Data suggests remote consultations are acceptable and convenient to some patients. However, patients and clinicians vary significantly in their preferences for modes of remote consultation. For example, data from south London on mental health consultations suggests lower uptake among older people.
Although current research is addressing some of the research gaps, there is no research on the unintended consequences, harms and equity of the recent rapid shift to remote consultations.
Proposing a service evaluation
The three London ARCs – ARC South London, ARC North Thames and ARC Northwest London – working with the London Academic Health Science Networks (AHSNs), developed this evaluation in response. The service evaluation aims to address the following evidence gaps to inform the adoption of remote consultation or triage in care settings and identify gaps in available data:
- Efficiencies, as measured by completed care episodes, and inefficiencies, eg duplicated consultations, waiting lists
- Unintended consequences, eg truncated consultations (where data available), use of unplanned care
- Inequities in uptake across demographic groups for specific care pathways, with reference to disease severity where data is available
This project seeks to build on the available data, to develop information that can help inform health care practices. Our initial research will focus on mental health and cardiology outpatient pathways.
Aim of the project
This is a mixed-methods study with the overall aim to assess the use of, and experiences with, remote consultations during the Covid-19 pandemic in primary and secondary care on health care pathways in London in cardiology and mental health services and patient outcomes.
Our objectives are to:
- Map and stratify remote consultations by demographics (age, gender, ethnicity, deprivation) and care pathways, across the comparable north-west and couth London datasets
- Quantify the proportion of remote consultation (phone, video, e-consultation) that result in no follow up care (‘closed’ consultations) for both physical and mental health conditions
- Examine associations between patterns in remote and face-to-face consultations and relevant quality and economic metrics, such as repeat appointments, subsequent use of unplanned or crisis secondary care, secondary care waiting list entry, rates of investigations and adverse outcomes during peak and less active phases of Covid-19
- Examine the above for different conditions (eg, musculoskeletal conditions, dermatology, depression) starting with cardiology and mental health
- Explore the barriers and facilitators to accessing primary and secondary health care during the pandemic, from the perspective of patients and clinicians
Expected benefits
Data on how remote consultations are being used across London will be helpful for health service planning. This will expand the knowledge available and help to establish best practices.
In particular, this research will provide the following outputs:
- Rapid synthesis of available quantitative data to inform decision making across London, including identifying patient, care pathway and provider factors leading to increased risk of unplanned healthcare use or of dropping out of care pathways following remote consultations
- Recommendations for refining current monitoring surveys to identify where patients may need additional support
- Recommendations on how to support patients to access healthcare remotely, particularly for excluded (digitally and otherwise) populations
- Recommendations on how to support coherent and patient-centred service provision by healthcare providers, including advice on how to implement remote consultations
- Development of a health informatics collaboration between north-west London and south London including shared data definitions, data dictionaries and shared learning
- Recommendations for routine data monitoring using cross-sector data and informatics. The aim is to develop a ‘Learning Health System’ for London, where the impacts of future service transformation on service use can be monitored in real time.
This project is funded by the NIHR Beneficial Changes Network.
