Severe Mental DisorderNCT04792268
Electronic Clinical Decision Support for Diabetes and Dysglycaemia in Secondary Mental Healthcare
A study testing Access to eCDSS on wards in people with Severe Mental Disorder and Diabetes Mellitus, run by King's College London at 1 site in United Kingdom.
Trial summary
People with serious mental illness (SMI) such as schizophrenia, schizoaffective disorder and bipolar affective disorder have a significantly reduced life expectancy, caused in part by increased incidences of mortality from physical health conditions such as cardiovascular disease (CVD) and diabetes. Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Severe Mental Disorder, Diabetes Mellitus, Dysglycemia
- What is being tested?
- Access to eCDSS on wards
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 4 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 May 2022to 1 Dec 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- General adult psychiatry inpatient wards at South London and Maudsley NHS Foundation Trust. Wards will be entered into the study if their respective management are agreeable to participate.
- All clinical staff on recruited wards will be eligible to participate and will be invited to take part in a preliminary survey and individual interview with the research team at the start of the study.
- Staff on intervention wards will also be asked to complete a survey and individual interview at the end of the study.
Who cannot join
- Staff on recruited wards who are not of a clinical or healthcare professional background.
- Staff who lack capacity to provide informed consent to participate.
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
South London and Maudsley NHS Foundation Trust
London
About this study
Official title: Implementation of an Electronic Clinical Decision Support System (eCDSS) for the Early Recognition and Management of Diabetes and Dysglycaemia in Secondary Mental Healthcare : Feasibility Study
People with serious mental illness (SMI) such as schizophrenia, schizoaffective disorder and bipolar affective disorder have a significantly reduced life expectancy, caused in part by increased incidences of mortality from physical health conditions such as cardiovascular disease (CVD) and diabetes.
Electronic clinical decision support systems (eCDSS) offer clinicians patient-specific advice and recommendations based on clinical guidelines, theoretically overcoming obstacles in the use of existing paper-based guidelines. Adoption of eCDSS to address CVD risk in people with SMI presents a unique opportunity for research, but requires evidence of acceptability and feasibility before scaling up of research.
Read more
The key objective of this study is to establish the feasibility and acceptability of an eCDSS (CogStack @ Maudsley) compromising a real-time electronic health record powered alerting and clinical decision support system for diabetes management in secondary inpatient mental healthcare settings. End-users of the eCDSS will be clinicians only.
Firstly we will conduct initial surveys and interviews with clinicians on inpatient wards to scope experiences of managing diabetes in secondary mental healthcare settings and attitudes towards use of digital technologies to aid in clinical decision making.
A feasibility study will then be run to evaluate the acceptability and feasibility of implementing eCDSS on inpatient wards. This will involve a cluster RCT on inpatient general adult psychiatry wards, where 4 months of eCDSS use by clinicians on intervention wards will be compared to 4 months of treatment as usual on control wards. All clinicians on recruited wards will be eligible to participate.
At the end of the study, participating clinicians on intervention wards will be invited to take part in a survey and interview which will explore their experiences and attitudes towards using the eCDSS, and an implementation science framework will be applied to inform future implementation of eCDSS. Group level pseudonymised outcome data will be gathered through a separate study.
Read the detailed description
People with serious mental illness (SMI) such as schizophrenia, schizoaffective disorder and bipolar affective disorder have a significantly reduced life expectancy in comparison to the general population. Improvements to the primary prevention of physical health illnesses like diabetes in the general population have not been mirrored to the same extent in people with SMI.
Diabetes is a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. If left untreated or poorly managed, diabetes can lead to various long term health complications including cardiovascular disease, stroke, chronic kidney disease, foot ulcers, damage to the nerves and damage to the eyes. Diabetes accounts for approximately 10% of healthcare resources in the UK, and this is set to rise to 17% with an estimated cost of £39.8billion by 2035 when direct healthcare costs and indirect costs on productivity are taken into account.
