How I Make Technology Decisions

The open source community has responded to the increasing complexity of software systems by creating many frameworks which are supposed to facilitate the work of developing software. Software developers spend a considerable amount of time researching, learning, and integrating these frameworks to build new software products. Selecting the wrong technology can cost an organization millions of dollars. In this post, I describe my approach to selecting these frameworks. I also discuss the frameworks that have made it to my software development toolbox.

Understanding the Business

The first step is to build a strong understanding of the following:

• The business goals and challenges of the organization. For example, the healthcare industry is currently shifting to a value-based payment model in an increasingly tightening regulatory environment. Healthcare organizations are looking for a computing infrastructure that support new demands such as the Accountable Care Organization (ACO) model, patient-centered outcomes, patient engagement, care coordination, quality measures, bundled payments, and Patient-Centered Medical Homes (PCMH).


• The intended buyers and users of the system and their concerns. For example, what are their pain points? which devices are they using? and what are their security and privacy concerns?


• The standards and regulations of the industry.


• The competitive landscape in the industry. To build a system that is relevant, it is important to have some ideas about the following: what is the competition? what are the current capabilities of their systems? what is on their road map? and what are customers saying about their products. This knowledge can help shape a Blue Ocean Strategy.


• Emerging trends in technologies.

This type of knowledge comes with industry experience and a habit of continuously paying attention to these issues. For example, on a daily basis, I read industry news as well as scientific and technical publications. As a member of the American Medical Informatics Association (AMIA), I receive the latest issue of the Journal of the American Medical Informatics Association (JAMIA) which allows me to access cutting-edge research in medical informatics. I speak at industry conferences when possible and this allows me not only to hone my presentation skills, but also attend all sessions for free or at a discounted price. For the latest in software development, I turn to publications like InfoQ, DZone, and TechCrunch.

To better understand the users and their needs and concerns, I perform early usability testing (using sketches, wireframes, or mockups) to test design ideas and obtain feedback before actual development starts. For generating innovative design ideas, I recommend the following book: Universal Methods of Design: 100 Ways to Research Complex Problems, Develop Innovative Ideas, and Design Effective Solutions by Bruce Hanington and Bella Martin.

 

Architecting the Solution

Armed with a solid understanding of the business and technological landscape as well as the domain, I can start creating a solution architecture. Software development projects can be chaotic. Based on my experience working on many software development projects across industries, I found that Domain Driven Design (DDD) can help foster a disciplined approach to software development. For more on my experience with DDD, see my previous post entitled How Not to Build A Big Ball of Mud, Part 2.

Frameworks evolve over time. So, I make sure that the architecture is framework-agnostic and focused on supporting the domain. This allows me to retrofit the system in the future with new frameworks as they emerge.

 

Due Diligence

Software development is a rapidly evolving field. I keep my eyes on the radar and try not to drink the vendors Kool-Aid. For example, not all vendors have a good track record in supporting standards, interoperability, and cross-platform solutions.

The ThoughtWorks Technology Radar is an excellent source of information and analysis on emerging trends in software. Its contributors include software thought leaders like Martin Fowler and Rebecca Parson. I also look at surveys of the developers community to determine the popularity, community size, and usage statistics of competing frameworks and tools. Sites like InfoQ often conduct these types of surveys like the recent InfoQ survey on Top JavaScript MVC Frameworks. I also like Matt Raible's Comparing JVM Web Frameworks.

I value the opinion of recognized experts in the field of interest. I read their books, blogs, and watch their presentations. Before formulating my own position, I make sure that I read expert opinions on opposing sides of the argument. For example, in deciding on a pure Java EE vs. Spring Framework approach, I read arguments by experts on both sides (experts like Arun Gupta, Java EE Evangelist at Oracle and Adrian Colyer, CTO at SpringSource).

Finally, consider a peer review of the architecture using a methodology like the Architecture Tradeoff Analysis Method (ATAM). Simply going through the exercise of explaining the architecture to stakeholders and receiving feedback can significantly help in improving it.

Rapid Prototyping 

 

It's generally a good idea to create a rapid prototype to quickly learn and demonstrate the capabilities and value of the framework to the business. This can also generate excitement in the development team, particularly if the framework can enhance the productivity of developers and make their life easier.

 

The Frameworks I've Selected

The Spring Framework

I am a big fan of the Spring Framework. I believe it is really designed to support the need of developers from a productivity standpoint. In addition to dependency injection (DI), Aspect Oriented Programming (AOP), and Spring MVC, I like the Spring Data repository abstraction for JPA, MongoDB, Neo4J, and Hadoop. Spring supports Polyglot Persistence and Big Data today. I use Spring Roo for rapid application development and this allows me to focus on modeling the domain. I use the Roo scaffolding feature to generate a lot of Spring configuration and Java code for the domain, repository (Roo supports JPA and MongDB), service, and web layers (Roo supports Spring MVC, JSF, and GWT). Spring also support for unit and integration testing with the recent release of Spring MVC Test.

