Electronic Clinical Decision Support for Diabetes and Dysglycaemia in Secondary Mental Healthcare
Study Details
Study Description
Brief 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.
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
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;
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Rates of clinician-delivered evidence-based interventions for patients with dysglycaemia
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Clinical outcomes relating to diabetes care
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Electronic clinical decision support 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. |
Other: Access to eCDSS on wards
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).
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| No Intervention: Treatment as usual Clinicians will not have access to eCDSS on wards recruited to this arm and will deliver care as usual. |
Outcome Measures
Primary Outcome Measures
- Extent to which eCDSS is perceived by clinician users to be acceptable [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 [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 [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.
Secondary Outcome Measures
- Rate of HbA1c testing [12 months]
Rates of HbA1c testing - Inpatient for initial test, inpatient and community for follow-up tests.
- Rate of documentation of dysglycaemia/diabetes in clinical notes [4 months]
Documentation of diabetes or pre-diabetes diagnosis in case notes during inpatient stay (where indicated)
- Rate of documentation of discussion with patient regarding exercise, diet and smoking cessation [4 months]
Documentation of advice by clinician given to patient regarding lifestyle changes- exercise, diet and smoking cessation in patients with dysglycaemia
- Rates of documentation of diabetes related screening interventions [4 months]
Documentation of completed foot check for patients with dysglycaemia
- Rate of delivery of evidence-based pharmacological interventions for diabetes or pre-diabetes where clinically indicated [4 months]
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.
- Rates of communication with GP/CMHT regarding diabetes or dysglycaemia follow up [4 months]
Documentation to relevant community team(s) and GP regarding follow up plans for diabetes care post-discharge where indicated.
Other Outcome Measures
- Assessment of the implementation of the eCDSS on inpatient ward settings [4 months]
Process evaluation to be conducted to evaluate the overall implementation of the system. Data will be gathered from qualitative analysis of individual semi-structured interviews with clinician users.
Eligibility Criteria
Criteria
Inclusion Criteria:
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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.
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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.
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Staff on intervention wards will also be asked to complete a survey and individual interview at the end of the study.
Exclusion Criteria:
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Staff on recruited wards who are not of a clinical or healthcare professional background.
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Staff who lack capacity to provide informed consent to participate.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | South London and Maudsley NHS Foundation Trust | London | United Kingdom |
Sponsors and Collaborators
- King's College London
- National Institute for Health Research, United Kingdom
- South London and Maudsley NHS Foundation Trust
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
- Fernandes 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.
- 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. Erratum in: Psychol Med. 2015;45(12):2631. Eberherd, J [corrected to Eberhard, J].
- Gaughran 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.
- Grol R, Grimshaw J. From best evidence to best practice: effective implementation of change in patients' care. Lancet. 2003 Oct 11;362(9391):1225-30. Review.
- Hayes 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.
- Hex 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.
- Jackson 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.
- Kawamoto 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. Epub 2005 Mar 14. Review.
- Perera 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.
- Stewart 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.
- Vancampfort 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.
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