People with SMI have higher rates of cardiovascular disease (CVD) risk factors such as central obesity, high blood pressure, raised cholesterol levels, and raised blood sugar levels compared to the general population. Local rates of diabetes in people with a diagnosis of established psychosis are 20% with a further 30% evidencing dysglycaemia (raised blood sugar levels). Again locally, rates of glucose dysregulation (indicator for high risk of developing diabetes) doubles in the first year after a first psychotic episode, creating a unique window for prevention strategies to address these risks as early as possible.
A key inequality in healthcare provision in people with SMI is the less than adequate assessment and treatment of physical health conditions such as diabetes in secondary mental healthcare settings. There is therefore a need for more targeted and clinically informed interventions, that improve the standard of physical healthcare screening and interventions offered to people with SMI across both primary and secondary care settings.
Globally, studies evaluating the provision of care by clinicians reveal that there is a sub-optimal uptake of guidelines into actual practice. The underlying factors for this are complex and occur at a combination of patient, clinician and system levels.
Adoption of digital technology to improve physical health in people with a diagnosis of SMI presents a unique opportunity, but requires evidence of acceptability, feasibility and effectiveness. Given the rising disease burden from diabetes in SMI, and deficits in providing evidence-based care for diabetes prevention and treatment, there is a pressing need to identify more systems-focused solutions.
Electronic clinical decision support systems (eCDSS) are well established as a strategic method of improving care for prevention and management of chronic conditions. eCDSS is defined as "any electronic information system based on a software algorithm designed to aid directly in clinical decision making, in which characteristics of individual patients are used to generate patient-specific assessments or recommendations that are then presented to clinicians for consideration".
Clinical guidelines remain under-utilized in clinical practice, thus eCDSS has the potential to overcome problems associated with the use of traditional paper-based guidelines. However, the existing evidence base for eCDSSs improving clinical performance and patient outcomes in mental healthcare settings remains sparse. In addition, electronic systems that are not accepted by their users cannot be expected to contribute to improving quality of care, hence facilitators, barriers and other consequences need to be understood for successful implementation of novel digital tools and could also serve as a basis for future system re-engineering. Hence there is call for research to include evaluating its implementation for successful future scalability.
The key digital tool to be used for eCDSS in this study is CogStack, a software platform developed by the National Institute for Health Research Maudsley Biomedical Research Centre (NIHR Maudsley BRC) and PhiDataLab. CogStack is an open source information retrieval and extraction system with the capability to offer near real-time natural language processing (NLP) of electronic health records. CogStack implements new data mining techniques, specifically the ability to search any clinical data source (unstructured and structured), and NLP applications developed to automate information extraction of medical concepts. The platform has shown early potential to be of value to clinicians in monitoring, intervention and follow up for their patients.
The primary objective of this study is to establish the feasibility and acceptability of an eCDSS (Cogstack@Maudsley) compromising a real-time computerised alerting and clinical decision support system for dysglycaemia management in secondary mental healthcare.
Our secondary objectives are to assess whether the system leads to changes in screening and follow-up testing rates for dysglycaemia, and subsequent clinician-led evidence-based interventions for dysglycaemia and diabetes (this will be measured using pseudonymised group observational data gathered from the South London and Maudsley NHS Foundation Trust (SLaM) Biomedical Research Centre (BRC) Clinical Records Interactive Search (CRIS) system once ward access to the eCDSS has ended). Since 2006, South London and Maudsley NHS Trust has operated fully electronic health records. The Clinical Record Interactive Search (CRIS) system, established in 2008, is an ethically approved electronic health records interface system that allows researchers to access deidentified electronic health records from this Trust for research purposes.
We will conduct a process evaluation to assess the barriers, facilitators, unintended consequences, and indicative costs of implementing the system onto inpatient general adult psychiatry wards.
Data gathered from this study will allow the research team to refine the system, address potential problems with future successful implementation, and inform a larger and more definitive effectiveness trial which will examine for hypothesised improvements in;
- Rates of clinician-delivered evidence-based interventions for patients with dysglycaemia
- Clinical outcomes relating to diabetes care
- Conditions
- Severe Mental Disorder, Diabetes Mellitus, Dysglycemia, Staff Attitude
- Being tested
- Access to eCDSS on wards
- Medical subject headings
- Mental Disorders, Diabetes Mellitus
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Access to eCDSS on wardsOther
Electronic clinical decision support (eCDSS) will be available to clinicians on wards recruited to this arm.
