Improving Health Outcomes for First Nations with Robotic Process Automation (RPA)
Updated: Dec 18, 2020
“This is not the country you believe it is,” said Chief Chris Moonias of Neskantaga First Nation to Canadians on CBC radio on October 26th.
Chief Moonias was referring to the harsh reality that life in this country is not as fair and equal for everyone, contrary to what many Canadians like to believe.
Neskantaga First Nation in Northern Ontario has been without safe drinking water for more than 25 years. Families have been forced to flee their homes to find shelter in Thunder Bay, more than 400km away, because all water services for the community had been shut down due to an ‘oily sheen’ found on the local water reservoir. 
First Nations living in Canada experience disproportionately more socio-economic hardships, illnesses, and negative health outcomes compared to non-First Nations.
Did you know that First Nations living in Alberta in 2015 had a life expectancy similar to that of the general population of Canada in the early 1960s? 
The data shows that the average life expectancy for non-First Nations is increasing every year, but that First Nations are experiencing no such improvements. 
The Alberta First Nations Information Governance Center reported the following health indicators comparing First Nations and non-First Nations in the province:
Infant mortality rates are two times higher for First Nations 
The suicide rate is three times higher for First Nations, and the majority of those suicides occured in those between the age of 15 and 29 
The incidence rate of diabetes among 30 to 34 year olds was 3.8 times higher for First Nations females 
The bad news is that these findings prove that there are still significant inequalities in healthcare between First Nations and non-First Nations.
The not so bad news is that there are many opportunities for making a positive impact on the lives of around 1,000,000 First Nations, and another 650,000 Inuit and Métis people in Canada.
So how can we make things better? Is it even possible to close the gap?
If it is possible, what can we do? And how will we know that things are getting better?
Like that old adage says, “if you can't measure it, you can't improve it”. One of the most important things we can do is keep track and see whether our actions are making a difference and to keep going, or if we should stop and try something else.
In this article I will discuss a number of issues that make it challenging for First Nation communities to improve health outcomes and close the gap as they are not able to easily measure success because of the effort required to create community-level reports by hand.
RPA enables First Nation communities to be more independent by making it easier to collect and understand data from their own sources
I will discuss how First Nation communities can leverage external data sources to create a more comprehensive picture of the community’s health. I will explain why First Nation communities should not rely on external data in all cases, and why communities should continue building their internal statistical capacity instead.
Then I will explain how Robotic Process Automation or RPA can assist with various activities so that First Nation leaders can make more timely and informed decisions and improve health outcomes through the use of more traditional First Nation indicators of well-being; and finally I will show how RPA can be used to augment the self-governing capacities of First Nations able to carry out their monitoring and reporting responsibilities as part of the new fiscal relationship with the Government of Canada.
Before we go any further, let’s go through an example so you can understand Robotic Process Automation (RPA) and how it works.
Robotic Process Automation (RPA) is technology that enables a computer to carry out repetitive, rules based tasks by interacting with software applications and data the same way a person would
Imagine that you are sitting in front of your computer and you wanted to copy a couple of CBC podcasts onto your phone so you could listen to them later.
So you plug your phone into your computer. Since RPA isn’t a physical robot, it can’t do that. RPA is just software that runs on a computer.
Then you open your internet browser and type in www.cbc.ca. You navigate to your favorite show and click the download button for a couple episodes.
Then on your computer, you open the downloads folder and your phone's download folder. Then you click and drag each episode (an audio file) and copy it from your computer to your phone.
That’s what RPA can do. And it can do it in exactly the same way; by using applications, clicking buttons on the screen and typing when required.
RPA can open internet browsers and go to websites and click on links. RPA can do a Google search. RPA could send an email to a friend of yours through Gmail or Outlook. RPA can open any Windows application, even old ones from the 90s. RPA can use Excel. RPA can create invoices and upload digital receipts to accounting software. RPA could do your taxes and add it to the CRA website. RPA could even pay the CRA from your online bank account.
