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National Mental Health Intelligence Developments David Kingdon Professor of Mental Health Care Delivery

‘Big Data’ David Kingdon Professor of Mental Health Care Delivery

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Page 1: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery

National Mental Health Intelligence DevelopmentsDavid Kingdon

Professor of Mental Health Care Delivery

Page 2: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery
Page 3: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery

Mental Health, Dementia & Neurology Intelligence Network (MHIN): Short and long term goals• Practical support to strategic clinical networks, CCGs and others –

providers, clinicians, service users – CCG leadership programme• Getting health intelligence tools and resources to the right people at the

right time in the right place: data briefing (Feb 2015) • Eyes on the long term prize:

– data linkage across clinical pathways – efficient translation of research findings– shift in resources towards primary and secondary prevention– better outcomes and reduced health inequalities

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System-wide approach

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A Care Pathway approach• The MHIN is driven by 7 External Reference Groups:

Mental Wellbeing Common Mental Health Disorders (CMHD) Severe Mental Illness (SMI) Drug & Alcohol/Dual Diagnosis Children and Young People (with Chi-Mat) Dementia (with the Dementia Programme Board) Neurology

• The ERGs are providing system-wide expert leadership in improving data, e.g. prevalence, and developing evidence based care pathways with applied data, e.g. Imperial/Wessex Psychosis pathway & revision of 2002 ‘Dual diagnosis’ guidance.

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Mental Health System Profiling Tools

• PHE Fingertips platform (used for Living Longer Lives) provides Atlases of Variation

• Presents range of public available data (MHMDS, QoF, PbR Cluster, IAPT, Social Care, ONS, Reference Costs)

• By CCG/LA enabling local benchmarking by geography, statistical neighbours and England (http://fingertips.phe.org.uk/profile-group/mental-health)

• Currently covers Psychosis, MI/SA, CMHD and CYP

• Wellbeing to focus on presenting key data items to support better evidence to support promotion and prevention commissioning

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Page 8: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery

We can also benchmark general outcomes and care standards against peer CCGs

Sources: Commissioning for Value Datapack, NHS Rightcare, Nov 2014

Figures show performance on a few key indicators, when compared to 10 “similar” CCGs

Figures show performance on a few key indicators, when compared to 10 “similar” CCGs

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http://54.171.100.130/

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Psychosis prevalence

• Current estimation based on ONS Household survey limited by – absence of homeless & institutions– Interview methodology– under-estimates of drug-induced, delusional disorder– categorical vs dimensional approach

• Could that used for incidence improve it?• Could data linkage?

– Scandinavian studies– HES, Social care, primary care, welfare benefits, CJS,

housing, MPs/councillors/Queen!• NMHIN & MQ considering way forward

Page 13: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery

A DETECTIVE STORY…… WHO’S STEALING THE DATA?

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Why is diagnostic coding low?

• Diagnostic (ICD 10) coding in MHLDDS is 15-20%

• Isn’t it because CMHTs and psychiatrists don’t make diagnoses? Or don’t complete coding?

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The clues…

• Investigations have revealed that the discrepancy between HSCIC and provider levels is largely due to the current data processing specification and rules.  

• The current data processing rules include the requirement to ignore all codings outside a limited reporting period and this requirement results in the majority of cases being excluded from the diagnosis data.

• The current data processing rules do not reflect good clinical practice because patient diagnosis would not normally change within the reporting period.

Page 16: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery

The clues

• There has been no improvement over the past 5 years

• Specialist mental health providers eg Oxleas, St Andrews, can demonstrate much higher levels of diagnostic coding than are reported in HSCIC reports.

• Hospital Episode Statistics (HES), which is also collected by HSCIC, receives complete diagnostic coding for inpatients - MHLDDS reports only 30% of inpatients being coded.

Page 17: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery

The investigation …

• MHLDDS technical specification discovered

• The current data processing rules include the requirement to ignore all codings outside a limited reporting period and this requirement results in the majority of cases being excluded from the diagnosis data.

• The current data processing rules do not reflect good clinical practice because patient diagnosis would not normally change within the reporting period.  

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The culprit…

• Investigations have revealed that the discrepancy between HSCIC and provider levels is largely due to the current data processing specification and rules.

Page 19: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery

The solution…

• The current definition of EVENTDATE “The date that a diagnosis was made for the patient. This should be the first date that the specific diagnosis was made.” Therefore, the requirement to ignore all codings outside of the reporting period is not consistent with the definition of EVENTDATE.

• The problem could be addressed by removing the requirement to ignore coding outside the reporting period.

• Specifically, this would involve removing coding responsible for data exclusions in Table 22 and 23 of the MHLDDS technical specification v1.1: “EVENTDATE Not Null AND >= RP Start Date and <=RP End date. Any records not meeting the qualifying criteria will be ignored.”

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Why does it matter?

• NICE use broad diagnoses so compliance requires use of diagnosis

• National funding allocations for mental health• The measurement of new access standards for specific

mental health conditions• CQUIN for physical health estimation• Research and evaluation: e.g. UK Biobank• The establishment of national training and workforce

competency planning • Regulatory assurance of effective high quality, safe care• The commissioning of evidence based services and care

pathways for different mental health conditions appropriate to the needs of a local (CCG level) population

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Intervention coding (use of use of SNOMED?SNOMED?)

Page 23: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery

Intervention codingAssessment: •1. Initial Assessment of holistic needs & aspirations & strengths •2. Ongoing Assessment of holistic needs & strengths Information: •3. Information -shared formulation, building on strengths, risk/safety management•4. Information – on conditions, interventions, services, self-management Care coordination/ MDT functions •5. Care Coordination - Care planning & safety/risk planning •6. Care Coordination – CPA / care plan & safety/ risk review/personalized budgets•7. Care Coordination – Discharge meeting•8. Care Coordination – Liaison, supervision & consultancy•9. Care Coordination - Administration/ data entry/reports •10. Care coordination : crisis contingency/advance decision planningPsychological therapy/ empowerment•11.Therapeutic Intervention – structured psychological therapy•12. Therapeutic Intervention - group psychological therapy•13.Therapeutic Intervention – relapse prevention/ coping strategies•14. Therapeutic Intervention – reminiscence & life historyPhysical health interventions•15.Physical health –cardiovascular assessment and treatment •16.Physical health _diabetes assessment and treatment •17.Physical health –smoking assessment and treatment

Recovery & Lifestyle •18. Recovery & Lifestyle – accommodation support•19. Recovery & Lifestyle – financial support •20. Recovery & Lifestyle - practical daily living support •21. Recovery & Lifestyle - personalized budgets & social inclusion activities •22. Recovery & Lifestyle – education/training/employment •23. Recovery & Lifestyle – enabling activities/emotional support•24. Recovery & Lifestyle - physical healthy lifestyle activities•25. Recovery & Lifestyle – alcohol assessment and intervention •26. Recovery & Lifestyle – illicit drugs assessment and interventions Medication/ECT•27. Intervention - ECT•28. Medicines optimization –medicine information and choice •29 Medicines optimization –baseline investigations pre prescribing & prescribing•30 Medicines optimization – administration•31 Medicines optimization- routine side effects assessment•32 Medicines optimization – medication plan monitoring/review•33 Medicines optimization – adherence supportFamily/Carer•34 Family/Carer - information on condition, coping strategies & relapse prevention •35 Family/Carer - carers assessment•36 Family/Carer - safeguarding•37 Family/Carer - carer individual or group psychological therapy •38 Family/Carer - family therapy

Page 24: ‘Big Data’ David Kingdon Professor of Mental Health Care Delivery