The world of the homeless is a tough and interesting world. Many have come dangerously close to accepting that the homeless situation is a problem that we just can't solve. Numerous studies have hinted that we just cannot stop people from being homeless if that is their choice. They are safe but they are not in their homes and if they are city-less, then how can policy makers coordinate their efforts where jurisdictional boundaries lack meaning?
The Government of Canada recently released their 2018 national snapshot of homelessness in collaboration with the provinces and 61 communities. The second nationally coordinated Point-in-Time (PiT) count of homelessness in Canadian Communities provides a one-day snapshot of homelessness, including people experiencing homelessness in shelters, unsheltered locations, and transitional housing. They can also include people experiencing homelessness who are in health or correctional facilities or who are staying with others because they have no access to a permanent residence. On a given night, 25,216 people across 61 communities were experiencing absolute homelessness in shelters or unsheltered locations. An additional 6,789 people were in a transitional program. The 2016 survey was extrapolated to estimate that at least 235,000 Canadians experience homelessness in a given year. Information collected through these surveys allows leaders and service providers to understand the scope of homelessness in their communities, investigate trends, identify needs, and plan to allocate resourceseffectively. However, many communities conduct their PiT counts using time‐consuming paper processes that are prone to data entry and transcription errors. As a result, they are not collecting data that’s as complete, accurate, or useful as it could be. Furthermore, this information is not captured through administrative data systems and would be enriched through integrated government data sets that could capture patterns that were not possible to identify previously due to this data sludge that is opaque.
Just like the healthcare industry, homelessness could benefit from digitally derived datasets to create personalised plans to address individual needs. Today, healthcare systems abandon vast amounts of data in electronic health records, test tubes, and X-ray machines, and scheduling systems. If front line caregivers can start to pull out that rich data and be more predictive for who is going to live and who’s going to die, who’s going to need this intervention and who needs that test; we can completely change the entire paradigm of health care and homelessness.
To modernize the point‐in‐time count survey process, SAP partner ESRI has applied best practices and input from leading homeless survey methodology experts to develop a series of homeless count apps. Surprisingly, the design thinking user research found that potential survey respondents were more receptive to being approached by a worker with a mobile phone as opposed to the traditional clipboard carrying ‘government worker’. These apps allow communities to not only simplify the survey collection process but also more readily visualize and analyze the results. When users capture data with the app, the information is automatically stored in a database and can be immediately used for reporting, analysis, and decision making.
Exhibit 1 – Homeless Count App
As an overall mobile first approach, a Federal or Provincial strategy could consider creating a shared services model to provide mobile resources for local government case workers and homeless resource partners through a centralized platform and not siloed databases. The ability to collect data via mobile devices will lead to expedited insight creation which will then lead to better access to services, reporting and overall visibility into progress. As more individuals are helped, more data is created, and a virtuous circle of improvement happens through the machine learning algorithms that provide recommendations to care providers.
The SAP platform for advanced analytics capabilities with predictive geospatial functions uses a graph engine which can provide easy access to the nearest location for available housing and other related services in real time based on a person’s individual needs. A small nudge to help those in the cold. Services could be accessed by mobile devices which would be put in the hands of the various agencies or “doors” which people are using to gain information about where to go and/or self-service kiosks could be located at shelters allowing for the automation of the check-in process. SAP’s machine learning capabilities can be used to increase efficiencies for manual data entry with integration to capture information from multiple data sources/systems (email, PDF, Silo Applications) to arrive at data driven insights.
Exhibit 2 – SAP Digital Homeless Dashboard
Gaining a deeper understanding of those individuals in need of support enables front line workers to provide recommendations for immediate support and provides a foundation for personalised improvement plans after some restorative sleep. There is an international trend in the personalisation of funding for care services, from the National Health Service in the England to the Brukerstyrt Personlig Assistanse in Norway. Part of this trend is the Australian National Disability Insurance Scheme (NDIS). The NDIS seeks to secure gains in health for hundreds of thousands of Australians living with a disability through developing personalised treatment plans for 460,000 people. The different model of care (i.e. personalisation enabled by SAP) used by the NDIS is meant to enable access to more appropriate services, empowerment, social and economic participation – all of which are known social determinants of health.
The potential effects in Canada could be most profound for people with little or no direct access to support, shelters or doctors. Getting a better grasp on the data regarding homelessness is a crucial first step to providing personalised plans for those that are vulnerable to the harshness of The North.
Homeless data is notoriously imprecise, and made harder to interpret by the fact that disparate approaches - often lacking funding - have been deployed in the past. Many reasons may prevent adoption, but the technology is here, and other countries are embracing how to do good with data as opposed to the opposite. Let’s clear the sludge in Canada to provide some helpful small nudges to increase prosperity for all Canadians.