RPA can even automate legacy software from the 80s
Basically RPA is like macros in Excel (the ones that save you time by automatically performing calculations, adding new cells and filling in rows, etc) but to another level. Any application on your computer can be automated, and RPA will do it the same way every time. RPA can do almost any digital task.
[video] Automated scraping of Public Health Unit COVID-19 counts
Here’s a video of RPA gathering the contact details and population count of all 634 First Nation bands in Canada and writing those details to a spreadsheet.
[video] Automatically Collecting First Nation Community Data from INAC
Here’s another video of RPA gathering COVID-19 case counts from Ontario’s 34 Public Health Units. It goes to the website of each Public Health Unit, finds the latest case counts, copies it, and adds the count to a spreadsheet with details about that specific Public Health Unit.
Now that you know a little bit about RPA and how it works, let's take a look at common use-cases for data that First Nation communities have, how those communities store data and then the steps required to make that data into something useful.
Common use cases for data
There are different ways to put data to use. All of these use-cases depend on information being stored somewhere, and then we need to know how to get that information out. We will refer back to these use cases soon.
Getting the answer to a question about a single area
Getting the answer to a complex question about multiple areas
Getting a comprehensive view of data across all areas
Data storage and reporting for First Nation communities; silos galore
First Nation communities generate a lot of data, and commonly this data is stored in many different locations, which are referred to as silos.
It could be that COVID-19 cases are stored on a shared network drive; while doctor visits, cancer, and chronic disease data may be stored together in Electronic Medical Record software; and health survey responses are stored in a digital survey application.
For clarification, a “silo”, “data silo”, or “information silo” is the idea that data stored in one silo is isolated from data in other silos. Much like with an agricultural grain silo, the grain stored in one silo is completely inaccessible from the others. Often these are different applications that don’t talk to one another.
The fact is that when data is isolated in silos, it creates a challenging situation for health leaders who would like to ask questions and find answers, or to see the big picture and investigate patterns and trends in the data. Since the data is not connected and there is no way to get data from all of the silos at once, it makes it challenging to get answers to questions that may span many different areas.
That’s because it can be challenging to get data out of each of the individual silos and to combine it all into one unified location. Basically, you need to get all of the data together in one place in order to generate a report that brings together multiple categories of health information and compares it all.
Use case #1: Getting the answer to a question about a single area
Let’s say we are a data analyst doing a bit of research on diabetes in the community and we had to get the answer to a simple question: “how many people in the community have diabetes as of September 2020?”
We need to think about where we get the data to answer this question. Diabetes is a chronic disease, and when we look at the “Internal Data Sources” diagram, we can see that chronic disease data is stored in Health Data Silo #4.
Since this question is only asking about information from a single area, diabetes, it is a very narrow question.
Since all diabetes information is stored in one silo, we could just run a report using the Electronic Medical Record software.
It wouldn’t take long at all, maybe 15 minutes at the most.
Use case #2: Getting the answer to a complex question about multiple areas
We’ve been tasked with finding the answer to a slightly different question: “how many people with diabetes visited a healing center in September 2020?”
We already know that data about diabetes is stored in Health Data Silo #4.
And we can see that healing centre data is stored in Health Data Silo #1.
Since our data is stored in two different places, we can’t just run a report in the Electronic Medical Record software and get the right answer because healing centre data is not stored there; it is stored on a different computer system outside of the data analysts office.
So we put on our mask and walked to the main office of the healing centre and got a list of all the appointments in September 2020 on an encrypted USB stick.
To get the answer to this question, we just need to combine the two sets of data to see which community members with diabetes also had an appointment at a healing centre.
When we combine data like this from two sources or silos, it’s called data linking. Basically the idea is that we want to match up all related information about a person from multiple sources.
Then we load the spreadsheets into Excel and look at the data and thought that the best way to link them together was by using first and last name, since that is information stored in both lists.
This question could take a couple of hours to answer because of how much more involved the process was. It wasn’t possible to get the answer automatically because the data had to be manually collected and saved, then linked together to get the answer.
It is significantly more work to get when you need to link data from two sources together.