An eCDSS is a health information technology system designed to assist clinicians and other health care professionals in clinical decision-making.
The key digital tool to be used for eCDSS in this study is CogStack. This eCDSS has been developed to alert clinicians automatically regarding patients admitted under their care, triggered by the presence of new, old or absent HbA1c pathology reports on the electronic health record (EHR).
| Study group | Role | What they receive |
|---|---|---|
| Electronic clinical decision support | Experimental | Electronic clinical decision support (eCDSS) will be available to clinicians on wards recruited to this arm. An eCDSS is a health information technology system designed to assist clinicians and other health care professionals in clinical decision-making.
Automated electronic decision support will be provided as a combination of visual prompts on the individual patient's dashboard, accessed by clinicians when they view a patient record on the electronic health record supplemented by an email sent to the NHS Trust email account addresses of the participating ward clinician(s).
Alerts will include locally approved guideline-based recommendations for clinician-led monitoring and management of dysglycaemia and known diabetes, tailored to the individual patient based upon reported HbA1c values. Receives: Other: Access to eCDSS on wards |
| Treatment as usual | No intervention | Clinicians will not have access to eCDSS on wards recruited to this arm and will deliver care as usual. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Extent to which eCDSS is perceived by clinician users to be acceptable
This outcome measure will explore clinician perceptions on how acceptable the eCDSS is in improving evidence-based dysglycaemia management, and where applicable, diabetes care.
Data will be gathered through qualitative analysis of individual semi-structured interviews with clinician users.
Measured: 4 months
Extent to which eCDSS is perceived by clinician users to be acceptable
This outcome measure will explore clinician perceptions on how acceptable the eCDSS is in improving evidence-based dysglycaemia management, and where applicable, diabetes care.
Data will be gathered through qualitative analysis of survey questionnaires of clinician users
Measured: 4 months
Number of wards and clinician end-users recruited to the study
Ability to recruit wards and clinicians to the study. Retention and participation of clinicians on recruited wards through to end of study.
Availability of data to fulfil outcome measures.
Measured: 4 months
Secondary outcomes (6)
Rate of HbA1c testing
Rates of HbA1c testing - Inpatient for initial test, inpatient and community for follow-up tests.
Measured: 12 months
Rate of documentation of dysglycaemia/diabetes in clinical notes
Documentation of diabetes or pre-diabetes diagnosis in case notes during inpatient stay (where indicated)
Measured: 4 months
Rate of documentation of discussion with patient regarding exercise, diet and smoking cessation
Documentation of advice by clinician given to patient regarding lifestyle changes- exercise, diet and smoking cessation in patients with dysglycaemia
Measured: 4 months
Rates of documentation of diabetes related screening interventions
Documentation of completed foot check for patients with dysglycaemia
Measured: 4 months
Rate of delivery of evidence-based pharmacological interventions for diabetes or pre-diabetes where clinically indicated
Documentation of diabetes-related medication changes post-alerting where clinically indicated:
- Initiation of diabetes medication
- Intensification of medication (dose change or introduction of new agent in accordance with algorithm)
- Documentation of antipsychotic medication changes to reduce risk of dysglycaemia in patients at risk of Hyperosmolar Hyperglycaemic State.
Measured: 4 months
Rates of communication with GP/CMHT regarding diabetes or dysglycaemia follow up
Documentation to relevant community team(s) and GP regarding follow up plans for diabetes care post-discharge where indicated.
Measured: 4 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Mar 2021
- Study start1 May 2022
- Primary completion1 Aug 2023
- Study completion1 Dec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Severe Mental DisorderStudies of the same condition, open and under way.
- More trials run by King's College LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- This is a feasibility study of a two-arm randomized controlled cluster trial conducted in general adult psychiatry inpatient ward settings. Wards will be the unit of recruitment and assigned to either the intervention or control group in a 1:1 ratio, to receive either the eCDSS platform or to follow usual care process.
