Impact of COVID-19 on telepsychiatry at the service and individual patient level across two UK NHS mental health Trusts

Background The effects of COVID-19 on the shift to remote consultations remain to be properly investigated. Objective To quantify the extent, nature and clinical impact of the use of telepsychiatry during the COVID-19 pandemic and compare it with the data in the same period of the 2 years before the outbreak. Methods We used deidentified electronic health records routinely collected from two UK mental health Foundation Trusts (Oxford Health (OHFT) and Southern Health (SHFT)) between January and September in 2018, 2019 and 2020. We considered three outcomes: (1) service activity, (2) in-person versus remote modalities of consultation and (3) clinical outcomes using Health of the Nation Outcome Scales (HoNOS) data. HoNOS data were collected from two cohorts of patients (cohort 1: patients with ≥1 HoNOS assessment each year in 2018, 2019 and 2020; cohort 2: patients with ≥1 HoNOS assessment each year in 2019 and 2020), and analysed in clusters using superclasses (namely, psychotic, non-psychotic and organic), which are used to assess overall healthcare complexity in the National Health Service. All statistical analyses were done in Python. Findings Mental health service activity in 2020 increased in all scheduled community appointments (by 15.4% and 5.6% in OHFT and SHFT, respectively). Remote consultations registered a 3.5-fold to 6-fold increase from February to June 2020 (from 4685 to a peak of 26 245 appointments in OHFT and from 7117 to 24 987 appointments in SHFT), with post-lockdown monthly averages of 23 030 and 22 977 remote appointments/month in OHFT and SHFT, respectively. Video consultations comprised up to one-third of total telepsychiatric services per month from April to September 2020. For patients with dementia, non-attendance rates at in-person appointments were higher than remote appointments (17.2% vs 3.9%). The overall HoNOS cluster value increased only in the organic superclass (clusters 18–21, n=174; p<0.001) from 2019 to 2020, suggesting a specific impact of the COVID-19 pandemic on this population of patients. Conclusions and clinical implications The rapid shift to remote service delivery has not reached some groups of patients who may require more tailored management with telepsychiatry.


Background and rationale
Mental health and wellbeing are a major public health concern during the COVID-19 pandemic (Rathod et al., 2020) and higher prevalence of suicidal ideation and lower prevalence of wellbeing reported in the general public . Health anxiety, as well as the impact of social distancing, and the accompanying economic disruption are likely to have a wide and far reaching impact on the mental health and wellbeing of the population, both now and into the future .
As well as the health impact on the general population, there is likely to be increased demand from existing service users, who are particularly vulnerable at this difficult time. In addition, the needs of the general public are likely to prompt increased numbers of referrals for assessment by mental health services. However, the need for health services to follow social distancing measures to reduce spread of COVID-19 means that there have been changes in the way health services have been working. Face-face contact with patients has been minimised and health staff have been encouraged to offer consultations remotely. However, early indications are that the uptake of different types of telehealth consultations (telephone or video-based) are patchy, with significantly more video consultation in Child and Adolescent services and very few in adult and older age services (Oxford Health).
Whilst this may be a new approach for many clinicians and patients, telepsychiatry is a well-recognised sub-discipline of telemedicine and includes psychiatric assessments or follow-up interviews conducted using telephone calls, audio and video digital platforms. It has been used since the 1950s and, particularly in the USA and Australia has been subject to evaluations with promising outcomes and clear guidelines.(APA 2021, https://www.psychiatry.org/psychiatrists/practice/telepsychiatry/toolkit/history-of-telepsychiatry). The evidence suggests that it is effective and well accepted across a range of age groups and cultural backgrounds. It may also be able to offer additional benefits, particularly when combined with other digital technologies such as online platforms and apps for recording and tracking of symptoms and treatment strategies. However, it is unclear to what extent UK clinicians are aware of this and are utilising the research and guidelines related to telepsychiatry during this current response to the COVID-19 pandemic.
The current crisis represents both a challenge and an opportunity for mental health services. Whereas there is evidence that telepsychiatry can be used to offer effective care for mental health problems (https://oxfordhealthbrc.nihr.ac.uk/our-work/oxppl/table-5-digital-technologies-and-BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) telepsychiatry/), there is a dearth of evidence regarding incorporation of telepsychiatry into routine mental health services in the context of a disaster. Therefore, the onus is on mental health services to optimise the use of telemedicine using the best available evidence and also to conduct meaningful research into its implementation at a time of crisis.
Even prior to the COVID-19 pandemic mental health services were already looking at service transformation to increase the use of digital technologies for consultations. The COVID-19 pandemic and social distancing has made evaluating such a transformation even more urgent.
The results of such an evaluation will be essential both during and in the aftermath of the pandemic.

Aims
We aim to conduct a feasibility study to evaluate the uptake, acceptability (including barriers to use), clinical utility and overall service impact of telepsychiatry during the COVID-19 pandemic within the NHS. This will lay the groundwork for larger-scale studies, in due course, evaluating the impact of interventions such as remote training, introduction of apps (to support teleconsultations), and/or use of clinical-decision making tools in teleconsultations.

