Outcome for a skilled digital team to develop a Strategic Insights Tool for Rough Sleeping

Description

Summary of work LOTI is working with London Councils, the Greater London Authority and Bloomberg Associates to design and deliver a data tool that supports the strategic aims of London’s Life off the Streets programme (LOTS). The key aim of LOTS is to help make rough sleeping rare, brief and non-recurrent in London. The London Office of Technology and Innovation and London Councils, seeks to develop an integrated rough sleeping data warehouse across London to better understand rough sleeping flows, plan services, report to the Department for Levelling Up, Homes and Communities (DLUHC), manage performance, and streamline processes. Strategic decision makers currently lack the data to understand the pathways into and out of rough sleeping. The lack of data hinders the development of preventative approaches. Blind spots include the circumstances and institutions that people are in immediately prior to entering rough sleeping, and the medium and long term outcomes for those who are provided with services such as hostels or temporary accommodation. For example, by analysing how people move through the rough sleeping ecosystem, we can begin to build a picture of how many people are likely to re-enter rough sleeping after leaving short-term accommodation or the probation service and what their specific circumstances and needs are. This provides the required insights to design targeted services that can be deployed to the right people at the right time to prevent rough sleeping. This tender builds upon significant previous work done by the Greater London Authority (GLA), London Councils (LC), Bloomberg Associates (BA) and London Office of Technology and Innovation (LOTI) to engage relevant stakeholders, develop the user stories and user requirements for the proposed data warehouse, and technical analysis on the data sources. Building upon this work, we intend to create a tool that ingests data from London Councils and local service providers and presents the data in a way that enables strategic decision makers to have deeper insights into the pathways into rough sleeping and the effectiveness of interventions. The tool needs to be delivered in Q1/Q2 2023, to meet the requirement that all London boroughs are onboarded and able to report data to DLUHC by July 2023 in an ideal scenario, or by the end of 2023 at the latest. Where the supplied staff will work London Where the supplied staff will work No specific location (for example they can work remotely) Who the organisation using the products or services is LOTI contracting via London Councils Why the work is being done No single actor is fully accountable for ending rough sleeping in a community. A collaborative, strategic, data-driven approach, with shared commitments, must be undertaken by partners to make rough sleeping rare, brief, and non-recurrent. London seeks to implement a data-informed approach, but this cannot be accomplished without improved collective action that cuts across institutions, services, and sectors. However, Greater London (and most of England) lacks consistency in data collection metrics, practices, and management. Without improved collective action, data informed policy cannot be accomplished. The fundamental challenges are: Too little join up between the different data systems and stakeholders in local areas Lack of integration between systems and stakeholders in some local areas leads to data silos Too much duplication across different systems with reporting requirements Extracting the data needed for casework, reporting and performance management is often difficult due to an inability to match and correlate individual records across different systems The data that commissioners need for reporting, management and commissioning is not always collected and is inconsistent This leads to a situation where the collective power of all the data being created and collected by the various systems and service providers across the ecosystem is not being realised. For example, at present, it is not possible to follow an individual’s journey from rough sleeping, to short-term accommodation to long-term accommodation. DLUHC has been working with the Centre for Homelessness Impact to propose a definition of ‘ending rough sleeping’ in England and develop an approach to operationalising this definition across the rough sleeping sector. The proposed definition is ‘rare, brief and non-recurrent’. Any local data integration efforts must align with these efforts by the national Government. We intend to create a tool that ingests data from London Councils and local service providers and presents the data in a way that enables strategic decision makers to have deeper insights into the pathways into rough sleeping and the effectiveness of interventions. The tool needs to be delivered in Q1/Q2 2023, to meet the requirement that all London boroughs are onboarded and able to report data to DLUHC by July 2023 in an ideal scenario, or by the end of 2023 at the latest. The data to be used by the tool will be sourced from a number of systems currently operated by stakeholders in the rough sleeping ecosystem, including CHAIN, Inform, and data held in borough Homelessness Case Level Collection (H-CLIC) returns. It should be noted that whilst these are three consistent data sets, there are multiple sources of these data sets that need to be joined up, and individual records matched. The data to be used by the tool will be sourced from a number of systems currently operated by stakeholders in the rough sleeping ecosystem, including CHAIN, Inform, and data held in borough Homelessness Case Level Collection (H-CLIC) returns. Data tool objectives: Measure success in preventing rough sleeping and making it rare, brief, non-recurrent Improve understanding of the population (including complexity of needs and flow) Improve understanding of individual outcomes for rough sleepers (anonymised) Learn what strategies work and inform public policy, service delivery and commissioning Enhance the effective operation of services for those who are rough sleeping Ease and enhance reporting and performance management We have taken an iterative approach to building the tool, and to date have identified the priority functionality and user stories. In this procurement, we intend to; Develop the minimum viable product for the rough sleeping data insights tool Match data records across the data sets as far as possible Conduct data analysis and insights to meet user requirements (see PDF) Identify opportunities for automation to reduce repeat workload Provide access for early user testing and feedback Iteratively develop this with a small group of pilot borough and their hostel providers: Camden, Lambeth, Hillingdon and Westminster and four service providers Review MVP Onboard highest rough sleeping need boroughs, remaining boroughs and hostel providers (29 London boroughs, and up to 11 service providers) Continue to build out new functionality as time/resources allow The key users will be local commissioners, the Government (DLUHC), commissioned rough sleeping service providers and non commissioned service providers. The results of the user requirements mapping exercise, and prioritisation of requirements relative to different user types is set out in the linked PDF. The user requirements were tested with different suppliers as part of the pre-procurement engagement to ensure that they could be delivered as part of the MVP. The business problem Cost savings of reduced rough sleeping The benefits of this project will be primarily social in terms of reduced rough sleeping, both length of and risk of becoming rough sleeping. Due to the manner in which costs and budgets are spread across different government departments and public health agencies, savings will accrue across the public system and may not be directly attributable to one intervention. What this means is that there may not be direct cashable savings that can be realised that might for example lead to a reduction in the need for budgets. The cost of a single person sleeping rough in the UK for 12 months is estimated at £20,128, compared to a cost of successful intervention at £1,426. As a proxy, if this project prevents one person at risk of rough sleeping in each London borough in one year, then the savings would be £20,128 across 33 boroughs, making a total saving of £664,244. This also translates into reduced pressure on already stretched public services. This project should also enable a reduction in the time and costs of the dispersed administration of managing rough sleeping across the 33 boroughs, where you have people doing similar tasks across each borough. Streamlining processes by building automation and data matching into the data warehouse has the potential to reduce the administrative burden where many local authorities are collecting and reporting data across multiple spreadsheets. Measuring and reporting what works Developing this data warehouse will enable the London boroughs to provide the measurement reports on the new national set of indicators for rough sleeping that are required by DLUHC. The new data points listed below, are not currently measured but should be able to be extracted from this new system, and this system is needed in order to show even at a basic level the progress on these indicators. P1 - Number of new people sleeping rough. P2 - People seen rough sleeping after leaving a statutory institution. R1 - Number of people sleeping out. B1 - Time between a person being seen sleeping rough and off-the-street accommodation B2 - Time between a person being seen sleeping rough and moving into long-term accommodation B3 - Number of long-term rough sleepers NR1 - Number of people returning to rough sleeping. NR2 - Number of people sleeping rough who had previously moved into long-term accommodation We should then be able to extract this data at a disaggregated level, for example understanding all the different typologies of people in different hostels. This would enable an understanding of the typology of people that could be prevented from rough sleeping. Bringing predictive analytics into the data warehouse might enable preventative measures to be taken in advance of someone at risk of rough sleeping. Effective, targeted use of resources Fundamentally what this data warehouse seeks to enable in the longer-term, is a shift from a reactive approach to what is happening out on the streets, to a proactive and preventative approach that seeks to stop the problem occurring at source. To reiterate, the cost of a single person sleeping rough in the UK for 12 months is estimated at £20,128, compared to a cost of successful intervention at £1,426. These savings could for example be used on long-term housing instead of costly temporary accommodation, mental health support, etc. Being able to quantitatively demonstrate that the money is being spent more effectively and actually achieving results, may open up more budgets to tackle the problem as you can demonstrate the evidence behind how that money is being spent. As mentioned above, the cost savings that will be realised will not necessarily be cashable, but the savings should enable a more effective and targeted approach to the limited resources available. Measurable data that allows us to track people across the system, understanding