DSfC - NHS West Kent CCG - Comm

Commissioning

To use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the CCG area.

The CCGs commission services from a range of providers covering a wide array of services. 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:

- Secondary Uses Service (SUS+)

- Local Provider Flows

o Acute

o Ambulance

o Community

o Demand for Service

o Diagnostic Service

o Emergency Care

o Experience, Quality and Outcomes

o Mental Health

o Other Not Elsewhere Classified

o Population Data

o Primary Care Services

o Public Health Screening

- 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)

- Community Services Data Set (CSDS)

- Diagnostic Imaging Data Set (DIDS)

- National Cancer Waiting Times Monitoring Data Set (CWT)

- Civil Registries Data (CRD) (Births and Deaths)

The pseudonymised data is required to for the following purposes:

 Population health management:

• Understanding the interdependency of care services

• Targeting care more effectively

• Using value as the redesign principle

 Data Quality and Validation – allowing data quality checks on the submitted data

 Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them

 Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs

 Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated

 Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another

 Service redesign

 Health Needs Assessment – identification of underlying disease prevalence within the local population

 Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models

The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.

Processing for commissioning will be conducted by:

- MedeAnalytics and Optum Health Solutions

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.

Patient level data will not be shared outside of the CCG 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.

All access to data is managed under Roles-Based Access Controls.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality and that data required by the applicant.

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)

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.

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 -

For the purpose of Commissioning:

• Patients who are normally registered and/or resident within the NHS West Kent CCG (including historical activity where the patient was previously registered or resident in another commissioner).

and/or

• Patients treated by a provider where NHS West Kent CCG 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 NHS West Kent CCG - this is only for commissioning and relates to both national and local flows.

Local Identifiers:

If a Data Controller organisation (or the Data Processor working on their behalf):

a. only receives a DSCRO disseminated identifiable (NHS Number) flow, then it can receive clear local identifiers.

b. receives and pseudonymised flow, then clear local identifiers can be included and used only for the purpose outlined within the Data Sharing Agreement

c. receives both DSCRO disseminated identifiable and pseudonymised flows, the identifiable flow must have the local identifiers pseudonymised or removed.

Data Processor 1 – MedeAnalytics

Data quality management and pseudonymisation is completed within the DSCRO using the MedeAnalytics tool specific to the CCG and is then disseminated as follows:

1) Pseudonymised SUS+, Local Provider data, 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), Diagnostic Imaging data (DIDS), National Cancer Waiting Times (CWT) and Civil Registries Data (CRD) only is securely transferred from the DSCRO to MedeAnalytics.

2) MedeAnalytics also receives the following pseudonymised data from providers that has been pseudonymised at source using the MedeAnalytics pseudonymisation tool:

o Community Data

o Mental Health Data

o Social Care Data

o GP Data

o Any Qualified Provider data

3) MedeAnalytics add derived fields, link data and provide analysis to:

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

o Check recorded activity against contracts or invoices and facilitate discussions with providers

o Undertake population health management

o Undertake data quality and validation checks

o Thoroughly investigate the needs of the population

o Understand cohorts of residents who are at risk

o Conduct Health Needs Assessments

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

5) MedeAnalytics then pass the processed, pseudonymised and linked data to the CCG.

6) Aggregation of required data for CCG management use will be completed by MedeAnalytics or the CCG as instructed by the CCG.

7) Patient level data will not be shared outside of the CCG and will only be shared within the CCG 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.

8) MedeAnalytics also pass pseudonymised SUS+ and GP data to Optum Health Solutions.

Data Processor 2 – Optum Health Solutions

9) Optum Health Solutions provide analysis to

o Data integration

o Undertake population health management

10) Aggregation of data is completed by Optum Health Solutions.

11) Patient level data will not be shared outside of Optum Health Solutions and will only be shared within Optum Health Solutions 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.

MedeAnalytics outputs only (Direct Care only)

Re-identification (managed under RBAC) requires an additional step to access re-identification keys held by an independent third party key management service that has no access to the data. Disabling a user’s account in the key management system immediately removes the ability of that user to access re-identification keys.

Each Re-identification requires a different key, so inappropriate retention of keys (which is neither allowed, nor easy to accomplish by design) will not result in compromise of data

Only GP Practice users are able to re-identify patients and only when they have a legitimate reason and a legal right to re-identify, and can only access data to which they have rights under RBAC (which is CG/SIRO approved – within the CCG)

All data providers for a particular region (according to contract) are issued with encryption keys that ensure data for their region can only be linked to data from other providers for the same region. This means that data for two different regional customers cannot be accidentally mixed.

For clarity: Optum require data for our more transformational Public Health facing tools such as Health Population Manager whereas MedeAnalytics will be dealing with the day to day more transactional (SUS, SLAM, MH, Community…) data feeds required for contracting and commissioning purposes.

Commissioning

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 CCG 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.

9. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports

10. Data Quality and Validation measures allowing data quality checks on the submitted data

11. Contract Management and Modelling

12. Patient Stratification, such as:

a. Patients at highest risk of admission

b. Most expensive patients (top 15%)

c. Frail and elderly

d. Patients that are currently in hospital

e. Patients with most referrals to secondary care

f. Patients with most emergency activity

g. Patients with most expensive prescriptions

h. Patients recently moving from one care setting to another

i. Discharged from hospital

ii. Discharged from community

13. Identifying and managing preventable and existing conditions

a. Identifying types of individuals and population cohorts at risk of non-elective re-admission

b. Risk stratification to identify populations suitable for case management

c. Risk profiling and predictive modelling

d. Risk stratification for planning services for population cohorts

e. Identification of disease incidence and diagnosis stratification

14. Reducing health inequalities

a. Identifying cohorts of patients who have worse health outcomes typically deprived, ethnic groups, homeless, travellers etc. to enable services to proactively target their needs

b. Socio-demographic analysis

15. Managing demand

a. Waiting times analysis

b. Service demand and supply modelling

c. Understanding cross-border and overseas visitor

d. Winter planning

e. Emergency preparedness, business continuity, recovery and contingency planning

16. Care co-ordination and planning

a. Planning packages of care

b. Service planning

c. Planning care co-ordination

17. Monitoring individual patient health, service utilisation, pathway compliance experience & outcomes across the heath and care system

a. Patient pathway analysis across health and care

b. Outcomes & experience analysis

c. Analysis to support services to react to terror situations

d. Analysis to identify vulnerable patients with potential safeguarding issues

e. Understanding equity of care and unwarranted variation

f. Modelling patient flow

g. Tracking patient pathways

h. Monitoring to support NMoC, ACOs, STPs

i. Identifying duplications in care

j. Identifying gaps in care, missed diagnoses and triple fail events

k. Analysing individual and aggregated timelines

18. Undertaking budget planning, management and reporting

a. Tracking financial performance against plans

b. Budget reporting

c. Tariff development

d. Developing and monitoring capitated budgets

e. Developing and monitoring individual-level budgets

f. Future budget planning and forecasting

g. Paying for care of overseas visitors and cross-border flow

19. Monitoring the value for money

a. Service-level costing & comparisons

b. Identification of cost pressures

c. Cost benefit analysis

d. Equity of spend across services and population cohorts

e. Finance impact assessment

20. Comparing population groups, peers, national and international best practice

a. Identification of variation in productivity, cost, outcomes, quality, experience, compared with peers, national and international & best practice

b. Benchmarking against other parts of the country

c. Identifying unwarranted variations

21. Comparing expected levels

a. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations

22. Comparing local targets & plan

a. Monitoring of local variation in productivity, cost, outcomes, quality and experience

b. Local performance dashboards by service provider, commissioner, geography, NMOC, STPs

23. Monitoring activity and cost compliance against contract and agreed plans

a. Contract monitoring

b. Contract reconciliation and challenge

24. Monitoring provider quality, demand, experience and outcomes against contract and agreed plans

a. Performance dashboards

b. CQUIN reporting

c. Clinical audit

d. Patient experience surveys

e. Demand, supply, outcome & experience analysis

f. Monitoring cross-border flows and overseas visitor activity

25. Improving provider data quality

a. Coding audit

b. Data quality validation and review

c. Checking validity of patient identity and commissioner assignment

Analytics Insights

Reports, charts and dashboards providing insights into:

1. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports

2. Data Quality and Validation measures allowing data quality checks on the submitted data

3. Contract Management and Modelling

4. Health needs assessment and predictive modelling instead, such as:

o Patients at highest risk of admission

o Most expensive patients (top 15%)

o Frail and elderly

o Patients that are currently in hospital

o Patients with most referrals to secondary care

o Patients with most emergency activity

o Patients with most expensive prescriptions

o Patients recently moving from one care setting to another

i. Discharged from hospital

ii. Discharged from community

5. Understanding impacts and interdependency of care services

Commissioning

1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.

a. Analysis to support full business cases.

b. Develop business models.

c. Monitor In year projects.

2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.

3. Health economic modelling using:

a. Analysis on provider performance against 18 weeks wait targets.

b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.

c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.

d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).

4. Commissioning cycle support for grouping and re-costing previous activity.

5. Enables monitoring of:

a. CCG outcome indicators.

b. Financial and Non-financial validation of activity.

c. Successful delivery of integrated care within the CCG.

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. List size verification by GP practices.

i. Understanding the care of patients in nursing homes.

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

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

8. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.

9. 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.

10. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.

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

12. 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

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

14. Reviewing current service provision

a. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy

b. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)

c. Impact analysis for different models or productivity measures, efficiency and experience

d. Service and pathway review

e. Service utilisation review

15. Ensuring compliance with evidence and guidance

a. Testing approaches with evidence and compliance with guidance.

16. Monitoring outcomes

a. Analysis of variation in outcomes across population group

17. Understanding how services impact across the health economy

a. Service evaluation

b. Programme reviews

c. Analysis of productivity, outcomes, experience, plan, targets and actuals

d. Assessing value for money and efficiency gains

e. Understanding impact of services on health inequalities

18. Understanding how services impact on the health of the population and patient cohorts

a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this

b. Propensity matching and scoring

c. Triple aim analysis

19. Understanding future drivers for change across health economy

a. Forecasting health and care needs for population and population cohorts across STPs

b. Identifying changes in disease trends and prevalence

c. Efficiencies that can be gained from procuring services across wider footprints, from new innovations

d. Predictive modelling

20. Delivering services that meet changing needs of population

a. Analysis to support policy development

b. Ethical and equality impact assessments

c. Implementation of NMOC

d. What do next years contracts need to include?

e. Workforce planning

21. Maximising services and outcomes within financial envelopes across health economy

a. What-if analysis

b. Cost-benefit analysis

c. Health economics analysis

d. Scenario planning and modelling

e. Investment and disinvestment in services analysis

f. Opportunity analysis

All of the above will lead to improved patient experience through more effective commissioning of services and enable the CCG and providers to direct finite health and social care (public health) resources more efficiently and effectively.

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 use to ensure that the services they commission:

• deliver the best outcomes for their patients

• 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

Not stated in the register.