associated infections Quality standard.

6-15%

of inpatients develop a hospital acquired infection.⁽¹⁾

² Arefian, H., Hagel, S., Fischer, D., Scherag, A., Brunkhorst,
F.M., Maschmann, J. and Hartmann, M. (2019). Estimating
extra length of stay due to healthcare-associated infections
before and after implementation of a hospital-wide infection
control program. PLOS ONE, 14(5), p.e0217159.

³ Guest, J.F., Keating, T. and Gould, D. Modelling the annual
NHS costs and outcomes attributable to healthcare-associated
infections in England, BMJ Open 2020:10

8-12 days

in increased length of stay after a complication⁽²⁾, impacting patient flow across healthcare systems equating to 7.2-10.8M bed days.⁽³⁾

⁴ TheyWorkForYou. (n.d.). Hospital Beds: Costs.

~£10bn

in direct costs for the NHS arising from longer length of stay and cost of care.⁽⁴⁾

⁵ Schreiber, P.W., Sax, H., Wolfensberger, A., Clack, L. and
Kuster, S.P. (2018). The preventable proportion of
healthcare-associated infections 2005–2016: Systematic
review and meta-analysis. Infection Control & Hospital
Epidemiology, 39(11), pp.1277–1295.

35-55%

of infections are avoidable through earlier detection and intervention⁽⁵⁾

Solution

MEMORI is an AI Clinical Decision Support tool that helps detect emerging risks in patients

Certified as a Class IIb Software-as-a-Medical Device (EU:MDR)

Co-designed with clinical teams

Fully embedded into EPR and workflows

Explainable AI to build trust

Software-as-a-Medical Device

Features

1

Real-time visualisation of key trends

2

Hospital's defined next best actions

3

Intelligent alerting to address alert fatigue

4

Explainable AI to build trust and transparency in supported decision-making

5

Time-aware to understand when a patient’s health changes

Compliance, safety, and AI-bias detection

MEMORI is regulated as a Class IIb Software-as-a-Medical Device.

In addition, Sanome adheres to the latest healthcare safety and quality standards such as DSPT, ISO14971:2019, EN62304: 2006, EN82304-1:2016, ISO13485 and IEC62366 and GDPR.

Sanome has a rigorous testing framework for our machine learning models (MLOps) which includes tests for architectural robustness, adversarial robustness, domain shift, and explainability. Robust assessments help ensure our models minimise any AI bias.

Integration

Integration with hospital EPR systems is essential for easy adoption and to reduce workload for clinical teams.

MEMORI works with any system and easily connects to existing EPRs and health technologies like virtual wards or care management platforms.

Designed for interoperability, MEMORI can handle data from various sources and protocols, enhancing its ability to support clinical decisions.

FAQs

Contact us – we can provide you with more information on our progress, and how you can be part of the journey.

We offer flexible deployment options to suit your organisation’s needs. Our solutions can be deployed in the cloud, providing scalability and accessibility, or on-premises for those requiring full control over infrastructure and data security.

We can integrate seamlessly with any EPR that uses common interoperability standards such as HL7v2, FHIR.

Our platform uses robust encryption protocols and role-based access control to ensure the highest levels of data security. Our platform is designed with GDPR principles embedded from the outset, prioritising privacy and compliance. We assess and enhance our security measures on a regular basis to monitor emerging threats. Additionally, the company holds certifications such as DSPT and Cyber Essentials.

Sanome has a rigorous testing framework for our machine learning models (MLOps) which includes tests for architectural robustness, adversarial robustness, domain shift, and explainability. Robust assessments help ensure our models minimise any AI bias.

We strive to empower clinicians by supporting clinical decision making. This decreases cognitive burden and allows teams to reinvest that time into patient care. ​

The prevention of patient deterioration positively impacts a hospital’s average length of stay, and thus bed capacity. This presents the opportunity to increase elective activity, get Trusts closer to the 107% needed to access the Elective Recovery Fund, and generate the income needed to reinvest into their services. ​

The current demand for health services drastically outweighs the system’s capacity. Earlier detection and prevention of deterioration keeps patients out of hospital and decreases the care burden. ​

MEMORI seeks to positively contribute to the eradication of health inequalities, as we aim to champion prevention and mitigate against digital exclusion by delivering cutting-edge AI to all, regardless of their digital literacy. ​

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