Rapid Application Development Public Health Style

If you work on front-ends or back-ends of health applications, you are probably already familiar with the concepts of Agile and rapid application development.

“Rapid application development”, or RAD for short, refers to an approach to designing computer applications. I had not heard the term for the first part of my career, because I was busy analyzing data from computer applications – not developing them.

But as my career proceeded, I ended up on an application development team. It was an eye-opening experience for me, because I was not used to serving on such a team. I was used to serving on public health research teams, which I describe in this blog post.

Application development teams are not run like public health research teams. In public health teams – as I explain on my blog post – you have a strict hierarchy. Actually, academia is literally full of hierarchy. This hierarchy has the advantage of building in a lot of oversight, but has the downside of dampening innovation. It’s hard to develop applications in an academic setting with public health research teams. If you are wondering what happens when that happens, read my article on LinkedIn about a webinar I attended sponsored by AHRQ.

What Does “Rapid Application Development” (RAD) Mean?

As I explain in my online course titled “Applications Basics”, there are three main philosophies that guide application development approaches. First, there is what is termed the “traditional” approach – which would not sound very traditional to people trained in public health.

In the traditional approach, there is a slight hierarchy, where there is a Sponsor at the top of the hierarchy, and that person has a Project Manager (PM) who reports to them. Then, the PM uses matrix management to manage an application development team. The course goes into detail about the different roles on the team, in terms of what they are named, and what they do.

Second, there is the Agile approach. In my opinion, the rise of Agile in the early 2000s was a backlash in response to the traditional approach. Even though the concept of Agile is outside the public health space, most healthcare data analysts and biostatisticians have at least heard of Agile. I know when I was learning about the “rapid application development” approach, Agile came up in the discussion. I realized I had heard of Agile a lot, but when asked to focus on it, I realized had no idea what it actually meant.

The traditional approach was what was used in the 1990s to develop software. As technology improved and the internet became more accessible, the traditional approach became too slow. I remember some software coming out in the early 2000s that I was waiting for – usually upgrades of applications I used a lot, like Microsoft Excel. Sometimes, the minute they came out, the seemed obsolete. Much of the software that came out in the early 2000s was incompatible with the internet. So you can imagine why something called “Agile” might be attractive!

The problem was that Agile was a little too Agile. Watch my video to learn about all the problems with Agile.

Watch Monika Wahi's data science tutorials on YouTube!

Watch my video to learn about the rapid application development, or RAD, approach to application development.

The video also provides are more detailed explanation of rapid application development (RAD). Briefly, saying you are doing RAD is basically saying you are mixing some components of the traditional approach and components of Agile to get an approach that is faster and more efficient than the traditional approach, but avoids all the pitfalls of the Agile approach.

So to be fair, the traditional approach was guided by some overarching management approaches and standards, like the same ones that guide the Plan Do Study Act (PDSA) model – which has nothing to do with public health. However, the Agile approach was not really guided by any overarching standard in any field. In fact, while the traditional approach was quite rigid, the Agile approach was so loose, everyone had to essentially develop their own version of Agile.

So that means – if you are following me – if someone is going to develop a rapid application development (RAD) approach, it’s going to be their own version, because it is a mixture of Agile and traditional. That’s exactly what I have done. I have developed a RAD approach specifically suited for the development of applications in the healthcare and public health space.

My video above gives you a short summary of my RAD approach. If you want to more details, please watch the full case study video on the Applications Basics course site – I made it free to the public under Chapter 3: Monika’s RAD in Action.

 

Updated June 12, 2023.

Read all of our data science blog posts!

Descriptive Analysis of Black Friday Death Count Database: Creative Classification

Descriptive analysis of Black Friday Death Count Database provides an example of how creative classification [...]

Classification Crosswalks: Strategies in Data Transformation

Classification crosswalks are easy to make, and can help you reduce cardinality in categorical variables, [...]

FAERS Data: Getting Creative with an Adverse Event Surveillance Dashboard

FAERS data are like any post-market surveillance pharmacy data – notoriously messy. But if you [...]

Dataset Source Documentation: Necessary for Data Science Projects with Multiple Data Sources

Dataset source documentation is good to keep when you are doing an analysis with data [...]

Joins in Base R: Alternative to SQL-like dplyr

Joins in base R must be executed properly or you will lose data. Read my [...]

NHANES Data: Pitfalls, Pranks, Possibilities, and Practical Advice

NHANES data piqued your interest? It’s not all sunshine and roses. Read my blog post [...]

Color in Visualizations: Using it to its Full Communicative Advantage

Color in visualizations of data curation and other data science documentation can be used to [...]

Defaults in PowerPoint: Setting Them Up for Data Visualizations

Defaults in PowerPoint are set up for slides – not data visualizations. Read my blog [...]

Text and Arrows in Dataviz Can Greatly Improve Understanding

Text and arrows in dataviz, if used wisely, can help your audience understand something very [...]

Shapes and Images in Dataviz: Making Choices for Optimal Communication

Shapes and images in dataviz, if chosen wisely, can greatly enhance the communicative value of [...]

Table Editing in R is Easy! Here Are a Few Tricks…

Table editing in R is easier than in SAS, because you can refer to columns, [...]

R for Logistic Regression: Example from Epidemiology and Biostatistics

R for logistic regression in health data analytics is a reasonable choice, if you know [...]

1 Comments

Connecting SAS to Other Applications: Different Strategies

Connecting SAS to other applications is often necessary, and there are many ways to do [...]

Portfolio Project Examples for Independent Data Science Projects

Portfolio project examples are sometimes needed for newbies in data science who are looking to [...]

Project Management Terminology for Public Health Data Scientists

Project management terminology is often used around epidemiologists, biostatisticians, and health data scientists, and it’s [...]

