| Literature DB >> 33889733 |
Stephanie Steels1,2, John Ainsworth1, Tjeerd P van Staa1,3.
Abstract
BACKGROUND: The "learning health system" has been proposed to deliver better outcomes for patients and communities by analyzing routinely captured health information and feeding back results to clinical staff. This approach has been piloted in the Connected Health Cities (CHC) programme in four regions in the North of England. This paper presents the results of the evaluation of this program conducted between February and December 2018.Entities:
Keywords: LHS infrastructure; evaluation; learning health system; quality improvement
Year: 2020 PMID: 33889733 PMCID: PMC8051340 DOI: 10.1002/lrh2.10224
Source DB: PubMed Journal: Learn Health Syst ISSN: 2379-6146
The Connected Health City programme deliverables
| Deliverable | Description of deliverable |
|---|---|
| Deliverable 1 | The establishment of data sharing strategies and data sharing agreements for each CHC region. |
| Deliverable 2 | The establishment and delivery of governance arrangement for the sharing and usage of data for each CHC region, across the North and the United Kingdom. |
| Deliverable 3 | The optimization of Ark workforce arrangements, including the identification of long‐term CPD requirements the establishment of new skill bases. |
| Deliverable 4 | The creation of the Ark as an analytical platform for investigating linked data. |
| Deliverable 5 | The analysis of eight care pathways, identification of any pathway variations, and proposals for any improvements if possible. |
| Deliverable 6 | The creation and implementation of frameworks for potential integration with R&D partners and the future rising of Foreign Direct Investment. |
| Deliverable 7 | The production of a CHC business model suitable for scaling across the North and sustainable for delivery in the NHS. |
Description of care pathways included for evaluation, by region
| Connected Health City region | Title of care pathway | Objectives of care pathway | Description of care pathway |
|---|---|---|---|
| Connected Yorkshire | Supporting community care and reducing demand on A&E services |
To link de‐identified routine NHS data to describe a detailed profile of patient demand across both prehospital, primary care and hospital emergency, and urgent care settings in Yorkshire. | To collect routine NHS data from a number of emergency and urgent care (EUC) providers and link the data to provide a coherent picture of EUC demand. |
| Safer prescribing for frailty |
To reduce inappropriate polypharmacy for people with frailty. | To work with GPs to change behaviors related to deprescribing for older people with moderate or severe frailty as identified by electronic Frailty Index scores. This includes developing interventions using which apply evidenced tools to support deprescribing. | |
| Greater Manchester | BRIT—Using data to tackle antibiotic resistance |
To provide the NHS and clinical care teams with better information on what is happening and who is getting antibiotics. To assist in determining whether the use of antibiotics is reasonable given local resistance patterns to antibiotics | Analysis of patient records from GPs for effectiveness of antibiotic prescribing in general practices. This includes the development of a DataLab feeding back advanced analytics to clinical staff and policy makers and the evaluation of interventions to optimize prescribing. |
| Using technology and data to improve the diagnosis and treatment of stroke |
Improve the recognition of stroke by paramedics to maximize the proportion of acute stroke patients taken directly to a specialist stroke center for timely expert care and minimizing the number of nonstroke patients entering the stroke pathway. Provide timely and focused referral to neurosurgery for patients in Greater Manchester with stroke caused by a brain hemorrhage. Ensure that all patients get all the right treatments that they need to reduce the risk of another stroke when they are discharged from hospital. | To improve stroke recognition by paramedics by linking ambulance data to data at Salford Royal; using primary and secondary care data to create a large cohort of stroke and TIA patients for creating a predictive model of patients who are at high risk of stroke; and using acute trust data to identify predictive factors of early deterioration and death. | |
| North East North Cumbria | Predictive modeling for unplanned care |
To develop predictive modelling tools for unplanned care forecasting to support demand management and service planning in relevant health and social care services. | To produce statistical models that can be used by health/local authority/other analytics teams to produce daily forecasts up to 6 mo in advance with the pertinent associated uncertainties and variations in urgent and emergency care. |
| SILVER: Smart Interventions for Local Vulnerable Families |
To develop data sharing agreements to allow the linking of existing health data across multiple health agencies via one platform that provides recommendations to key workers. | To link data across multiple agencies including health (physical and mental), social care, criminal justice, housing, and education to develop a more complete Learning Health System. | |
| North West coast | Development of a learning system for alcohol |
To be able to inform health professionals about local clinical care. To define best care or treatments, implement and demonstrate benefits. | Improving the way information is collected, analyzed and shared between agencies and service users to bring opportunities for news was to respond collectively. |
| Development of a learning system for unplanned care |
To improve how data is used to enhance patient care admitted to hospital for emergency care. | Linking NHS data with social services data to improve the care pathway for patients with COPD and epilepsy. |