We pull the right innovations into routine NHS practice

A flagship NHS Virtual Hospital - the centre of clinical and academic operations of a modern health service

  • Design, deployment and validation of multidisciplinary remote and home-based care pathways at Imperial College Healthcare NHS Trust. Working with stakeholders at divisional, Trust, inter-Trust and ICB level to deliver first-line management of NHS patients in North West London

  • Technology-enabled pathways using established protocols, ensuring continuity of care between primary and secondary care services.

  • Our proprietary Care Navigators - the NHS ‘Genius Bar’ - ensure that no patient is left behind in the digital transformation of healthcare

Translational MedTech science - clinical and health economic evidence at speed and scale

  • Streamlined, proprietary processes for rapid approvals:

    • UK Health Research Authority (ethics)

    • New Systems and Data Protection Office approvals including Data Processing Agreements, International Data Transfer Agreements (at NHS Trust and NHS ICB-level)

    • Clinical guideline design and validation for incorporation of medical technology into real clinical pathways

  • High volume research recruitment (track record including >1000 patients in under 4 months) to validate the most promising MedTech innovations

  • Experts in decentralised, pragmatic clinical trials and implementation studies

  • Track record of prestigious translational funding awards (NIHR, BHF)

  • Outputs in high-impact outlets (Lancet, BMJ, European Heart Journal)

Point of Care AI Detection of Cardiovascular Disease

  • Clinician and patient self-administered technology for detection of heart failure, rhythm disturbances, and valvular heart disease using artificial intelligence.

  • Validated in:

    • 25 primary care practices (with a further 100 on the waiting list)

    • 3 UK NHS Integrated Care Boards

Consumer health to population health - identifying digital biomarkers through wearable sensors

  • Extensive industry collaboration - selecting the best-of-breed across boutique remote patient monitoring SMEs and Big Tech offerings to address real clinical problems

    • Digital biomarkers as predictors of disease and deterioration - from commercial smartwatches

    • Naturalizing use of wearable sensor data in routine clinical practice (population based physical activity programmes, cardiac rehabilitation) to improve meaningful clinical outcomes

Selected publications

Bachtiger P, Kelshiker MA, Petri CF, et al., 2023, Survival and health economic outcomes in heart failure diagnosed at hospital admission versus community settings: a propensity-matched analysis, BMJ Health & Care Informatics, Vol: 30, ISSN: 2632-1009

Kramer DB, Moe MEG & Peters NS, 2023, A universal programmer for cardiac implantable electrical devices-clinical, technical, and ethical considerations, JAMA Cardiology, Pages: 1-2, ISSN: 2380-6583

Wu H, Patel KHK, Xinyang L, et al., 2022, A fully-automated paper ECG digitisation algorithm using deep learning, Scientific Reports, Vol: 12, ISSN: 2045-2322

Sau A, Ibrahim S, Handa B, et al., 2022, Artificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanisms, European Heart Journal – Digital Health, Vol: 3, Pages: 405-414, ISSN: 2634-3916

Davies HJ, Bachtiger P, Williams I, et al., 2022, Wearable in-ear PPG: detailed respiratory variations enable classification of COPD, IEEE Transactions on Biomedical Engineering, Vol: 69, ISSN: 0018-9294

Bachtiger P, Petri CF, Scott FE, et al., 2022, Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study, The Lancet Digital Health, Vol: 4, ISSN: 2589-7500

Zaman S, Petri C, Vimalesvaran K, et al., 2022, Automatic diagnosis labeling of cardiovascular MRI by using semisupervised natural language processing of text reports, Radiology: Artificial Intelligence, Vol: 4, ISSN: 2638-6100

Patel K, Li X, Sun L, Peters N, & Ng FS, 2021, Neural networks applied to 12-lead electrocardiograms predict body mass index, visceral adiposity and concurrent cardiometabolic ill-health, Cardiovascular Digital Health Journal, Vol: 2, Pages: S1-S10, ISSN: 2666-6936

