Co-producing a safe mobility and falls informatics platform to drive meaningful quality improvement in the hospital setting: a mixed-methods protocol for the insightFall study - Ben Glampson; Clare Leon-Villapalos; Erik Mayer; Rachael Lear; Phoebe Averill; Catalina Carenzo; Rachel Tao; Robert Latchford (BMJ Open)
Introduction Manual investigation of falls incidents for quality improvement is time-consuming for clinical staff. Routine care delivery generates a large volume of relevant data in disparate systems, yet these data are seldom integrated and transformed into real-time, actionable insights for frontline staff. This protocol describes the co-design and testing of a safe mobility and falls informatics platform for automated, real-time insights to support the learning response to inpatient falls.Methods Underpinned by the learning health system model and human-centred design principles, this mixed-methods study will involve (1) collaboration between healthcare professionals, patients, data scientists and researchers to co-design a safe mobility and falls informatics platform; (2) co-production of natural language processing pipelines and integration with a user interface for automated, near-real-time insights and (3) platform usability testing. Platform features (data taxonomy and insights display) will be co-designed during workshops with lay partners and clinical staff. The data to be included in the informatics platform will be curated from electronic health records and incident reports within an existing secure data environment, with appropriate data access approvals and controls. Exploratory analysis of a preliminary static dataset will examine the variety (structured/unstructured), veracity (accuracy/completeness) and value (clinical utility) of the data. Based on these initial insights and further consultation with lay partners and clinical staff, a final data extraction template will be agreed. Natural language processing pipelines will be co-produced, clinically validated and integrated with QlikView. Prototype testing will be underpinned by the Technology Acceptance Model, comprising a validated survey and think-aloud interviews to inform platform optimisation.Ethics and dissemination This study protocol was approved by the National Institute for Health Research Imperial Biomedical Research Centre Data Access and Prioritisation Committee (Database: iCARE—Research Data Environment; REC reference: 21/SW/0120). Our dissemination plan includes presenting our findings to the National Falls Prevention Coordination Group, publication in peer-reviewed journals, conference presentations and sharing findings with patient groups most affected by falls in hospital.