DSfC - Kent County Council Optum Application - Comm

To use pseudonymised data to provide intelligence to support commissioning of health and social care services. The pseudonymised data is required to ensure that analysis of health and social care provision can be completed to support the needs of the health and social care profile of the population within the County Council area, based on full analysis of multiple pseudonymised datasets.

The County Council commissions services from a range of providers covering a wide array of services. It also has joint statutory responsibility (with CCGs) to manage the Kent Joint Strategic Needs Assessment which covers the purposes below.

Each of the data flow categories requested supports the commissioned activity of one or more providers.

The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:

From NHS Digital:

• Secondary Uses Service (SUS)

• Mental Health Minimum Data Set (MHMDS)

• Mental Health Learning Disability Data Set (MHLDDS)

• Mental Health Services Data Set (MHSDS)

• Maternity Services Data Set (MSDS)

• Improving Access to Psychological Therapy (IAPT)

• Child and Young People Health Service (CYPHS)

• Diagnostic Imaging Data Set (DIDS)

• Community Services Dataset (CSDS)

From Providers:

• Community care

• Mental health

• Primary care services

• Social care

• Hospice care

• Out of hours primary care

• AQP

The pseudonymised data is required for the following purposes:

1) Population health management

-evaluating the effectiveness of targeted care

-supporting and informing service redesign plans

-understanding the interdependence of care services

For example -

- From a preventative perspective Kent County Council routinely monitor the uptake (including equity of uptake) of Public Health programmes - e.g. NHS Health Checks or smoking cessation services - by key equity vectors such as age, gender, deprivation and socio-economic circumstance.

- The County Council also conduct analysis to better understand, for cohorts of patients - e.g. those with Learning Disability, the number of people receiving services concurrently from multiple providers.

2) Data Quality and Validation:

- checking the accuracy and quality of submitted data so that decisions and activities that result from using the data are properly founded.

- calculating GP denominators – comparing patient list sizes direct from local primary care services to nationally available data sources such as the Primary Care Information Service.

- exploration of variability in GP coding and the impact on recorded disease and activity prevalence.

3) Thoroughly investigating the needs of the population, to ensure the right services are available for members of the population when and where they need them. Outputs would include:

- Health Needs Assessments for identification of underlying disease prevalence within the local population.

- Production of an annual public health report

- Production of a Joint Strategic Needs Assessment (JSNA) - report summarising population needs by Kent, CCG and district population as well as key programme/commissioning areas.

- Production of emergency planning materials.

4) Understanding Service utilisation / care inefficiency for vulnerable groups such as aggregate patient pathways.

5) Understanding cohorts of residents who are at risk of becoming users of some of the more expensive, specialised or limited availability services, to better understand and manage those needs

6) Modelling activity across all linked datasets to understand how services interact with each other, and to understand how changes in one service may affect flows through another. This describes JSNAs cohort model approach to simulate and estimate the impact of future commissioning schemes.

7) Service redesign to support continuous improvement

Recent examples of analytics designed and completed to support service re-design with Kent County Council include:

- Decision to further invest in Extra care housing

- Estimating the volume and care-needs of carers ahead of re-tendering carers support.

- Estimating disease and multimorbidity projections for the planning of care in the home and enablement services

8) Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, initially computed using algorithms executed against linked de-identified data and then reviewed by healthcare analysts in support of preventative services. Patient stratification and predictive modelling will also be used for identification of future service delivery models

PROCESSING CONDITIONS:

Data must only be used for the purposes stipulated within this Data Sharing Agreement. Any additional disclosure / publication will require further approval from NHS Digital.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

All access to data is managed under Role-Based Access Controls. Users can only access data authorised by their role and the tasks that they are required to undertake.

Patient level data will not be linked other than as specifically detailed within this Data Sharing Agreement. Data released will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement.

NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

ONWARD SHARING:

Patient level data will not be shared outside of the Local Authority unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.

Aggregated reports only with small number suppression can be shared externally as set out within NHS Digital guidance applicable to each data set.

