DEV Community: Vibha Shastry

Public Data API as a Healthcare Tool

Vibha Shastry — Wed, 16 Sep 2020 22:15:08 +0000

APIs in Healthcare

In today’s world, the exchange of data is a key element that supports industries which rely on the Internet and thrive on acquiring data. However, in the healthcare industry, the exchange of health data is underinvested, affecting the quality of patient care and other health care services. For example, a patient may not be able to easily access their health records or hospitals may not be able to efficiently transfer health records. Recently, application program interface (API) has been touted as a tool that can streamline this exchange of data, further improving patient care.

APIs are essentially code that allow computer systems to interact with one another. They are supported by the concept of interoperability — the exchange of information between computer systems. In healthcare, we see interoperability in the form of electronic health records (EHR). While such records can be effective in storing patient data, hospitals frequently use multiple health records per patient. This makes retrieving and sharing patient data complicated. With APIs, multiple EHRs can be reduced to one profile or system, which is a more convenient method to transfer patient data.

Considering that APIs are frequently used in tech industries, APIs have many potential applications to healthcare. Clinical studies can address underrepresented groups with a greater variety of data. Greater accessibility to health data can affect understanding of diseases and treatments in various populations. In addition, expanding the use of APIs in the healthcare system can also make data more transportable through the internet. To illustrate how exactly APIs can be used, we will go through an example that involves accessing data using an API and R, a statistical and data analysis programming language. For this activity, we will be using RStudio.

API Mini Tutorial

Suppose that you are conducting a clinical research study on COVID-19 cases in the United States. While you have data on the number of confirmed cases from your local hospitals, you would like to compare those to the nationwide number of cases, preferably by state. Here, we will use an open API, which is publicly available on the Internet. This open API has been retrieved from a COVID-19 API website: https://api.covid19api.com/live/country/usa/status/confirmed.

In RStudio, we will first need to load a package using the library() function. We will download a package called jsonlite, which converts JSON (Javascript Object Notation) data and makes them more readable and organized. JSON is a syntax used in R that helps structure data.

> library(json) 
> covid_data <- fromJSON("https://api.covid19api.com/live/country/usa/status/confirmed")
> print(covid_data)

We then want to be able to view the data on R. The fromJSON() function parses JSON data in R. To avoid having to repeat this command over and over again, we will create a variable and assign it to the fromJSON() command using an assignment operator (symbolized by “<-”). This also makes it easier to refer back to the data when we work with it later. Here, we created the variable “covid_data”:

If you type print(covid_data), the following statistics will show up on R. Here, we can see the number of confirmed cases by state (note that this is not the entire dataset- just a view of how the data would look like):

                   Country CountryCode                 Province City CityCode   Lat     Lon  Confirmed 
1  United States of America          US                 Michigan               43.33  -84.54     24244   
2  United States of America          US                 Colorado               39.06 -105.31      7307   
3  United States of America          US               Washington                47.4 -121.49     10530    
4  United States of America          US              Mississippi               32.74  -89.68      2781    
5  United States of America          US                Tennessee               35.75  -86.69      5508    
6  United States of America          US             South Dakota                44.3  -99.44       730      
7  United States of America          US             Pennsylvania               40.59  -77.21     22997   
8  United States of America          US              Puerto Rico               18.22  -66.59       897     
9  United States of America          US           Grand Princess               37.65 -122.67       103

What if we only wanted to look at a smaller dataset — that is, what if we wanted to randomly sample only 60% of the data we have? We first start by using the set.seed() function, which helps in generating a random sequence of numbers (and therefore will help in generating a random 60% subset of our data). We define the “population size” (the original data) with nrow(covid_data), which returns the number of columns or rows. Then we define the “sample size” (our 60% subset) using round(). To identify the sample drawn from our data, we set sample_id equal to sample(pop_size, sample_size).

The command sample(pop_size, sample_size) tells us that from the "population" data, we will take a sample that is 60% of the dataset (notice what we had set sample_size equal to). Then the resulting sample can be viewed through print(covid_data_sample):

> set.seed(1342)
> pop_size = nrow(covid_data)
> sample_size = round(pop_size*0.6)
> sample_id = sample(pop_size,sample_size)
> covid_data_sample = covid_data[sample_id, ]
> print(covid_data_sample)

Oftentimes, datasets can be huge. Therefore, it is helpful to take smaller random samples and perform statistical analysis. Not only are APIs helpful in interpreting such information, but they can be used to efficiently transfer and interpret data. APIs can help create better information systems in healthcare, a sector where data is extremely valuable.