- Masking
- None (open label)
- Enrolment
- 4 participants (actual)
Sponsor and investigators
- Lead sponsor
- King's College London
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute for Health Research, United Kingdom, South London and Maudsley NHS Foundation Trust
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04792268
- Sponsor's protocol ID
- 285509
- Status last verified
- Mar 2024
- First submitted
- 28 Oct 2020
- Last updated
- 15 Mar 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Gardner-Sood P, Lally J, Smith S, Atakan Z, Ismail K, Greenwood KE, Keen A, O'Brien C, Onagbesan O, Fung C, Papanastasiou E, Eberhard J, Patel A, Ohlsen R, Stahl D, David A, Hopkins D, Murray RM, Gaughran F; IMPaCT team. Cardiovascular risk factors and metabolic syndrome in people with established psychotic illnesses: baseline data from the IMPaCT randomized controlled trial. Psychol Med. 2015;45(12):2619-29. doi: 10.1017/S0033291715000562. Epub 2015 May 12.
PubMed 25961431Gaughran F, Stahl D, Stringer D, Hopkins D, Atakan Z, Greenwood K, Patel A, Smith S, Gardner-Sood P, Lally J, Heslin M, Stubbs B, Bonaccorso S, Kolliakou A, Howes O, Taylor D, Forti MD, David AS, Murray RM, Ismail K; IMPACT team. Effect of lifestyle, medication and ethnicity on cardiometabolic risk in the year following the first episode of psychosis: prospective cohort study. Br J Psychiatry. 2019 Dec;215(6):712-719. doi: 10.1192/bjp.2019.159.
PubMed 31347480Hayes JF, Marston L, Walters K, King MB, Osborn DPJ. Mortality gap for people with bipolar disorder and schizophrenia: UK-based cohort study 2000-2014. Br J Psychiatry. 2017 Sep;211(3):175-181. doi: 10.1192/bjp.bp.117.202606. Epub 2017 Jul 6.
PubMed 28684403Grol R, Grimshaw J. From best evidence to best practice: effective implementation of change in patients' care. Lancet. 2003 Oct 11;362(9391):1225-30. doi: 10.1016/S0140-6736(03)14546-1.
PubMed 14568747Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005 Apr 2;330(7494):765. doi: 10.1136/bmj.38398.500764.8F. Epub 2005 Mar 14.
PubMed 15767266Vancampfort D, Stubbs B, Mitchell AJ, De Hert M, Wampers M, Ward PB, Rosenbaum S, Correll CU. Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder: a systematic review and meta-analysis. World Psychiatry. 2015 Oct;14(3):339-47. doi: 10.1002/wps.20252.
PubMed 26407790Jackson R, Kartoglu I, Stringer C, Gorrell G, Roberts A, Song X, Wu H, Agrawal A, Lui K, Groza T, Lewsley D, Northwood D, Folarin A, Stewart R, Dobson R. CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital. BMC Med Inform Decis Mak. 2018 Jun 25;18(1):47. doi: 10.1186/s12911-018-0623-9.
PubMed 29941004Hex N, Bartlett C, Wright D, Taylor M, Varley D. Estimating the current and future costs of Type 1 and Type 2 diabetes in the UK, including direct health costs and indirect societal and productivity costs. Diabet Med. 2012 Jul;29(7):855-62. doi: 10.1111/j.1464-5491.2012.03698.x.
PubMed 22537247Fernandes AC, Cloete D, Broadbent MT, Hayes RD, Chang CK, Jackson RG, Roberts A, Tsang J, Soncul M, Liebscher J, Stewart R, Callard F. Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records. BMC Med Inform Decis Mak. 2013 Jul 11;13:71. doi: 10.1186/1472-6947-13-71.
PubMed 23842533Perera G, Broadbent M, Callard F, Chang CK, Downs J, Dutta R, Fernandes A, Hayes RD, Henderson M, Jackson R, Jewell A, Kadra G, Little R, Pritchard M, Shetty H, Tulloch A, Stewart R. Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource. BMJ Open. 2016 Mar 1;6(3):e008721. doi: 10.1136/bmjopen-2015-008721.
