Have you ever gone through a long and painful electronic health record (EHR) or laboratory information system (LIS) implementation only to find that there are significant workflow gaps? Or maybe, while working, you found yourself wondering if there was an easier way to complete a repetitive task? Well, there might be an app for that! You won’t want to miss today’s session, “Mind the App: Application Development as a Solution to Unmet Needs in Laboratory Workflows,” where moderator Amrom Obstfeld, MD, PhD, will lead a discussion about the ways custom apps can be deployed to help laboratorians solve practical problems with improved efficiency and reduced risk of errors.
It should not come as a surprise that software vendors cannot meet every need of every workflow for every customer, but it is a frustrating reality. This means that a local solution is required, which often translates into a “manual workaround” with associated potential for error, inefficiencies, and propensity for safety risk. Vendor software change requests may offer relief, but can be expensive, time-consuming, and slow to evolve. The availability of Python and R as freely available open-source tools to conduct machine learning, data analytics, statistical computing, and graphing projects has facilitated their expansion into the clinical laboratory community, offering a no-cost option to experiment with innovative ideas.
This session aims to educate a general audience about the creation of web-based applications that can be simple in design, quick to make, and—most importantly—solve your most annoying problem(s). There is no prerequisite to be a programmer or know anything about writing code to attend this session.
Daniel Holmes, MD, from the University of British Columbia, will present his talk, “Why Use R and What Kinds of Apps Can I Make?” and Stephan Kadauke, MD, PhD, from the Children’s Hospital of Philadelphia, will present his talk, “Making Reliable Lab Apps: Demystifying Good Software Engineering Practices.” They will share examples of how to use an application to curate data from multiple sources to monitor a lab process and present key performance indicators. This can be used to inform decisions or automate manual processes. The benefit of a customized app goes beyond automating annoying, repetitive tasks. A well-designed “lab app” can more accurately model real lab workflows, thereby reducing risk of errors and implementing innovative functionality, such as automated machine learning-supported decision support.
The presenters will provide the audience with strategies to design and implement custom apps in their own institutions. They will discuss the importance of and strategies for clinical validation to ensure data integrity; aspects of change management; and the importance of getting buy-in from leadership, as well as other intended users. They will exchange views on the merits of a governance structure, as well as the adoption of good software engineering practices to support these apps once implemented.