Sunday, April 11, 2010

Green EHRs, Genomics, Decision Support, and Checklists

I am back in the blogosphere after a three month hiatus. As you may have noticed from my last post, my focus lately has been on health information technology and how to unleash its potential to improve the quality of care and reduce costs. Healthcare Reform has been signed into law in the US as I have predicted in my last post. I believe that the healthcare sector is going through what former Intel CEO Andy Grove calls a "Strategic Inflection Point" driven by a combination of the following:

  • A new regulatory framework including Healthcare Reform, ARRA and the HITECH Act
  • Technology: electronic health records, advances in Genomics, and leveraging the ubiquity and ease of use of Web 2.0/3.0 platforms
  • Innovations in the practice of healthcare delivery such as personalized medicine, automated execution of clinical guidelines (evidence-based medicine and clinical decision support), and the growing awareness of the effectiveness of checklists in high-pressure clinical settings such as surgery and intensive care.

Developing robust and easy to use health IT standards is key, so I've joined the HL7 standard development organization to follow and hopefully contribute. Specifically, I am interested in the following HL7 working groups:

The HL7 Structured Document Working Group and the Greening of the CDA

This group develops structured document standards for healthcare such as the Clinical Document Architecture (CDA) and the Continuity of Care Document (CCD). I am particularly interested in the greenCDA effort to simplify the CDA. I believe that simplifying the CDA can greatly facilitate and accelerate adoption. A simpler version of the CDA will allow adopters to quickly learn it and use their familiar software development tools (such as XML databinding tools and IDEs) to write software applications. To achieve that objective, data components of the greenCDA will be based on data semantics as opposed to the CDA R-MIM (Refined Message Information Model) classes. The CDA R-MIM is a graphical representation of the CDA specification and a step in the HL7 V3 Reference Information Model (RIM) refinement process.

HITSP C32 took the right approach by first defining required data elements for various content modules such as conditions and medications and then mapping those data elements to the HL7 CCD XML Schema structure. However the latter is quite complex. Fortunately, it is possible to map those HITSP C32 data elements to a simpler XML Schema than what is defined by the HL7 CCD. That simpler XML Schema can take different forms. A National Information Exchange Model (NIEM) Information Exchange Package Documentation (IEPD) for healthcare data is an approach that is being considered by the ONC. The NIEM approach is proven and has been successfully used in complex data exchange scenarios in criminal justice and other sensitive domains. Another approach is to create a lighter version of the HITSP C32 specification like the L32 proposed by the MITRE corporation.

There are many new interesting features of the XML Schema 1.1 specification such as assertions and conditional type assignments that can be leveraged as well to consolidate both structural and business rules data constraints in a single schema.

While there are different approaches to simplifying the CDA, instances of any well-designed simplified version of the CDA/CCD should easily be mapped to the original plain vanilla CDA/CCD schemas for exchange.

The Clinical Decision Support Working Group

This group develops standards such as the Virtual Medical Record (vMR), GELLO (an expression and query language for computer-interpretable clinical guidelines), Infobutton (a context-aware information retrieval standard specification), and Clinical Decision Support Services (CDSS).

Clinical Decision Support systems had a limited adoption but are likely to get a boost from the new ARRA EHR certification requirements.

The Clinical Genomics Data Working Group

This group develops standards for clinical and personalized genomic data such as DNA sequence variations and gene expression levels. Combined with clinical decision support services, this work has the potential to revolutionize personalized medicine as we know it. For example, a medication dose can be adjusted based on a patient's genotype. Family history and genetic screening will play an important role in preventive medicine for diseases such as diabetes and cancer.

Procedures and Checklists in the Cockpit and Intensive Care Units

The Flight Operation Interests Group (FOIG) of the Air Transport Association (ATA) of which I am a member is developing a data model and XML Schema for flight deck procedures and checklists. Adherence to well designed flight deck procedures and checklists can contribute significantly to the improvement of flight safety. For example, several no-flap/no-slat takeoff crashes have been caused by a deviation from basic operating procedures.

Aircraft manufacturers have different philosophies and policies on the design of flight deck procedures that must be reconciled. Another challenge is the need to take human factors into consideration in any effort related to the design and execution of flight deck procedures.

While the idea of using checklists and standard operating procedures has been fully embraced and adopted by aviation professionals for the last 70 years, it is only now making inroads into the field of medicine particularly in intensive care units. The use of checklists in medicine has already shown the potential to save patients live and reduce human errors. However, the main challenge remains acceptance and full embrace of checklists by clinicians. If you are interested in learning more about checklists in medicine, I recommend the book "The Checklist Manifesto: How to Get Things Right" by Atul Gawande.

A checklist could take the form of a tasks list automatically generated as output of clinical decision support services that take as inputs electronic health records as well as patient genomic data for a more effective form of personalized medicine.