trends, predicting likely outcomes will add a richness to the user journeys that have been previously developed, and enable targeted interventions for different types of people. This should in turn lead to better outcomes for people who are at risk of rough sleeping, or those that are rough sleeping, and being helped out of the system. Facilitating even stronger partnership and contracted work to end rough sleeping in London The project objectives respond to the national strategy to end Rough Sleeping by strengthening and improving existing data in such a way that is radical to rough sleeping services in London. The sector is made up of very interconnected services and this data tool project aims across the board throughout the iterations to enable the users - including government , devolved government and local government commissioners, service providers - to strengthen and shift their relationships and ways of working together. The people who will use the product or service User type: Senior Officials & Policy Makers, Directors & Local Authorities Definition: Senior Officials & Policy Makers, Directors & Local Authorities need to; > Understand the common barriers preventing those sleeping rough to improve access the support they need and move off the street > Understand what happens to rough sleepers after they move on to short-term accommodation, in order to understand any needs for improvement to services > Understand what happens to rough sleepers after they move on to long-term accommodation or other solutions (e.g., reconnection), in order to understand any needs for improvement to services > Track statutory offers and outcomes of statutory interventions to understand effectiveness of supports and providers > Identify trends and emerging issues and promptly act, commissioning a solution > Segment the rough sleeping population into different cohorts according to their housing status/needs to better tailor services > Understand the pathways into rough sleeping and take action to reduce the factors that cause and contribute to rough sleeping through preventive or diversion services User type: Senior Officials and Policy Makers Definition: Senior Officials and Policy Makers need to be able to generate reports for these users. Functionality should include: > Ability to report in multiple formats, to ease administrative burdens of required reporting to multiple entities/funders > Allows self-generation of reports, rather than that having to make a data request > Run reports on activity/services available outside immediate area of responsibility to support collaboration > Run reports to provide evidence of what works and examples of good practice to guide policy, funding and commissioning > Helps fulfil/report on Performance (KPI) of services delivered/commissioned User type: Service Managers Definition: Service Managers need to; > Informs a process of standardised data collection across the sector to align measures, improve comparability, and reduce administrative waste > Capability to easily adapt to new data requirements (e.g., changes to existing databases, add-on of new databases, dataset modifications, new policies such as GDPR update) User type: Case Workers Definition: Case workers need to; > Upload or link (user owned data) with other data systems or modules (e.g., other case management systems) Any pre-market engagement done potential suppliers were interviewed to test our initial proposals for their desirability (the degree to which each option meets the strategic objectives and priorities of stakeholders), viability (the degree to which each option is financially viable and sustainable), and feasibility (the degree to which each option can be implemented). This exercise allowed a preferred approach to be taken, and The summary of relevant findings from this early market engagement can be found in the document attached to this procurement. Work done so far A project design and discovery phase has taken place to determine the following; Sample user journeys User requirements Minimum data set requirements Data set quality qualifications Information governance and ethics requirements We used the LOTI Data Projects Methodology to guide our project design and discovery. We started by taking the outcomes defined by the Life of the Streets programme as our goal and working backwards to determine the type of solution and data that might be suitable for helping us to meet the goal of making rough sleeping, rare, brief and non-recurrent. We conducted a user journey mapping exercise that involved London Councils engaging with rough sleeping leads and practitioners from across the boroughs to examine the pathways of people accessing rough sleeping, homelessness and related services across London. The result of the work was to produce journeys for a number of common personas including which services they touched and the data systems they would be recorded in. This allowed us to identify the core systems that should contribute to our insights tool. Our initial tool will include data from the Combined Homelessness and Information Network (CHAIN), data from hostel providers stored in Inform, and data held in borough Homelessness Case Level Collection (H-CLIC) returns. In the attached document to this tender, are diagrams that illustrate an example of a user journey. The user case might be the tenancy abandonment, with key service touch points in local council housing team, landlord or housing provider, DWP job centre, housing first, general practitioner or health professionals, mental health and substance use treatment providers, police anti-social behaviour enforcement team. The key data systems would then be CHAIN, HCLC, NHS (EMIS, GP