PubMed 26932138Stewart R, Soremekun M, Perera G, Broadbent M, Callard F, Denis M, Hotopf M, Thornicroft G, Lovestone S. The South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register: development and descriptive data. BMC Psychiatry. 2009 Aug 12;9:51. doi: 10.1186/1471-244X-9-51.
PubMed 19674459Patel D, Msosa YJ, Wang T, Williams J, Mustafa OG, Gee S, Arroyo B, Larkin D, Tiedt T, Roberts A, Dobson RJB, Gaughran F. Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial. JMIR Res Protoc. 2024 Apr 5;13:e49548. doi: 10.2196/49548.
PubMed 38578666
Study at a glance
From the registry record, last updated by the sponsor on 15 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Implementation of an Electronic Clinical Decision Support System (eCDSS) for the Early Recognition and Management of Diabetes and Dysglycaemia in Secondary Mental Healthcare : Feasibility Study
- ClinicalTrials.gov ID
- NCT04792268
- Acronym / sponsor ID
- 285509
- Conditions
- Severe Mental Disorder, Diabetes Mellitus, Dysglycemia, Staff Attitude
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 4 participants (actual)
- Lead sponsor
- King's College LondonUniversity, hospital or other
- Collaborators
- National Institute for Health Research, United Kingdom, South London and Maudsley NHS Foundation Trust
- Study start
- 1 May 2022 (actual)
- Primary completion
- 1 Aug 2023 (actual)
- Study completion
- 1 Dec 2023 (actual)
- First posted
- 10 Mar 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
People with serious mental illness (SMI) such as schizophrenia, schizoaffective disorder and bipolar affective disorder have a significantly reduced life expectancy, caused in part by increased incidences of mortality from physical health conditions such as cardiovascular disease (CVD) and diabetes.
Electronic clinical decision support systems (eCDSS) offer clinicians patient-specific advice and recommendations based on clinical guidelines, theoretically overcoming obstacles in the use of existing paper-based guidelines. Adoption of eCDSS to address CVD risk in people with SMI presents a unique opportunity for research, but requires evidence of acceptability and feasibility before scaling up of research.
The key objective of this study is to establish the feasibility and acceptability of an eCDSS (CogStack @ Maudsley) compromising a real-time electronic health record powered alerting and clinical decision support system for diabetes management in secondary inpatient mental healthcare settings. End-users of the eCDSS will be clinicians only.
Firstly we will conduct initial surveys and interviews with clinicians on inpatient wards to scope experiences of managing diabetes in secondary mental healthcare settings and attitudes towards use of digital technologies to aid in clinical decision making.
A feasibility study will then be run to evaluate the acceptability and feasibility of implementing eCDSS on inpatient wards. This will involve a cluster RCT on inpatient general adult psychiatry wards, where 4 months of eCDSS use by clinicians on intervention wards will be compared to 4 months of treatment as usual on control wards. All clinicians on recruited wards will be eligible to participate.
At the end of the study, participating clinicians on intervention wards will be invited to take part in a survey and interview which will explore their experiences and attitudes towards using the eCDSS, and an implementation science framework will be applied to inform future implementation of eCDSS. Group level pseudonymised outcome data will be gathered through a separate study.
Detailed description
People with serious mental illness (SMI) such as schizophrenia, schizoaffective disorder and bipolar affective disorder have a significantly reduced life expectancy in comparison to the general population. Improvements to the primary prevention of physical health illnesses like diabetes in the general population have not been mirrored to the same extent in people with SMI.
Diabetes is a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. If left untreated or poorly managed, diabetes can lead to various long term health complications including cardiovascular disease, stroke, chronic kidney disease, foot ulcers, damage to the nerves and damage to the eyes. Diabetes accounts for approximately 10% of healthcare resources in the UK, and this is set to rise to 17% with an estimated cost of £39.8billion by 2035 when direct healthcare costs and indirect costs on productivity are taken into account.
