Digitization of Surgical Flowsheets; Big Data and Inequity in Healthcare

Data can be an incredibly important tool in improving medical care and better developing medical research and knowledge. But what happens when no infrastructure is in place to collect data? Or on the other hand, how do we ensure that data is used in ways that are fair and equitable? Through my technical project and STS research I look at data in both developed and developing data infrastructures in Rwanda and the United States and examine not only the importance of utilizing data in healthcare, but also the importance of understanding and working with the potential equity issues in the data.
My technical project focuses on optimizing and improving a data digitization system built for low and middle income countries (LMIC). Due to various barriers such as cost and existing infrastructure many healthcare systems in LMICs record medical data manually on papers. This method for data collection does not allow for easy aggregation and analysis of data, and therefore makes it nearly impossible for these providers to gain insights from data. My project worked to improve an existing system at the University Teaching Hospital of Kigali (CHUK) to digitize surgical records. One of the team’s main improvements was the development of a mobile upload application to replace an existing web application that not only had several technical issues, but was also very inefficient in uploading data. In addition to redevelopment of the upload application we made several improvements throughout the process including changes to hardware. With the upgraded system users should be able to upload surgical flowsheets at a much faster rate, ideally leading to more data uploaded.
Where my technical work focuses on creating a data infrastructure to allow for more efficient data analytics, my STS research moves into looking at how the use of sophisticated data analytics has the potential to further perpetuate inequities in healthcare, namely in regards to inequities concerning people of color. As I discuss in my technical project, the use of data analytics can be a powerful tool in healthcare, and combined with big data tools can help provide powerful insights. However, without proper consideration, biases in outcomes and predictions may form that may be harmful and may disproportionately affect minority racial groups. In my research I explore possible root causes of biases in these analytical methods and then present some solutions to combat the given issues.
Both my technical project and STS research delve into the use of data analytics in a healthcare setting, from the creation of data infrastructures to allow for analysis, to ensuring equity once robust and complex data systems are created. With my team’s work we hope that the increased efficiency to upload data will increase the amount of data and quality of data collected, so the hospital may have a higher capacity to use such data for further research and clinical care. Furthermore, through my STS research I’ve discussed several reasons as to why big data may perpetuate biases and inequity and some potential solutions.