Use case #3: Getting a comprehensive view of data across all areas
The final use case we are going to cover here is building a comprehensive view of the overall health of our community. We need to get information from each of the individual silos and combine them all, we need to clean the data up because it might be kind of messy, then we need to link the data together in different ways, and finally put the data all into the final report.
This is a fairly complex process and it will require a lot of clicking, typing, and thinking.
Let’s get started.
Collecting the data
The first step, of course, is to get the data from each of the 11 data silos, one at a time.
Just getting the data could be a challenge. If the data is stored in different physical locations or in different applications, we will need to have access to each of those buildings or computer systems.
Furthermore, access to a silo may be controlled by different organizations within the community and the data could be difficult to access due to political reasons. And maybe we can’t access it because the person who is in charge of that data is away from the office that week.
We also have to remember the right way of actually getting the data out in the correct fashion. Since there are possibly 11 different applications or databases, this could be tricky to remember the right menus to navigate into, buttons to click and fields to fill out.
Cleaning and linking the data
Let’s say we have all of our freshly exported data in a number of spreadsheets.
The thing is that often the files exported from software are not in the format that we need. So we need to clean the data up and make some adjustments so that we can easily combine it all later.
Now we can go ahead and combine all of the data we took from the silos so that we will end up with a complete picture of that person and their overall health.
This can be quite tricky. Hopefully we can just run an Excel function and automatically connect the data.
Numerous data linkages may be required to create a single report
Let's say one of the reports that we want to create (though there may be many more) shows confirmed resident COVID-19 cases matched against hospital admissions.
We get the list of COVID-19 cases from the COVID-19 Data Silo. Then we get the list of hospitalization records from the Government Data Silo #2.
But there’s a problem. We can’t link these two lists because the info to identify the resident in the COVID-19 case list isn’t the same as the info that identifies the patient in the hospital.
To solve this, we need to bring in a third data source as an intermediary so that we can connect both spreadsheets. For that we can use the On-reserve / Off-reserve list from the First Nation Data Silo.
So we go ahead and link the COVID-19 data with the On-reserve / Off-reserve list which we then link with the hospitalization records.
We get rid of any unnecessary data, and now we have a single list that connects COVID-19 cases with hospitalizations.
We are still far from done and there are still many more records to link together and reports to generate until we have a single, cohesive view of the overall health of the community.
A team of data analysts would make this whole thing go a lot faster
This might seem impossible for a single person to do such a task for all of the healthcare information for an entire First Nation community. Surely it is not impossible. It will just take a very long time.
That’s why data collection and linkages of numerous sources of data are often performed by teams of data analysts; the amount of manual effort required can be significant.
Massive amount of manual labour required to create a one-time comprehensive report
All of this manual effort, which, depending on the complexity of the data and number of sources could be tens of hours to tens of thousands of hours over the course of a number of days, weeks, months, or even years is put into generating a single point-in-time report which is then almost immediately out of date.
The data in the report would be fresh for a period of time, but soon enough new data will be required in order to stay on top of the healthcare reporting needs for that First Nation. So the entire process must once again be undertaken, manually gathering, cleaning, linking and reporting; again and again and again.
Mistakes can occur often due to human error while building reports
It is quite possible that errors could arise at any of the above mentioned stages. There are countless opportunities for mistakes to be made.
Perhaps a column was accidentally deleted from a spreadsheet and now there are zero mental health issues in the final report, or a mistake occurred while linking two datasets and chronic diseases are now being associated with the wrong people.
Mistakes that occur while preparing data for reporting is a real problem.
Less than a month ago, on October 5th 2020 the BBC said that 15,841 COVID-19 cases went unreported in England because Public Health England, a government agency, had stored too many rows in a very old version of Excel and as a result lost track of thousands of COVID-19 cases. 
Sometimes mistakes are very obvious, but in Britain it took 2 days before this the issue was identified. To think that almost 16,000 people with confirmed cases COVID-19 were walking around infecting others, due to a poor use of Excel really demonstrates why it is important to have reliable and error free reporting processes.