etc.), Inform and multiple criminal justice databases. The research phase saw further engagement with rough sleeping practitioners across the ecosystem through workshops and one on one interviews. This helped us to understand the key operational and strategic challenges across London and develop a draft minimum data set of indicators that could be used to help address these challenges. Bloomberg Associates have been leading the work to curate the minimum data set which has included an exploration of the data fields and how they are coded across CHAIN, Inform powered systems and HCLIC returns. This work has enabled us to see the similarities and differences in how fields are defined and recorded, exposing interesting challenges around fields with a temporal element such as pregnancy status and where support needs are recorded in more general qualitative fields versus coded. As we moved towards specifying a product, we focused on the use cases that would deliver the most value to those commissioning rough sleeping services in London. We conducted further workshops to refine a long list of user stories down to a prioritised list that will help form our initial development backlog. One of the use cases will be to support reporting on the new Department for Levelling Up Housing and Communities (DLUHC) rough sleeping KPIs that all local authorities will need to report on in the future. A further detailed discovery has been carried out with the four pilot London Councils and service providers to determine the data sets, technical specifications, processes and systems. A summary of the outputs from these two discovery phases can be found in the document attached to this procurement. Which phase the project is in Beta Existing team The project will be managed by a Project Manager from LOTI, who will act as the single point of contact with the supplier, and help manage the relationships with the wider stakeholder group. The project manager will work closely with LOTI’s Programme Manager for Data and the project working group. The project working group consists of the following organisations and individuals; LOTI - Project Delivery Eddie Copeland, Director, LOTI Jay Saggar, Programme Manager: Data, Smart Cities and Cyber Security London Councils - Project Sponsors Michelle Binfield, Rough Sleeping Programme Director Ben Ridley-Johnson, Principal Policy and Project Officer GLA - Project advisors (GLA will take on the data insights tool in the long-term) David Eastwood, Rough Sleeping Lead Manager Luke Oates - Programme Manager (Rough Sleeping), Housing & Land Bloomberg Associates - Project consultants Tamiru Manno, Consultant Bridget Ackeifi, Consultant Clyde Hunt, Consultant Information Governance for London Victoria Blythe, LOTI Rukhsana Hussain, London Borough of Hillingdon Pilot London Boroughs & service providers London Borough of Camden London Borough of Lambeth London Borough of Hillingdon London Borough of Westminster Thames Reach St Mungo’s Evolve SHP Address where the work will be done Most of the work will be done remotely. In person meetings will be at; London Councils 59½ Southwark Street SE1 0AL Working arrangements The vast majority of this work will be conducted fully remotely. The Contractor may be asked, from time to time, to attend a meeting or event in person where needed for the successful delivery of an activity that falls within the scope of this contract. Client meetings in person will be held in central London, either at London Councils offices, or one of the pilot boroughs and providers. Security and vetting requirements No security clearance needed Latest start date 1 May 2023 Expected contract length Contract length: 0 years 8 months 0 days Special terms and conditions special term or condition: The Client will have the right to recover, share, reuse and publish: all data that is entered into the system; any data that is augmented through the use of the system (e.g. linked data); and any data generated through the operation of the system. Budget Indicative maximum: £250000 Indicative minimum: The contract value is not specified by the buyer Further information: Up to a maximum of £250,000 for the development of the MVP with the four pilot boroughs, data matching, analysis and visualisation, and onboarding of the other London boroughs. Contracted out service or supply of resource? Contracted out service: the off-payroll rules do not apply Terms and acronyms Term or acronym: DLUHC Definition: Department for Levelling Up, Housing and Communities Term or acronym: GLA Definition: Greater London Authority Term or acronym: LOTI Definition: London Office of Technology & Information Term or acronym: CHAIN Definition: Combined Homelessness and Information Network Term or acronym: H-CLIC Definition: Homelessness Case Level Collection Term or acronym: BA Definition: Bloomberg Associates Questions and Clarifications 1. Subject: Clarifications on the Essential questions Hi, I am assuming you are expecting Our approach and experience for each of the 10 questions. Can I get bit more clarification on the following questions please? What is the expectation? Question 9 - Data sharing agreements Question 10- Cyber-security essentials certification Many Thanks Hello Yes we are expecting responses to each of the 10 questions. With regard to questions 9 and 10; Q9: What experience you have of working with data sharing agreements between stakeholders. LOTI has a draft data sharing agreement Q10: Do you have cyber-essentials certification, or are in the process of getting it? If you don't have it, how will you ensure cyber-security on this project, given that we are dealing with personal data records. Regards LOTI Last Updated : <strong>10/03/2023</strong>