SEGREGATION:

Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.

Where the Data Processor and/or the Data Controller hold identifiable data with opt outs applied and identifiable data with opt outs not applied, the data will be held separately so data cannot be linked.

All access to data is auditable by NHS Digital.

DATA MINIMISATION

Data Minimisation in relation to the data sets listed within section 3 are listed below. This also includes the purpose on which they would be applied -

• Patients who are normally registered and/or resident within any 1 of the 7 CCGs which are coterminous with Kent County Council (including historical activity where the patient was previously registered or resident in another commissioner). The 7 CCGs are:

- NHS Ashford CCG

- NHS Canterbury and Coastal CCG

- NHS Dartford, Gravesham & Swanley CCG

- NHS South Kent Coast CCG

- NHS Swale CCG

- NHS Thanet CCG

- NHS West Kent CCG

and/or

• Patients treated by a provider where any 1 of the 7 CCGs which are coterminous with Kent County Council is the host/co-ordinating commissioner and/or has the primary responsibility for the provider services in the local health economy – this is only for commissioning and relates to both national and local flows.

and/or

• Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of the 7 CCGs which are coterminous with Kent County Council - this is only for commissioning and relates to both national and local flows.

COMMISSIONING

The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:

1. SUS+

2. Mental Health Minimum Data Set (MHMDS)

3. Mental Health Learning Disability Data Set (MHLDDS)

4. Mental Health Services Data Set (MHSDS)

5. Maternity Services Data Set (MSDS)

6. Improving Access to Psychological Therapy (IAPT)

7. Child and Young People Health Service (CYPHS)

8. Community Services Data Set (CSDS)

9. Diagnostic Imaging Data Set (DIDS)

Data quality management and pseudonymisation is completed within the DSCRO using the MedeAnalytics International Limited pseudonymisation tool and is then disseminated as follows:

Data Processor 1 – MedeAnalytics International Limited

1. Pseudonymised SUS+, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set (CSDS) and Diagnostic Imaging data (DIDS) only is securely transferred from the DSCRO to MedeAnalytics International Limited.

2. Providers pseudonymise data at source using the MedeAnalytics pseudonymisation tool. Data includes:

i. Community data

ii. Mental Health data

iii. Social Care data

iv. GP data

v. AQP data

vi. Out of Hours data

vii. Hospice data

3. MedeAnalytics International Limited add derived fields, link data and provide analysis to:

a. Undertake population health management

b. Undertake data quality and validation checks

c. Understand service utilisation for vulnerable groups such as aggregate patient pathways.

d. Understand cohorts of residents who are at risk

e. Model activity across all linked datasets to understand how services interact with each other

f. See patient journeys for pathways or service design, re-design and de-commissioning.

g. Undertake patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors

4. Allowed linkage is between the data sets contained within point 1 and 2.

5. MedeAnalytics International Limited then provide online access to the processed, pseudonymised and linked data to the County Council under roles based access controls.

6. Patient level data will not be shared outside of the County Council and will only be shared within the County Council on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

7. MedeAnalytics International Limited also pass, consistently pseudonymised, SUS+ data and primary care data only to Optum Health Solutions UK Limited.

Data Processor 2 – Optum Health Solutions UK Limited

8. Optum Health Solutions UK Limited provide analysis to thoroughly investigate the needs of the population

9. Optum Health Solutions UK Limited makes data and analysis available to the County Council via Role Based Access Controls.

10. Patient level data will not be shared outside of the County Council and will only be shared within the County Council on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

11. Aggregation of required data for County Council management use will be completed by MedeAnalytics International Limited, Optum Health Solutions UK Limited or the County Council as instructed by the County Council.

Analytics Insights:

Key outputs in supporting the council in making evidenced based commissioning decisions include:

1. Annual public health report (statutory requirement)

2. Annual refresh of Joint Strategic Needs Assessment (statutory requirement), including all component elements: infographic summary, exception report, and predictive cohort model.