Attia ZI, Kapa S, Dugan J, et al., 2021, Rapid exclusion of COVID infection with the artificial intelligence electrocardiogram, Mayo Clinic Proceedings, Vol: 96, Pages: 2081-2094, ISSN: 0025-6196

Li X, Shi X, Handa BS, et al., 2021, Classification of fibrillation organisation using electrocardiograms to guide mechanism-directed treatments, Frontiers in Physiology, Vol: 12, Pages: 1-14, ISSN: 1664-042X

Bachtiger P, Adamson A, Maclean WA, et al., 2021, Determinants of shielding behaviour during the COVID-19 pandemic and associations with wellbeing in >7,000 NHS patients: 17-week longitudinal observational study., JMIR Public Health and Surveillance, Vol: 7, Pages: 1-14, ISSN: 2369-2960

Bachtiger P, Adamson A, Chow J-J, et al., 2021, The impact of the Covid-19 pandemic on uptake of influenza vaccine: a UK-wide observational study., JMIR Public Health and Surveillance, Vol: 7, Pages: 1-14, ISSN: 2369-2960

Arnold AD, Howard JP, Gopi AA, et al., 2020, Discriminating electrocardiographic responses to His-bundle pacing using machine learning., Cardiovascular Digital Health Journal, Vol: 1, Pages: 11-20

Davies HJ, Williams I, Peters NS, & Mandic DP, 2020, In-ear SpO2: a tool for wearable, unobtrusive monitoring of core blood oxygen saturation, Sensors (Basel, Switzerland), Vol: 20, ISSN: 1424-8220

Bachtiger P, Adamson A, Quint JK, & Peters NS, 2020, Belief of having had unconfirmed Covid-19 infection reduces willingness to participate in app-based contact tracing, NPJ Digital Medicine, Vol: 3, Pages: 1-7, ISSN: 2398-6352

Bachtiger P, Plymen CM, Pabari PA, et al., 2020, Artificial intelligence, data sensors and interconnectivity: future Opportunities for heart failure, Cardiac Failure Review, Vol: 6, Pages: e11-e11, ISSN: 2057-7540

Bachtiger P, Peters NS, Walsh SLF, 2020, Machine learning for COVID-19-asking the right questions, The Lancet Digital Health, Vol: 2, Pages: E391-E392, ISSN: 2589-7500

Ding EY, Svennberg E, Wurster C, et al., 2020, Survey of current perspectives on consumer-available digital health devices for detecting atrial fibrillation., Cardiovascular Digital Health Journal, Vol: 1, Pages: 21-29, ISSN: 2666-6936

Cohen IG, Gerke S, Kramer DB. Ethical and Legal Implications of Remote Monitoring of Medical Devices. The Milbank Quarterly. 2020;98(4): 1257–1289.

Holtzman JN, Wadhera RK, Choi E, et al. Trends in utilization and spending on remote monitoring of pacemakers and implantable cardioverter-defibrillators among Medicare beneficiaries. Heart Rhythm. 2020;17(11): 1917–1921.

Hu SY, Santus E, Forsyth AW, et al. Can machine learning improve patient selection for cardiac resynchronization therapy? PloS One. 2019;14(10): e0222397.

Stern AD, Gordon WJ, Landman AB, Kramer DB. Cybersecurity features of digital medical devices: an analysis of FDA product summaries. BMJ Open. 2019;9(6): e025374.

Dauvin A, Donado C, Bachtiger P, et al., 2019, Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients, NPJ Digital Medicine, Vol: 2, ISSN: 2398-6352

McGillivray MF, Cheng W, Peters NS, et al., 2018, Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation, Royal Society Open Science, Vol: 5, ISSN: 2054-5703

Kramer DB, Fu K. Cybersecurity Concerns and Medical Devices: Lessons From a Pacemaker Advisory. JAMA. 2017;318(21): 2077–2078.