1)
To evaluate the uptake of teleconsultations, using routine clinical data to analyse numbers of telephone, digital and face to face contacts 2) To evaluate the acceptability of teleconsultations, using online feedback from clinicians, service-users and carers, as to how these consultations are experienced. Barriers to use, such as lack of confidence in using technology, or lack of access to the technology itself will be assessed, with options for written or telephone feedback if easier 3) To evaluate the utility of teleconsultations, using routine clinical data (such as DNAs, consultation time, amount/quality/rapidity of documentation) alongside online questionnaire data from service-users and clinicians (perceived communication and rapport) 4) To evaluate the effectiveness of teleconsultations using routine clinical data (such as prescriptions, hospitalisation) alongside online questionnaires from clinicians (PHQ/global state) and service users (self-report measures of global state) 5) Overall impact on the service-qualitative data on overall effects from clinicians, service users and carers. 6) To develop an intervention to improve telepsychiatry within the NHS 7) To develop a protocol for a trial to evaluate the intervention to implement telepsychiatry in the NHS

Approach
It will be important to frame the evaluation within the context of the current, and growing, research evidence base relating to telepsychiatry initiatives and the application of this evidence as is reflected in existing training and guidance materials available within the Trust.

Method
In Phase 1 we will collect and analyse data, using routine clinical data and self-report questionnaires developed specifically for this study (as outlined in objectives 1-5). For each of the above outcomes we will analyse by service factors (e.g. types of service), clinician factors (e.g. discipline) and service user factors (e.g. degree of disability, diagnosis, sex, age), which will be available from routine clinical data. Services to be included in this study will be agreed with the Trust.
In Phase 2 we will develop an intervention aimed at improving the uptake, acceptability, utility and/or effectiveness of telepsychiatry within NHS services. Most likely it will consist of a package of information and training. The content, format and delivery of which will be decided in consultation with service users, clinicians and managers and using the best available evidence in the literature. Focus groups with clinicians and patients, together with other feedback, will be used to develop this intervention with expert input.
In Phase 3 we will develop a protocol for a trial to evaluate the developed intervention, most likely using a stepped wedge design.

Participants
Patients, carers and clinicians in NHS mental health services (including children and adolescents, adults, older adults, pharmacies and care homes).

Out of Scope
This study will exclude any form of evaluation of types of digital platforms being used in the Trust.

Expected impact
This is a feasibility study to evaluate the current use of telepsychiatry and barriers to its use, in the context of COVID-19. The information would be used to design an intervention study to improve patient care (nationally and possibly internationally) in the context of the current pandemic and telepsychiatry beyond this.

Total number of patients with an open referral
Extracted in accordance to how the Trust report Nationally -Open referral is defined as a referral accepted date with no end date or where the end date occurred within the period. Exclusions: rejected referrals. This is a distinct count of patients with an open referral within the period

Total number of open referrals
Extracted in accordance to how the Trust report Nationally -Open referral is defined as a referral accepted date with no end date or where the end date occurred within the period. Exclusions: rejected referrals. This counts number of referrals within the period (more than one per patient is possible)

Total number of new patients with an accepted referral
Extracted in accordance to how the Trust report Nationally -Accepted referral is defined as a referral accepted date within the period. Exclusions: rejected referrals. This is a distinct count of patients with an accepted referral within the period. The patient could have been subsequently discharged within the same period or had multiple referrals accepted within the period, they will only be counted once.

Total number of new referrals
Extracted in accordance to how the Trust report Nationally -Accepted referral is defined as a referral accepted date within the period. Exclusions: rejected referrals. This is a count of number of accepted referrals within the period. More than one per patient is possible.

Number of patients discharged
Discharge is where the referral has a discharge date within the period. Distinct count of patients. If multiple referrals and discharged within the same period then only one is counted.
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Number of Discharges
Discharge is where the referral has a discharge date within the period. Excluding test patients. Count of number of discharges within the period. If a patient has multiple discharges within the same period then all discharges are counted

Number of patient deaths
Count of patient deaths which fall within the periods

Number of Inpatients (number of occupied beds per day)
Number of occupied beds per day -If a patient has a bed for 5 days then 7 days within the same period then this will count as 12 Number of inpatients (were in hospital at least once during the period) Number of distinct patient with a hospital stay

Number of new inpatients (admissions)
Number of admission start dates for inpatients within the study period. Can be multiple per patient if more then one admission occurs within the period.

Number of discharged inpatients
Number of discharge dates for inpatients within the study period. Can be multiple per patient if more then one discharge occurs within the period.

Section 5: Demographic data Section 5A: Southern Health NHS Foundation Trust (SHFT) demographic data
Tables 5Ai-iv below show: SHFT service data by age, gender, ethnicity, and team-level % figures. Calculations were done using initial absolute number of numbers of accepted referrals, for all services including inpatient and community. Note that a single patient can have multiple referrals. Percentages in the table below were calculated by dividing the total number of accepted referrals for the respective year.

Cohort 1 Selection criteria (Also described in Methods of the main text)
Step 1 -Identify patients have at least 1 assessment in 2018, 2019 & 2020 Step 2 -If a patient has more than one assessment in a year, then the most recent assessment is included in the analysis.
Step 3identify patient moved between superclass in 2018, 2019 and 2020 and exclude them from the analysis Var: Cluster 0 is called the 'variance' cluster, which codes patients who need mental health care, but cannot be classified into one of the other clusters. Cluster 9 is a 'blank cluster' and is not used.

Please refer to the Methods section of the main text for a description of HoNOS scores, clusters, and superclasses.
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Post-hoc Pairwise test
Post-hoc pairwise tests are commonly performed after a significant difference has been found when there are three or more levels of a factor. The Python package scikit-posthocs provides post hoc tests for pairwise multiple comparisons.
The result of applying Related-Sample Wilcoxon Signed-Rank post-hoc tests on Psychotic and Organic superclass are shown in Tables 7G and 7H respectively.

Cohort 2 Selection Criteria (Also described in Methods of the main text)
Step 1 -Identify patients have at least 1 assessment in each year in 2019 and 2020 Step 2 -If a patient has more than one assessment in a year, then the most recent assessment is included in the analysis.
Step 3identify patient moved between superclass between 2019 and 2020 and exclude them from the analysis