Leveraging external data sources to see new trends
So far we've looked at some of the issues that First Nation communities may face when generating reports using information they already have access to.
But sometimes the goal that we're trying to achieve requires information that the Nation does not have already. The First Nation community might want to compare the rates of diabetes in its community against another community in its province, or even compare its data against the national averages.
The nation might also want to fill the gaps in its reporting with information about its community members from local hospitals or COVID-19 testing centres.
If the First Nation does not have the information already, we have two options going forward.
Option A would be for the first Nation community to collect the data itself by directly surveying relevant stakeholders or community members; or it could modify existing data collection processes that would make the new information available, like adding a new question to a form that already exists.
However it can be impossible for a community to collect certain kinds of information by itself. Like hospitalization records, because hospitals are run by governments and to my knowledge there are currently no First Nation hospitals in existence in Canada.
Option B would be another way First Nations could acquire new information if that information had already been collected by an external organization which had made that data available for First Nation communities to use.
There are many Canadian organizations and agencies that do just that.
They vary in size and scope considerably. Ranging from tiny community organizations run by a handful of people all the way up to national federal agencies that have $500M budgets and thousands of employees.
But just because an organization is huge and has tons of resources does not mean that the data that they have available is actually of any use to the first Nation community.
We have to keep in mind the specific outcomes that the community would like to achieve. If the goal is to see why some of the members of the community are much worse off than everyone else, then it's important that this new data source has information that is directly related and specific to the community itself, or other communities that are similar.
There are a number of factors that make data useful:
Relevance or specificity
Comprehensiveness / breadth
Availability and Accessibility
For example, data from the Census (which is compiled by Statistics Canada) can be used to assess socio-economic gaps across time, geographies, and populations,  which would allow the community to see how it is doing compared to other first Nation communities on very broad and non-specific indicators.
But the Census data is lacking in depth  and may not be useful because the first Nation community can't dive deep into the results and understand complex interrelationships that indicators may have.
Another issue with the census is that it is only released every five years. And that's where timeliness becomes an issue, that first nation community would only see if it's on the right track or not.
It is important for First Nation communities to improve their own statistical capacity
Chief Public Health Officer Dr. Theresa Tam mentioned in her most recent Report on the State of Public Health in Canada that in order to understand and address inequalities, there is a need for community-led data collection activities that “collect, link and disaggregate data related to socio-demographic factors” that have the “ability to integrate intersectional data from various sources, such as self-reported survey data with administrative data.” 
“The ultimate goal,” Dr. Tam writes “is to supply decision-makers and public health authorities with the information needed to identify the most appropriate interventions for a specific targeted group.” 
That's why I created the diagram on the next page to show a small slice of the organizations that make this information available.
How RPA can help First Nation communities report on their own data and fill in gaps of knowledge on their own terms
With all of the issues brought up in this article it may sound as if it is nearly impossible for a Nation to build reliable data gathering and reporting processes without having a ten person data analytics team.
The good news is that it is possible for First Nation communities to build a unified data storage solution using the Nation's own data sources by using Robotic Process Automation (RPA).
This solution would solve the following reporting problems and enable First Nation communities to make the most of their siloed data:
Reporting Issue #1: Data silos waste time and cause headaches by making reporting processes and linkages more complicated. Silos also make teams less effective by disrupting the sharing of information and knowledge.
Reporting Issue #2: It will be significantly more complicated to combine the data of multiple First Nations (or a Tribal Council) into a comprehensive report because the same type of information may be stored in different ways and many more linkages will be required.
Reporting Issue #3: Manually having to gather data from numerous silos is inefficient and slows the entire reporting process.
Reporting Issue #4: Manually linking data by hand with Excel is extremely time consuming. If we were using a database we could link 10,000 records in less than a second.
Reporting Issue #5: It wouldn’t make sense for a First Nation to hire a team of data analysts just to get more timely reports. A team of 10 people won’t be 10X faster than a single analyst. If it costs $60,000 for a single analyst, is $600,000 a worthwhile investment to save a couple of days or weeks and gain no other advantage? Not worth it.