I use Spring Security which allows me to use AOP and annotations to secure methods and supports advanced features like Remenber Me and regular expressions for URLs. I think that JAAS is too low-level. Spring Security allows me to meet all OWASP Top Ten requirements (see my previous post entitled  Application-Level Security in Health IT Systems: A Roadmap).

Spring Social makes it easy to connect a Spring application to social network sites like Facebook, Twitter, and LinkedIn using the OAuth2 protocol. From a tooling standpoint, Spring STS supports many Spring features and I can deploy directly to Cloud Foundry from Spring STS. I look forward to evaluating Grails and the Play Framework which use convention over configuration and are built on Groovy and Scala respectively.

Thymeleaf, Twitter Boostrap, and JQuery

I use Twitter Boostrap because it is based on HTML5, CSS3, JQuery, LESS, and also supports a Responsive Web Design (RWD) approach. The size of the components library and the community is quite impressive.

Thymeleaf is an HTML5 templating engine and a replacement for traditional JSP. It is well integrated with Spring MVC and supports a clear division of labor between back-end and front-end developers. Twitter Boostrap and Thymeleaf work well together.

AngularJS

For Single Page Applications (SPA) my definitive choice is AngularJS. It provides everything I need including a clean MVC pattern implementation, directives, view routing, Deep Linking (for bookmarking), dependency injection, two-way databinding, and BDD-style unit testing with Jasmine. AngularJS has its own dedicated debugging tool called Batarang. There are also several learning resources (including books) on AngularJS.

Check this page comparing the performance of AngulaJS vs. KnockoutJS. This is a survey of the popularity of  Top JavaScript MVC Frameworks.

 

D3.js 

D3.js is my favorite for data visualization in data-intensive applications. It is based on HTML5, SVG, and Javascript. For simple charting and plotting, I use jqPlot which is based on JQuery. See my previous post entitled Visual Analytics for Clinical Decision Making.

 

R 

I use R for statistical computing, data analysis, and predictive analytics. See my previous post entitled Statistical Computing and Data Mining with R.

Development Tools

My development tools include: Git (Distributed Version Control), Maven or Gradle (build), Jenkins (Continuous Integration), Artifactory (Repository Manager), and Sonar (source code quality management). My testing toolkit includes Mockito, DBUnit, Cucumber JVM, JMeter, and Selenium.

Visual Analytics for Clinical Decision Making

In my last post, I talked about the era of Big Data in medicine, Evidence-Based Practice (EBP),  Practice-Based Evidence (PBE), and the need for a human-centered approach to building intelligent health IT (iHIT) systems. In this post, I discuss Visual Analytics, an emerging discipline in Data Science. In a report titled "Illuminating the Path: The R&D Agenda for Visual Analytics" published in 2004 by the National Visualization and Analytics Center (NVAC), Visual Analytics is defined as "the science of analytical reasoning facilitated by visual interactive interfaces."

The goal of Visual Analytics is to obtain deep insight for effective understanding, reasoning, and decision making through the visual exploration of massive, complex, and often ambiguous data. As a multidisciplinary field, Visual Analytics combines several disciplines such as human perception and cognition, interactive graphic design, statistical computing, data mining, spatio-temporal data analysis, and even art.

In his book titled "Beautiful Evidence", Edward Tufte illustrates the fundamental principles of analytical design by using Charles Minard's famous map known as "Carte figurative des pertes successives en hommes de l'Armée Française dans la campagne de Russie 1812-1813" (Figurative Map of the successive losses in men of the French Army in the Russian Campaign 1812-1813). The map is a dramatic account of the heavy losses of the french army during Napoleon's Russian campaign of 1812. Edward Tuffe calls the map the "best statistical graphics ever". Click on the image below to enlarge it.

Visual Analytics is also an emerging discipline in healthcare informatics. For example, similar to Minard's map of the Russian Campaign of 1812-1813, Visual Analytics can help in comparing different interventions and care pathways and their respective clinical outcomes over a certain period of time through the vivid showing of causes, variables, comparisons, and explanations. This approach contrasts with the traditional display of clinical data in table rows that is so common in electronic health record (EHR) systems interfaces.

Another Visual Analytics technique called Visual Cluster Analysis can be particularly helpful in Comparative Effectiveness in clinical care settings where the goal is to compare the benefits and harms of different interventions for different subgroups (groups of patients sharing similar clinical characteristics such as age, gender, race, genetic profile, and comorbidities). Given a specific patient, Visual Cluster Analysis can help the clinician visually explore what works and what doesn't work for "similar patients".

