Sunday, December 30, 2012

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:
" 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!

Saturday, December 8, 2012

A Journey into Software Excellence

I am back in the blogosphere after a seven month hiatus. It was about time I get my blogging act together. Software development has never been so much fun. In this post, I will share some thoughts on using tools, methods, and practices that can really help in your search for software excellence from the initial prototyping of the user interface to deployment.

  1. With the rapid proliferation of mobile and desktop devices, adopt a Responsive Wed Design (RWD) strategy to reach the largest audience possible.
  2. Create responsive sketches, wireframes, or mockups and apply usability guidelines during the initial prototyping. The NHS Common User Interface (CUI) Program is a good example of usability guidelines for healthcare IT applications. also has many interesting resources as well.
  3. Perform usability testing to test your design ideas and obtain early feedback from future users of your product before actual development starts. Use metrics such as the System Usability Scale (SUS) to assess the results.
  4. Carefuly select the right HTML5, CSS3, and Javascript libraries and frameworks. The Single Page Application (SPA) architecture is becoming popular and can provide a more fluid user experience.
  5. Consider "Specification By Example" and Behaviour Driven Development (BDD) tools like Cucumber-JVM to create executable user stories.
  6. Pattern languages like Domain Driven Design (DDD) can help you avoid a "Big Ball of Mud" in architecting your software. DDD concepts such as "Strategic Design", "Bounded Context", "Published Language", and "Anti-Corruption Layer" can help you put your architecture in the right perspective, particularly if there is a need to support industry interoperability standards such as HL7 and IHE. However, beware that the practice of DDD has evolved over the last 8 years and new lessons have been learned particularly in the area of "Aggregate" design. So keep up-to-date with new developments in the field in order to leverage the experience of the community. I also found the concept of "Hexagonal Architecture" very helpful in visualizing the complexity of an architecture from different angles.
  7. Consider a peer review of the architecture using a methodology like the Architecture Tradeoff Analysis Method (ATAM).
  8. Embrace Polyglot Persistence (the use of different persistence mechanisms such as relational, document, and graph databases within the same application). However, use the right application development framework to make this easy. Beware of the peculiarities of modeling data for NoSQL databases and remember that "Persistence Ignorance" is not always easy to achieve in practice.
  9. Add a social dimension to your product by integrating the user experience with existing social networking sites that your users already belong to.
  10. Make your application more intelligent through the use of techniques such as Machine Learning (e.g., a recommendation engine), ontologies and rule engines (e.g., automated reasoning), and Natural Language Processing (NLP) (e.g., automated question answering). As Richard Hamming said: "The purpose of computing is insight, not numbers".
  11. To enhance the user experience, adopt HTML5, SVG, and Javascript-based graphing and data visualization techniques for data-intensive applications.
  12. Consider the benefits of deploying the application to the cloud and if you decide to deploy to the cloud, factor that into your entire design and development process including the selection of development tools. Choosing the right Platform-as-a-Service (PaaS) provider can facilitate the process.
  13. Create a Continuous Delivery pipeline based on the core concept of automated testing. Leverage tools like Git (Distributed Version Control), Gradle (build), Jenkins (Continuous Integration), and Artifactory. Continuous Delivery allows you to go to market faster and with confidence in the quality of your product. Save infrastructure costs by using these tools in the cloud during development.
  14. Although there is still a place for manual testing, all tests should be automated as much as possible. In addition to the traditional unit tests (using tools like JUnit, TestNG, and Mockito), embrace automated cross-device, cross-browser, and cross-platform user interface (UI) testing using a tool like Selenium.
  15. Web services and performance testing should also become part of your build and Continuous Delivery pipeline using tools like soapUI and JMeter respectively. Performance testing should not be an afterthought.
  16. Adopt automated code quality inspection with tools like Sonar, Checkstyle, FindBugs, and PMD. This can supplement your peer code review process and can provide you with concrete code quality metrics in addition to automatically flagging bugs (including insecure code) in your code base.
  17. Write secure code by carefully studying the OWASP Top Ten. Adopt OWASP guidelines related to security testing and secure code reviews. Perform penetration testing to find vulnerabilities in your application before it is too late.
  18. Do your due diligence in protecting the privacy of your users data. Put the users in control of their privacy in your system by adopting standards such as OAuth2, OpenID Connect, and the User Managed Access (UMA) protocol of the Kantara Initiative. Consider increasing the strength of authentication using multi-factor authentication (e.g., two-factor authentication using the user's phone).
  19. Invest in learning and training your development team. Software excellence can only be achieved by skilled professionals.
  20. Relax, have fun, and remember that software excellence is a journey.