People with SMI have higher rates of cardiovascular disease (CVD) risk factors such as central obesity, high blood pressure, raised cholesterol levels, and raised blood sugar levels compared to the general population. Local rates of diabetes in people with a diagnosis of established psychosis are 20% with a further 30% evidencing dysglycaemia (raised blood sugar levels). Again locally, rates of glucose dysregulation (indicator for high risk of developing diabetes) doubles in the first year after a first psychotic episode, creating a unique window for prevention strategies to address these risks as early as possible.
A key inequality in healthcare provision in people with SMI is the less than adequate assessment and treatment of physical health conditions such as diabetes in secondary mental healthcare settings. There is therefore a need for more targeted and clinically informed interventions, that improve the standard of physical healthcare screening and interventions offered to people with SMI across both primary and secondary care settings.
Globally, studies evaluating the provision of care by clinicians reveal that there is a sub-optimal uptake of guidelines into actual practice. The underlying factors for this are complex and occur at a combination of patient, clinician and system levels.
Adoption of digital technology to improve physical health in people with a diagnosis of SMI presents a unique opportunity, but requires evidence of acceptability, feasibility and effectiveness. Given the rising disease burden from diabetes in SMI, and deficits in providing evidence-based care for diabetes prevention and treatment, there is a pressing need to identify more systems-focused solutions.
Electronic clinical decision support systems (eCDSS) are well established as a strategic method of improving care for prevention and management of chronic conditions. eCDSS is defined as "any electronic information system based on a software algorithm designed to aid directly in clinical decision making, in which characteristics of individual patients are used to generate patient-specific assessments or recommendations that are then presented to clinicians for consideration".
Clinical guidelines remain under-utilized in clinical practice, thus eCDSS has the potential to overcome problems associated with the use of traditional paper-based guidelines. However, the existing evidence base for eCDSSs improving clinical performance and patient outcomes in mental healthcare settings remains sparse. In addition, electronic systems that are not accepted by their users cannot be expected to contribute to improving quality of care, hence facilitators, barriers and other consequences need to be understood for successful implementation of novel digital tools and could also serve as a basis for future system re-engineering. Hence there is call for research to include evaluating its implementation for successful future scalability.
The key digital tool to be used for eCDSS in this study is CogStack, a software platform developed by the National Institute for Health Research Maudsley Biomedical Research Centre (NIHR Maudsley BRC) and PhiDataLab. CogStack is an open source information retrieval and extraction system with the capability to offer near real-time natural language processing (NLP) of electronic health records. CogStack implements new data mining techniques, specifically the ability to search any clinical data source (unstructured and structured), and NLP applications developed to automate information extraction of medical concepts. The platform has shown early potential to be of value to clinicians in monitoring, intervention and follow up for their patients.
The primary objective of this study is to establish the feasibility and acceptability of an eCDSS (Cogstack@Maudsley) compromising a real-time computerised alerting and clinical decision support system for dysglycaemia management in secondary mental healthcare.
Our secondary objectives are to assess whether the system leads to changes in screening and follow-up testing rates for dysglycaemia, and subsequent clinician-led evidence-based interventions for dysglycaemia and diabetes (this will be measured using pseudonymised group observational data gathered from the South London and Maudsley NHS Foundation Trust (SLaM) Biomedical Research Centre (BRC) Clinical Records Interactive Search (CRIS) system once ward access to the eCDSS has ended). Since 2006, South London and Maudsley NHS Trust has operated fully electronic health records. The Clinical Record Interactive Search (CRIS) system, established in 2008, is an ethically approved electronic health records interface system that allows researchers to access deidentified electronic health records from this Trust for research purposes.
We will conduct a process evaluation to assess the barriers, facilitators, unintended consequences, and indicative costs of implementing the system onto inpatient general adult psychiatry wards.