The Government of Canada recently released their 2018 national snapshot of homelessness in collaboration with the provinces and 61 communities. The second nationally coordinated Point-in-Time (PiT) count of homelessness in Canadian Communities provides a one-day snapshot of homelessness, including people experiencing homelessness in shelters, unsheltered locations, and transitional housing. They can also include people experiencing homelessness who are in health or correctional facilities or who are staying with others because they have no access to a permanent residence. On a given night, 25,216 people across 61 communities were experiencing absolute homelessness in shelters or unsheltered locations. An additional 6,789 people were in a transitional program. The 2016 survey was extrapolated to estimate that at least 235,000 Canadians experience homelessness in a given year. Information collected through these surveys allows leaders and service providers to understand the scope of homelessness in their communities, investigate trends, identify needs, and plan to allocate resourceseffectively. However, many communities conduct their PiT counts using time‐consuming paper processes that are prone to data entry and transcription errors. As a result, they are not collecting data that’s as complete, accurate, or useful as it could be. Furthermore, this information is not captured through administrative data systems and would be enriched through integrated government data sets that could capture patterns that were not possible to identify previously due to this data sludge that is opaque.
Just like the healthcare industry, homelessness could benefit from digitally derived datasets to create personalised plans to address individual needs. Today, healthcare systems abandon vast amounts of data in electronic health records, test tubes, and X-ray machines, and scheduling systems. If front line caregivers can start to pull out that rich data and be more predictive for who is going to live and who’s going to die, who’s going to need this intervention and who needs that test; we can completely change the entire paradigm of health care and homelessness.
To modernize the point‐in‐time count survey process, SAP partner ESRI has applied best practices and input from leading homeless survey methodology experts to develop a series of homeless count apps. Surprisingly, the design thinking user research found that potential survey respondents were more receptive to being approached by a worker with a mobile phone as opposed to the traditional clipboard carrying ‘government worker’. These apps allow communities to not only simplify the survey collection process but also more readily visualize and analyze the results. When users capture data with the app, the information is automatically stored in a database and can be immediately used for reporting, analysis, and decision making.
Exhibit 1 – Homeless Count App
As an overall mobile first approach, a Federal or Provincial strategy could consider creating a shared services model to provide mobile resources for local government case workers and homeless resource partners through a centralized platform and not siloed databases. The ability to collect data via mobile devices will lead to expedited insight creation which will then lead to better access to services, reporting and overall visibility into progress. As more individuals are helped, more data is created, and a virtuous circle of improvement happens through the machine learning algorithms that provide recommendations to care providers.
The SAP platform for advanced analytics capabilities with predictive geospatial functions uses a graph engine which can provide easy access to the nearest location for available housing and other related services in real time based on a person’s individual needs. A small nudge to help those in the cold. Services could be accessed by mobile devices which would be put in the hands of the various agencies or “doors” which people are using to gain information about where to go and/or self-service kiosks could be located at shelters allowing for the automation of the check-in process. SAP’s machine learning capabilities can be used to increase efficiencies for manual data entry with integration to capture information from multiple data sources/systems (email, PDF, Silo Applications) to arrive at data driven insights.
Exhibit 2 – SAP Digital Homeless Dashboard
Gaining a deeper understanding of those individuals in need of support enables front line workers to provide recommendations for immediate support and provides a foundation for personalised improvement plans after some restorative sleep. There is an international trend in the personalisation of funding for care services, from the National Health Service in the England to the Brukerstyrt Personlig Assistanse in Norway. Part of this trend is the Australian National Disability Insurance Scheme (NDIS). The NDIS seeks to secure gains in health for hundreds of thousands of Australians living with a disability through developing personalised treatment plans for 460,000 people. The different model of care (i.e. personalisation enabled by SAP) used by the NDIS is meant to enable access to more appropriate services, empowerment, social and economic participation – all of which are known social determinants of health.
The potential effects in Canada could be most profound for people with little or no direct access to support, shelters or doctors. Getting a better grasp on the data regarding homelessness is a crucial first step to providing personalised plans for those that are vulnerable to the harshness of The North.
Homeless data is notoriously imprecise, and made harder to interpret by the fact that disparate approaches - often lacking funding - have been deployed in the past. Many reasons may prevent adoption, but the technology is here, and other countries are embracing how to do good with data as opposed to the opposite. Let’s clear the sludge in Canada to provide some helpful small nudges to increase prosperity for all Canadians.
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