Reporting Issue #6: Manually updating reports is time consuming whether you are selective and inserting a couple of new records, or whether you are starting from scratch because 90% of your time would still need to be spent on the previous stages preparing the data.
Reporting Issue #7: Mistakes at the start of the reporting process will continue on through the process. Decisions made (or not made) based on false information could lead to worse outcomes for certain people.
Reporting Issue #8: Time spent collecting, linking and reporting data is not actively helping community members.
RPA can act as the glue to connect all of these disparate data silos and create a single source of truth that allows for easier reporting and analysis. No matter where or how the digital data is stored, RPA is able to access it and store it in a centralized repository.
RPA can gather new data from countless sources on a daily basis ensuring that the Nation has access to the most up-to-date information at all times. After gathering the data, RPA can clean and organize data in a systematic fashion, which ensures that the data stored is high quality, accurate, and tidy.
One way to think about RPA is to consider it as a digital data-analyst that performs the same repeated tasks over and over again.
As previously explained in this article, in order for a Nation to overcome the barriers of data being stored in disparate silos without RPA, that Nation would have to allocate a person to spend quite literally all of their working hours collecting and processing data. This person would have to go through all of the steps of cleaning up that data, performing complicated linkages and finally building a report out of it all. This person would have to carry out the same steps, every single time a new report is required.
But with RPA, we don’t need to expend a person on these repetitive tasks. Instead we can have the computer do all of these data gathering, cleaning, linking and reporting tasks for us. These activities can be completed from three to five times as fast as a person. Sometimes, RPA can even be tens or hundreds of times faster than a person.
As long as RPA has been properly programmed and tested, it doesn’t make mistakes. Even after working 24 hours straight. And since RPA is just computer software, it doesn’t need to sleep or take breaks.
RPA is OCAP®️ compliant because each Nation can decide how RPA will work for it
The FNIGC’s OCAP®️ framework (Ownership, Control, Access and Possession) sets the standard for how First Nations data should be collected, protected, used, or shared.
RPA is OCAP®️ compliant because at the most simple level, RPA is just a collection of rules and instructions that tells the computer what to do.
Since RPA must be built from the ground up, each Nation can write their own rules about how they would like to collect, process, store, report on and share their information. OCAP®️ compliance can be attained when the principles are an integral part of implementing RPA and when those principles are considered every step of the way.
RPA enables First Nation communities to be more independent and in control of their own future
By being able to easily analyze and report on their own data, First Nation communities will be able to make more timely and informed decisions based on real data and not on educated guesses.
Because Nations can use their own data, the data is more trustworthy, detailed, and specific to that Nation and its community. If information in a certain area is lacking, the Nation could ask residents to fill out surveys which could then be added to the Nation’s unified database.
Since survey data will remain under the ownership of the Nation and because that data will be used to improve the community they are a part of, residents will feel more comfortable and be more honest when sharing their thoughts, feelings and opinions as opposed to possibly distrusting government surveys.
RPA can enable the measurement of more traditional indicators of health and well-being
The indicators used to measure the healthiness of non-First Nations populations may not be effective when applied to First Nations populations.
But it also isn’t fair to assume that an indicator of health that works for one First Nation band will work for all Nations in Canada. While there may be similarities between First Nation bands, each has their own unique history, cultural background, and definitions of well-being.
That is why RPA is such a wonderful tool because each Nation can pick and choose what data they would like to measure and report on. RPA allows for Nations to customize their own indicators of health and monitor their progress on their own terms.
Furthermore, it is possible for a Nation to customize their measurements and reports over time. If it turns out that a certain measurement has not been as insightful as previously believed, then it can be changed, or new data can be collected, or that measurement can be thrown out and a new one brought in.
RPA can help First Nations in their discussions of a new fiscal relationship with the Government of Canada by providing relevant, accurate, and timely data
While this article has mainly focused on putting RPA to use in relation to tracking health indicators, this is not the only kind of information that can be measured with RPA.