Data gathered from this study will allow the research team to refine the system, address potential problems with future successful implementation, and inform a larger and more definitive effectiveness trial which will examine for hypothesised improvements in;
- Rates of clinician-delivered evidence-based interventions for patients with dysglycaemia
- Clinical outcomes relating to diabetes care
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- This is a feasibility study of a two-arm randomized controlled cluster trial conducted in general adult psychiatry inpatient ward settings. Wards will be the unit of recruitment and assigned to either the intervention or control group in a 1:1 ratio, to receive either the eCDSS platform or to follow usual care process.
| Arm | Type | Receives |
|---|---|---|
| Electronic clinical decision support | Experimental | Other: Access to eCDSS on wardsElectronic clinical decision support (eCDSS) will be available to clinicians on wards recruited to this arm. An eCDSS is a health information technology system designed to assist clinicians and other health care professionals in clinical decision-making. Automated electronic decision support will be provided as a combination of visual prompts on the individual patient's dashboard, accessed by clinicians when they view a patient record on the electronic health record supplemented by an email sent to the NHS Trust email account addresses of the participating ward clinician(s). Alerts will include locally approved guideline-based recommendations for clinician-led monitoring and management of dysglycaemia and known diabetes, tailored to the individual patient based upon reported HbA1c values. |
| Treatment as usual | No intervention | –Clinicians will not have access to eCDSS on wards recruited to this arm and will deliver care as usual. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- General adult psychiatry inpatient wards at South London and Maudsley NHS Foundation Trust. Wards will be entered into the study if their respective management are agreeable to participate.
- All clinical staff on recruited wards will be eligible to participate and will be invited to take part in a preliminary survey and individual interview with the research team at the start of the study.
- Staff on intervention wards will also be asked to complete a survey and individual interview at the end of the study.
Key exclusion criteria (2 listed)
- Staff on recruited wards who are not of a clinical or healthcare professional background.
- Staff who lack capacity to provide informed consent to participate.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Access to eCDSS on wardsElectronic clinical decision support (eCDSS) will be available to clinicians on wards recruited to this arm. An eCDSS is a health information technology system designed to assist clinicians and other health care professionals in clinical decision-making. The key digital tool to be used for eCDSS in this study is CogStack. This eCDSS has been developed to alert clinicians automatically regarding patients admitted under their care, triggered by the presence of new, old or absent HbA1c pathology reports on the electronic health record (EHR). | Other | Electronic clinical decision support |
Primary outcome
Extent to which eCDSS is perceived by clinician users to be acceptable
Time frame: 4 months
This outcome measure will explore clinician perceptions on how acceptable the eCDSS is in improving evidence-based dysglycaemia management, and where applicable, diabetes care. Data will be gathered through qualitative analysis of individual semi-structured interviews with clinician users.
Extent to which eCDSS is perceived by clinician users to be acceptable
Time frame: 4 months
This outcome measure will explore clinician perceptions on how acceptable the eCDSS is in improving evidence-based dysglycaemia management, and where applicable, diabetes care. Data will be gathered through qualitative analysis of survey questionnaires of clinician users
Number of wards and clinician end-users recruited to the study
Time frame: 4 months
Ability to recruit wards and clinicians to the study. Retention and participation of clinicians on recruited wards through to end of study. Availability of data to fulfil outcome measures.
6 secondary outcomes
Rate of HbA1c testing
Time frame: 12 months
Rate of documentation of dysglycaemia/diabetes in clinical notes
Time frame: 4 months
Rate of documentation of discussion with patient regarding exercise, diet and smoking cessation
Time frame: 4 months
Rates of documentation of diabetes related screening interventions
Time frame: 4 months
Rate of delivery of evidence-based pharmacological interventions for diabetes or pre-diabetes where clinically indicated
Time frame: 4 months
Rates of communication with GP/CMHT regarding diabetes or dysglycaemia follow up
Time frame: 4 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Mar 2024.
- Background: PubMed 25961431Gardner-Sood P, Lally J, Smith S, Atakan Z, Ismail K, Greenwood KE, Keen A, O'Brien C, Onagbesan O, Fung C, Papanastasiou E, Eberhard J, Patel A, Ohlsen R, Stahl D, David A, Hopkins D, Murray RM, Gaughran F; IMPaCT team. Cardiovascular risk factors and metabolic syndrome in people with established psychotic illnesses: baseline data from the IMPaCT randomized controlled trial. Psychol Med. 2015;45(12):2619-29. doi: 10.1017/S0033291715000562. Epub 2015 May 12.