Rapid Application Development Public Health Style

“Rapid application development” (RAD) refers to an approach to designing and developing computer applications. In [...]

Understanding Legacy Data in a Relational World

Understanding legacy data is necessary if you want to analyze datasets that are extracted from [...]

Front-end Decisions Impact Back-end Data (and Your Data Science Experience!)

Front-end decisions are made when applications are designed. They are even made when you design [...]

Reducing Query Cost (and Making Better Use of Your Time)

Reducing query cost is especially important in SAS – but do you know how to [...]

Curated Datasets: Great for Data Science Portfolio Projects!

Curated datasets are useful to know about if you want to do a data science [...]

Statistics Trivia for Data Scientists

Statistics trivia for data scientists will refresh your memory from the courses you’ve taken – [...]

Management Tips for Data Scientists

Management tips for data scientists can be used by anyone – at work and in [...]

REDCap Mess: How it Got There, and How to Clean it Up

REDCap mess happens often in research shops, and it’s an analysis showstopper! Read my blog [...]

GitHub Beginners in Data Science: Here’s an Easy Way to Start!

GitHub beginners – even in data science – often feel intimidated when starting their GitHub [...]

ETL Pipeline Documentation: Here are my Tips and Tricks!

ETL pipeline documentation is great for team communication as well as data stewardship! Read my [...]

Benchmarking Runtime is Different in SAS Compared to Other Programs

Benchmarking runtime is different in SAS compared to other programs, where you have to request [...]

End-to-End AI Pipelines: Can Academics Be Taught How to Do Them?

End-to-end AI pipelines are being created routinely in industry, and one complaint is that academics [...]

Referring to Columns in R by Name Rather than Number has Pros and Cons

Referring to columns in R can be done using both number and field name syntax. [...]

The Paste Command in R is Great for Labels on Plots and Reports

The paste command in R is used to concatenate strings. You can leverage the paste [...]

Coloring Plots in R using Hexadecimal Codes Makes Them Fabulous!

Recoloring plots in R? Want to learn how to use an image to inspire R [...]

Adding Error Bars to ggplot2 Plots Can be Made Easy Through Dataframe Structure

Adding error bars to ggplot2 in R plots is easiest if you include the width [...]

AI on the Edge: What it is, and Data Storage Challenges it Poses

“AI on the edge” was a new term for me that I learned from Marc [...]

Pie Chart ggplot Style is Surprisingly Hard! Here’s How I Did it

Pie chart ggplot style is surprisingly hard to make, mainly because ggplot2 did not give [...]

Time Series Plots in R Using ggplot2 Are Ultimately Customizable

Time series plots in R are totally customizable using the ggplot2 package, and can come [...]

Data Curation Solution to Confusing Options in R Package UpSetR

Data curation solution that I posted recently with my blog post showing how to do [...]

Making Upset Plots with R Package UpSetR Helps Visualize Patterns of Attributes

Making upset plots with R package UpSetR is an easy way to visualize patterns of [...]

4 Comments

Making Box Plots Different Ways is Easy in R!

Making box plots in R affords you many different approaches and features. My blog post [...]

Convert CSV to RDS When Using R for Easier Data Handling

Convert CSV to RDS is what you want to do if you are working with [...]

GPower Case Example Shows How to Calculate and Document Sample Size

GPower case example shows a use-case where we needed to select an outcome measure for [...]

Querying the GHDx Database: Demonstration and Review of Application

Querying the GHDx database is challenging because of its difficult user interface, but mastering it [...]

Variable Names in SAS and R Have Different Restrictions and Rules

Variable names in SAS and R are subject to different “rules and regulations”, and these [...]

Referring to Variables in Processing Data is Different in SAS Compared to R

Referring to variables in processing is different conceptually when thinking about SAS compared to R. [...]

Counting Rows in SAS and R Use Totally Different Strategies

Counting rows in SAS and R is approached differently, because the two programs process data [...]

Native Formats in SAS and R for Data Are Different: Here’s How!

Native formats in SAS and R of data objects have different qualities – and there [...]

SAS-R Integration Example: Transform in R, Analyze in SAS!

Looking for a SAS-R integration example that uses the best of both worlds? I show [...]

Dumbbell Plot for Comparison of Rated Items: Which is Rated More Highly – Harvard or the U of MN?

Want to compare multiple rankings on two competing items – like hotels, restaurants, or colleges? [...]

2 Comments

Data for Meta-analysis Need to be Prepared a Certain Way – Here’s How

Getting data for meta-analysis together can be challenging, so I walk you through the simple [...]

Sort Order, Formats, and Operators: A Tour of The SAS Documentation Page

Get to know three of my favorite SAS documentation pages: the one with sort order, [...]

Confused when Downloading BRFSS Data? Here is a Guide

I use the datasets from the Behavioral Risk Factor Surveillance Survey (BRFSS) to demonstrate in [...]

2 Comments

Doing Surveys? Try my R Likert Plot Data Hack!

I love the Likert package in R, and use it often to visualize data. The [...]

2 Comments

I Used the R Package EpiCurve to Make an Epidemiologic Curve. Here’s How It Turned Out.

With all this talk about “flattening the curve” of the coronavirus, I thought I would [...]

Which Independent Variables Belong in a Regression Equation? We Don’t All Agree, But Here’s What I Do.

During my failed attempt to get a PhD from the University of South Florida, my [...]

“Rapid application development” (RAD) refers to an approach to designing and developing computer applications. In public health and healthcare, we are not taught about application development – but it’s good for us to learn about it, since we have to deal with data from health applications. My blog post talks about the RAD approach I have developed that is customized for the healthcare space.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Verified by MonsterInsights