With the advent of a new fiscal relationship between First Nations and the Government of Canada that “moves towards sufficient, predictable and sustained funding for First Nations communities”,  and ultimately towards self-government for the Nations themselves.
As a result of that change, in order to govern themselves effectively, First Nations communities in Canada will need to improve their reporting capabilities across the board, especially in regards to financial and economic statistics. 
Additionally, First Nations communities “will be increasingly responsible for making decisions about what programs and services are needed in their communities, program planning, funding allocations, and monitoring whether programs and services are having an impact.” 
RPA is the best tool to use for First Nation communities looking for better data.
RPA can be applied in this area and to any type of data, and it can assist First Nations communities in maintaining its new fiscal relationship with the Government of Canada by automatically gathering, cleaning, linking, and reporting on various statistics that are required to support the new government-to-government relationship.
What matters most is that the gap between First Nations and non-First Nations is closed and that First Nations are living longer, healthier, happier, and more connected lives
While this article surely paints Robotic Process Automation (RPA) in a positive light and demonstrates how it can do some pretty amazing things and solve certain problems faced by First Nations communities; but it is only a tool, a means to an end.
In this case, the end goal is ultimately a Canada where all First Nations, Inuit, and Metis, regardless of their race, religion, gender, age, sexual orientation, geographical location, or anything else, is able to live a life that is just as lengthy, healthy, and happy as the rest of the general population.
RPA can help First Nations leaders identify patterns in data, but RPA can not make inferences from that data and see why the numbers are that way. RPA just gathers and displays facts to whoever is looking. It doesn’t tell you what to do.
The real outcomes and improvements in people’s lives are not made by RPA, but instead they are made by the people who understand the big picture, hidden behind all those numbers and charts. The real difference is made by the teams and people who come up with new ideas, put plans into action and aim to make a difference in the lives of those around them.
RPA is a revolutionary technology that can help First Nation communities get access to more timely, accurate and relevant information about their home. But the most important thing is about what you are going to DO with the data. Because after all, these aren’t just random numbers floating around on the screen. Each number could be a life.
Let’s get started on making Canada into the country that everyone wants to believe it is, and a place where First Nations are treated equally and given the opportunity to live their best lives.
 'This is not the country you believe it is,' Neskantaga First Nation chief says amid water crisis
 Trends in life expectancy over time for First Nations in Alberta
First Nations - Health Trends Alberta - May 31, 2016 - AFNIGC and AHS
 Infant Mortality Rates in First Nations in Alberta
First Nations - Health Trends Alberta - April 26, 2016 - AFNIGC and AHS
 Suicide Rates among First Nations people in Alberta
First Nations - Health Trends Alberta - June 28, 2016 - AFNIGC and AHS
 Diabetes Incidence among First Nations in Alberta
First Nations - Health Trends Alberta - April 4, 2017 - AFNIGC and AHS
 Excel: Why using Microsoft's tool caused Covid-19 results to be lost
Leo Kelion - 5 October
 About ClinicalConnect
 Strengthening the Availability of First Nations Data
Prepared for: Indigenous Services Canada & The Assembly of First Nations by Shelley Trevethan from QMR Consulting - January 30, 2019
 Data Resources and Challenges for First Nations Communities
 The First Nations Principles of OCAP®
OCAP® is a registered trademark of the First Nations Information Governance Centre (FNIGC)
 A new approach: Co-development of a new fiscal relationship between Canada and First Nations
 Fiscal Relations
Assembly of First Nations
 Memorandum of Understanding
Memorandum of Understanding between the Assembly of First Nations and the Indigenous and Northern Affairs Canada - July 2016
 First Nations Regional Health Survey - General Information Page
 The First Nations Regional Health Survey - Phase 3: Volume Two
 From risk to resilience: An equity approach to COVID-19
Chief Public Health Officer of Canada's Report on the State of Public Health in Canada 2020
 Ignored to death: Brian Sinclair's death caused by racism, inquest inadequate, grou