- Background: PubMed 31347480Gaughran F, Stahl D, Stringer D, Hopkins D, Atakan Z, Greenwood K, Patel A, Smith S, Gardner-Sood P, Lally J, Heslin M, Stubbs B, Bonaccorso S, Kolliakou A, Howes O, Taylor D, Forti MD, David AS, Murray RM, Ismail K; IMPACT team. Effect of lifestyle, medication and ethnicity on cardiometabolic risk in the year following the first episode of psychosis: prospective cohort study. Br J Psychiatry. 2019 Dec;215(6):712-719. doi: 10.1192/bjp.2019.159.
- Background: PubMed 28684403Hayes JF, Marston L, Walters K, King MB, Osborn DPJ. Mortality gap for people with bipolar disorder and schizophrenia: UK-based cohort study 2000-2014. Br J Psychiatry. 2017 Sep;211(3):175-181. doi: 10.1192/bjp.bp.117.202606. Epub 2017 Jul 6.
- Background: PubMed 14568747Grol R, Grimshaw J. From best evidence to best practice: effective implementation of change in patients' care. Lancet. 2003 Oct 11;362(9391):1225-30. doi: 10.1016/S0140-6736(03)14546-1.
- Background: PubMed 15767266Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005 Apr 2;330(7494):765. doi: 10.1136/bmj.38398.500764.8F. Epub 2005 Mar 14.
- Background: PubMed 26407790Vancampfort D, Stubbs B, Mitchell AJ, De Hert M, Wampers M, Ward PB, Rosenbaum S, Correll CU. Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder: a systematic review and meta-analysis. World Psychiatry. 2015 Oct;14(3):339-47. doi: 10.1002/wps.20252.
- Background: PubMed 29941004Jackson R, Kartoglu I, Stringer C, Gorrell G, Roberts A, Song X, Wu H, Agrawal A, Lui K, Groza T, Lewsley D, Northwood D, Folarin A, Stewart R, Dobson R. CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital. BMC Med Inform Decis Mak. 2018 Jun 25;18(1):47. doi: 10.1186/s12911-018-0623-9.
- Background: PubMed 22537247Hex N, Bartlett C, Wright D, Taylor M, Varley D. Estimating the current and future costs of Type 1 and Type 2 diabetes in the UK, including direct health costs and indirect societal and productivity costs. Diabet Med. 2012 Jul;29(7):855-62. doi: 10.1111/j.1464-5491.2012.03698.x.
- Background: PubMed 23842533Fernandes AC, Cloete D, Broadbent MT, Hayes RD, Chang CK, Jackson RG, Roberts A, Tsang J, Soncul M, Liebscher J, Stewart R, Callard F. Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records. BMC Med Inform Decis Mak. 2013 Jul 11;13:71. doi: 10.1186/1472-6947-13-71.
- Background: PubMed 26932138Perera G, Broadbent M, Callard F, Chang CK, Downs J, Dutta R, Fernandes A, Hayes RD, Henderson M, Jackson R, Jewell A, Kadra G, Little R, Pritchard M, Shetty H, Tulloch A, Stewart R. Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource. BMJ Open. 2016 Mar 1;6(3):e008721. doi: 10.1136/bmjopen-2015-008721.
- Background: PubMed 19674459Stewart R, Soremekun M, Perera G, Broadbent M, Callard F, Denis M, Hotopf M, Thornicroft G, Lovestone S. The South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register: development and descriptive data. BMC Psychiatry. 2009 Aug 12;9:51. doi: 10.1186/1471-244X-9-51.
- Derived from this study: PubMed 38578666Patel D, Msosa YJ, Wang T, Williams J, Mustafa OG, Gee S, Arroyo B, Larkin D, Tiedt T, Roberts A, Dobson RJB, Gaughran F. Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial. JMIR Res Protoc. 2024 Apr 5;13:e49548. doi: 10.2196/49548.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Access to eCDSS on wards in people with Severe Mental Disorder and Diabetes Mellitus, run by King's College London at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Extent to which eCDSS is perceived by clinician users to be acceptable (time frame: 4 months). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04792268, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Mar 2024.