3. Needs assessment reports to support service planning

4. Briefing papers/reports/slide decks to support strategic commissioning decisions

These outputs frequently include charts and tables providing insights into:

1. Comparators of GP/CCG/LA performance with similar areas, as set out by a specific range of care quality and performance measures

2. Assessments of equity of access by key vectors, including age, gender and socio-economic circumstance

3. Data Quality and validation measures

4. Modelling

5. Patient Stratification and regression modelling, for identification of:

o Patients at highest risk of admission

o High cost patients

o Patients with most activity in secondary care settings

o Patients with most emergency activity

o Frail and elderly

o Patients with social care packages

o Delayed transfers of care

6. Impacts and inter-dependency of care services

Commissioning insights

General reporting

1. Commissioner reporting:

a. Summary by provider view - plan & actuals year to date (YTD).

b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.

c. Summary by provider view - activity & finance variance by POD.

d. Planned care by provider view - activity & finance plan & actuals YTD.

e. Planned care by POD view - activity plan & actuals YTD.

f. Provider reporting.

g. Statutory returns.

h. Statutory returns - monthly activity return.

i. Statutory returns - quarterly activity return.

j. Delayed discharges.

k. Quality & performance referral to treatment reporting.

2. Readmissions analysis.

3. Production of aggregate reports for County Council Business Intelligence.

4. Production of project / programme level dashboards.

5. Monitoring of acute / community / mental health quality matrix.

6. Clinical coding reviews / audits.

7. Budget reporting down to individual GP Practice level.

8. GP Practice level dashboard reports include high flyers.

COMMISSIONING

1. Supporting service improvement initiatives such as Quality Innovation Productivity and Prevention (QIPP) and Better Care Fund to review demand management, integrated care and care pathways.

a. Analysis to support full business cases.

b. Develop business models.

c. Monitor In year projects.

2. Supporting and improving Joint Strategic Needs Assessment (JSNA) development process as part of our statutory requirement which includes evaluation and monitoring of health inequalities and inequalities in health and care provision

3. A range of applied analytics, modelling, and simulation methods for:

a. capacity planning and estimating future population health and needs, service demand and workforce requirements

b. patient care pathways

c. estimating the impact of service development and care model initiatives.

4. Enables monitoring of:

a. County Council outcome indicators.

b. Non-financial validation of activity.

c. Successful delivery of integrated care within the County Council.

d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.

e. Case management.

f. Care service planning.

g. Commissioning and performance management.

h. Understanding the care of patients in nursing, residential homes and other social care settings.

5. Feedback to NHS and social care service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.

6. Improved planning by better understanding patient flows through the health and social care system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.

7. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.

8. Potentially reduced premature mortality by more targeted intervention in primary and social care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the Local Authority Outcome Framework.

9. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.

10. Better understanding of contract requirements, contract execution, and required services for management of existing contracts, and to assist with identification and planning of future contracts

11. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.

All of the above lead to improved patient experience through more effective commissioning of services. Users of the same MedeAnalytics service have fed back that:

Users can better understand variation in their system, and make comparisons between populations and organisations in a fair and meaningful way with a greater understanding of what normal is. This will support routine opportunity analyses that they carry out in order to best target resources and best understand which activities have had a genuine benefit, and helped reduce costs to the system.

In addition, the platform provides access to comprehensive supporting information that commissioning organisations such as Clinical Commissioning Groups and Local Authorities use to ensure that the services they commission:

• deliver the best outcomes for their patients and clients

• cater for and meet the needs of the population they are responsible for;

• monitor condition prevalence within the population

• identify health inequalities and work with local organisations and agencies to remove them

Also, for Acute Trusts and other care providers, it provides access to comprehensive supporting information that helps to:

• Ensure that services they provide are of high quality, efficient and effective

• plan and re-engineer services to meet the changing requirements and developments in technology

Direct measurement of the benefits associated with an enabling self-service system such as this is challenging, however, proxies can be provided through use metrics (number of individual users and frequency of use) as well as examples of decisions made by customers in the management and delivery of their services that have been supported by reports / information from the Mede tool.

Not stated in the register.