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Service evaluation of the implementation of a digitally-enabled care pathway for the recognition and management of acute kidney injury.
Connell, Alistair; Montgomery, Hugh; Morris, Stephen; Nightingale, Claire; Stanley, Sarah; Emerson, Mary; Jones, Gareth; Sadeghi-Alavijeh, Omid; Merrick, Charles; King, Dominic; Karthikesalingam, Alan; Hughes, Cian; Ledsam, Joseph; Back, Trevor; Rees, Geraint; Raine, Rosalind; Laing, Christopher.
Afiliación
  • Connell A; Centre for Human Health and Performance, University College London, 170 Tottenham Court Road, London, W1T 7HA, UK.
  • Montgomery H; Institute of Sport, Exercise and Health, London, W1T 7HA, UK.
  • Morris S; Centre for Human Health and Performance, University College London, 170 Tottenham Court Road, London, W1T 7HA, UK.
  • Nightingale C; Institute of Sport, Exercise and Health, London, W1T 7HA, UK.
  • Stanley S; Department of Applied Health Research, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
  • Emerson M; Department of Applied Health Research, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
  • Jones G; Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, UK.
  • Sadeghi-Alavijeh O; Royal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
  • Merrick C; Royal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
  • King D; Royal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
  • Karthikesalingam A; Royal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
  • Hughes C; Royal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
  • Ledsam J; DeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.
  • Back T; DeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.
  • Rees G; DeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.
  • Raine R; DeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.
  • Laing C; DeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.
F1000Res ; 6: 1033, 2017.
Article en En | MEDLINE | ID: mdl-28751970
ABSTRACT
Acute Kidney Injury (AKI), an abrupt deterioration in kidney function, is defined by changes in urine output or serum creatinine. AKI is common (affecting up to 20% of acute hospital admissions in the United Kingdom), associated with significant morbidity and mortality, and expensive (excess costs to the National Health Service in England alone may exceed £1 billion per year). NHS England has mandated the implementation of an automated algorithm to detect AKI based on changes in serum creatinine, and to alert clinicians. It is uncertain, however, whether 'alerting' alone improves care quality. We have thus developed a digitally-enabled care pathway as a clinical service to inpatients in the Royal Free Hospital (RFH), a large London hospital. This pathway incorporates a mobile software application - the "Streams-AKI" app, developed by DeepMind Health - that applies the NHS AKI algorithm to routinely collected serum creatinine data in hospital inpatients. Streams-AKI alerts clinicians to potential AKI cases, furnishing them with a trend view of kidney function alongside other relevant data, in real-time, on a mobile device. A clinical response team comprising nephrologists and critical care nurses responds to these AKI alerts by reviewing individual patients and administering interventions according to existing clinical practice guidelines. We propose a mixed methods service evaluation of the implementation of this care pathway. This evaluation will assess how the care pathway meets the health and care needs of service users (RFH inpatients), in terms of clinical outcome, processes of care, and NHS costs. It will also seek to assess acceptance of the pathway by members of the response team and wider hospital community. All analyses will be undertaken by the service evaluation team from UCL (Department of Applied Health Research) and St George's, University of London (Population Health Research Institute).
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Evaluation_studies / Guideline Idioma: En Revista: F1000Res Año: 2017 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Evaluation_studies / Guideline Idioma: En Revista: F1000Res Año: 2017 Tipo del documento: Article País de afiliación: Reino Unido