The Future of Digitizing Health Information

Besides multiple applications to patient care, APIs can have many potential roles in health research. Clinical studies, which involve testing human participants to learn more about disease and other health-related topics, can also get access to a variety of data through healthcare APIs. Rather than directly downloading data from a database (which often results in bad formatting), APIs allow for the data to be more readable and organized. In addition, expanding the use of APIs in the healthcare system can also make data more transportable through the internet. Through our mini tutorial, we got to see how data can be easily looked it through an API. Such an approach is not only useful in areas of clinical research — it will also be an important tool that could help make health data more accessible in the future.

Sourcing To Recruit Top Tech Students & Leaders at a Student-Run Non-Profit

Vibha Shastry — Sat, 29 Aug 2020 19:49:19 +0000

Bit Project's Sourcing Goals & Values

Bit Project is a student-led non-profit organization that aims to make tech education more accessible. This goal is important to each of our members, who collaborate together and are passionate about creating STEM education materials for fellow students. Our People Department seeks to recruit individuals who are interested in promoting STEM education on an open source platform. To make Bit Project a learning hub for STEM education, the People Department uses various sourcing strategies to attract top tech students.

How Sourcing Works at Bit Project

Sourcing is a process that involves setting criteria in place to fulfill recruitment goals. Over the course of Bit Project’s expansion, the process of sourcing has changed to make Bit Project a diverse organization. When the organization started, one of the People Department’s goal was to encourage many students to apply.

“We have done a bunch of email blasts and marketing to colleges across the nation,” said Becca Tran, a management consultant within the People Department. Inviting students from all majors and backgrounds has sped Bit Project's growth and output, while also building a tight knit community invested in tech education.

As Bit Project continues to create projects like educational tutorials and blogs, sourcing has been redirected to search for top tech students. This has led to the People Team creating a group of sourcers who are directly involved in recruitment. Su-Ting Tan, a recruiter, notes that detailed guidelines have been set in place to increase the yield of top tech students. The guidelines help sourcers identify ideal traits in candidates and suggest places where sourcers can look for candidates. For example, sourcers are encouraged to source from digital platforms such as LinkedIn, DevPost, and Github. On these websites, sourcers can message individuals whose profiles indicate a strong interest in technology. Increasing Bit Project’s online presence allows the organization to directly connect with candidates.

These guidelines also encourage sourcers to look for key “buzzwords” and experiences that describe a candidate’s programming skills. When a sourcer is looking at an online profile, they can take note of skills that make a potential candidate ideal for Bit Project. For example, qualities such as tutoring and hackathon participation can be sorted into tiers of green, yellow, and red flags. Green flags, such as extensive coding experience and online projects, indicate qualities that are the most ideal and important. Red flags, such as lack of internships and “filler” experiences, are qualities that can be irrelevant to a position at Bit Project. A large number of green flags in a candidate’s profile permits the sourcer to add the candidate to a sourcing list. The sourcer can then add additional comments on why the candidate would be a good fit for the position.

Assessing Candidates Through Filters

To filter through applications, three rounds of interviews are scheduled to assess a candidate’s skills and competency. The first screen interview is informal and allows the candidate to discuss their projects and experiences. These interviews are used to inform candidates of Bit Project’s goals, allowing sourcers to determine how well a candidate aligns with Bit Project’s mission and objectives. An entry task is also designed to allow the candidate to demonstrate their skills, giving an opportunity for candidates to see what their role within Bit Project would look like. The entry task also gives the sourcers and relevant departments a chance to see the candidate’s performance and passion for their potential role. Once the departments review the candidate, the candidate can be onboarded and become a part of Bit Project!

The ongoing pandemic has made Bit Project reevaluate its methods of connection with members. With the organization going remote, remote sourcing has enabled Bit Project to connect with individuals across the globe! To learn more about how remote sourcing and onboarding work at Bit Project, read here.

The Power of Improved Sourcing

Over time, the sourcing process has become more organized, resulting in the recruitment of top students who create quality educational content. Investing time into sourcing strategies has enabled Bit Project to develop innovative projects that are an integral part of the organization’s mission. By looking for passionate individuals, the organization ensures that the goal to improve accessibility to tech education can be fulfilled. The Bit Project community thrives as a result of individuals who bring new ideas and perspectives, all of which are possible through improved sourcing.

Bit Project encourages students of all backgrounds and experience to become part of the Bit community. The organization is constantly looking for dedicated people! Take a look at Bit Project's home page to learn more.