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!

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