You can find interesting examples of research projects and implementations in the proceedings of the Visual Analytics in Healthcare Workshop which has been held in conjunction with the IEEE VisWeek for the past three years. The 2013 Visual Analytics in Healthcare Summit (VHAC 2013) will be held in conjunction with the AMIA 2013 conference in Washington DC. There are a number of open source toolkits that can be used to implement Visual Analytics. Some of them are based on open web standards such as HTML5, CSS3, SVG, and Javascript. My favorite is D3.js. DC.js and Crossfilter are built on top of D3.js and facilitate the creation of interactive visualization of multivariate datasets in the browser.

Prediction for 2013: Intelligent Health IT Systems (iHIT) Go Mainstream

iHIT systems represent an evolution of clinical decision support (CDS) systems. Traditionally, CDS systems have provided functionalities such as Alerts and Reminders, Order Sets, Infobuttons, and Documentation Templates. iHIT systems go beyond these basic functionalities and are poised to go mainstream in 2013. This evolution is enabled by recent developments in both computing and healthcare. Notably in computing:

• The emergence of Big Data and massively parallel computing platforms like Hadoop.
• The entrance of the following disciplines into the mainstream of computing: Machine Learning (a branch of Artificial Intelligence), Statistical Computing, Visual Analytics, Natural Language Processing, Information Retrieval, Rule engines, and Semantic Web Technologies (like RDF, OWL, SPARQL, and SWRL). These disciplines have been around for many years, but have been largely confined into Academia, very large organizations, and niche markets.
• The availability of open source tools, platforms, and resources to support the technologies mentioned above. Examples include: R (a statistical engine), Apache Hadoop, Apache Mahout, Apache Jena, Apache Stanbol, Apache OpenNLP, and Apache UIMA. The number of books, courses, and conferences dedicated to these topics has increased dramatically over the last two years signalling an entrance into the mainstream.

In addition, the healthcare industry itself is currently going through a significant transformation from a business model based on the number of patients treated to a value-based payment model. The Accountable Care Organization (ACO) is an example of this new model. This model puts an increased emphasis on meeting certain quality and performance metrics driven by the latest scientific evidence (this is called Evidence Based Practice or EBP).

Although very costly, Randomized Control Trials (RCTs) are considered the strongest form of evidence in EBP. Despite their inherent methodological challenges (lack of randomization leading to possible bias and confounding), observational studies (using real world data) are increasingly recognized as complementary to RCTs and an important tool in clinical decision making and health policy. According to a report titled "Clinical Practice Guidelines (CPGs) We Can Trust"  published by the Institute Of Medicine (IoM):

"Randomized trials commonly have an under representation of important subgroups, including those with comorbidities, older persons, racial and ethnic minorities, and low-income, less educated, or low-literacy patients."

Investments into Comparative Effectiveness Research (CER) are increasing as well. CER, an emerging trend in Evidence Based Practice (EBP), has been defined by the Federal Coordinating Council for CER as "the conduct and synthesis of research comparing the benefits and harms of different interventions and strategies to prevent, diagnose, treat and monitor health conditions in 'real world' settings." CER is important not only for discovering what works and what doesn't work in practice, but also for an informed shared decision making process between the patient and her provider.

The use of predictive risk models for personalized medicine is becoming a common practice. These models can predict the health risks of patients based on their individual health profiles (including genetic profiles). These models often take the form of logistic regression models. Examples include models for predicting cardiovascular disease, ICU mortality, and hospital readmission (an important ACO performance measure).

Thanks to the Meaningful Use incentive program, adoption of electronic health record (EHR) systems by providers is rapidly increasing. This translates into the availability of huge amount of EHR data which can be harvested to provide Practice Based Evidence (PBE) necessary to close the evidence loop. PBE is the key to a learning health system. The Institute of Medicine (IOM) released a report last year titled "Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care". The report describes a learning health system as:

"...delivery of best practice guidance at the point of choice, continuous learning and feedback in both health and health care, and seamless, ongoing communication among participants, all facilitated through the application of IT."

Both EBP and PBE will require not only rigorous scientific methodologies, but also a computing platform suitable for the era of Big Data in medicine. As Williams Osler (1849-1919) famously said:

"Medicine is a science of uncertainty and an art of probability."

Lastly, to be successful, the emergence of iHIT systems will require a human-centered design approach. This will be facilitated by the use of techniques that can enhance human cognitive abilities. Examples are: Electronic Checklists (an approach that originates from the aviation industry and has been proven to save lives in healthcare delivery as well) and Visual Analytics.

Happy New Year to You and Your Family!